Package: a4 Version: 1.34.0 Depends: a4Base, a4Preproc, a4Classif, a4Core, a4Reporting Suggests: MLP, nlcv, ALL, Cairo License: GPL-3 MD5sum: 7210d289aaa5b6fee8863d57f9c69767 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Umbrella Package Description: Automated Affymetrix Array Analysis Umbrella Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg git_url: https://git.bioconductor.org/packages/a4 git_branch: RELEASE_3_10 git_last_commit: 2fe2ace git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/a4_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/a4_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/a4_1.34.0.tgz vignettes: vignettes/a4/inst/doc/a4vignette.pdf vignetteTitles: a4vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4/inst/doc/a4vignette.R dependencyCount: 117 Package: a4Base Version: 1.34.1 Depends: methods, graphics, grid, Biobase, AnnotationDbi, annaffy, mpm, genefilter, limma, multtest, glmnet, a4Preproc, a4Core, gplots Suggests: Cairo, ALL, hgu95av2.db Enhances: gridSVG, JavaGD License: GPL-3 MD5sum: 2a246e9e272b9e1ceff48724b1233408 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Base Package Description: Automated Affymetrix Array Analysis biocViews: Microarray Author: Willem Talloen, Tobias Verbeke, Tine Casneuf, An De Bondt, Steven Osselaer and Hinrich Goehlmann, Willem Ligtenberg Maintainer: Tobias Verbeke , Laure Cougnaud git_url: https://git.bioconductor.org/packages/a4Base git_branch: RELEASE_3_10 git_last_commit: d8bb208 git_last_commit_date: 2020-04-02 Date/Publication: 2020-04-02 source.ver: src/contrib/a4Base_1.34.1.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/a4Base_1.34.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 dependencyCount: 57 Package: a4Classif Version: 1.34.0 Depends: methods, a4Core, a4Preproc, MLInterfaces, ROCR, pamr, glmnet, varSelRF Imports: a4Core Suggests: ALL License: GPL-3 MD5sum: 04152facc7027fb92828c6768078c035 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Classification Package Description: Automated Affymetrix Array Analysis Classification Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg git_url: https://git.bioconductor.org/packages/a4Classif git_branch: RELEASE_3_10 git_last_commit: cbf7c5c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/a4Classif_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/a4Classif_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/a4Classif_1.34.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 dependencyCount: 108 Package: a4Core Version: 1.34.0 Depends: methods, Biobase, glmnet License: GPL-3 MD5sum: 44d2e2fea2d4a6c4d88a236cd59a7ff5 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Core Package Description: Automated Affymetrix Array Analysis Core Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg git_url: https://git.bioconductor.org/packages/a4Core git_branch: RELEASE_3_10 git_last_commit: 61c1d88 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/a4Core_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/a4Core_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/a4Core_1.34.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4, a4Base, a4Classif importsMe: a4Classif dependencyCount: 16 Package: a4Preproc Version: 1.34.0 Depends: methods, AnnotationDbi Suggests: ALL, hgu95av2.db License: GPL-3 MD5sum: d8f7bc01c2d6f4f7d4fc5c544088a228 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Preprocessing Package Description: Automated Affymetrix Array Analysis Preprocessing Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg git_url: https://git.bioconductor.org/packages/a4Preproc git_branch: RELEASE_3_10 git_last_commit: bfe8262 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/a4Preproc_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/a4Preproc_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/a4Preproc_1.34.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4, a4Base, a4Classif suggestsMe: graphite dependencyCount: 26 Package: a4Reporting Version: 1.34.0 Depends: methods, annaffy Imports: xtable, utils License: GPL-3 MD5sum: 14ea517069c3e850720afa918761cadb NeedsCompilation: no Title: Automated Affymetrix Array Analysis Reporting Package Description: Automated Affymetrix Array Analysis Reporting Package biocViews: Microarray Author: Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg git_url: https://git.bioconductor.org/packages/a4Reporting git_branch: RELEASE_3_10 git_last_commit: 967b263 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/a4Reporting_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/a4Reporting_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/a4Reporting_1.34.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 dependencyCount: 30 Package: ABAEnrichment Version: 1.16.0 Depends: R (>= 3.4) Imports: Rcpp (>= 0.11.5), gplots (>= 2.14.2), gtools (>= 3.5.0), ABAData (>= 0.99.2), data.table (>= 1.10.4), GOfuncR (>= 1.1.2), grDevices, stats, graphics, utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: b8f4dbfdb7520c1fdeb912456058f69e NeedsCompilation: yes Title: Gene expression enrichment in human brain regions Description: The package ABAEnrichment is designed to test for enrichment of user defined candidate genes in the set of expressed genes in different human brain regions. The core function 'aba_enrich' integrates the expression of the candidate gene set (averaged across donors) and the structural information of the brain using an ontology, both provided by the Allen Brain Atlas project. 'aba_enrich' interfaces the ontology enrichment software FUNC to perform the statistical analyses. Additional functions provided in this package like 'get_expression' and 'plot_expression' facilitate exploring the expression data, and besides the standard candidate vs. background gene set enrichment, also three additional tests are implemented, e.g. for cases when genes are ranked instead of divided into candidate and background. biocViews: GeneSetEnrichment, GeneExpression Author: Steffi Grote Maintainer: Steffi Grote VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ABAEnrichment git_branch: RELEASE_3_10 git_last_commit: 32cd3e7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ABAEnrichment_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ABAEnrichment_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ABAEnrichment_1.16.0.tgz vignettes: vignettes/ABAEnrichment/inst/doc/ABAEnrichment.html vignetteTitles: Introduction to ABAEnrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ABAEnrichment/inst/doc/ABAEnrichment.R dependencyCount: 48 Package: ABarray Version: 1.54.0 Imports: Biobase, graphics, grDevices, methods, multtest, stats, tcltk, utils Suggests: limma, LPE License: GPL MD5sum: 1aec5158d7b4a20326b87f737be578a4 NeedsCompilation: no Title: Microarray QA and statistical data analysis for Applied Biosystems Genome Survey Microrarray (AB1700) gene expression data. Description: Automated pipline to perform gene expression analysis for Applied Biosystems Genome Survey Microarray (AB1700) data format. Functions include data preprocessing, filtering, control probe analysis, statistical analysis in one single function. A GUI interface is also provided. The raw data, processed data, graphics output and statistical results are organized into folders according to the analysis settings used. biocViews: Microarray, OneChannel, Preprocessing Author: Yongming Andrew Sun Maintainer: Yongming Andrew Sun git_url: https://git.bioconductor.org/packages/ABarray git_branch: RELEASE_3_10 git_last_commit: a5e3ee6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ABarray_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ABarray_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ABarray_1.54.0.tgz vignettes: vignettes/ABarray/inst/doc/ABarray.pdf, vignettes/ABarray/inst/doc/ABarrayGUI.pdf vignetteTitles: ABarray gene expression, ABarray gene expression GUI interface hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 17 Package: abseqR Version: 1.4.0 Depends: R (>= 3.5.0) Imports: ggplot2, RColorBrewer, circlize, reshape2, VennDiagram, plyr, flexdashboard, BiocParallel (>= 1.1.25), png, grid, gridExtra, rmarkdown, knitr, vegan, ggcorrplot, ggdendro, plotly, BiocStyle, stringr, utils, methods, grDevices, stats, tools, graphics Suggests: testthat License: GPL-3 | file LICENSE MD5sum: d1ea38dd76a559ecfcd783c0addc4659 NeedsCompilation: no Title: Reporting and data analysis functionalities for Rep-Seq datasets of antibody libraries Description: AbSeq is a comprehensive bioinformatic pipeline for the analysis of sequencing datasets generated from antibody libraries and abseqR is one of its packages. abseqR empowers the users of abseqPy (https://github.com/malhamdoosh/abseqPy) with plotting and reporting capabilities and allows them to generate interactive HTML reports for the convenience of viewing and sharing with other researchers. Additionally, abseqR extends abseqPy to compare multiple repertoire analyses and perform further downstream analysis on its output. biocViews: Sequencing, Visualization, ReportWriting, QualityControl, MultipleComparison Author: JiaHong Fong [cre, aut], Monther Alhamdoosh [aut] Maintainer: JiaHong Fong URL: https://github.com/malhamdoosh/abseqR SystemRequirements: pandoc (>= 1.19.2.1) VignetteBuilder: knitr BugReports: https://github.com/malhamdoosh/abseqR/issues git_url: https://git.bioconductor.org/packages/abseqR git_branch: RELEASE_3_10 git_last_commit: d30efb4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/abseqR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/abseqR_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/abseqR_1.4.0.tgz vignettes: vignettes/abseqR/inst/doc/abseqR.pdf vignetteTitles: Introduction to abseqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/abseqR/inst/doc/abseqR.R dependencyCount: 115 Package: ABSSeq Version: 1.40.0 Depends: R (>= 2.10), methods Imports: locfit, limma Suggests: edgeR License: GPL (>= 3) MD5sum: 927d350826d221155051534db24f9419 NeedsCompilation: no Title: ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences Description: Inferring differential expression genes by absolute counts difference between two groups, utilizing Negative binomial distribution and moderating fold-change according to heterogeneity of dispersion across expression level. biocViews: DifferentialExpression Author: Wentao Yang Maintainer: Wentao Yang git_url: https://git.bioconductor.org/packages/ABSSeq git_branch: RELEASE_3_10 git_last_commit: 13635c2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ABSSeq_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ABSSeq_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ABSSeq_1.40.0.tgz vignettes: vignettes/ABSSeq/inst/doc/ABSSeq.pdf vignetteTitles: ABSSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ABSSeq/inst/doc/ABSSeq.R dependencyCount: 9 Package: acde Version: 1.16.0 Depends: R(>= 3.3), boot(>= 1.3) Imports: stats, graphics Suggests: BiocGenerics, RUnit License: GPL-3 MD5sum: 31c3e2e540ec0772a89c59ddfb9b629e NeedsCompilation: no Title: Artificial Components Detection of Differentially Expressed Genes Description: This package provides a multivariate inferential analysis method for detecting differentially expressed genes in gene expression data. It uses artificial components, close to the data's principal components but with an exact interpretation in terms of differential genetic expression, to identify differentially expressed genes while controlling the false discovery rate (FDR). The methods on this package are described in the vignette or in the article 'Multivariate Method for Inferential Identification of Differentially Expressed Genes in Gene Expression Experiments' by J. P. Acosta, L. Lopez-Kleine and S. Restrepo (2015, pending publication). biocViews: DifferentialExpression, TimeCourse, PrincipalComponent, GeneExpression, Microarray, mRNAMicroarray Author: Juan Pablo Acosta, Liliana Lopez-Kleine Maintainer: Juan Pablo Acosta git_url: https://git.bioconductor.org/packages/acde git_branch: RELEASE_3_10 git_last_commit: 3c8dc1a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/acde_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/acde_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/acde_1.16.0.tgz vignettes: vignettes/acde/inst/doc/acde.pdf vignetteTitles: Identification of Differentially Expressed Genes with Artificial Components hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/acde/inst/doc/acde.R importsMe: coexnet dependencyCount: 3 Package: ACE Version: 1.4.0 Depends: R (>= 3.4) Imports: Biobase, QDNAseq, ggplot2, grid, stats, utils, methods, grDevices, GenomicRanges Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 3dd79ed785cd50780f2c7c72dc856739 NeedsCompilation: no Title: Absolute Copy Number Estimation from Low-coverage Whole Genome Sequencing Description: Uses segmented copy number data to estimate tumor cell percentage and produce copy number plots displaying absolute copy numbers. biocViews: CopyNumberVariation, DNASeq, Coverage, WholeGenome, Visualization, Sequencing Author: Jos B Poell Maintainer: Jos B Poell URL: https://github.com/tgac-vumc/ACE VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ACE git_branch: RELEASE_3_10 git_last_commit: 246b8d7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ACE_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ACE_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ACE_1.4.0.tgz vignettes: vignettes/ACE/inst/doc/ACE_vignette.html vignetteTitles: ACE vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ACE/inst/doc/ACE_vignette.R dependencyCount: 94 Package: aCGH Version: 1.64.0 Depends: R (>= 2.10), cluster, survival, multtest Imports: Biobase, cluster, grDevices, graphics, methods, multtest, stats, survival, splines, utils License: GPL-2 Archs: i386, x64 MD5sum: 8069c332618b981a8fa82bffdeffc3b9 NeedsCompilation: yes Title: Classes and functions for Array Comparative Genomic Hybridization data. Description: Functions for reading aCGH data from image analysis output files and clone information files, creation of aCGH S3 objects for storing these data. Basic methods for accessing/replacing, subsetting, printing and plotting aCGH objects. biocViews: CopyNumberVariation, DataImport, Genetics Author: Jane Fridlyand , Peter Dimitrov Maintainer: Peter Dimitrov git_url: https://git.bioconductor.org/packages/aCGH git_branch: RELEASE_3_10 git_last_commit: 42470b5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/aCGH_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/aCGH_1.64.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/aCGH_1.64.0.tgz vignettes: vignettes/aCGH/inst/doc/aCGH.pdf vignetteTitles: aCGH Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/aCGH/inst/doc/aCGH.R dependsOnMe: CRImage importsMe: ADaCGH2, snapCGH suggestsMe: beadarraySNP dependencyCount: 17 Package: ACME Version: 2.42.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), methods, BiocGenerics Imports: graphics, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: 21d1c3a39668220ba3db33b3425e5af7 NeedsCompilation: yes Title: Algorithms for Calculating Microarray Enrichment (ACME) Description: ACME (Algorithms for Calculating Microarray Enrichment) is a set of tools for analysing tiling array ChIP/chip, DNAse hypersensitivity, or other experiments that result in regions of the genome showing "enrichment". It does not rely on a specific array technology (although the array should be a "tiling" array), is very general (can be applied in experiments resulting in regions of enrichment), and is very insensitive to array noise or normalization methods. It is also very fast and can be applied on whole-genome tiling array experiments quite easily with enough memory. biocViews: Technology, Microarray, Normalization Author: Sean Davis Maintainer: Sean Davis URL: http://watson.nci.nih.gov/~sdavis git_url: https://git.bioconductor.org/packages/ACME git_branch: RELEASE_3_10 git_last_commit: 7f41657 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ACME_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ACME_2.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ACME_2.42.0.tgz vignettes: vignettes/ACME/inst/doc/ACME.pdf vignetteTitles: ACME hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ACME/inst/doc/ACME.R suggestsMe: oligo dependencyCount: 7 Package: ADaCGH2 Version: 2.26.0 Depends: R (>= 3.2.0), parallel, ff, GLAD Imports: bit, ffbase, DNAcopy, tilingArray, waveslim, cluster, aCGH, snapCGH Suggests: CGHregions, Cairo, limma Enhances: Rmpi License: GPL (>= 3) Archs: i386, x64 MD5sum: 1d96652e3c8df48d280d86e8133ef1e2 NeedsCompilation: yes Title: Analysis of big data from aCGH experiments using parallel computing and ff objects Description: Analysis and plotting of array CGH data. Allows usage of Circular Binary Segementation, wavelet-based smoothing (both as in Liu et al., and HaarSeg as in Ben-Yaacov and Eldar), HMM, BioHMM, GLAD, CGHseg. Most computations are parallelized (either via forking or with clusters, including MPI and sockets clusters) and use ff for storing data. biocViews: Microarray, CopyNumberVariants Author: Ramon Diaz-Uriarte and Oscar M. Rueda . Wavelet-based aCGH smoothing code from Li Hsu and Douglas Grove . Imagemap code from Barry Rowlingson . HaarSeg code from Erez Ben-Yaacov; downloaded from . Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/adacgh2 git_url: https://git.bioconductor.org/packages/ADaCGH2 git_branch: RELEASE_3_10 git_last_commit: e93a6c9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ADaCGH2_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ADaCGH2_2.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ADaCGH2_2.26.0.tgz vignettes: vignettes/ADaCGH2/inst/doc/ADaCGH2-long-examples.pdf, vignettes/ADaCGH2/inst/doc/ADaCGH2.pdf, vignettes/ADaCGH2/inst/doc/benchmarks.pdf vignetteTitles: ADaCGH2-long-examples.pdf, ADaCGH2 Overview, benchmarks.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADaCGH2/inst/doc/ADaCGH2.R dependencyCount: 101 Package: ADAM Version: 1.2.0 Depends: R(>= 3.5), stats, utils, methods Imports: Rcpp (>= 0.12.18), GO.db (>= 3.6.0), KEGGREST (>= 1.20.2), knitr, pbapply (>= 1.3-4), dplyr (>= 0.7.6), DT (>= 0.4), stringr (>= 1.3.1), SummarizedExperiment (>= 1.10.1) LinkingTo: Rcpp Suggests: testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: f65b88767651d4d62bae2892df3d7bd3 NeedsCompilation: yes Title: ADAM: Activity and Diversity Analysis Module Description: ADAM is a GSEA R package created to group a set of genes from comparative samples (control versus experiment) belonging to different species according to their respective functions (Gene Ontology and KEGG pathways as default) and show their significance by calculating p-values referring togene diversity and activity. Each group of genes is called GFAG (Group of Functionally Associated Genes). biocViews: GeneSetEnrichment, Pathways, KEGG Author: André Luiz Molan , Giordano Bruno Sanches Seco , Agnes Alessandra Sekijima Takeda , Jose Luiz Rybarczyk Filho Maintainer: Jose Luiz Rybarczyk Filho SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ADAM git_branch: RELEASE_3_10 git_last_commit: 42c70ad git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ADAM_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ADAM_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ADAM_1.2.0.tgz vignettes: vignettes/ADAM/inst/doc/ADAM.html vignetteTitles: "Using ADAM" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADAM/inst/doc/ADAM.R dependsOnMe: ADAMgui dependencyCount: 87 Package: ADAMgui Version: 1.2.0 Depends: R(>= 3.6), stats, utils, methods, ADAM Imports: GO.db (>= 3.5.0), dplyr (>= 0.7.6), shiny (>= 1.1.0), stringr (>= 1.3.1), stringi (>= 1.2.4), varhandle (>= 2.0.3), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), ggpubr (>= 0.1.8), ggsignif (>= 0.4.0), reshape2 (>= 1.4.3), RColorBrewer (>= 1.1-2), colorRamps (>= 2.3), DT (>= 0.4), data.table (>= 1.11.4), gridExtra (>= 2.3), shinyjs (>= 1.0), knitr, testthat Suggests: BiocStyle License: GPL (>= 2) MD5sum: 3bd52a1cff7957b7682dacf297ab8fa8 NeedsCompilation: no Title: Activity and Diversity Analysis Module Graphical User Interface Description: ADAMgui is a Graphical User Interface for the ADAM package. The ADAMgui package provides 2 shiny-based applications that allows the user to study the output of the ADAM package files through different plots. It's possible, for example, to choose a specific GFAG and observe the gene expression behavior with the plots created with the GFAGtargetUi function. Features such as differential expression and foldchange can be easily seen with aid of the plots made with GFAGpathUi function. biocViews: GeneSetEnrichment, Pathways, KEGG Author: Giordano Bruno Sanches Seco , André Luiz Molan , Agnes Alessandra Sekijima Takeda , Jose Luiz Rybarczyk Filho Maintainer: Jose Luiz Rybarczyk Filho URL: TBA VignetteBuilder: knitr BugReports: https://github.com/jrybarczyk/ADAMgui/issues git_url: https://git.bioconductor.org/packages/ADAMgui git_branch: RELEASE_3_10 git_last_commit: 989f760 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ADAMgui_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ADAMgui_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ADAMgui_1.2.0.tgz vignettes: vignettes/ADAMgui/inst/doc/ADAMgui.html vignetteTitles: "Using ADAMgui" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADAMgui/inst/doc/ADAMgui.R dependencyCount: 134 Package: adaptest Version: 1.6.1 Depends: R (>= 3.6.0) Imports: methods, graphics, stats, utils, calibrate, origami (>= 1.0.0), SummarizedExperiment, S4Vectors, tmle Suggests: Matrix, testthat, rmarkdown, knitr, BiocStyle, SuperLearner, earth, gam, nnls, airway License: GPL-2 MD5sum: 7d562719a192afff62949be7c61e17cd NeedsCompilation: no Title: Data-Adaptive Statistics for High-Dimensional Multiple Testing Description: Data-adaptive test statistics represent a general methodology for performing multiple hypothesis testing on effects sizes while maintaining honest statistical inference when operating in high-dimensional settings (). The utilities provided here extend the use of this general methodology to many common data analytic challenges that arise in modern computational and genomic biology. biocViews: Genetics, GeneExpression, DifferentialExpression, Sequencing, Microarray, Regression, DimensionReduction, MultipleComparison Author: Weixin Cai [aut, cre, cph] (), Nima Hejazi [aut] (), Alan Hubbard [ctb, ths] () Maintainer: Weixin Cai URL: https://github.com/wilsoncai1992/adaptest VignetteBuilder: knitr BugReports: https://github.com/wilsoncai1992/adaptest/issues git_url: https://git.bioconductor.org/packages/adaptest git_branch: RELEASE_3_10 git_last_commit: bb7509d git_last_commit_date: 2020-01-05 Date/Publication: 2020-01-06 source.ver: src/contrib/adaptest_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/adaptest_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/adaptest_1.6.1.tgz vignettes: vignettes/adaptest/inst/doc/differentialExpression.html vignetteTitles: Data-Mining Biomarkers and High-Dimensional Testing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/adaptest/inst/doc/differentialExpression.R dependencyCount: 58 Package: adductomicsR Version: 1.2.0 Depends: R (>= 3.6), adductData, ExperimentHub, AnnotationHub Imports: parallel (>= 3.3.2), data.table (>= 1.10.4), OrgMassSpecR (>= 0.4.6), foreach (>= 1.4.3), mzR (>= 2.14.0), ade4 (>= 1.7.6), rvest (>= 0.3.2), pastecs (>= 1.3.18), reshape2 (>= 1.4.2), pracma (>= 2.0.4), DT (>= 0.2), fpc (>= 2.1.10), doSNOW (>= 1.0.14), fastcluster (>= 1.1.22), RcppEigen (>= 0.3.3.3.0), bootstrap (>= 2017.2), smoother (>= 1.1), dplyr (>= 0.7.5), zoo (>= 1.8), stats (>= 3.5.0), utils (>= 3.5.0), graphics (>= 3.5.0), grDevices (>= 3.5.0), methods (>= 3.5.0), datasets (>= 3.5.0) Suggests: knitr (>= 1.15.1), rmarkdown (>= 1.5), Rdisop (>= 1.34.0), testthat License: Artistic-2.0 MD5sum: 7946090e4b56e02960c79439c3f93905 NeedsCompilation: no Title: Processing of adductomic mass spectral datasets Description: Processes MS2 data to identify potentially adducted peptides from spectra that has been corrected for mass drift and retention time drift and quantifies MS1 level mass spectral peaks. biocViews: MassSpectrometry,Metabolomics,Software,ThirdPartyClient,DataImport, GUI Author: Josie Hayes Maintainer: Josie Hayes VignetteBuilder: knitr BugReports: https://github.com/JosieLHayes/adductomicsR/issues git_url: https://git.bioconductor.org/packages/adductomicsR git_branch: RELEASE_3_10 git_last_commit: b9699e7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/adductomicsR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/adductomicsR_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/adductomicsR_1.2.0.tgz vignettes: vignettes/adductomicsR/inst/doc/adductomicsRWorkflow.html vignetteTitles: Adductomics workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/adductomicsR/inst/doc/adductomicsRWorkflow.R dependencyCount: 119 Package: adSplit Version: 1.56.0 Depends: R (>= 2.1.0), methods (>= 2.1.0) Imports: AnnotationDbi, Biobase (>= 1.5.12), cluster (>= 1.9.1), GO.db (>= 1.8.1), graphics, grDevices, KEGG.db (>= 1.8.1), methods, multtest (>= 1.6.0), stats (>= 2.1.0) Suggests: golubEsets (>= 1.0), vsn (>= 1.5.0), hu6800.db (>= 1.8.1) License: GPL (>= 2) Archs: i386, x64 MD5sum: 3e6ccd7107b4ab04f66ee675819d8f42 NeedsCompilation: yes Title: Annotation-Driven Clustering Description: This package implements clustering of microarray gene expression profiles according to functional annotations. For each term genes are annotated to, splits into two subclasses are computed and a significance of the supporting gene set is determined. biocViews: Microarray, Clustering Author: Claudio Lottaz, Joern Toedling Maintainer: Claudio Lottaz URL: http://compdiag.molgen.mpg.de/software/index.shtml git_url: https://git.bioconductor.org/packages/adSplit git_branch: RELEASE_3_10 git_last_commit: 5f31deb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/adSplit_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/adSplit_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/adSplit_1.56.0.tgz vignettes: vignettes/adSplit/inst/doc/tr_2005_02.pdf vignetteTitles: Annotation-Driven Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/adSplit/inst/doc/tr_2005_02.R dependencyCount: 37 Package: AffiXcan Version: 1.4.0 Depends: R (>= 3.6), SummarizedExperiment Imports: MultiAssayExperiment, BiocParallel, crayon Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: b2523a93e70db65b14d403054dda4612 NeedsCompilation: no Title: A Functional Approach To Impute Genetically Regulated Expression Description: Impute a GReX (Genetically Regulated Expression) for a set of genes in a sample of individuals, using a method based on the Total Binding Affinity (TBA). Statistical models to impute GReX can be trained with a training dataset where the real total expression values are known. biocViews: GeneExpression, Transcription, GeneRegulation, DimensionReduction, Regression, PrincipalComponent Author: Alessandro Lussana [aut, cre] Maintainer: Alessandro Lussana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AffiXcan git_branch: RELEASE_3_10 git_last_commit: 268b82b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AffiXcan_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AffiXcan_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AffiXcan_1.4.0.tgz vignettes: vignettes/AffiXcan/inst/doc/AffiXcan.html vignetteTitles: AffiXcan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffiXcan/inst/doc/AffiXcan.R dependencyCount: 56 Package: affxparser Version: 1.58.0 Depends: R (>= 2.14.0) Suggests: R.oo (>= 1.22.0), R.utils (>= 2.7.0), AffymetrixDataTestFiles License: LGPL (>= 2) Archs: i386, x64 MD5sum: 187069d0740c0b25d8b21d3ba9cdf3b0 NeedsCompilation: yes Title: Affymetrix File Parsing SDK Description: Package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR). It provides methods for fast and memory efficient parsing of Affymetrix files using the Affymetrix' Fusion SDK. Both ASCII- and binary-based files are supported. Currently, there are methods for reading chip definition file (CDF) and a cell intensity file (CEL). These files can be read either in full or in part. For example, probe signals from a few probesets can be extracted very quickly from a set of CEL files into a convenient list structure. biocViews: Infrastructure, DataImport, Microarray, ProprietaryPlatforms, OneChannel Author: Henrik Bengtsson [aut], James Bullard [aut], Robert Gentleman [ctb], Kasper Daniel Hansen [aut, cre], Jim Hester [ctb], Martin Morgan [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/affxparser BugReports: https://github.com/HenrikBengtsson/affxparser/issues git_url: https://git.bioconductor.org/packages/affxparser git_branch: RELEASE_3_10 git_last_commit: 2dd0fef git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/affxparser_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/affxparser_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/affxparser_1.58.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder, Starr importsMe: affyILM, cn.farms, crossmeta, EventPointer, GCSscore, GeneRegionScan, ITALICS, oligo suggestsMe: TIN dependencyCount: 0 Package: affy Version: 1.64.0 Depends: R (>= 2.8.0), BiocGenerics (>= 0.1.12), Biobase (>= 2.5.5) Imports: affyio (>= 1.13.3), BiocManager, graphics, grDevices, methods, preprocessCore, stats, utils, zlibbioc LinkingTo: preprocessCore Suggests: tkWidgets (>= 1.19.0), affydata, widgetTools License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: edf0f0d94650dd988e8d795ded9982f7 NeedsCompilation: yes Title: Methods for Affymetrix Oligonucleotide Arrays Description: The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it. biocViews: Microarray, OneChannel, Preprocessing Author: Rafael A. Irizarry , Laurent Gautier , Benjamin Milo Bolstad , and Crispin Miller with contributions from Magnus Astrand , Leslie M. Cope , Robert Gentleman, Jeff Gentry, Conrad Halling , Wolfgang Huber, James MacDonald , Benjamin I. P. Rubinstein, Christopher Workman , John Zhang Maintainer: Rafael A. Irizarry git_url: https://git.bioconductor.org/packages/affy git_branch: RELEASE_3_10 git_last_commit: 82d96e6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/affy_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/affy_1.64.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/affy_1.64.0.tgz vignettes: vignettes/affy/inst/doc/affy.pdf, vignettes/affy/inst/doc/builtinMethods.pdf, vignettes/affy/inst/doc/customMethods.pdf, vignettes/affy/inst/doc/vim.pdf vignetteTitles: 1. Primer, 2. Built-in Processing Methods, 3. Custom Processing Methods, 4. Import Methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affy/inst/doc/affy.R, vignettes/affy/inst/doc/builtinMethods.R, vignettes/affy/inst/doc/customMethods.R, vignettes/affy/inst/doc/vim.R dependsOnMe: affyContam, AffyExpress, affyPara, affypdnn, affyPLM, affyQCReport, AffyRNADegradation, altcdfenvs, arrayMvout, ArrayTools, bgx, Cormotif, DrugVsDisease, dualKS, ExiMiR, farms, frmaTools, gcrma, LMGene, logitT, maskBAD, MLP, panp, plw, prebs, qpcrNorm, RefPlus, Risa, RPA, SCAN.UPC, simpleaffy, sscore, Starr, webbioc importsMe: affycoretools, affyILM, affylmGUI, affyQCReport, arrayQualityMetrics, ArrayTools, CAFE, ChIPXpress, coexnet, Cormotif, crossmeta, Doscheda, EGAD, farms, ffpe, frma, gcrma, GEOsubmission, Harshlight, HTqPCR, iCheck, lumi, LVSmiRNA, makecdfenv, mimager, MSnbase, PECA, plier, plw, puma, pvac, Rnits, simpleaffy, STATegRa, tilingArray, TurboNorm, vsn, waveTiling suggestsMe: AnnotationForge, ArrayExpress, beadarray, beadarraySNP, BiocCaseStudies, BiocGenerics, Biostrings, BufferedMatrixMethods, categoryCompare, ecolitk, ExpressionView, factDesign, gCMAPWeb, GeneRegionScan, limma, made4, paxtoolsr, piano, PREDA, qcmetrics, runibic, siggenes, TCGAbiolinks dependencyCount: 12 Package: affycomp Version: 1.62.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.3.3) Suggests: splines, affycompData License: GPL (>= 2) MD5sum: e9616ae3db640d2811a1fef48476a350 NeedsCompilation: no Title: Graphics Toolbox for Assessment of Affymetrix Expression Measures Description: The package contains functions that can be used to compare expression measures for Affymetrix Oligonucleotide Arrays. biocViews: OneChannel, Microarray, Preprocessing Author: Rafael A. Irizarry and Zhijin Wu with contributions from Simon Cawley Maintainer: Rafael A. Irizarry git_url: https://git.bioconductor.org/packages/affycomp git_branch: RELEASE_3_10 git_last_commit: d5b30ad git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/affycomp_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/affycomp_1.62.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/affycomp_1.62.0.tgz vignettes: vignettes/affycomp/inst/doc/affycomp.pdf vignetteTitles: affycomp primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycomp/inst/doc/affycomp.R dependencyCount: 7 Package: AffyCompatible Version: 1.46.0 Depends: R (>= 2.7.0), XML (>= 2.8-1), RCurl (>= 0.8-1), methods Imports: Biostrings License: Artistic-2.0 MD5sum: b155168bc80f545b148f54c4b4d21374 NeedsCompilation: no Title: Affymetrix GeneChip software compatibility Description: This package provides an interface to Affymetrix chip annotation and sample attribute files. The package allows an easy way for users to download and manage local data bases of Affynmetrix NetAffx annotation files. The package also provides access to GeneChip Operating System (GCOS) and GeneChip Command Console (AGCC)-compatible sample annotation files. biocViews: Infrastructure, Microarray, OneChannel Author: Martin Morgan, Robert Gentleman Maintainer: Martin Morgan git_url: https://git.bioconductor.org/packages/AffyCompatible git_branch: RELEASE_3_10 git_last_commit: 57551c1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AffyCompatible_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AffyCompatible_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AffyCompatible_1.46.0.tgz vignettes: vignettes/AffyCompatible/inst/doc/MAGEAndARR.pdf, vignettes/AffyCompatible/inst/doc/NetAffxResource.pdf vignetteTitles: Retrieving MAGE and ARR sample attributes, Annotation retrieval with NetAffxResource hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyCompatible/inst/doc/MAGEAndARR.R, vignettes/AffyCompatible/inst/doc/NetAffxResource.R importsMe: IdMappingRetrieval dependencyCount: 15 Package: affyContam Version: 1.44.0 Depends: R (>= 2.7.0), tools, methods, utils, Biobase, affy, affydata Suggests: hgu95av2cdf License: Artistic-2.0 MD5sum: ca0f4ad7736706c395a7037768d85d1c NeedsCompilation: no Title: structured corruption of affymetrix cel file data Description: structured corruption of cel file data to demonstrate QA effectiveness biocViews: Infrastructure Author: V. Carey Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/affyContam git_branch: RELEASE_3_10 git_last_commit: fddfe14 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/affyContam_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/affyContam_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/affyContam_1.44.0.tgz vignettes: vignettes/affyContam/inst/doc/affyContam.pdf vignetteTitles: affy contamination tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyContam/inst/doc/affyContam.R dependencyCount: 15 Package: affycoretools Version: 1.58.4 Depends: Biobase, methods Imports: affy, limma, GOstats, gcrma, splines, xtable, AnnotationDbi, ggplot2, gplots, oligoClasses, ReportingTools, hwriter, lattice, S4Vectors, edgeR, RSQLite, BiocGenerics, DBI, Glimma Suggests: affydata, hgfocuscdf, BiocStyle, knitr, hgu95av2.db, rgl, rmarkdown License: Artistic-2.0 MD5sum: 590a90e026388ddd4da0dc552741fa02 NeedsCompilation: no Title: Functions useful for those doing repetitive analyses with Affymetrix GeneChips Description: Various wrapper functions that have been written to streamline the more common analyses that a core Biostatistician might see. biocViews: ReportWriting, Microarray, OneChannel, GeneExpression Author: James W. MacDonald Maintainer: James W. MacDonald VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/affycoretools git_branch: RELEASE_3_10 git_last_commit: e8ef0a9 git_last_commit_date: 2020-01-06 Date/Publication: 2020-01-06 source.ver: src/contrib/affycoretools_1.58.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/affycoretools_1.58.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/affycoretools_1.58.4.tgz vignettes: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.html vignetteTitles: Creating annotated output with \Biocpkg{affycoretools} and ReportingTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.R dependencyCount: 187 Package: AffyExpress Version: 1.52.0 Depends: R (>= 2.10), affy (>= 1.23.4), limma Suggests: simpleaffy, R2HTML, affyPLM, hgu95av2cdf, hgu95av2, test3cdf, genefilter, estrogen, annaffy, gcrma License: LGPL MD5sum: b5c2acf42fb5dc4a455ad331f66465f6 NeedsCompilation: no Title: Affymetrix Quality Assessment and Analysis Tool Description: The purpose of this package is to provide a comprehensive and easy-to-use tool for quality assessment and to identify differentially expressed genes in the Affymetrix gene expression data. biocViews: Microarray, OneChannel, QualityControl, Preprocessing, DifferentialExpression, Annotation, ReportWriting, Visualization Author: Xiwei Wu , Xuejun Arthur Li Maintainer: Xuejun Arthur Li git_url: https://git.bioconductor.org/packages/AffyExpress git_branch: RELEASE_3_10 git_last_commit: cab3660 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AffyExpress_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AffyExpress_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AffyExpress_1.52.0.tgz vignettes: vignettes/AffyExpress/inst/doc/AffyExpress.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyExpress/inst/doc/AffyExpress.R dependencyCount: 14 Package: affyILM Version: 1.38.0 Depends: R (>= 2.10.0), methods, gcrma Imports: affxparser (>= 1.16.0), affy, graphics, Biobase Suggests: AffymetrixDataTestFiles, hgfocusprobe License: GPL-3 MD5sum: 5cde8ea99c8f1d9b5abb6ef96a3d6453 NeedsCompilation: no Title: Linear Model of background subtraction and the Langmuir isotherm Description: affyILM is a preprocessing tool which estimates gene expression levels for Affymetrix Gene Chips. Input from physical chemistry is employed to first background subtract intensities before calculating concentrations on behalf of the Langmuir model. biocViews: Microarray, OneChannel, Preprocessing Author: K. Myriam Kroll, Fabrice Berger, Gerard Barkema, Enrico Carlon Maintainer: Myriam Kroll and Fabrice Berger git_url: https://git.bioconductor.org/packages/affyILM git_branch: RELEASE_3_10 git_last_commit: e6abfe8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/affyILM_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/affyILM_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/affyILM_1.38.0.tgz vignettes: vignettes/affyILM/inst/doc/affyILM.pdf vignetteTitles: affyILM1.3.0 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyILM/inst/doc/affyILM.R dependencyCount: 21 Package: affyio Version: 1.56.0 Depends: R (>= 2.6.0) Imports: zlibbioc, methods License: LGPL (>= 2) Archs: i386, x64 MD5sum: e8a56137b02d9ec691b75ee7b6bb34e7 NeedsCompilation: yes Title: Tools for parsing Affymetrix data files Description: Routines for parsing Affymetrix data files based upon file format information. Primary focus is on accessing the CEL and CDF file formats. biocViews: Microarray, DataImport, Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/affyio git_url: https://git.bioconductor.org/packages/affyio git_branch: RELEASE_3_10 git_last_commit: fd0e865 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/affyio_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/affyio_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/affyio_1.56.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPara, makecdfenv, SCAN.UPC, sscore importsMe: affy, affylmGUI, crlmm, ExiMiR, gcrma, oligo, oligoClasses, puma suggestsMe: BufferedMatrixMethods dependencyCount: 2 Package: affylmGUI Version: 1.60.0 Imports: grDevices, graphics, stats, utils, tcltk, tkrplot, limma, affy, affyio, affyPLM, gcrma, BiocGenerics, BiocManager, R2HTML, xtable License: GPL (>=2) MD5sum: 6f27922f83a5ce9c7dda37cdd9fc78dc NeedsCompilation: no Title: GUI for limma Package with Affymetrix Microarrays Description: A Graphical User Interface (GUI) for analysis of Affymetrix microarray gene expression data using the affy and limma packages. biocViews: GUI, GeneExpression, Transcription, DifferentialExpression, DataImport, Bayesian, Regression, TimeCourse, Microarray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: James Wettenhall [cre,aut], Ken Simpson [aut], Gordon Smyth [aut], Keith Satterley [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/affylmGUI/ git_url: https://git.bioconductor.org/packages/affylmGUI git_branch: RELEASE_3_10 git_last_commit: 255d7db git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/affylmGUI_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/affylmGUI_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/affylmGUI_1.60.0.tgz vignettes: vignettes/affylmGUI/inst/doc/affylmGUI.pdf, vignettes/affylmGUI/inst/doc/extract.pdf, vignettes/affylmGUI/inst/doc/about.html, vignettes/affylmGUI/inst/doc/CustMenu.html, vignettes/affylmGUI/inst/doc/index.html, vignettes/affylmGUI/inst/doc/windowsFocus.html vignetteTitles: affylmGUI Vignette, Extracting affy and limma objects from affylmGUI files, about.html, CustMenu.html, index.html, windowsFocus.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affylmGUI/inst/doc/affylmGUI.R dependencyCount: 26 Package: affyPara Version: 1.46.0 Depends: R (>= 2.5.0), methods, affy (>= 1.20.0), snow (>= 0.2-3), vsn (>= 3.6.0), aplpack (>= 1.1.1), affyio Suggests: affydata Enhances: affy License: GPL-3 MD5sum: 338e5457f5568aa59a30bc87506684fc NeedsCompilation: no Title: Parallelized preprocessing methods for Affymetrix Oligonucleotide Arrays Description: The package contains parallelized functions for exploratory oligonucleotide array analysis. The package is designed for large numbers of microarray data. biocViews: Microarray, Preprocessing Author: Markus Schmidberger , Esmeralda Vicedo , Ulrich Mansmann Maintainer: Markus Schmidberger URL: http://www.ibe.med.uni-muenchen.de git_url: https://git.bioconductor.org/packages/affyPara git_branch: RELEASE_3_10 git_last_commit: cbe3d99 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/affyPara_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/affyPara_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/affyPara_1.46.0.tgz vignettes: vignettes/affyPara/inst/doc/affyPara.pdf, vignettes/affyPara/inst/doc/vsnStudy.pdf vignetteTitles: Parallelized affy functions for preprocessing, Simulation Study for VSN Add-On Normalization and Subsample Size hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyPara/inst/doc/affyPara.R, vignettes/affyPara/inst/doc/vsnStudy.R dependencyCount: 66 Package: affypdnn Version: 1.60.0 Depends: R (>= 2.13.0), affy (>= 1.5) Suggests: affydata, hgu95av2probe License: LGPL MD5sum: 73074c64343b4512bba19bdeacb51800 NeedsCompilation: no Title: Probe Dependent Nearest Neighbours (PDNN) for the affy package Description: The package contains functions to perform the PDNN method described by Li Zhang et al. biocViews: OneChannel, Microarray, Preprocessing Author: H. Bjorn Nielsen and Laurent Gautier (Many thanks to Li Zhang early communications about the existence of the PDNN program and related publications). Maintainer: Laurent Gautier git_url: https://git.bioconductor.org/packages/affypdnn git_branch: RELEASE_3_10 git_last_commit: 9f8998f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/affypdnn_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/affypdnn_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/affypdnn_1.60.0.tgz vignettes: vignettes/affypdnn/inst/doc/affypdnn.pdf vignetteTitles: affypdnn hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affypdnn/inst/doc/affypdnn.R dependencyCount: 13 Package: affyPLM Version: 1.62.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), affy (>= 1.11.0), Biobase (>= 2.17.8), gcrma, stats, preprocessCore (>= 1.5.1) Imports: zlibbioc, graphics, grDevices, methods LinkingTo: preprocessCore Suggests: affydata, MASS License: GPL (>= 2) Archs: i386, x64 MD5sum: 2bdb5c1de7bd37c93cf87a9bd559f1c5 NeedsCompilation: yes Title: Methods for fitting probe-level models Description: A package that extends and improves the functionality of the base affy package. Routines that make heavy use of compiled code for speed. Central focus is on implementation of methods for fitting probe-level models and tools using these models. PLM based quality assessment tools. biocViews: Microarray, OneChannel, Preprocessing, QualityControl Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/affyPLM git_url: https://git.bioconductor.org/packages/affyPLM git_branch: RELEASE_3_10 git_last_commit: c61a1ea git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/affyPLM_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/affyPLM_1.62.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/affyPLM_1.62.0.tgz vignettes: vignettes/affyPLM/inst/doc/AffyExtensions.pdf, vignettes/affyPLM/inst/doc/MAplots.pdf, vignettes/affyPLM/inst/doc/QualityAssess.pdf, vignettes/affyPLM/inst/doc/ThreeStep.pdf vignetteTitles: affyPLM: Fitting Probe Level Models, affyPLM: Advanced use of the MAplot function, affyPLM: Model Based QC Assessment of Affymetrix GeneChips, affyPLM: the threestep function hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyPLM/inst/doc/AffyExtensions.R, vignettes/affyPLM/inst/doc/MAplots.R, vignettes/affyPLM/inst/doc/QualityAssess.R, vignettes/affyPLM/inst/doc/ThreeStep.R dependsOnMe: RefPlus importsMe: affylmGUI, affyQCReport, arrayQualityMetrics, mimager suggestsMe: AffyExpress, arrayMvout, ArrayTools, BiocCaseStudies, BiocGenerics, ELBOW, frmaTools, metahdep, piano dependencyCount: 20 Package: affyQCReport Version: 1.64.0 Depends: Biobase (>= 1.13.16), affy, lattice Imports: affy, affyPLM, Biobase, genefilter, graphics, grDevices, lattice, RColorBrewer, simpleaffy, stats, utils, xtable Suggests: tkWidgets (>= 1.5.23), affydata (>= 1.4.1) License: LGPL (>= 2) MD5sum: e14208f1bdc34113b61700ede9f6b78c NeedsCompilation: no Title: QC Report Generation for affyBatch objects Description: This package creates a QC report for an AffyBatch object. The report is intended to allow the user to quickly assess the quality of a set of arrays in an AffyBatch object. biocViews: Microarray,OneChannel,QualityControl Author: Craig Parman , Conrad Halling , Robert Gentleman Maintainer: Craig Parman git_url: https://git.bioconductor.org/packages/affyQCReport git_branch: RELEASE_3_10 git_last_commit: c97d070 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/affyQCReport_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/affyQCReport_1.64.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/affyQCReport_1.64.0.tgz vignettes: vignettes/affyQCReport/inst/doc/affyQCReport.pdf vignetteTitles: affyQCReport: Methods for Generating Affymetrix QC Reports hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyQCReport/inst/doc/affyQCReport.R suggestsMe: BiocCaseStudies dependencyCount: 49 Package: AffyRNADegradation Version: 1.32.0 Depends: R (>= 2.9.0), methods, affy Suggests: AmpAffyExample License: GPL-2 MD5sum: a5a08fad72cbc6272a716bf6a4409ab1 NeedsCompilation: no Title: Analyze and correct probe positional bias in microarray data due to RNA degradation Description: The package helps with the assessment and correction of RNA degradation effects in Affymetrix 3' expression arrays. The parameter d gives a robust and accurate measure of RNA integrity. The correction removes the probe positional bias, and thus improves comparability of samples that are affected by RNA degradation. biocViews: GeneExpression, Microarray, OneChannel, Preprocessing, QualityControl Author: Mario Fasold Maintainer: Mario Fasold git_url: https://git.bioconductor.org/packages/AffyRNADegradation git_branch: RELEASE_3_10 git_last_commit: f5821da git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AffyRNADegradation_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AffyRNADegradation_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AffyRNADegradation_1.32.0.tgz vignettes: vignettes/AffyRNADegradation/inst/doc/vignette.pdf vignetteTitles: AffyRNADegradation Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyRNADegradation/inst/doc/vignette.R dependencyCount: 13 Package: AGDEX Version: 1.34.0 Depends: R (>= 2.10), Biobase, GSEABase Imports: stats License: GPL Version 2 or later MD5sum: ad9ededbdb13e45f16acd3500371b0ba NeedsCompilation: no Title: Agreement of Differential Expression Analysis Description: A tool to evaluate agreement of differential expression for cross-species genomics biocViews: Microarray, Genetics, GeneExpression Author: Stan Pounds ; Cuilan Lani Gao Maintainer: Cuilan lani Gao git_url: https://git.bioconductor.org/packages/AGDEX git_branch: RELEASE_3_10 git_last_commit: f34d12b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AGDEX_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AGDEX_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AGDEX_1.34.0.tgz vignettes: vignettes/AGDEX/inst/doc/AGDEX.pdf vignetteTitles: AGDEX.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AGDEX/inst/doc/AGDEX.R dependencyCount: 33 Package: agilp Version: 3.18.0 Depends: R (>= 2.14.0) License: GPL-3 MD5sum: 4e7d129f39239b8cf25f90492c4a69b6 NeedsCompilation: no Title: Agilent expression array processing package Description: More about what it does (maybe more than one line) Author: Benny Chain Maintainer: Benny Chain git_url: https://git.bioconductor.org/packages/agilp git_branch: RELEASE_3_10 git_last_commit: b69a5d7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/agilp_3.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/agilp_3.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/agilp_3.18.0.tgz vignettes: vignettes/agilp/inst/doc/agilp_manual.pdf vignetteTitles: An R Package for processing expression microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/agilp/inst/doc/agilp_manual.R dependencyCount: 0 Package: AgiMicroRna Version: 2.36.0 Depends: R (>= 2.10),methods,Biobase,limma,affy (>= 1.22),preprocessCore,affycoretools Imports: Biobase Suggests: geneplotter,marray,gplots,gtools,gdata,codelink License: GPL-3 MD5sum: 33fb5e0c50fbd48dd816c819cf1e3aa3 NeedsCompilation: no Title: Processing and Differential Expression Analysis of Agilent microRNA chips Description: Processing and Analysis of Agilent microRNA data biocViews: Microarray, AgilentChip, OneChannel, Preprocessing, DifferentialExpression Author: Pedro Lopez-Romero Maintainer: Pedro Lopez-Romero git_url: https://git.bioconductor.org/packages/AgiMicroRna git_branch: RELEASE_3_10 git_last_commit: a8b23d2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AgiMicroRna_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AgiMicroRna_2.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AgiMicroRna_2.36.0.tgz vignettes: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.pdf vignetteTitles: AgiMicroRna hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.R dependencyCount: 188 Package: AIMS Version: 1.18.0 Depends: R (>= 2.10), e1071, Biobase Suggests: breastCancerVDX, hgu133a.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 506b9d20b05f7864b2e93919dfa2cd4f NeedsCompilation: no Title: AIMS : Absolute Assignment of Breast Cancer Intrinsic Molecular Subtype Description: This package contains the AIMS implementation. It contains necessary functions to assign the five intrinsic molecular subtypes (Luminal A, Luminal B, Her2-enriched, Basal-like, Normal-like). Assignments could be done on individual samples as well as on dataset of gene expression data. biocViews: ImmunoOncology, Classification, RNASeq, Microarray, Software, GeneExpression Author: Eric R. Paquet, Michael T. Hallett Maintainer: Eric R Paquet URL: http://www.bci.mcgill.ca/AIMS git_url: https://git.bioconductor.org/packages/AIMS git_branch: RELEASE_3_10 git_last_commit: 16edc7b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AIMS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AIMS_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AIMS_1.18.0.tgz vignettes: vignettes/AIMS/inst/doc/AIMS.pdf vignetteTitles: AIMS An Introduction (HowTo) hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AIMS/inst/doc/AIMS.R dependsOnMe: genefu dependencyCount: 11 Package: ALDEx2 Version: 1.18.0 Depends: methods, stats Imports: BiocParallel, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, multtest Suggests: testthat, BiocStyle, knitr, rmarkdown License: file LICENSE MD5sum: ee3c2f25c8c37c0318a852dad0f2b593 NeedsCompilation: no Title: Analysis Of Differential Abundance Taking Sample Variation Into Account Description: A differential abundance analysis for the comparison of two or more conditions. Useful for analyzing data from standard RNA-seq or meta-RNA-seq assays as well as selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, optimized for three or more experimental replicates. The method infers biological and sampling variation to calculate the expected false discovery rate, given the variation, based on a Wilcoxon Rank Sum test and Welch's t-test (via aldex.ttest), a Kruskal-Wallis test (via aldex.kw), a generalized linear model (via aldex.glm), or a correlation test (via aldex.corr). All tests report p-values and Benjamini-Hochberg corrected p-values. biocViews: DifferentialExpression, RNASeq, Transcriptomics, GeneExpression, DNASeq, ChIPSeq, Bayesian, Sequencing, Software, Microbiome, Metagenomics, ImmunoOncology Author: Greg Gloor, Andrew Fernandes, Jean Macklaim, Arianne Albert, Matt Links, Thomas Quinn, Jia Rong Wu, Ruth Grace Wong, Brandon Lieng Maintainer: Greg Gloor URL: https://github.com/ggloor/ALDEx_bioc VignetteBuilder: knitr BugReports: https://github.com/ggloor/ALDEx_bioc/issues git_url: https://git.bioconductor.org/packages/ALDEx2 git_branch: RELEASE_3_10 git_last_commit: 3355bad git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ALDEx2_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ALDEx2_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ALDEx2_1.18.0.tgz vignettes: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.pdf vignetteTitles: ANOVA-Like Differential Expression tool for high throughput sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.R dependsOnMe: omicplotR dependencyCount: 36 Package: alevinQC Version: 1.2.0 Depends: R (>= 3.6) Imports: rmarkdown, tools, methods, ggplot2, GGally, dplyr, rjson, shiny, shinydashboard, DT, stats, utils, tximport (>= 1.11.5), cowplot Suggests: knitr, BiocStyle, testthat, License: MIT + file LICENSE MD5sum: 89838fb1933a80e04b74dc36f4bd15b6 NeedsCompilation: no Title: Generate QC Reports For Alevin Output Description: Generate QC reports summarizing the output from an alevin run. Reports can be generated as html or pdf files, or as shiny applications. biocViews: QualityControl, SingleCell Author: Charlotte Soneson [aut, cre] (), Avi Srivastava [aut] Maintainer: Charlotte Soneson URL: https://github.com/csoneson/alevinQC VignetteBuilder: knitr BugReports: https://github.com/csoneson/alevinQC/issues git_url: https://git.bioconductor.org/packages/alevinQC git_branch: RELEASE_3_10 git_last_commit: 8ca336f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/alevinQC_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/alevinQC_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/alevinQC_1.2.0.tgz vignettes: vignettes/alevinQC/inst/doc/alevinqc.html vignetteTitles: alevinQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alevinQC/inst/doc/alevinqc.R dependencyCount: 92 Package: AllelicImbalance Version: 1.24.0 Depends: R (>= 3.2.0), grid, GenomicRanges (>= 1.31.8), SummarizedExperiment (>= 0.2.0), GenomicAlignments (>= 1.15.6) Imports: methods, BiocGenerics, AnnotationDbi, BSgenome (>= 1.47.3), VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Rsamtools (>= 1.99.3), GenomicFeatures (>= 1.31.3), Gviz, lattice, latticeExtra, gridExtra, seqinr, GenomeInfoDb, nlme Suggests: testthat, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: a8ce00e0c0283c1366de59c4cfa06a5f NeedsCompilation: no Title: Investigates Allele Specific Expression Description: Provides a framework for allelic specific expression investigation using RNA-seq data. biocViews: Genetics, Infrastructure, Sequencing Author: Jesper R Gadin, Lasse Folkersen Maintainer: Jesper R Gadin URL: https://github.com/pappewaio/AllelicImbalance VignetteBuilder: knitr BugReports: https://github.com/pappewaio/AllelicImbalance/issues git_url: https://git.bioconductor.org/packages/AllelicImbalance git_branch: RELEASE_3_10 git_last_commit: 068ad8b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AllelicImbalance_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AllelicImbalance_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AllelicImbalance_1.24.0.tgz vignettes: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.pdf vignetteTitles: AllelicImbalance Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.R dependencyCount: 148 Package: AlphaBeta Version: 1.0.0 Depends: R (>= 3.6.0) Imports: dplyr (>= 0.7), data.table (>= 1.10), stringr (>= 1.3), utils (>= 3.6.0), gtools (>= 3.8.0), optimx (>= 2018-7.10), expm (>= 0.999-4), stats (>= 3.6), BiocParallel (>= 1.18) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: ccaa9ed2ef12a6aed23804e1af82af81 NeedsCompilation: no Title: Computational inference of epimutation rates and spectra from high-throughput DNA methylation data in plants Description: AlphaBeta is a computational method for estimating epimutation rates and spectra from high-throughput DNA methylation data in plants. The method has been specifically designed to: 1. analyze 'germline' epimutations in the context of multi-generational mutation accumulation lines (MA-lines). 2. analyze 'somatic' epimutations in the context of plant development and aging. biocViews: Epigenetics, FunctionalGenomics, Genetics, MathematicalBiology Author: Yadollah Shahryary Dizaji [cre, aut], Frank Johannes [aut], Rashmi Hazarika [aut] Maintainer: Yadollah Shahryary Dizaji VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AlphaBeta git_branch: RELEASE_3_10 git_last_commit: ce752ca git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AlphaBeta_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AlphaBeta_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AlphaBeta_1.0.0.tgz vignettes: vignettes/AlphaBeta/inst/doc/AlphaBeta.pdf vignetteTitles: AlphaBeta hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AlphaBeta/inst/doc/AlphaBeta.R dependencyCount: 45 Package: alpine Version: 1.12.0 Depends: R (>= 3.3) Imports: Biostrings, IRanges, GenomicRanges, GenomicAlignments, Rsamtools, SummarizedExperiment, GenomicFeatures, speedglm, splines, graph, RBGL, stringr, stats, methods, graphics, GenomeInfoDb, S4Vectors Suggests: knitr, testthat, alpineData, rtracklayer, ensembldb, BSgenome.Hsapiens.NCBI.GRCh38, RColorBrewer License: GPL (>=2) MD5sum: 9ed211426ec5f68b923a198c74b7d98d NeedsCompilation: no Title: alpine Description: Fragment sequence bias modeling and correction for RNA-seq transcript abundance estimation. biocViews: Sequencing, RNASeq, AlternativeSplicing, DifferentialSplicing, GeneExpression, Transcription, Coverage, BatchEffect, Normalization, Visualization, QualityControl Author: Michael Love, Rafael Irizarry Maintainer: Michael Love VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alpine git_branch: RELEASE_3_10 git_last_commit: a46f925 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/alpine_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/alpine_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/alpine_1.12.0.tgz vignettes: vignettes/alpine/inst/doc/alpine.html vignetteTitles: alpine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/alpine/inst/doc/alpine.R dependencyCount: 88 Package: ALPS Version: 1.0.0 Depends: R (>= 3.6) Imports: assertthat, BiocParallel, ChIPseeker, corrplot, data.table, dplyr, GenomicRanges, GGally, genefilter, gghalves, ggplot2, ggseqlogo, Gviz, magrittr, org.Hs.eg.db, plyr, reshape2, rtracklayer, stats, stringr, tibble, tidyr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, utils Suggests: knitr, rmarkdown, ComplexHeatmap, circlize, testthat License: MIT + file LICENSE MD5sum: a9cb681881f0efc652502e19a3d0592e NeedsCompilation: no Title: AnaLysis routines for ePigenomicS data Description: The package provides analysis and publication quality visualization routines for genome-wide epigenomics data such as histone modification or transcription factor ChIP-seq, ATAC-seq, DNase-seq etc. The functions in the package can be used with any type of data that can be represented with bigwig files at any resolution. The goal of the ALPS is to provide analysis tools for most downstream analysis without leaving the R environment and most tools in the package require a minimal input that can be prepared with basic R, unix or excel skills. biocViews: Epigenetics, Sequencing, ChIPSeq, ATACSeq, Visualization, Transcription, HistoneModification Author: Venu Thatikonda, Natalie Jäger Maintainer: Venu Thatikonda URL: https://github.com/itsvenu/ALPS VignetteBuilder: knitr BugReports: https://github.com/itsvenu/ALPS/issues git_url: https://git.bioconductor.org/packages/ALPS git_branch: RELEASE_3_10 git_last_commit: 25ef438 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ALPS_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ALPS_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ALPS_1.0.0.tgz vignettes: vignettes/ALPS/inst/doc/ALPS-vignette.html vignetteTitles: ALPS-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ALPS/inst/doc/ALPS-vignette.R dependencyCount: 193 Package: alsace Version: 1.22.0 Depends: R (>= 2.10), ALS, ptw (>= 1.0.6) Suggests: lattice, knitr License: GPL (>= 2) MD5sum: 3af6fbced2231e1d93deda35676e78c5 NeedsCompilation: no Title: ALS for the Automatic Chemical Exploration of mixtures Description: Alternating Least Squares (or Multivariate Curve Resolution) for analytical chemical data, in particular hyphenated data where the first direction is a retention time axis, and the second a spectral axis. Package builds on the basic als function from the ALS package and adds functionality for high-throughput analysis, including definition of time windows, clustering of profiles, retention time correction, etcetera. Author: Ron Wehrens Maintainer: Ron Wehrens URL: https://github.com/rwehrens/alsace VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alsace git_branch: RELEASE_3_10 git_last_commit: 58bfd6c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/alsace_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/alsace_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/alsace_1.22.0.tgz vignettes: vignettes/alsace/inst/doc/alsace.pdf vignetteTitles: alsace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: altcdfenvs Version: 2.48.0 Depends: R (>= 2.7), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.9.25), Biobase (>= 2.15.1), affy, makecdfenv, Biostrings, hypergraph Suggests: plasmodiumanophelescdf, hgu95acdf, hgu133aprobe, hgu133a.db, hgu133acdf, Rgraphviz, RColorBrewer License: GPL (>= 2) MD5sum: 5f660d3ea670c4d92720b4a66ff41f21 NeedsCompilation: no Title: alternative CDF environments (aka probeset mappings) Description: Convenience data structures and functions to handle cdfenvs biocViews: Microarray, OneChannel, QualityControl, Preprocessing, Annotation, ProprietaryPlatforms, Transcription Author: Laurent Gautier Maintainer: Laurent Gautier git_url: https://git.bioconductor.org/packages/altcdfenvs git_branch: RELEASE_3_10 git_last_commit: ae877d2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/altcdfenvs_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/altcdfenvs_2.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/altcdfenvs_2.48.0.tgz vignettes: vignettes/altcdfenvs/inst/doc/altcdfenvs.pdf, vignettes/altcdfenvs/inst/doc/modify.pdf, vignettes/altcdfenvs/inst/doc/ngenomeschips.pdf vignetteTitles: altcdfenvs, Modifying existing CDF environments to make alternative CDF environments, Alternative CDF environments for 2(or more)-genomes chips hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/altcdfenvs/inst/doc/altcdfenvs.R, vignettes/altcdfenvs/inst/doc/modify.R, vignettes/altcdfenvs/inst/doc/ngenomeschips.R importsMe: Harshlight dependencyCount: 21 Package: AMARETTO Version: 1.2.0 Depends: R (>= 3.6), impute, doParallel, grDevices, dplyr, methods, ComplexHeatmap Imports: callr (>= 3.0.0.9001), Matrix, Rcpp, BiocFileCache, DT, MultiAssayExperiment, circlize, curatedTCGAData, foreach, glmnet, httr, limma, matrixStats, readr, reshape2, tibble, rmarkdown, graphics, grid, parallel, stats, knitr, ggplot2, gridExtra, utils LinkingTo: Rcpp Suggests: testthat, MASS, knitr License: Apache License (== 2.0) + file LICENSE MD5sum: c0910257d7cb92e27d475a1c5b50fdbf NeedsCompilation: no Title: Regulatory Network Inference and Driver Gene Evaluation using Integrative Multi-Omics Analysis and Penalized Regression Description: Integrating an increasing number of available multi-omics cancer data remains one of the main challenges to improve our understanding of cancer. One of the main challenges is using multi-omics data for identifying novel cancer driver genes. We have developed an algorithm, called AMARETTO, that integrates copy number, DNA methylation and gene expression data to identify a set of driver genes by analyzing cancer samples and connects them to clusters of co-expressed genes, which we define as modules. We applied AMARETTO in a pancancer setting to identify cancer driver genes and their modules on multiple cancer sites. AMARETTO captures modules enriched in angiogenesis, cell cycle and EMT, and modules that accurately predict survival and molecular subtypes. This allows AMARETTO to identify novel cancer driver genes directing canonical cancer pathways. biocViews: StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,Transcription,Preprocessing,BatchEffect,DataImport,mRNAMicroarray,MicroRNAArray,Regression,Clustering,RNASeq,CopyNumberVariation,Sequencing,Microarray,Normalization,Network,Bayesian,ExonArray,OneChannel,TwoChannel,ProprietaryPlatforms,AlternativeSplicing,DifferentialExpression,DifferentialSplicing,GeneSetEnrichment,MultipleComparison,QualityControl,TimeCourse Author: Jayendra Shinde, Celine Everaert, Shaimaa Bakr, Mohsen Nabian, Jishu Xu, Vincent Carey, Nathalie Pochet and Olivier Gevaert Maintainer: Olivier Gevaert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AMARETTO git_branch: RELEASE_3_10 git_last_commit: 8b0d34e git_last_commit_date: 2019-11-26 Date/Publication: 2019-12-09 source.ver: src/contrib/AMARETTO_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AMARETTO_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AMARETTO_1.2.0.tgz vignettes: vignettes/AMARETTO/inst/doc/amaretto.html vignetteTitles: "1. Introduction" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AMARETTO/inst/doc/amaretto.R dependencyCount: 153 Package: AMOUNTAIN Version: 1.12.0 Depends: R (>= 3.3.0) Imports: stats Suggests: BiocStyle, qgraph, knitr, rmarkdown License: GPL (>= 2) Archs: i386, x64 MD5sum: 48b11312302a0adaebb01d8b0699995f NeedsCompilation: yes Title: Active modules for multilayer weighted gene co-expression networks: a continuous optimization approach Description: A pure data-driven gene network, weighted gene co-expression network (WGCN) could be constructed only from expression profile. Different layers in such networks may represent different time points, multiple conditions or various species. AMOUNTAIN aims to search active modules in multi-layer WGCN using a continuous optimization approach. biocViews: GeneExpression, Microarray, DifferentialExpression, Network Author: Dong Li, Shan He, Zhisong Pan and Guyu Hu Maintainer: Dong Li SystemRequirements: gsl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AMOUNTAIN git_branch: RELEASE_3_10 git_last_commit: fc4db0b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AMOUNTAIN_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AMOUNTAIN_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AMOUNTAIN_1.12.0.tgz vignettes: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.R importsMe: MODA dependencyCount: 1 Package: amplican Version: 1.8.2 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.22.0), Biostrings (>= 2.44.2), data.table (>= 1.10.4-3) Imports: Rcpp, utils (>= 3.4.1), S4Vectors (>= 0.14.3), ShortRead (>= 1.34.0), IRanges (>= 2.10.2), GenomicRanges (>= 1.28.4), GenomeInfoDb (>= 1.12.2), BiocParallel (>= 1.10.1), gtable (>= 0.2.0), gridExtra (>= 2.2.1), ggplot2 (>= 2.2.0), ggthemes (>= 3.4.0), waffle (>= 0.7.0), stringr (>= 1.2.0), stats (>= 3.4.1), matrixStats (>= 0.52.2), Matrix (>= 1.2-10), dplyr (>= 0.7.2), rmarkdown (>= 1.6), knitr (>= 1.16), clusterCrit (>= 1.2.7) LinkingTo: Rcpp Suggests: testthat, BiocStyle, GenomicAlignments License: GPL-3 Archs: i386, x64 MD5sum: bfe93b4353ff1322c0e0ceba24cf85cc NeedsCompilation: yes Title: Automated analysis of CRISPR experiments Description: `amplican` performs alignment of the amplicon reads, normalizes gathered data, calculates multiple statistics (e.g. cut rates, frameshifts) and presents results in form of aggregated reports. Data and statistics can be broken down by experiments, barcodes, user defined groups, guides and amplicons allowing for quick identification of potential problems. biocViews: ImmunoOncology, Technology, Alignment, qPCR, CRISPR Author: Kornel Labun [aut], Eivind Valen [cph, cre] Maintainer: Eivind Valen URL: https://github.com/valenlab/amplican VignetteBuilder: knitr BugReports: https://github.com/valenlab/amplican/issues git_url: https://git.bioconductor.org/packages/amplican git_branch: RELEASE_3_10 git_last_commit: e1a60b3 git_last_commit_date: 2019-12-10 Date/Publication: 2019-12-16 source.ver: src/contrib/amplican_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/amplican_1.8.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/amplican_1.8.2.tgz vignettes: vignettes/amplican/inst/doc/amplicanFAQ.html, vignettes/amplican/inst/doc/amplicanOverview.html, vignettes/amplican/inst/doc/example_amplicon_report.html, vignettes/amplican/inst/doc/example_barcode_report.html, vignettes/amplican/inst/doc/example_group_report.html, vignettes/amplican/inst/doc/example_guide_report.html, vignettes/amplican/inst/doc/example_id_report.html, vignettes/amplican/inst/doc/example_index.html vignetteTitles: amplican FAQ, amplican overview, example amplicon_report report, example barcode_report report, example group_report report, example guide_report report, example id_report report, example index report hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/amplican/inst/doc/amplicanOverview.R, vignettes/amplican/inst/doc/example_amplicon_report.R, vignettes/amplican/inst/doc/example_barcode_report.R, vignettes/amplican/inst/doc/example_group_report.R, vignettes/amplican/inst/doc/example_guide_report.R, vignettes/amplican/inst/doc/example_id_report.R, vignettes/amplican/inst/doc/example_index.R dependencyCount: 111 Package: AnalysisPageServer Version: 1.20.0 Imports: methods, log4r, tools, rjson, Biobase, graph Suggests: RUnit, XML, knitr Enhances: Rook (>= 1.1), fork, FastRWeb, ggplot2 License: Artistic-2.0 Archs: i386, x64 MD5sum: 18d59c02b70c4e50ece98a20dbf00078 NeedsCompilation: yes Title: A framework for sharing interactive data and plots from R through the web Description: AnalysisPageServer is a modular system that enables sharing of customizable R analyses via the web. biocViews: GUI, Visualization, DataRepresentation Author: Brad Friedman , Adrian Nowicki, Hunter Whitney , Matthew Brauer , Sara Moore, Konrad Debski Maintainer: Brad Friedman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnalysisPageServer git_branch: RELEASE_3_10 git_last_commit: aafd036 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AnalysisPageServer_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AnalysisPageServer_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AnalysisPageServer_1.20.0.tgz vignettes: vignettes/AnalysisPageServer/inst/doc/AnalysisPageServer.html, vignettes/AnalysisPageServer/inst/doc/ApacheDeployment.html, vignettes/AnalysisPageServer/inst/doc/embedding.html, vignettes/AnalysisPageServer/inst/doc/ExampleServers.html, vignettes/AnalysisPageServer/inst/doc/FastRWebDeployment.html, vignettes/AnalysisPageServer/inst/doc/InteractiveApps.html, vignettes/AnalysisPageServer/inst/doc/Interactivity.html, vignettes/AnalysisPageServer/inst/doc/Licenses.html, vignettes/AnalysisPageServer/inst/doc/StaticContent.html, vignettes/AnalysisPageServer/inst/doc/TrappingConditions.html vignetteTitles: 0. AnalysisPageServer, 6. Apache Deployment, 2. Embedding APS datasets in other documents, 4. Non-interactive servers and Rook Deployment, 7. FastRWeb Deployment, 5. Interactive Apps AnalysisPageServer, 3. AnalysisPageServer Interactivity, 8. Licenses, 1. Making Static Content Interactive with AnalysisPageServer, 8. Condition Trapping hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/AnalysisPageServer/inst/doc/AnalysisPageServer.R, vignettes/AnalysisPageServer/inst/doc/ApacheDeployment.R, vignettes/AnalysisPageServer/inst/doc/embedding.R, vignettes/AnalysisPageServer/inst/doc/ExampleServers.R, vignettes/AnalysisPageServer/inst/doc/FastRWebDeployment.R, vignettes/AnalysisPageServer/inst/doc/InteractiveApps.R, vignettes/AnalysisPageServer/inst/doc/Interactivity.R, vignettes/AnalysisPageServer/inst/doc/StaticContent.R, vignettes/AnalysisPageServer/inst/doc/TrappingConditions.R dependencyCount: 12 Package: anamiR Version: 1.13.0 Depends: R (>= 3.5) Imports: stats, DBI, limma, lumi, agricolae, RMySQL, DESeq2, SummarizedExperiment, gplots, gage, S4Vectors Suggests: knitr, rmarkdown, data.table License: GPL-2 MD5sum: 5c84f56890cb459233440c444012b3e5 NeedsCompilation: no Title: An integrated analysis package of miRNA and mRNA expression data Description: This package is intended to identify potential interactions of miRNA-target gene interactions from miRNA and mRNA expression data. It contains functions for statistical test, databases of miRNA-target gene interaction and functional analysis. biocViews: Software, AssayDomain, GeneExpression, BiologicalQuestion, GeneSetEnrichment, GeneTarget, Normalization, Pathways, DifferentialExpression, GeneRegulation, ResearchField, Genetics, Technology, Microarray, Sequencing, miRNA, WorkflowStep Author: Ti-Tai Wang [aut, cre], Tzu-Pin Lu [aut], Chien-Yueh Lee[ctb,] Eric Y. Chuang [aut] Maintainer: Ti-Tai Wang URL: https://github.com/AllenTiTaiWang/anamiR VignetteBuilder: knitr BugReports: https://github.com/AllenTiTaiWang/anamiR/issues git_url: https://git.bioconductor.org/packages/anamiR git_branch: master git_last_commit: 322d81d git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-09 source.ver: src/contrib/anamiR_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/anamiR_1.13.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/anamiR_1.13.0.tgz vignettes: vignettes/anamiR/inst/doc/IntroductionToanamiR.html vignetteTitles: Introduction to anamiR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anamiR/inst/doc/IntroductionToanamiR.R dependencyCount: 207 Package: Anaquin Version: 2.10.0 Depends: R (>= 3.3), ggplot2 (>= 2.2.0) Imports: ggplot2, ROCR, knitr, qvalue, locfit, methods, stats, utils, plyr, DESeq2 Suggests: RUnit, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: acb440a81a55fe5ab368da9ce2c18202 NeedsCompilation: no Title: Statistical analysis of sequins Description: The project is intended to support the use of sequins (synthetic sequencing spike-in controls) owned and made available by the Garvan Institute of Medical Research. The goal is to provide a standard open source library for quantitative analysis, modelling and visualization of spike-in controls. biocViews: ImmunoOncology, DifferentialExpression, Preprocessing, RNASeq, GeneExpression, Software Author: Ted Wong Maintainer: Ted Wong URL: www.sequin.xyz VignetteBuilder: knitr BugReports: https://github.com/student-t/RAnaquin/issues git_url: https://git.bioconductor.org/packages/Anaquin git_branch: RELEASE_3_10 git_last_commit: fb0476a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Anaquin_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Anaquin_2.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Anaquin_2.10.0.tgz vignettes: vignettes/Anaquin/inst/doc/Anaquin.pdf vignetteTitles: Anaquin - Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Anaquin/inst/doc/Anaquin.R dependencyCount: 130 Package: AneuFinder Version: 1.14.0 Depends: R (>= 3.5), GenomicRanges, ggplot2, cowplot, AneuFinderData Imports: methods, utils, grDevices, graphics, stats, foreach, doParallel, BiocGenerics (>= 0.31.6), S4Vectors, GenomeInfoDb, IRanges, Rsamtools, bamsignals, DNAcopy, ecp, Biostrings, GenomicAlignments, reshape2, ggdendro, ggrepel, ReorderCluster, mclust Suggests: knitr, BiocStyle, testthat, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10 License: Artistic-2.0 Archs: i386, x64 MD5sum: 5bb7fe50ca6067529b6256ae54461283 NeedsCompilation: yes Title: Analysis of Copy Number Variation in Single-Cell-Sequencing Data Description: AneuFinder implements functions for copy-number detection, breakpoint detection, and karyotype and heterogeneity analysis in single-cell whole genome sequencing and strand-seq data. biocViews: ImmunoOncology, Software, Sequencing, SingleCell, CopyNumberVariation, GenomicVariation, HiddenMarkovModel, WholeGenome Author: Aaron Taudt, Bjorn Bakker, David Porubsky Maintainer: Aaron Taudt URL: https://github.com/ataudt/aneufinder.git VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AneuFinder git_branch: RELEASE_3_10 git_last_commit: 536431e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AneuFinder_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AneuFinder_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AneuFinder_1.14.0.tgz vignettes: vignettes/AneuFinder/inst/doc/AneuFinder.pdf vignetteTitles: A quick introduction to AneuFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AneuFinder/inst/doc/AneuFinder.R dependencyCount: 103 Package: ANF Version: 1.8.0 Imports: igraph, Biobase, survival, MASS, stats, RColorBrewer Suggests: ExperimentHub, SNFtool, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 5b63bcd06d6dda78d1f943edbd11317d NeedsCompilation: no Title: Affinity Network Fusion for Complex Patient Clustering Description: This package is used for complex patient clustering by integrating multi-omic data through affinity network fusion. biocViews: Clustering, GraphAndNetwork, Network Author: Tianle Ma, Aidong Zhang Maintainer: Tianle Ma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ANF git_branch: RELEASE_3_10 git_last_commit: 9804022 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ANF_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ANF_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ANF_1.8.0.tgz vignettes: vignettes/ANF/inst/doc/ANF.html vignetteTitles: Cancer Patient Clustering with ANF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ANF/inst/doc/ANF.R dependencyCount: 18 Package: animalcules Version: 1.2.1 Depends: R (>= 3.6.0) Imports: assertthat, shiny, shinyjs, DESeq2, caret, plotly, ggplot2, rentrez, reshape2, covr, ape, vegan, dplyr, magrittr, MultiAssayExperiment, SummarizedExperiment, S4Vectors (>= 0.23.19), XML, forcats, scales, lattice, glmnet, tsne, DMwR, plotROC, DT, reactable, utils, limma, methods, stats, tibble, biomformat, umap, Matrix Suggests: BiocStyle, knitr, rmarkdown, testthat, usethis License: Artistic-2.0 MD5sum: 0dbe62a53b1080ac6ec7c340879ac5d0 NeedsCompilation: no Title: Interactive microbiome analysis toolkit Description: animalcules is an R package for utilizing up-to-date data analytics, visualization methods, and machine learning models to provide users an easy-to-use interactive microbiome analysis framework. It can be used as a standalone software package or users can explore their data with the accompanying interactive R Shiny application. Traditional microbiome analysis such as alpha/beta diversity and differential abundance analysis are enhanced, while new methods like biomarker identification are introduced by animalcules. Powerful interactive and dynamic figures generated by animalcules enable users to understand their data better and discover new insights. biocViews: Microbiome, Metagenomics, Coverage, Visualization Author: Yue Zhao [aut, cre] (), Anthony Federico [aut] (), W. Evan Johnson [aut] () Maintainer: Yue Zhao URL: https://github.com/compbiomed/animalcules VignetteBuilder: knitr BugReports: https://github.com/compbiomed/animalcules/issues git_url: https://git.bioconductor.org/packages/animalcules git_branch: RELEASE_3_10 git_last_commit: d934ddb git_last_commit_date: 2020-03-16 Date/Publication: 2020-03-16 source.ver: src/contrib/animalcules_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/animalcules_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/animalcules_1.2.1.tgz vignettes: vignettes/animalcules/inst/doc/animalcules.html vignetteTitles: animalcules hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/animalcules/inst/doc/animalcules.R dependencyCount: 197 Package: annaffy Version: 1.58.0 Depends: R (>= 2.5.0), methods, Biobase, GO.db, KEGG.db Imports: AnnotationDbi (>= 0.1.15), DBI Suggests: hgu95av2.db, multtest, tcltk License: LGPL MD5sum: 09034da5d4ed2b062a152e03d82a7fdb NeedsCompilation: no Title: Annotation tools for Affymetrix biological metadata Description: Functions for handling data from Bioconductor Affymetrix annotation data packages. Produces compact HTML and text reports including experimental data and URL links to many online databases. Allows searching biological metadata using various criteria. biocViews: OneChannel, Microarray, Annotation, GO, Pathways, ReportWriting Author: Colin A. Smith Maintainer: Colin A. Smith git_url: https://git.bioconductor.org/packages/annaffy git_branch: RELEASE_3_10 git_last_commit: 5617008 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/annaffy_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/annaffy_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/annaffy_1.58.0.tgz vignettes: vignettes/annaffy/inst/doc/annaffy.pdf vignetteTitles: annaffy Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annaffy/inst/doc/annaffy.R dependsOnMe: a4Base, a4Reporting, PGSEA, webbioc suggestsMe: AffyExpress, ArrayTools, BiocCaseStudies dependencyCount: 28 Package: annmap Version: 1.28.0 Depends: R (>= 2.15.0), methods, GenomicRanges Imports: DBI, RMySQL (>= 0.6-0), digest, Biobase, grid, lattice, Rsamtools, genefilter, IRanges, BiocGenerics Suggests: RUnit, rjson, Gviz License: GPL-2 MD5sum: 8de5af6772ce2f7e303c6f8f883b6c50 NeedsCompilation: no Title: Genome annotation and visualisation package pertaining to Affymetrix arrays and NGS analysis. Description: annmap provides annotation mappings for Affymetrix exon arrays and coordinate based queries to support deep sequencing data analysis. Database access is hidden behind the API which provides a set of functions such as genesInRange(), geneToExon(), exonDetails(), etc. Functions to plot gene architecture and BAM file data are also provided. Underlying data are from Ensembl. biocViews: Annotation, Microarray, OneChannel, ReportWriting, Transcription, Visualization Author: Tim Yates Maintainer: Chris Wirth URL: http://annmap.cruk.manchester.ac.uk git_url: https://git.bioconductor.org/packages/annmap git_branch: RELEASE_3_10 git_last_commit: 0822a1a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/annmap_1.28.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/annmap_1.28.0.tgz vignettes: vignettes/annmap/inst/doc/annmap.pdf, vignettes/annmap/inst/doc/cookbook.pdf, vignettes/annmap/inst/doc/INSTALL.pdf vignetteTitles: annmap primer, The Annmap Cookbook, annmap installation instruction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 53 Package: annotate Version: 1.64.0 Depends: R (>= 2.10), AnnotationDbi (>= 1.27.5), XML Imports: Biobase, DBI, xtable, graphics, utils, stats, methods, BiocGenerics (>= 0.13.8), RCurl Suggests: hgu95av2.db, genefilter, Biostrings (>= 2.25.10), IRanges, rae230a.db, rae230aprobe, tkWidgets, GO.db, org.Hs.eg.db, org.Mm.eg.db, hom.Hs.inp.db, humanCHRLOC, Rgraphviz, RUnit, License: Artistic-2.0 MD5sum: d32f547fd75a4e4c5b9420957b2aca17 NeedsCompilation: no Title: Annotation for microarrays Description: Using R enviroments for annotation. biocViews: Annotation, Pathways, GO Author: R. Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/annotate git_branch: RELEASE_3_10 git_last_commit: e272e0b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/annotate_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/annotate_1.64.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/annotate_1.64.0.tgz vignettes: vignettes/annotate/inst/doc/annotate.pdf, vignettes/annotate/inst/doc/chromLoc.pdf, vignettes/annotate/inst/doc/GOusage.pdf, vignettes/annotate/inst/doc/prettyOutput.pdf, vignettes/annotate/inst/doc/query.pdf, vignettes/annotate/inst/doc/useDataPkgs.pdf, vignettes/annotate/inst/doc/useHomology.pdf, vignettes/annotate/inst/doc/useProbeInfo.pdf vignetteTitles: Annotation Overview, HowTo: use chromosomal information, Basic GO Usage, HowTo: Get HTML Output, HOWTO: Use the online query tools, Using Data Packages, Using the homology package, Using Affymetrix Probe Level Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotate/inst/doc/annotate.R, vignettes/annotate/inst/doc/chromLoc.R, vignettes/annotate/inst/doc/GOusage.R, vignettes/annotate/inst/doc/prettyOutput.R, vignettes/annotate/inst/doc/query.R, vignettes/annotate/inst/doc/useDataPkgs.R, vignettes/annotate/inst/doc/useHomology.R, vignettes/annotate/inst/doc/useProbeInfo.R dependsOnMe: ChromHeatMap, GeneAnswers, geneplotter, GOSim, GSEABase, idiogram, macat, MineICA, MLInterfaces, PCpheno, phenoTest, PREDA, RpsiXML, sampleClassifier, ScISI, SemDist importsMe: CAFE, Category, categoryCompare, CNEr, codelink, debrowser, DrugVsDisease, gCMAP, gCMAPWeb, GeneAnswers, genefilter, GlobalAncova, globaltest, GOstats, lumi, methyAnalysis, methylumi, MGFR, PathwaySplice, phenoTest, qpgraph, ScISI, splicegear, systemPipeR, tigre suggestsMe: BiocCaseStudies, BiocGenerics, biomaRt, GenomicRanges, GSAR, GSEAlm, hmdbQuery, maigesPack, metagenomeSeq, MLP, pcxn, RnBeads, siggenes, SummarizedExperiment dependencyCount: 30 Package: AnnotationDbi Version: 1.48.0 Depends: R (>= 2.7.0), methods, utils, stats4, BiocGenerics (>= 0.29.2), Biobase (>= 1.17.0), IRanges Imports: DBI, RSQLite, S4Vectors (>= 0.9.25) Suggests: hgu95av2.db, GO.db, org.Sc.sgd.db, org.At.tair.db, KEGG.db, RUnit, TxDb.Hsapiens.UCSC.hg19.knownGene, hom.Hs.inp.db, org.Hs.eg.db, reactome.db, AnnotationForge, graph, EnsDb.Hsapiens.v75, BiocStyle, knitr License: Artistic-2.0 MD5sum: a9df4412f03ea44ec8605c2fc77ed8b7 NeedsCompilation: no Title: Manipulation of SQLite-based annotations in Bioconductor Description: Implements a user-friendly interface for querying SQLite-based annotation data packages. biocViews: Annotation, Microarray, Sequencing, GenomeAnnotation Author: Hervé Pagès, Marc Carlson, Seth Falcon, Nianhua Li Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=8qvGNTVz3Ik git_url: https://git.bioconductor.org/packages/AnnotationDbi git_branch: RELEASE_3_10 git_last_commit: e8ca855 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AnnotationDbi_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AnnotationDbi_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AnnotationDbi_1.48.0.tgz vignettes: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.pdf, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf vignetteTitles: 2. (Deprecated) How to use bimaps from the ".db" annotation packages, 1. Introduction To Bioconductor Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.R, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.R dependsOnMe: a4Base, a4Preproc, annotate, AnnotationForge, AnnotationFuncs, attract, Category, chimera, ChromHeatMap, customProDB, deco, DEXSeq, EGSEA, eisa, ExpressionView, GenomicFeatures, GOFunction, goProfiles, GSReg, ipdDb, miRNAtap, MLP, OrganismDbi, pathRender, PGSEA, proBAMr, RpsiXML, safe, SemDist, topGO importsMe: adSplit, affycoretools, AllelicImbalance, annaffy, AnnotationHub, AnnotationHubData, annotatr, artMS, ASpli, beadarray, BiocSet, biomaRt, BioNet, biovizBase, bumphunter, BUSpaRse, CancerMutationAnalysis, categoryCompare, ccmap, cellity, chimeraviz, chipenrich, ChIPpeakAnno, ChIPseeker, clusterProfiler, CoCiteStats, compEpiTools, consensusDE, crisprseekplus, CrispRVariants, crossmeta, csaw, debrowser, derfinder, DominoEffect, DOSE, EDASeq, eegc, EnrichmentBrowser, enrichplot, ensembldb, erma, esATAC, ExpressionView, GA4GHshiny, gage, GAPGOM, gCMAP, gCMAPWeb, genefilter, geneplotter, geneXtendeR, GenVisR, GGBase, ggbio, GGtools, GlobalAncova, globaltest, GmicR, GOfuncR, GOFunction, GOpro, GOSemSim, goseq, GOSim, goSTAG, GOstats, goTools, gpart, gQTLstats, graphite, GSEABase, GSEABenchmarkeR, Gviz, gwascat, HTSanalyzeR, ideal, IMAS, InPAS, interactiveDisplay, iSEE, isomiRs, IVAS, karyoploteR, LRBaseDbi, lumi, mAPKL, MCbiclust, mdgsa, MeSHDbi, meshes, MetaboSignal, methyAnalysis, methylGSA, methylumi, MIGSA, MineICA, MiRaGE, mirIntegrator, miRNAmeConverter, missMethyl, multiMiR, NanoStringQCPro, nanotatoR, Onassis, ontoProc, ORFik, Organism.dplyr, PADOG, pathview, PathwaySplice, pcaExplorer, pcaGoPromoter, PCpheno, PGA, phantasus, phenoTest, psichomics, pwOmics, qpgraph, QuasR, RCAS, ReactomePA, REDseq, restfulSE, rgsepd, rTRM, SBGNview, ScISI, scPipe, scruff, scTensor, SGSeq, signatureSearch, singleCellTK, SLGI, SMITE, SpidermiR, StarBioTrek, SubCellBarCode, TCGAutils, tenXplore, tigre, trackViewer, trena, tximeta, Ularcirc, UniProt.ws, VariantAnnotation, VariantFiltering, ViSEAGO, dSimer suggestsMe: APAlyzer, BiocCaseStudies, BiocGenerics, BiocOncoTK, CellTrails, cicero, cola, DEGreport, edgeR, esetVis, FELLA, FGNet, fgsea, GA4GHclient, gCrisprTools, geecc, GeneAnswers, GeneRegionScan, GenomicRanges, limma, MmPalateMiRNA, oligo, OUTRIDER, piano, Pigengene, pRoloc, qcmetrics, R3CPET, recount, RGalaxy, sigPathway, SingleR, SummarizedExperiment, TFutils, topconfects, wiggleplotr dependencyCount: 25 Package: AnnotationFilter Version: 1.10.0 Depends: R (>= 3.4.0) Imports: utils, methods, GenomicRanges, lazyeval Suggests: BiocStyle, knitr, testthat, RSQLite, org.Hs.eg.db License: Artistic-2.0 MD5sum: f022485a9ad10534d92efb7c02dfdc23 NeedsCompilation: no Title: Facilities for Filtering Bioconductor Annotation Resources Description: This package provides class and other infrastructure to implement filters for manipulating Bioconductor annotation resources. The filters will be used by ensembldb, Organism.dplyr, and other packages. biocViews: Annotation, Infrastructure, Software Author: Martin Morgan [aut], Johannes Rainer [aut], Joachim Bargsten [ctb], Daniel Van Twisk [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/AnnotationFilter VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationFilter/issues git_url: https://git.bioconductor.org/packages/AnnotationFilter git_branch: RELEASE_3_10 git_last_commit: 23d7018 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AnnotationFilter_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AnnotationFilter_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AnnotationFilter_1.10.0.tgz vignettes: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.html vignetteTitles: Facilities for Filtering Bioconductor Annotation resources hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.R dependsOnMe: chimeraviz, ensembldb, Organism.dplyr importsMe: biovizBase, BUSpaRse, ggbio, Pbase, TVTB suggestsMe: TFutils, TxRegInfra, wiggleplotr dependencyCount: 17 Package: AnnotationForge Version: 1.28.0 Depends: R (>= 3.5.0), methods, utils, BiocGenerics (>= 0.15.10), Biobase (>= 1.17.0), AnnotationDbi (>= 1.33.14) Imports: DBI, RSQLite, XML, S4Vectors, RCurl Suggests: biomaRt, httr, GenomeInfoDb (>= 1.17.1), Biostrings, affy, hgu95av2.db, human.db0, org.Hs.eg.db, Homo.sapiens, hom.Hs.inp.db, GO.db, BiocStyle, knitr, BiocManager License: Artistic-2.0 MD5sum: 1f152e718ef96024db399ced8b7c02f1 NeedsCompilation: no Title: Tools for building SQLite-based annotation data packages Description: Provides code for generating Annotation packages and their databases. Packages produced are intended to be used with AnnotationDbi. biocViews: Annotation, Infrastructure Author: Marc Carlson, Hervé Pagès Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnnotationForge git_branch: RELEASE_3_10 git_last_commit: 61a9cb6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AnnotationForge_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AnnotationForge_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AnnotationForge_1.28.0.tgz vignettes: vignettes/AnnotationForge/inst/doc/makeProbePackage.pdf, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.pdf, vignettes/AnnotationForge/inst/doc/SQLForge.pdf, vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.html vignetteTitles: Creating probe packages, AnnotationForge: Creating select Interfaces for custom Annotation resources, SQLForge: An easy way to create a new annotation package with a standard database schema., Making New Organism Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationForge/inst/doc/makeProbePackage.R, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.R, vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.R, vignettes/AnnotationForge/inst/doc/SQLForge.R importsMe: AnnotationHubData, GCSscore, GOstats, ViSEAGO suggestsMe: AnnotationDbi, AnnotationHub dependencyCount: 29 Package: AnnotationFuncs Version: 1.36.0 Depends: R (>= 2.7.0), AnnotationDbi Imports: DBI Suggests: org.Bt.eg.db, GO.db, org.Hs.eg.db, hom.Hs.inp.db License: GPL-2 MD5sum: 71fbf39b6656ad4ef4afbcbcc1aa547a NeedsCompilation: no Title: Annotation translation functions Description: Functions for handling translating between different identifieres using the Biocore Data Team data-packages (e.g. org.Bt.eg.db). biocViews: AnnotationData, Software Author: Stefan McKinnon Edwards Maintainer: Stefan McKinnon Edwards URL: http://www.iysik.com/index.php?page=annotation-functions git_url: https://git.bioconductor.org/packages/AnnotationFuncs git_branch: RELEASE_3_10 git_last_commit: 73abe3e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AnnotationFuncs_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AnnotationFuncs_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AnnotationFuncs_1.36.0.tgz vignettes: vignettes/AnnotationFuncs/inst/doc/AnnotationFuncsUserguide.pdf vignetteTitles: Annotation mapping functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationFuncs/inst/doc/AnnotationFuncsUserguide.R importsMe: bioCancer dependencyCount: 26 Package: AnnotationHub Version: 2.18.0 Depends: BiocGenerics (>= 0.15.10), BiocFileCache (>= 1.5.1) Imports: utils, methods, grDevices, RSQLite, BiocManager, BiocVersion, curl, rappdirs, AnnotationDbi (>= 1.31.19), S4Vectors, interactiveDisplayBase, httr, yaml, dplyr Suggests: IRanges, GenomicRanges, GenomeInfoDb, VariantAnnotation, Rsamtools, rtracklayer, BiocStyle, knitr, AnnotationForge, rBiopaxParser, RUnit, GenomicFeatures, MSnbase, mzR, Biostrings, SummarizedExperiment, ExperimentHub, gdsfmt Enhances: AnnotationHubData License: Artistic-2.0 MD5sum: 4519881a751b29d40191d8e799b68adf NeedsCompilation: yes Title: Client to access AnnotationHub resources Description: This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, Ensembl) can be discovered. The resource includes metadata about each resource, e.g., a textual description, tags, and date of modification. The client creates and manages a local cache of files retrieved by the user, helping with quick and reproducible access. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Martin Morgan [cre], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Lori Shepherd [ctb] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnnotationHub git_branch: RELEASE_3_10 git_last_commit: 660ceb5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AnnotationHub_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AnnotationHub_2.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AnnotationHub_2.18.0.tgz vignettes: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.html, vignettes/AnnotationHub/inst/doc/AnnotationHub.html, vignettes/AnnotationHub/inst/doc/CreateAnAnnotationPackage.html, vignettes/AnnotationHub/inst/doc/TroubleshootingTheCache.html vignetteTitles: AnnotationHub: AnnotationHub HOW TO's, AnnotationHub: Access the AnnotationHub Web Service, AnnotationHub: Creating An AnnotationHub Package, Troubleshooting The Hubs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.R, vignettes/AnnotationHub/inst/doc/AnnotationHub.R, vignettes/AnnotationHub/inst/doc/TroubleshootingTheCache.R dependsOnMe: adductomicsR, AnnotationHubData, ExperimentHub, hipathia, ipdDb, ProteomicsAnnotationHubData, RefNet importsMe: annotatr, circRNAprofiler, dmrseq, ENCODExplorer, GenomicScores, GSEABenchmarkeR, PathwaySplice, psichomics, pwOmics, REMP, restfulSE, scmeth, scTensor, TSRchitect, Ularcirc suggestsMe: Chicago, CINdex, clusterProfiler, CNVRanger, COCOA, DNAshapeR, dupRadar, ensembldb, epiNEM, epivizrChart, epivizrData, GenomicRanges, GOSemSim, gwascat, maser, MIRA, MSnbase, OrganismDbi, Pbase, SingleR, VariantAnnotation dependencyCount: 63 Package: AnnotationHubData Version: 1.16.0 Depends: R (>= 3.2.2), methods, utils, S4Vectors (>= 0.7.21), IRanges (>= 2.3.23), GenomicRanges, AnnotationHub (>= 2.15.15) Imports: GenomicFeatures, Rsamtools, rtracklayer, BiocGenerics, jsonlite, BiocManager, biocViews, AnnotationDbi, Biobase, Biostrings, DBI, GenomeInfoDb (>= 1.15.4), OrganismDbi, RSQLite, rBiopaxParser, AnnotationForge, futile.logger (>= 1.3.0), XML, RCurl Suggests: RUnit, knitr, BiocStyle, grasp2db, GenomeInfoDbData License: Artistic-2.0 MD5sum: d5ca1fac4a9e3d9672ee447d315ec26e NeedsCompilation: no Title: Transform public data resources into Bioconductor Data Structures Description: These recipes convert a wide variety and a growing number of public bioinformatic data sets into easily-used standard Bioconductor data structures. biocViews: DataImport Author: Martin Morgan [ctb], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Paul Shannon [ctb], Lori Shepherd [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnnotationHubData git_branch: RELEASE_3_10 git_last_commit: 67b7ca8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AnnotationHubData_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AnnotationHubData_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AnnotationHubData_1.16.0.tgz vignettes: vignettes/AnnotationHubData/inst/doc/CreateAnAnnotationPackage.html, vignettes/AnnotationHubData/inst/doc/IntroductionToAnnotationHubData.html vignetteTitles: AnnotationHub: Creating An AnnotationHub Package, Introduction to AnnotationHubData hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ExperimentHubData dependencyCount: 104 Package: annotationTools Version: 1.60.0 Imports: Biobase, stats License: GPL MD5sum: 0fd3f1742af81c99be1f0dea73ba3034 NeedsCompilation: no Title: Annotate microarrays and perform cross-species gene expression analyses using flat file databases. Description: Functions to annotate microarrays, find orthologs, and integrate heterogeneous gene expression profiles using annotation and other molecular biology information available as flat file database (plain text files). biocViews: Microarray, Annotation Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/annotationTools git_branch: RELEASE_3_10 git_last_commit: 95df48e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/annotationTools_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/annotationTools_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/annotationTools_1.60.0.tgz vignettes: vignettes/annotationTools/inst/doc/annotationTools.pdf vignetteTitles: annotationTools Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotationTools/inst/doc/annotationTools.R dependencyCount: 7 Package: annotatr Version: 1.12.1 Depends: R (>= 3.4.0) Imports: AnnotationDbi, AnnotationHub, dplyr, GenomicFeatures, GenomicRanges, GenomeInfoDb (>= 1.10.3), ggplot2, IRanges, methods, readr, regioneR, reshape2, rtracklayer, S4Vectors (>= 0.23.10), stats, utils Suggests: BiocStyle, devtools, knitr, org.Dm.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, rmarkdown, roxygen2, testthat, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.refGene License: GPL-3 MD5sum: 0655a02357bed2d9f4052c7156d8bcaf NeedsCompilation: no Title: Annotation of Genomic Regions to Genomic Annotations Description: Given a set of genomic sites/regions (e.g. ChIP-seq peaks, CpGs, differentially methylated CpGs or regions, SNPs, etc.) it is often of interest to investigate the intersecting genomic annotations. Such annotations include those relating to gene models (promoters, 5'UTRs, exons, introns, and 3'UTRs), CpGs (CpG islands, CpG shores, CpG shelves), or regulatory sequences such as enhancers. The annotatr package provides an easy way to summarize and visualize the intersection of genomic sites/regions with genomic annotations. biocViews: Software, Annotation, GenomeAnnotation, FunctionalGenomics, Visualization Author: Raymond G. Cavalcante [aut, cre], Maureen A. Sartor [ths] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr BugReports: https://www.github.com/rcavalcante/annotatr/issues git_url: https://git.bioconductor.org/packages/annotatr git_branch: RELEASE_3_10 git_last_commit: 919d4e3 git_last_commit_date: 2019-11-12 Date/Publication: 2019-11-12 source.ver: src/contrib/annotatr_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/annotatr_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/annotatr_1.12.1.tgz vignettes: vignettes/annotatr/inst/doc/annotatr-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotatr/inst/doc/annotatr-vignette.R importsMe: dmrseq, scmeth dependencyCount: 129 Package: anota Version: 1.34.0 Depends: qvalue Imports: multtest, qvalue License: GPL-3 MD5sum: 3e5b4e03de0ad65eca5dd1c375039c4c NeedsCompilation: no Title: ANalysis Of Translational Activity (ANOTA). Description: Genome wide studies of translational control is emerging as a tool to study verious biological conditions. The output from such analysis is both the mRNA level (e.g. cytosolic mRNA level) and the levl of mRNA actively involved in translation (the actively translating mRNA level) for each mRNA. The standard analysis of such data strives towards identifying differential translational between two or more sample classes - i.e. differences in actively translated mRNA levels that are independent of underlying differences in cytosolic mRNA levels. This package allows for such analysis using partial variances and the random variance model. As 10s of thousands of mRNAs are analyzed in parallell the library performs a number of tests to assure that the data set is suitable for such analysis. biocViews: GeneExpression, DifferentialExpression, Microarray, Sequencing Author: Ola Larsson , Nahum Sonenberg , Robert Nadon Maintainer: Ola Larsson git_url: https://git.bioconductor.org/packages/anota git_branch: RELEASE_3_10 git_last_commit: 2d8ef45 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/anota_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/anota_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/anota_1.34.0.tgz vignettes: vignettes/anota/inst/doc/anota.pdf vignetteTitles: ANalysis Of Translational Activity (anota) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anota/inst/doc/anota.R dependsOnMe: tRanslatome dependencyCount: 65 Package: anota2seq Version: 1.8.0 Depends: R (>= 3.4.0), methods Imports: multtest,qvalue,limma,DESeq2,edgeR,RColorBrewer, grDevices, graphics, stats, utils, SummarizedExperiment Suggests: BiocStyle,knitr License: GPL-3 MD5sum: e0d07880d41d3d263e1b2334dd850f65 NeedsCompilation: no Title: Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq Description: anota2seq provides analysis of translational efficiency and differential expression analysis for polysome-profiling and ribosome-profiling studies (two or more sample classes) quantified by RNA sequencing or DNA-microarray. Polysome-profiling and ribosome-profiling typically generate data for two RNA sources; translated mRNA and total mRNA. Analysis of differential expression is used to estimate changes within each RNA source (i.e. translated mRNA or total mRNA). Analysis of translational efficiency aims to identify changes in translation efficiency leading to altered protein levels that are independent of total mRNA levels (i.e. changes in translated mRNA that are independent of levels of total mRNA) or buffering, a mechanism regulating translational efficiency so that protein levels remain constant despite fluctuating total mRNA levels (i.e. changes in total mRNA that are independent of levels of translated mRNA). anota2seq applies analysis of partial variance and the random variance model to fulfill these tasks. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Microarray,GenomeWideAssociation, BatchEffect, Normalization, RNASeq, Sequencing, GeneRegulation, Regression Author: Christian Oertlin , Julie Lorent , Ola Larsson Maintainer: Christian Oertlin , Julie Lorent VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/anota2seq git_branch: RELEASE_3_10 git_last_commit: 89e6122 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/anota2seq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/anota2seq_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/anota2seq_1.8.0.tgz vignettes: vignettes/anota2seq/inst/doc/anota2seq.pdf vignetteTitles: Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anota2seq/inst/doc/anota2seq.R dependencyCount: 127 Package: antiProfiles Version: 1.26.0 Depends: R (>= 3.0), matrixStats (>= 0.50.0), methods (>= 2.14), locfit (>= 1.5) Suggests: antiProfilesData, RColorBrewer License: Artistic-2.0 MD5sum: cd396cffde5d306911a1ac2e8ab26165 NeedsCompilation: no Title: Implementation of gene expression anti-profiles Description: Implements gene expression anti-profiles as described in Corrada Bravo et al., BMC Bioinformatics 2012, 13:272 doi:10.1186/1471-2105-13-272. biocViews: GeneExpression,Classification Author: Hector Corrada Bravo, Rafael A. Irizarry and Jeffrey T. Leek Maintainer: Hector Corrada Bravo URL: https://github.com/HCBravoLab/antiProfiles git_url: https://git.bioconductor.org/packages/antiProfiles git_branch: RELEASE_3_10 git_last_commit: fe78e7a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/antiProfiles_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/antiProfiles_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/antiProfiles_1.26.0.tgz vignettes: vignettes/antiProfiles/inst/doc/antiProfiles.pdf vignetteTitles: Introduction to antiProfiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/antiProfiles/inst/doc/antiProfiles.R dependencyCount: 9 Package: APAlyzer Version: 1.0.0 Imports: GenomicRanges, GenomicFeatures, GenomicAlignments, DESeq, SummarizedExperiment, Rsubread, stats, methods Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db, AnnotationDbi, TBX20BamSubset, Rsamtools, ggplot2, testthat License: LGPL-3 MD5sum: d45059af19cf2add28112c52a6cf2f77 NeedsCompilation: no Title: A toolkit for APA analysis using RNA-seq data Description: Perform 3'UTR APA, Intronic APA and gene expression analysis using RNA-seq data. biocViews: Sequencing, RNASeq, DifferentialExpression, GeneExpression, GeneRegulation, Annotation, DataImport, Software Author: Ruijia Wang [cre, aut] (), Bin Tian [aut] Maintainer: Ruijia Wang URL: https://github.com/RJWANGbioinfo/APAlyzer/ VignetteBuilder: knitr BugReports: https://github.com/RJWANGbioinfo/APAlyzer/issues git_url: https://git.bioconductor.org/packages/APAlyzer git_branch: RELEASE_3_10 git_last_commit: 2874b52 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/APAlyzer_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/APAlyzer_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/APAlyzer_1.0.0.tgz vignettes: vignettes/APAlyzer/inst/doc/APAlyzer.html vignetteTitles: APAlyzer: toolkit for RNA-seq APA analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/APAlyzer/inst/doc/APAlyzer.R dependencyCount: 94 Package: apComplex Version: 2.52.0 Depends: R (>= 2.10), graph, RBGL Imports: Rgraphviz, stats, org.Sc.sgd.db License: LGPL MD5sum: a0a747959f8c38e5bdf706f2276a6196 NeedsCompilation: no Title: Estimate protein complex membership using AP-MS protein data Description: Functions to estimate a bipartite graph of protein complex membership using AP-MS data. biocViews: ImmunoOncology, NetworkInference, MassSpectrometry, GraphAndNetwork Author: Denise Scholtens Maintainer: Denise Scholtens git_url: https://git.bioconductor.org/packages/apComplex git_branch: RELEASE_3_10 git_last_commit: 812b679 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/apComplex_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/apComplex_2.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/apComplex_2.52.0.tgz vignettes: vignettes/apComplex/inst/doc/apComplex.pdf vignetteTitles: apComplex hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/apComplex/inst/doc/apComplex.R dependsOnMe: ScISI suggestsMe: BiocCaseStudies dependencyCount: 32 Package: apeglm Version: 1.8.0 Imports: emdbook, SummarizedExperiment, GenomicRanges, methods, stats, utils, Rcpp LinkingTo: Rcpp, RcppEigen, RcppNumerical Suggests: DESeq2, airway, knitr, rmarkdown, testthat License: GPL-2 Archs: i386, x64 MD5sum: c2282b84a644131705d89ca0ac02ebc2 NeedsCompilation: yes Title: Approximate posterior estimation for GLM coefficients Description: apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, GeneExpression, Bayesian Author: Anqi Zhu [aut, cre], Joshua Zitovsky [ctb], Joseph Ibrahim [aut], Michael Love [aut] Maintainer: Anqi Zhu VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/apeglm git_branch: RELEASE_3_10 git_last_commit: e27f70c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/apeglm_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/apeglm_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/apeglm_1.8.0.tgz vignettes: vignettes/apeglm/inst/doc/apeglm.html vignetteTitles: Effect size estimation with apeglm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/apeglm/inst/doc/apeglm.R suggestsMe: DESeq2, fishpond dependencyCount: 43 Package: appreci8R Version: 1.4.0 Imports: shiny, shinyjs, DT, VariantAnnotation, BSgenome, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, Homo.sapiens, SNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, rsnps, Biostrings, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.hs37d5, MafDb.ESP6500SI.V2.SSA137.hs37d5, MafDb.gnomADex.r2.1.hs37d5, COSMIC.67, rentrez, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137, seqinr, openxlsx, Rsamtools, stringr, utils, stats, GenomicRanges, S4Vectors, GenomicFeatures, IRanges, GenomicScores, SummarizedExperiment Suggests: GO.db, org.Hs.eg.db License: LGPL-3 MD5sum: 1833fe8ec6df62cfcc667284cfa946c3 NeedsCompilation: no Title: appreci8R: an R/Bioconductor package for filtering SNVs and short indels with high sensitivity and high PPV Description: The appreci8R is an R version of our appreci8-algorithm - A Pipeline for PREcise variant Calling Integrating 8 tools. Variant calling results of our standard appreci8-tools (GATK, Platypus, VarScan, FreeBayes, LoFreq, SNVer, samtools and VarDict), as well as up to 5 additional tools is combined, evaluated and filtered. biocViews: VariantDetection, GeneticVariability, SNP, VariantAnnotation, Sequencing, Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/appreci8R git_branch: RELEASE_3_10 git_last_commit: ba9885a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/appreci8R_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/appreci8R_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/appreci8R_1.4.0.tgz vignettes: vignettes/appreci8R/inst/doc/appreci8R.pdf vignetteTitles: Using appreci8R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/appreci8R/inst/doc/appreci8R.R dependencyCount: 138 Package: aroma.light Version: 3.16.0 Depends: R (>= 2.15.2) Imports: stats, R.methodsS3 (>= 1.7.1), R.oo (>= 1.22.0), R.utils (>= 2.9.0), matrixStats (>= 0.54.0) Suggests: princurve (>= 2.1.4) License: GPL (>= 2) MD5sum: 9cd884ea8694f15d655f352d48236aec NeedsCompilation: no Title: Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types Description: Methods for microarray analysis that take basic data types such as matrices and lists of vectors. These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes. biocViews: Infrastructure, Microarray, OneChannel, TwoChannel, MultiChannel, Visualization, Preprocessing Author: Henrik Bengtsson [aut, cre, cph], Pierre Neuvial [ctb], Aaron Lun [ctb] Maintainer: Henrik Bengtsson URL: https://github.com/HenrikBengtsson/aroma.light, https://www.aroma-project.org BugReports: https://github.com/HenrikBengtsson/aroma.light/issues git_url: https://git.bioconductor.org/packages/aroma.light git_branch: RELEASE_3_10 git_last_commit: fc16179 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/aroma.light_3.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/aroma.light_3.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/aroma.light_3.16.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: EDASeq, scone suggestsMe: scran, TIN dependencyCount: 8 Package: ArrayExpress Version: 1.46.0 Depends: R (>= 2.9.0), Biobase (>= 2.4.0) Imports: XML, oligo, limma Suggests: affy License: Artistic-2.0 MD5sum: 32b86ec5c3368f60fdf4b886786e7ae0 NeedsCompilation: no Title: Access the ArrayExpress Microarray Database at EBI and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet Description: Access the ArrayExpress Repository at EBI and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet biocViews: Microarray, DataImport, OneChannel, TwoChannel Author: Audrey Kauffmann, Ibrahim Emam, Michael Schubert Maintainer: Suhaib Mohammed git_url: https://git.bioconductor.org/packages/ArrayExpress git_branch: RELEASE_3_10 git_last_commit: 4eb2ee5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ArrayExpress_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ArrayExpress_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ArrayExpress_1.46.0.tgz vignettes: vignettes/ArrayExpress/inst/doc/ArrayExpress.pdf vignetteTitles: ArrayExpress: Import and convert ArrayExpress data sets into R object hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpress/inst/doc/ArrayExpress.R dependsOnMe: DrugVsDisease suggestsMe: gCMAPWeb dependencyCount: 60 Package: ArrayExpressHTS Version: 1.36.0 Depends: sampling, Rsamtools (>= 1.99.0), snow Imports: Biobase, BiocGenerics, Biostrings, DESeq, GenomicRanges, Hmisc, IRanges (>= 2.13.11), R2HTML, RColorBrewer, Rsamtools, ShortRead, XML, biomaRt, edgeR, grDevices, graphics, methods, rJava, stats, svMisc, utils, sendmailR, bitops LinkingTo: Rhtslib (>= 1.15.3) License: Artistic License 2.0 MD5sum: 3c0693690f9e9fc1f4c2211e33c5efb5 NeedsCompilation: yes Title: ArrayExpress High Throughput Sequencing Processing Pipeline Description: RNA-Seq processing pipeline for public ArrayExpress experiments or local datasets biocViews: ImmunoOncology, RNASeq, Sequencing Author: Angela Goncalves, Andrew Tikhonov Maintainer: Angela Goncalves , Andrew Tikhonov SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/ArrayExpressHTS git_branch: RELEASE_3_10 git_last_commit: 1c23173 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ArrayExpressHTS_1.36.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ArrayExpressHTS_1.36.0.tgz vignettes: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.pdf vignetteTitles: ArrayExpressHTS: RNA-Seq Pipeline for transcription profiling experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.R dependencyCount: 148 Package: arrayMvout Version: 1.44.0 Depends: R (>= 2.6.0), tools, methods, utils, parody, Biobase, affy, lumi Imports: simpleaffy, mdqc, affyContam, Suggests: MAQCsubset, mvoutData, lumiBarnes, affyPLM, affydata, hgu133atagcdf License: Artistic-2.0 MD5sum: f3914ec62ee3cc2a805d716b66938ec4 NeedsCompilation: no Title: multivariate outlier detection for expression array QA Description: This package supports the application of diverse quality metrics to AffyBatch instances, summarizing these metrics via PCA, and then performing parametric outlier detection on the PCs to identify aberrant arrays with a fixed Type I error rate biocViews: Infrastructure, Microarray, QualityControl Author: Z. Gao, A. Asare, R. Wang, V. Carey Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/arrayMvout git_branch: RELEASE_3_10 git_last_commit: d65f39c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/arrayMvout_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/arrayMvout_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/arrayMvout_1.44.0.tgz vignettes: vignettes/arrayMvout/inst/doc/arrayMvout.pdf vignetteTitles: arrayMvout -- multivariate outlier algorithm for expression arrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrayMvout/inst/doc/arrayMvout.R dependencyCount: 163 Package: arrayQuality Version: 1.64.0 Depends: R (>= 2.2.0) Imports: graphics, grDevices, grid, gridBase, hexbin, limma, marray, methods, RColorBrewer, stats, utils Suggests: mclust, MEEBOdata, HEEBOdata License: LGPL MD5sum: fcaece1a0396c894ac944f47243bee56 NeedsCompilation: no Title: Assessing array quality on spotted arrays Description: Functions for performing print-run and array level quality assessment. biocViews: Microarray,TwoChannel,QualityControl,Visualization Author: Agnes Paquet and Jean Yee Hwa Yang Maintainer: Agnes Paquet URL: http://arrays.ucsf.edu/ git_url: https://git.bioconductor.org/packages/arrayQuality git_branch: RELEASE_3_10 git_last_commit: dd0c0b8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/arrayQuality_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/arrayQuality_1.64.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/arrayQuality_1.64.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: arrayQualityMetrics Version: 3.42.0 Imports: affy, affyPLM (>= 1.27.3), beadarray, Biobase, genefilter, graphics, grDevices, grid, gridSVG (>= 1.4-3), Hmisc, hwriter, lattice, latticeExtra, limma, methods, RColorBrewer, setRNG, stats, utils, vsn (>= 3.23.3), XML, svglite Suggests: ALLMLL, CCl4, BiocStyle, knitr License: LGPL (>= 2) MD5sum: 30fa46369c9131fa441f3fc7d6159d80 NeedsCompilation: no Title: Quality metrics report for microarray data sets Description: This package generates microarray quality metrics reports for data in Bioconductor microarray data containers (ExpressionSet, NChannelSet, AffyBatch). One and two color array platforms are supported. biocViews: Microarray, QualityControl, OneChannel, TwoChannel, ReportWriting Author: Audrey Kauffmann, Wolfgang Huber Maintainer: Mike Smith VignetteBuilder: knitr BugReports: https://github.com/grimbough/arrayQualityMetrics/issues git_url: https://git.bioconductor.org/packages/arrayQualityMetrics git_branch: RELEASE_3_10 git_last_commit: 6ebb828 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/arrayQualityMetrics_3.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/arrayQualityMetrics_3.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/arrayQualityMetrics_3.42.0.tgz vignettes: vignettes/arrayQualityMetrics/inst/doc/aqm.pdf, vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.pdf vignetteTitles: Advanced topics: Customizing arrayQualityMetrics reports and programmatic processing of the output, Introduction: microarray quality assessment with arrayQualityMetrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrayQualityMetrics/inst/doc/aqm.R, vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.R importsMe: EGAD, KnowSeq dependencyCount: 133 Package: ArrayTools Version: 1.46.0 Depends: R (>= 2.7.0), affy (>= 1.23.4), Biobase (>= 2.5.5), methods Imports: affy, Biobase, graphics, grDevices, limma, methods, stats, utils, xtable Suggests: simpleaffy, R2HTML, affydata, affyPLM, genefilter, annaffy, gcrma, hugene10sttranscriptcluster.db License: LGPL (>= 2.0) MD5sum: 46c3caa4abab8812fb2c2d5b0b2729e8 NeedsCompilation: no Title: geneChip Analysis Package Description: This package is designed to provide solutions for quality assessment and to detect differentially expressed genes for the Affymetrix GeneChips, including both 3' -arrays and gene 1.0-ST arrays. The package generates comprehensive analysis reports in HTML format. Hyperlinks on the report page will lead to a series of QC plots, processed data, and differentially expressed gene lists. Differentially expressed genes are reported in tabular format with annotations hyperlinked to online biological databases. biocViews: Microarray, OneChannel, QualityControl, Preprocessing, StatisticalMethod, DifferentialExpression, Annotation, ReportWriting, Visualization Author: Xiwei Wu, Arthur Li Maintainer: Arthur Li git_url: https://git.bioconductor.org/packages/ArrayTools git_branch: RELEASE_3_10 git_last_commit: 247759d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ArrayTools_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ArrayTools_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ArrayTools_1.46.0.tgz vignettes: vignettes/ArrayTools/inst/doc/ArrayTools.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayTools/inst/doc/ArrayTools.R dependencyCount: 15 Package: ArrayTV Version: 1.24.0 Depends: R (>= 2.14) Imports: methods, foreach, S4Vectors (>= 0.9.25), IRanges (>= 2.13.24), DNAcopy, oligoClasses (>= 1.21.3) Suggests: RColorBrewer, crlmm, ff, BSgenome.Hsapiens.UCSC.hg18,BSgenome.Hsapiens.UCSC.hg19, lattice, latticeExtra, RUnit, BiocGenerics Enhances: doMC, doSNOW, doParallel License: GPL (>= 2) MD5sum: 46cb8bbd414c015d972e9ddbc5c1ef8e NeedsCompilation: no Title: Implementation of wave correction for arrays Description: Wave correction for genotyping and copy number arrays biocViews: CopyNumberVariation Author: Eitan Halper-Stromberg Maintainer: Eitan Halper-Stromberg git_url: https://git.bioconductor.org/packages/ArrayTV git_branch: RELEASE_3_10 git_last_commit: b7398de git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ArrayTV_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ArrayTV_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ArrayTV_1.24.0.tgz vignettes: vignettes/ArrayTV/inst/doc/ArrayTV.pdf vignetteTitles: ArrayTV Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayTV/inst/doc/ArrayTV.R suggestsMe: VanillaICE dependencyCount: 55 Package: ARRmNormalization Version: 1.26.0 Depends: R (>= 2.15.1), ARRmData License: Artistic-2.0 MD5sum: 442e16836cfb7dd6374853cc182769eb NeedsCompilation: no Title: Adaptive Robust Regression normalization for Illumina methylation data Description: Perform the Adaptive Robust Regression method (ARRm) for the normalization of methylation data from the Illumina Infinium HumanMethylation 450k assay. biocViews: DNAMethylation, TwoChannel, Preprocessing, Microarray Author: Jean-Philippe Fortin, Celia M.T. Greenwood, Aurelie Labbe. Maintainer: Jean-Philippe Fortin git_url: https://git.bioconductor.org/packages/ARRmNormalization git_branch: RELEASE_3_10 git_last_commit: 014b76a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ARRmNormalization_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ARRmNormalization_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ARRmNormalization_1.26.0.tgz vignettes: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.pdf vignetteTitles: ARRmNormalization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.R dependencyCount: 1 Package: artMS Version: 1.4.3 Depends: R (>= 3.6.0) Imports: AnnotationDbi, biomaRt, bit64, circlize, cluster, ComplexHeatmap, corrplot, data.table, dplyr, factoextra, FactoMineR, getopt, ggdendro, ggplot2, gplots, ggrepel, gProfileR, graphics, grDevices, grid, limma, MSstats, openxlsx, org.Hs.eg.db, org.Mm.eg.db, PerformanceAnalytics, pheatmap, plotly, plyr, RColorBrewer, scales, seqinr, stats, stringr, tidyr, UpSetR, utils, VennDiagram, yaml Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL (>= 3) + file LICENSE MD5sum: 48205e214cbfdb6815ba1ba1541fcfab NeedsCompilation: no Title: Analytical R tools for Mass Spectrometry Description: artMS provides a set of tools for the analysis of proteomics label-free datasets. It takes as input the MaxQuant search result output (evidence.txt file) and performs quality control, relative quantification using MSstats, downstream analysis and integration. artMS also provides a set of functions to re-format and make it compatible with other analytical tools, including, SAINTq, SAINTexpress, Phosfate, and PHOTON. Check [http://artms.org](http://artms.org) for details. biocViews: Proteomics, DifferentialExpression, BiomedicalInformatics, SystemsBiology, MassSpectrometry, Annotation, QualityControl, GeneSetEnrichment, Clustering, Normalization, ImmunoOncology, MultipleComparison Author: David Jimenez-Morales, Alexandre Rosa Campos, John Von Dollen. Maintainer: David Jimenez-Morales URL: https://github.com/biodavidjm/artMS VignetteBuilder: knitr BugReports: https://github.com/biodavidjm/artMS/issues git_url: https://git.bioconductor.org/packages/artMS git_branch: RELEASE_3_10 git_last_commit: 539d4cb git_last_commit_date: 2020-02-08 Date/Publication: 2020-02-09 source.ver: src/contrib/artMS_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/artMS_1.4.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/artMS_1.4.3.tgz vignettes: vignettes/artMS/inst/doc/artMS_vignette.html vignetteTitles: Learn to use artMS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/artMS/inst/doc/artMS_vignette.R dependencyCount: 195 Package: ASAFE Version: 1.12.0 Depends: R (>= 3.2) Suggests: knitr, testthat License: Artistic-2.0 MD5sum: 91146c9cce0046edf29fc845effcac66 NeedsCompilation: no Title: Ancestry Specific Allele Frequency Estimation Description: Given admixed individuals' bi-allelic SNP genotypes and ancestry pairs (where each ancestry can take one of three values) for multiple SNPs, perform an EM algorithm to deal with the fact that SNP genotypes are unphased with respect to ancestry pairs, in order to estimate ancestry-specific allele frequencies for all SNPs. biocViews: SNP, GenomeWideAssociation, LinkageDisequilibrium, BiomedicalInformatics, Genetics, ExperimentalDesign Author: Qian Zhang Maintainer: Qian Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASAFE git_branch: RELEASE_3_10 git_last_commit: 0a912e6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ASAFE_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ASAFE_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ASAFE_1.12.0.tgz vignettes: vignettes/ASAFE/inst/doc/ASAFE.pdf vignetteTitles: ASAFE (Ancestry Specific Allele Frequency Estimation) hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASAFE/inst/doc/ASAFE.R dependencyCount: 0 Package: ASEB Version: 1.30.0 Depends: R (>= 2.8.0), methods Imports: graphics, methods, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: 2c3005a5c756d8fc161858ea8bb36b16 NeedsCompilation: yes Title: Predict Acetylated Lysine Sites Description: ASEB is an R package to predict lysine sites that can be acetylated by a specific KAT-family. biocViews: Proteomics Author: Likun Wang and Tingting Li . Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/ASEB git_branch: RELEASE_3_10 git_last_commit: 149eae9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ASEB_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ASEB_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ASEB_1.30.0.tgz vignettes: vignettes/ASEB/inst/doc/ASEB.pdf vignetteTitles: ASEB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASEB/inst/doc/ASEB.R dependencyCount: 3 Package: ASGSCA Version: 1.20.0 Imports: Matrix, MASS Suggests: BiocStyle License: GPL-3 MD5sum: ab6a004dfc37ea194ba37d79f1961ee0 NeedsCompilation: no Title: Association Studies for multiple SNPs and multiple traits using Generalized Structured Equation Models Description: The package provides tools to model and test the association between multiple genotypes and multiple traits, taking into account the prior biological knowledge. Genes, and clinical pathways are incorporated in the model as latent variables. The method is based on Generalized Structured Component Analysis (GSCA). biocViews: StructuralEquationModels Author: Hela Romdhani, Stepan Grinek , Heungsun Hwang and Aurelie Labbe. Maintainer: Hela Romdhani git_url: https://git.bioconductor.org/packages/ASGSCA git_branch: RELEASE_3_10 git_last_commit: eb3457d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ASGSCA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ASGSCA_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ASGSCA_1.20.0.tgz vignettes: vignettes/ASGSCA/inst/doc/ASGSCA.pdf vignetteTitles: Association Studies using Generalized Structured Equation Models. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASGSCA/inst/doc/ASGSCA.R dependencyCount: 9 Package: ASICS Version: 2.2.0 Depends: R (>= 3.5) Imports: BiocParallel, ggplot2, grDevices, gridExtra, methods, PepsNMR, plyr, quadprog, ropls, stats, SummarizedExperiment, TSdist, utils, Matrix, zoo Suggests: knitr, rmarkdown, BiocStyle, testthat, ASICSdata License: GPL (>= 2) MD5sum: fda8db787fade5373525ddabcd24064c NeedsCompilation: no Title: Automatic Statistical Identification in Complex Spectra Description: With a set of pure metabolite reference spectra, ASICS quantifies concentration of metabolites in a complex spectrum. The identification of metabolites is performed by fitting a mixture model to the spectra of the library with a sparse penalty. The method and its statistical properties are described in Tardivel et al. (2017) . biocViews: Software, DataImport, Cheminformatics, Metabolomics Author: Gaëlle Lefort [aut, cre], Rémi Servien [aut], Patrick Tardivel [aut], Nathalie Vialaneix [aut] Maintainer: Gaëlle Lefort VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASICS git_branch: RELEASE_3_10 git_last_commit: a8eba84 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ASICS_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ASICS_2.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ASICS_2.2.0.tgz vignettes: vignettes/ASICS/inst/doc/ASICS.html, vignettes/ASICS/inst/doc/ASICSUsersGuide.html vignetteTitles: ASICS, ASICS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASICS/inst/doc/ASICS.R, vignettes/ASICS/inst/doc/ASICSUsersGuide.R dependencyCount: 133 Package: ASpediaFI Version: 1.0.0 Depends: R (>= 3.6.0), SummarizedExperiment, ROCR Imports: BiocParallel, GenomicAlignments, GenomicFeatures, GenomicRanges, IRanges, IVAS, Rsamtools, biomaRt, limma, S4Vectors, stats, DRaWR, GenomeInfoDb, Gviz, Matrix, dplyr, fgsea, reshape2, igraph, graphics, e1071, methods, rtracklayer, scales, grid, ggplot2, mGSZ, utils Suggests: knitr License: GPL-3 MD5sum: a89e03ce9ac8af73f4d4dd8d2e021c3f NeedsCompilation: no Title: ASpedia-FI: Functional Interaction Analysis of Alternative Splicing Events Description: This package provides functionalities for a systematic and integrative analysis of alternative splicing events and their functional interactions. biocViews: AlternativeSplicing, Annotation, Coverage, GeneExpression, GeneSetEnrichment, GraphAndNetwork, KEGG, Network, NetworkInference, Pathways, Reactome, Transcription, Sequencing, Visualization Author: Doyeong Yu, Kyubin Lee, Daejin Hyung, Soo Young Cho, Charny Park Maintainer: Doyeong Yu VignetteBuilder: knitr BugReports: https://github.com/nachoryu/ASpediaFI git_url: https://git.bioconductor.org/packages/ASpediaFI git_branch: RELEASE_3_10 git_last_commit: 41d9e03 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ASpediaFI_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ASpediaFI_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ASpediaFI_1.0.0.tgz vignettes: vignettes/ASpediaFI/inst/doc/ASpediaFI.pdf vignetteTitles: ASpediaFI.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASpediaFI/inst/doc/ASpediaFI.R dependencyCount: 174 Package: ASpli Version: 1.12.0 Depends: methods, grDevices, stats, utils, parallel, edgeR Imports: GenomicRanges, GenomicFeatures, BiocGenerics, IRanges, GenomicAlignments, Gviz, S4Vectors, AnnotationDbi, Rsamtools, BiocStyle License: GPL MD5sum: 665e632810cbbd0c81388e8867eada9a NeedsCompilation: no Title: Analysis of alternative splicing using RNA-Seq Description: Integrative pipeline for the analysis of alternative splicing using RNAseq. biocViews: ImmunoOncology, GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, TimeCourse, RNASeq, GenomeAnnotation, Sequencing, Alignment Author: Estefania Mancini, Javier Iserte, Marcelo Yanovsky and Ariel Chernomoretz Maintainer: Estefania Mancini git_url: https://git.bioconductor.org/packages/ASpli git_branch: RELEASE_3_10 git_last_commit: 23929ac git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ASpli_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ASpli_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ASpli_1.12.0.tgz vignettes: vignettes/ASpli/inst/doc/ASpli.pdf vignetteTitles: Analysis of alternative splicing using ASpli hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASpli/inst/doc/ASpli.R dependencyCount: 151 Package: AssessORF Version: 1.4.0 Depends: R (>= 3.5.0), DECIPHER (>= 2.10.0) Imports: Biostrings, GenomicRanges, IRanges, graphics, grDevices, methods, stats, utils Suggests: AssessORFData, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 28198ec57b5efe7fef8fcefd30c7e56e NeedsCompilation: no Title: Assess Gene Predictions Using Proteomics and Evolutionary Conservation Description: In order to assess the quality of a set of predicted genes for a genome, evidence must first be mapped to that genome. Next, each gene must be categorized based on how strong the evidence is for or against that gene. The AssessORF package provides the functions and class structures necessary for accomplishing those tasks, using proteomic hits and evolutionarily conserved start codons as the forms of evidence. biocViews: ComparativeGenomics, GenePrediction, GenomeAnnotation, Genetics, Proteomics, QualityControl, Visualization Author: Deepank Korandla [aut, cre], Erik Wright [aut] Maintainer: Deepank Korandla VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AssessORF git_branch: RELEASE_3_10 git_last_commit: d7805a6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AssessORF_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AssessORF_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AssessORF_1.4.0.tgz vignettes: vignettes/AssessORF/inst/doc/UsingAssessORF.pdf vignetteTitles: Using AssessORF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AssessORF/inst/doc/UsingAssessORF.R dependencyCount: 34 Package: ASSET Version: 2.4.0 Depends: MASS, msm, rmeta Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: 1666e10e839ec92efd85ed36508c6540 NeedsCompilation: no Title: An R package for subset-based association analysis of heterogeneous traits and subtypes Description: An R package for subset-based analysis of heterogeneous traits and subtypes biocViews: Software Author: Samsiddhi Bhattacharjee, Nilanjan Chatterjee and William Wheeler Maintainer: Samsiddhi Bhattacharjee git_url: https://git.bioconductor.org/packages/ASSET git_branch: RELEASE_3_10 git_last_commit: 47effea git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ASSET_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ASSET_2.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ASSET_2.4.0.tgz vignettes: vignettes/ASSET/inst/doc/vignette.pdf vignetteTitles: ASSET Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASSET/inst/doc/vignette.R dependsOnMe: REBET dependencyCount: 15 Package: ASSIGN Version: 1.22.0 Depends: R (>= 3.4) Imports: gplots, graphics, grDevices, msm, Rlab, stats, sva, utils, ggplot2, yaml Suggests: testthat, BiocStyle, lintr, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 33bc63113b5155c7b81c022f67496bce NeedsCompilation: no Title: Adaptive Signature Selection and InteGratioN (ASSIGN) Description: ASSIGN is a computational tool to evaluate the pathway deregulation/activation status in individual patient samples. ASSIGN employs a flexible Bayesian factor analysis approach that adapts predetermined pathway signatures derived either from knowledge-based literature or from perturbation experiments to the cell-/tissue-specific pathway signatures. The deregulation/activation level of each context-specific pathway is quantified to a score, which represents the extent to which a patient sample encompasses the pathway deregulation/activation signature. biocViews: Software, GeneExpression, Pathways, Bayesian Author: Ying Shen, Andrea H. Bild, W. Evan Johnson, and Mumtehena Rahman Maintainer: Ying Shen , W. Evan Johnson , David Jenkins , Mumtehena Rahman URL: https://compbiomed.github.io/ASSIGN/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/ASSIGN/issues git_url: https://git.bioconductor.org/packages/ASSIGN git_branch: RELEASE_3_10 git_last_commit: c20ca70 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ASSIGN_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ASSIGN_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ASSIGN_1.22.0.tgz vignettes: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.html vignetteTitles: Primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.R dependencyCount: 95 Package: ATACseqQC Version: 1.10.4 Depends: R (>= 3.4), BiocGenerics, S4Vectors Imports: BSgenome, Biostrings, ChIPpeakAnno, IRanges, GenomicRanges, GenomicAlignments, GenomeInfoDb, GenomicScores, graphics, grid, limma, Rsamtools (>= 1.31.2), randomForest, rtracklayer, stats, motifStack, preseqR, utils, KernSmooth, edgeR Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19, MotifDb, trackViewer, testthat License: GPL (>= 2) MD5sum: 507013b5c7780329dabcd4d163b662cd NeedsCompilation: no Title: ATAC-seq Quality Control Description: ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. Comparing to the other methods, ATAC-seq requires less amount of the biological samples and time to process. In the process of analyzing several ATAC-seq dataset produced in our labs, we learned some of the unique aspects of the quality assessment for ATAC-seq data.To help users to quickly assess whether their ATAC-seq experiment is successful, we developed ATACseqQC package partially following the guideline published in Nature Method 2013 (Greenleaf et al.), including diagnostic plot of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints. biocViews: Sequencing, DNASeq, ATACSeq, GeneRegulation, QualityControl, Coverage, NucleosomePositioning, ImmunoOncology Author: Jianhong Ou, Haibo Liu, Feng Yan, Jun Yu, Michelle Kelliher, Lucio Castilla, Nathan Lawson, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ATACseqQC git_branch: RELEASE_3_10 git_last_commit: 2434b83 git_last_commit_date: 2020-04-09 Date/Publication: 2020-04-11 source.ver: src/contrib/ATACseqQC_1.10.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/ATACseqQC_1.10.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ATACseqQC_1.10.4.tgz vignettes: vignettes/ATACseqQC/inst/doc/ATACseqQC.html vignetteTitles: ATACseqQC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ATACseqQC/inst/doc/ATACseqQC.R dependencyCount: 159 Package: atSNP Version: 1.2.0 Depends: R (>= 3.6) Imports: BSgenome, BiocFileCache, BiocParallel, Rcpp, data.table, ggplot2, grDevices, graphics, grid, motifStack, rappdirs, stats, testit, utils LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 2d9e4e7c453eacfe4ca821f6dedf306a NeedsCompilation: yes Title: Affinity test for identifying regulatory SNPs Description: atSNP performs affinity tests of motif matches with the SNP or the reference genomes and SNP-led changes in motif matches. biocViews: Software, ChIPSeq, GenomeAnnotation, MotifAnnotation, Visualization Author: Chandler Zuo [aut], Sunyoung Shin [aut, cre], Sunduz Keles [aut] Maintainer: Sunyoung Shin URL: https://github.com/sunyoungshin/atSNP VignetteBuilder: knitr BugReports: https://github.com/sunyoungshin/atSNP/issues git_url: https://git.bioconductor.org/packages/atSNP git_branch: RELEASE_3_10 git_last_commit: 4c978e2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/atSNP_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/atSNP_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/atSNP_1.2.0.tgz vignettes: vignettes/atSNP/inst/doc/atsnp-vignette.html vignetteTitles: atsnp-vignette.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/atSNP/inst/doc/atsnp-vignette.R dependencyCount: 120 Package: attract Version: 1.38.0 Depends: R (>= 3.4.0), AnnotationDbi Imports: Biobase, limma, cluster, GOstats, graphics, stats, reactome.db, KEGGREST, org.Hs.eg.db, utils, methods Suggests: illuminaHumanv1.db License: LGPL (>= 2.0) MD5sum: 7ea8ddc5ea6413df5d2036fd3b6d0065 NeedsCompilation: no Title: Methods to Find the Gene Expression Modules that Represent the Drivers of Kauffman's Attractor Landscape Description: This package contains the functions to find the gene expression modules that represent the drivers of Kauffman's attractor landscape. The modules are the core attractor pathways that discriminate between different cell types of groups of interest. Each pathway has a set of synexpression groups, which show transcriptionally-coordinated changes in gene expression. biocViews: ImmunoOncology, KEGG, Reactome, GeneExpression, Pathways, GeneSetEnrichment, Microarray, RNASeq Author: Jessica Mar Maintainer: Samuel Zimmerman git_url: https://git.bioconductor.org/packages/attract git_branch: RELEASE_3_10 git_last_commit: d71a381 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/attract_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/attract_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/attract_1.38.0.tgz vignettes: vignettes/attract/inst/doc/attract.pdf vignetteTitles: Tutorial on How to Use the Functions in the \texttt{attract} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/attract/inst/doc/attract.R dependencyCount: 64 Package: AUCell Version: 1.8.0 Imports: data.table, graphics, grDevices, GSEABase, methods, mixtools, R.utils, shiny, stats, SummarizedExperiment, BiocGenerics, S4Vectors, utils Suggests: Biobase, BiocStyle, devtools, dynamicTreeCut, DT, GEOquery, knitr, NMF, plotly, R2HTML, rbokeh, rmarkdown, Rtsne, testthat, zoo Enhances: doMC, doRNG, doParallel, foreach License: GPL-3 MD5sum: 877deddcc88a65ef344d32b9de96ac54 NeedsCompilation: no Title: AUCell: Analysis of 'gene set' activity in single-cell RNA-seq data (e.g. identify cells with specific gene signatures) Description: AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched within the expressed genes for each cell. The distribution of AUC scores across all the cells allows exploring the relative expression of the signature. Since the scoring method is ranking-based, AUCell is independent of the gene expression units and the normalization procedure. In addition, since the cells are evaluated individually, it can easily be applied to bigger datasets, subsetting the expression matrix if needed. biocViews: SingleCell, GeneSetEnrichment, Transcriptomics, Transcription, GeneExpression, WorkflowStep, Normalization Author: Sara Aibar, Stein Aerts. Laboratory of Computational Biology. VIB-KU Leuven Center for Brain & Disease Research. Leuven, Belgium. Maintainer: Sara Aibar URL: http://scenic.aertslab.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AUCell git_branch: RELEASE_3_10 git_last_commit: b1f460c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/AUCell_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/AUCell_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AUCell_1.8.0.tgz vignettes: vignettes/AUCell/inst/doc/AUCell.html vignetteTitles: AUCell: Identifying cells with active gene sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AUCell/inst/doc/AUCell.R importsMe: RcisTarget dependencyCount: 74 Package: Autotuner Version: 1.0.1 Depends: R (>= 3.6) Imports: MSnbase, devtools, RColorBrewer, dplyr, rlang, plyr, mzR, assertthat, scales, methods, entropy, cluster, grDevices, graphics, stats, utils Suggests: testthat (>= 2.1.0), covr, knitr, rmarkdown, mtbls2 License: MIT + file LICENSE MD5sum: f24b8ce15156c4ae05996651011c9945 NeedsCompilation: no Title: Automated parameter selection for untargeted metabolomics data processing Description: This package is designed to help faciliate data processing in untargeted metabolomics. To do this, the algorithm contained within the package performs statistical inference on raw data to come up with the best set of parameters to process the raw data. biocViews: MassSpectrometry, Metabolomics Author: Craig McLean Maintainer: Craig McLean URL: https://github.com/crmclean/Autotuner/ VignetteBuilder: knitr BugReports: https://github.com/crmclean/Autotuner/issues git_url: https://git.bioconductor.org/packages/Autotuner git_branch: RELEASE_3_10 git_last_commit: 04527bb git_last_commit_date: 2020-02-26 Date/Publication: 2020-02-26 source.ver: src/contrib/Autotuner_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/Autotuner_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Autotuner_1.0.1.tgz vignettes: vignettes/Autotuner/inst/doc/Autotuner.html vignetteTitles: Autotuner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Autotuner/inst/doc/Autotuner.R dependencyCount: 137 Package: AWFisher Version: 1.0.1 Depends: R (>= 3.6) Imports: edgeR, limma, stats Suggests: knitr, tightClust License: GPL-3 Archs: i386, x64 MD5sum: 031f2784f10f29327a4aac0097287fa3 NeedsCompilation: yes Title: An R package for fast computing for adaptively weighted fisher's method Description: Implementation of the adaptively weighted fisher's method, including fast p-value computing, variability index, and meta-pattern. biocViews: StatisticalMethod, Software Author: Zhiguang Huo Maintainer: Zhiguang Huo VignetteBuilder: knitr BugReports: https://github.com/Caleb-Huo/AWFisher/issues git_url: https://git.bioconductor.org/packages/AWFisher git_branch: RELEASE_3_10 git_last_commit: 49eb663 git_last_commit_date: 2020-02-08 Date/Publication: 2020-02-08 source.ver: src/contrib/AWFisher_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/AWFisher_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/AWFisher_1.0.1.tgz vignettes: vignettes/AWFisher/inst/doc/AWFisher.html vignetteTitles: AWFisher hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AWFisher/inst/doc/AWFisher.R dependencyCount: 11 Package: BaalChIP Version: 1.12.0 Depends: R (>= 3.3.1), GenomicRanges, IRanges, Rsamtools, Imports: GenomicAlignments, GenomeInfoDb, doParallel, parallel, doBy, reshape2, scales, coda, foreach, ggplot2, methods, utils, graphics, stats Suggests: RUnit, BiocGenerics, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 1b093438118a9a7929f30f39b7e7aa19 NeedsCompilation: no Title: BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes Description: The package offers functions to process multiple ChIP-seq BAM files and detect allele-specific events. Computes allele counts at individual variants (SNPs/SNVs), implements extensive QC steps to remove problematic variants, and utilizes a bayesian framework to identify statistically significant allele- specific events. BaalChIP is able to account for copy number differences between the two alleles, a known phenotypical feature of cancer samples. biocViews: Software, ChIPSeq, Bayesian, Sequencing Author: Ines de Santiago, Wei Liu, Ke Yuan, Martin O'Reilly, Chandra SR Chilamakuri, Bruce Ponder, Kerstin Meyer, Florian Markowetz Maintainer: Ines de Santiago VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BaalChIP git_branch: RELEASE_3_10 git_last_commit: cdb93e0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BaalChIP_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BaalChIP_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BaalChIP_1.12.0.tgz vignettes: vignettes/BaalChIP/inst/doc/BaalChIP.html vignetteTitles: Analyzing ChIP-seq and FAIRE-seq data with the BaalChIP package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BaalChIP/inst/doc/BaalChIP.R dependencyCount: 106 Package: BAC Version: 1.46.0 Depends: R (>= 2.10) License: Artistic-2.0 Archs: i386, x64 MD5sum: 5f3e71dbcdbc8d388bf7e75d368afc56 NeedsCompilation: yes Title: Bayesian Analysis of Chip-chip experiment Description: This package uses a Bayesian hierarchical model to detect enriched regions from ChIP-chip experiments biocViews: Microarray, Transcription Author: Raphael Gottardo Maintainer: Raphael Gottardo git_url: https://git.bioconductor.org/packages/BAC git_branch: RELEASE_3_10 git_last_commit: bd93fcd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BAC_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BAC_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BAC_1.46.0.tgz vignettes: vignettes/BAC/inst/doc/BAC.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BAC/inst/doc/BAC.R dependencyCount: 0 Package: bacon Version: 1.14.0 Depends: R (>= 3.3), methods, stats, ggplot2, graphics, BiocParallel, ellipse Suggests: BiocStyle, knitr, rmarkdown, testthat, roxygen2 License: GPL (>= 2) Archs: i386, x64 MD5sum: 9867aeafac4ed5ccb8665ece81148b24 NeedsCompilation: yes Title: Controlling bias and inflation in association studies using the empirical null distribution Description: Bacon can be used to remove inflation and bias often observed in epigenome- and transcriptome-wide association studies. To this end bacon constructs an empirical null distribution using a Gibbs Sampling algorithm by fitting a three-component normal mixture on z-scores. biocViews: ImmunoOncology, StatisticalMethod, Bayesian, Regression, GenomeWideAssociation, Transcriptomics, RNASeq, MethylationArray, BatchEffect, MultipleComparison Author: Maarten van Iterson [aut, cre], Erik van Zwet [ctb] Maintainer: Maarten van Iterson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bacon git_branch: RELEASE_3_10 git_last_commit: f3ef756 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/bacon_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/bacon_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/bacon_1.14.0.tgz vignettes: vignettes/bacon/inst/doc/bacon.html vignetteTitles: Controlling bias and inflation in association studies using the empirical null distribution hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bacon/inst/doc/bacon.R dependencyCount: 63 Package: BADER Version: 1.24.0 Suggests: pasilla (>= 0.2.10) License: GPL-2 Archs: i386, x64 MD5sum: c673727b02fc87edc9a94d7764d98c88 NeedsCompilation: yes Title: Bayesian Analysis of Differential Expression in RNA Sequencing Data Description: For RNA sequencing count data, BADER fits a Bayesian hierarchical model. The algorithm returns the posterior probability of differential expression for each gene between two groups A and B. The joint posterior distribution of the variables in the model can be returned in the form of posterior samples, which can be used for further down-stream analyses such as gene set enrichment. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, Software, SAGE Author: Andreas Neudecker, Matthias Katzfuss Maintainer: Andreas Neudecker git_url: https://git.bioconductor.org/packages/BADER git_branch: RELEASE_3_10 git_last_commit: 0c5d8cb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BADER_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BADER_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BADER_1.24.0.tgz vignettes: vignettes/BADER/inst/doc/BADER.pdf vignetteTitles: Analysing RNA-Seq data with the "BADER" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BADER/inst/doc/BADER.R dependencyCount: 0 Package: BadRegionFinder Version: 1.14.0 Imports: VariantAnnotation, Rsamtools, biomaRt, GenomicRanges, S4Vectors, utils, stats, grDevices, graphics Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 413cd68c3d6e11e954d821f5bc722096 NeedsCompilation: no Title: BadRegionFinder: an R/Bioconductor package for identifying regions with bad coverage Description: BadRegionFinder is a package for identifying regions with a bad, acceptable and good coverage in sequence alignment data available as bam files. The whole genome may be considered as well as a set of target regions. Various visual and textual types of output are available. biocViews: Coverage, Sequencing, Alignment, WholeGenome, Classification Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/BadRegionFinder git_branch: RELEASE_3_10 git_last_commit: 49af136 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BadRegionFinder_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BadRegionFinder_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BadRegionFinder_1.14.0.tgz vignettes: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.pdf vignetteTitles: Using BadRegionFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.R dependencyCount: 85 Package: BAGS Version: 2.26.0 Depends: R (>= 2.10), breastCancerVDX, Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: c247de54501c19bd47e06986387f34ac NeedsCompilation: yes Title: A Bayesian Approach for Geneset Selection Description: R package providing functions to perform geneset significance analysis over simple cross-sectional data between 2 and 5 phenotypes of interest. biocViews: Bayesian Author: Alejandro Quiroz-Zarate Maintainer: Alejandro Quiroz-Zarate git_url: https://git.bioconductor.org/packages/BAGS git_branch: RELEASE_3_10 git_last_commit: 47e6a5c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BAGS_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BAGS_2.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BAGS_2.26.0.tgz vignettes: vignettes/BAGS/inst/doc/BAGS.pdf vignetteTitles: BAGS: A Bayesian Approach for Geneset Selection. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BAGS/inst/doc/BAGS.R dependencyCount: 8 Package: ballgown Version: 2.18.0 Depends: R (>= 3.1.1), methods Imports: GenomicRanges (>= 1.17.25), IRanges (>= 1.99.22), S4Vectors (>= 0.9.39), RColorBrewer, splines, sva, limma, rtracklayer (>= 1.29.25), Biobase (>= 2.25.0), GenomeInfoDb Suggests: testthat, knitr License: Artistic-2.0 MD5sum: d3e7672bde08d3e88d0e31d4814d6d41 NeedsCompilation: no Title: Flexible, isoform-level differential expression analysis Description: Tools for statistical analysis of assembled transcriptomes, including flexible differential expression analysis, visualization of transcript structures, and matching of assembled transcripts to annotation. biocViews: ImmunoOncology, RNASeq, StatisticalMethod, Preprocessing, DifferentialExpression Author: Jack Fu [aut], Alyssa C. Frazee [aut, cre], Leonardo Collado-Torres [aut], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Jack Fu VignetteBuilder: knitr BugReports: https://github.com/alyssafrazee/ballgown/issues git_url: https://git.bioconductor.org/packages/ballgown git_branch: RELEASE_3_10 git_last_commit: 3e1dcf2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ballgown_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ballgown_2.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ballgown_2.18.0.tgz vignettes: vignettes/ballgown/inst/doc/ballgown.html vignetteTitles: Flexible isoform-level differential expression analysis with Ballgown hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ballgown/inst/doc/ballgown.R importsMe: RNASeqR suggestsMe: polyester, variancePartition dependencyCount: 63 Package: bamsignals Version: 1.18.0 Depends: R (>= 3.2.0) Imports: methods, BiocGenerics, Rcpp (>= 0.10.6), IRanges, GenomicRanges, zlibbioc LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc Suggests: testthat (>= 0.9), Rsamtools, BiocStyle, knitr, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: a71616eb0eeecc4f1e49e00c02a176df NeedsCompilation: yes Title: Extract read count signals from bam files Description: This package allows to efficiently obtain count vectors from indexed bam files. It counts the number of reads in given genomic ranges and it computes reads profiles and coverage profiles. It also handles paired-end data. biocViews: DataImport, Sequencing, Coverage, Alignment Author: Alessandro Mammana [aut, cre], Johannes Helmuth [aut] Maintainer: Johannes Helmuth URL: https://github.com/lamortenera/bamsignals SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/lamortenera/bamsignals/issues git_url: https://git.bioconductor.org/packages/bamsignals git_branch: RELEASE_3_10 git_last_commit: 4ed80c9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/bamsignals_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/bamsignals_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/bamsignals_1.18.0.tgz vignettes: vignettes/bamsignals/inst/doc/bamsignals.html vignetteTitles: Introduction to the bamsignals package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bamsignals/inst/doc/bamsignals.R importsMe: AneuFinder, chromstaR, karyoploteR, normr dependencyCount: 18 Package: BANDITS Version: 1.2.3 Depends: R (>= 3.6.0) Imports: Rcpp, doRNG, MASS, data.table, R.utils, doParallel, parallel, foreach, methods, stats, graphics, ggplot2, DRIMSeq, BiocParallel LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, tximport, BiocStyle, GenomicFeatures, Biostrings License: GPL (>= 3) Archs: i386, x64 MD5sum: d7f135d6e295952290e65041764a3a59 NeedsCompilation: yes Title: BANDITS: Bayesian ANalysis of DIfferenTial Splicing Description: BANDITS is a Bayesian hierarchical model for detecting differential splicing of genes and transcripts, via differential transcript usage (DTU), between two or more conditions. The method uses a Bayesian hierarchical framework, which allows for sample specific proportions in a Dirichlet-Multinomial model, and samples the allocation of fragments to the transcripts. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques and a DTU test is performed via a multivariate Wald test on the posterior densities for the average relative abundance of transcripts. biocViews: DifferentialSplicing, AlternativeSplicing, Bayesian, Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, WorkflowStep, Transcription Author: Simone Tiberi [aut, cre], Mark D. Robinson [aut]. Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/BANDITS SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/BANDITS/issues git_url: https://git.bioconductor.org/packages/BANDITS git_branch: RELEASE_3_10 git_last_commit: fe1f6bb git_last_commit_date: 2020-04-14 Date/Publication: 2020-04-14 source.ver: src/contrib/BANDITS_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/BANDITS_1.2.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BANDITS_1.2.3.tgz vignettes: vignettes/BANDITS/inst/doc/BANDITS.html vignetteTitles: BANDITS: Bayesian ANalysis of DIfferenTial Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BANDITS/inst/doc/BANDITS.R dependencyCount: 92 Package: banocc Version: 1.10.0 Depends: R (>= 3.5.1), rstan (>= 2.17.4) Imports: coda (>= 0.18.1), mvtnorm, stringr Suggests: knitr, rmarkdown, methods, testthat License: MIT + file LICENSE MD5sum: 1cbae179e99c0ab7a3749af9c543f0fe NeedsCompilation: no Title: Bayesian ANalysis Of Compositional Covariance Description: BAnOCC is a package designed for compositional data, where each sample sums to one. It infers the approximate covariance of the unconstrained data using a Bayesian model coded with `rstan`. It provides as output the `stanfit` object as well as posterior median and credible interval estimates for each correlation element. biocViews: ImmunoOncology, Metagenomics, Software, Bayesian Author: Emma Schwager [aut, cre], Curtis Huttenhower [aut] Maintainer: George Weingart , Curtis Huttenhower VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/banocc git_branch: RELEASE_3_10 git_last_commit: 97df938 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/banocc_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/banocc_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/banocc_1.10.0.tgz vignettes: vignettes/banocc/inst/doc/banocc-vignette.html vignetteTitles: BAnOCC (Bayesian Analysis of Compositional Covariance) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/banocc/inst/doc/banocc-vignette.R dependencyCount: 69 Package: basecallQC Version: 1.10.0 Depends: R (>= 3.4), stats, utils, methods, rmarkdown, knitr, prettydoc, yaml Imports: ggplot2, stringr, XML, raster, dplyr, data.table, tidyr, magrittr, DT, lazyeval, ShortRead Suggests: testthat, BiocStyle License: GPL (>= 3) MD5sum: d5a15ae9110b1d60d9cb10bd9f4f8c95 NeedsCompilation: no Title: Working with Illumina Basecalling and Demultiplexing input and output files Description: The basecallQC package provides tools to work with Illumina bcl2Fastq (versions >= 2.1.7) software.Prior to basecalling and demultiplexing using the bcl2Fastq software, basecallQC functions allow the user to update Illumina sample sheets from versions <= 1.8.9 to >= 2.1.7 standards, clean sample sheets of common problems such as invalid sample names and IDs, create read and index basemasks and the bcl2Fastq command. Following the generation of basecalled and demultiplexed data, the basecallQC packages allows the user to generate HTML tables, plots and a self contained report of summary metrics from Illumina XML output files. biocViews: Sequencing, Infrastructure, DataImport, QualityControl Author: Thomas Carroll and Marian Dore Maintainer: Thomas Carroll SystemRequirements: bcl2Fastq (versions >= 2.1.7) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/basecallQC git_branch: RELEASE_3_10 git_last_commit: 421c582 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/basecallQC_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/basecallQC_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/basecallQC_1.10.0.tgz vignettes: vignettes/basecallQC/inst/doc/basecallQC.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/basecallQC/inst/doc/basecallQC.R dependencyCount: 115 Package: BaseSpaceR Version: 1.30.0 Depends: R (>= 2.15.0), RCurl, RJSONIO Imports: methods Suggests: RUnit, IRanges, Rsamtools License: Apache License 2.0 MD5sum: 4d01ddd54d8e812a62df73af6bb2c2d2 NeedsCompilation: no Title: R SDK for BaseSpace RESTful API Description: A rich R interface to Illumina's BaseSpace cloud computing environment, enabling the fast development of data analysis and visualisation tools. biocViews: Infrastructure, DataRepresentation, ConnectTools, Software, DataImport, HighThroughputSequencing, Sequencing, Genetics Author: Adrian Alexa Maintainer: Jared O'Connell git_url: https://git.bioconductor.org/packages/BaseSpaceR git_branch: RELEASE_3_10 git_last_commit: 7d1ce41 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BaseSpaceR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BaseSpaceR_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BaseSpaceR_1.30.0.tgz vignettes: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.pdf vignetteTitles: BaseSpaceR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.R dependencyCount: 4 Package: Basic4Cseq Version: 1.22.0 Depends: R (>= 3.4), Biostrings, GenomicAlignments, caTools, GenomicRanges, grDevices, graphics, stats, utils Imports: methods, RCircos, BSgenome.Ecoli.NCBI.20080805 Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 18403505909f846fcb4be6022c9dcf5a NeedsCompilation: no Title: Basic4Cseq: an R/Bioconductor package for analyzing 4C-seq data Description: Basic4Cseq is an R/Bioconductor package for basic filtering, analysis and subsequent visualization of 4C-seq data. Virtual fragment libraries can be created for any BSGenome package, and filter functions for both reads and fragments and basic quality controls are included. Fragment data in the vicinity of the experiment's viewpoint can be visualized as a coverage plot based on a running median approach and a multi-scale contact profile. biocViews: ImmunoOncology, Visualization, QualityControl, Sequencing, Coverage, Alignment, RNASeq, SequenceMatching, DataImport Author: Carolin Walter Maintainer: Carolin Walter git_url: https://git.bioconductor.org/packages/Basic4Cseq git_branch: RELEASE_3_10 git_last_commit: 25a3f99 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Basic4Cseq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Basic4Cseq_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Basic4Cseq_1.22.0.tgz vignettes: vignettes/Basic4Cseq/inst/doc/vignette.pdf vignetteTitles: Basic4Cseq: an R/Bioconductor package for the analysis of 4C-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Basic4Cseq/inst/doc/vignette.R dependencyCount: 42 Package: BASiCS Version: 1.8.1 Depends: R (>= 3.6), SingleCellExperiment Imports: Biobase, BiocGenerics, coda, cowplot, data.table, ggExtra, ggplot2, graphics, grDevices, KernSmooth, MASS, matrixStats, methods, Rcpp (>= 0.11.3), S4Vectors, scran, stats, stats4, SummarizedExperiment, viridis, utils, Matrix LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: c952d581032800f2c56105ac37e3e5a0 NeedsCompilation: yes Title: Bayesian Analysis of Single-Cell Sequencing data Description: Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, DifferentialExpression, Bayesian, CellBiology, ImmunoOncology Author: Catalina Vallejos [aut, cre], Nils Eling [aut], Alan O'Callaghan [aut], Sylvia Richardson [ctb], John Marioni [ctb] Maintainer: Catalina Vallejos URL: https://github.com/catavallejos/BASiCS SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/catavallejos/BASiCS/issues git_url: https://git.bioconductor.org/packages/BASiCS git_branch: RELEASE_3_10 git_last_commit: 42dc339 git_last_commit_date: 2020-02-10 Date/Publication: 2020-02-10 source.ver: src/contrib/BASiCS_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/BASiCS_1.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BASiCS_1.8.1.tgz vignettes: vignettes/BASiCS/inst/doc/BASiCS.html vignetteTitles: Introduction to BASiCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BASiCS/inst/doc/BASiCS.R suggestsMe: splatter dependencyCount: 124 Package: BasicSTARRseq Version: 1.14.0 Depends: GenomicRanges,GenomicAlignments Imports: S4Vectors,methods,IRanges,GenomeInfoDb,stats Suggests: knitr License: LGPL-3 MD5sum: 1961c8f75b9a6cc75aa9406f94e1550f NeedsCompilation: no Title: Basic peak calling on STARR-seq data Description: Basic peak calling on STARR-seq data based on a method introduced in "Genome-Wide Quantitative Enhancer Activity Maps Identified by STARR-seq" Arnold et al. Science. 2013 Mar 1;339(6123):1074-7. doi: 10.1126/science. 1232542. Epub 2013 Jan 17. biocViews: PeakDetection, GeneRegulation, FunctionalPrediction, FunctionalGenomics, Coverage Author: Annika Buerger Maintainer: Annika Buerger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BasicSTARRseq git_branch: RELEASE_3_10 git_last_commit: 73e356d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BasicSTARRseq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BasicSTARRseq_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BasicSTARRseq_1.14.0.tgz vignettes: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.pdf vignetteTitles: BasicSTARRseq.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.R dependencyCount: 36 Package: batchelor Version: 1.2.4 Depends: SingleCellExperiment Imports: SummarizedExperiment, S4Vectors, BiocGenerics, Rcpp, stats, methods, utils, BiocNeighbors, BiocSingular, Matrix, DelayedArray, DelayedMatrixStats, scater, BiocParallel LinkingTo: Rcpp, beachmat Suggests: testthat, BiocStyle, knitr, beachmat, scran, scRNAseq License: GPL-3 Archs: i386, x64 MD5sum: d6bce5c447b42887c6c6747bf3c240ab NeedsCompilation: yes Title: Single-Cell Batch Correction Methods Description: Implements a variety of methods for batch correction of single-cell (RNA sequencing) data. This includes methods based on detecting mutually nearest neighbors, as well as several efficient variants of linear regression of the log-expression values. Functions are also provided to perform global rescaling to remove differences in depth between batches, and to perform a principal components analysis that is robust to differences in the numbers of cells across batches. biocViews: Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, BatchEffect, Normalization Author: Aaron Lun [aut, cre], Laleh Haghverdi [ctb] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/batchelor git_branch: RELEASE_3_10 git_last_commit: 24cbcdd git_last_commit_date: 2020-01-01 Date/Publication: 2020-01-02 source.ver: src/contrib/batchelor_1.2.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/batchelor_1.2.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/batchelor_1.2.4.tgz vignettes: vignettes/batchelor/inst/doc/correction.html, vignettes/batchelor/inst/doc/extension.html vignetteTitles: 1. Correcting batch effects, 2. Extending methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/batchelor/inst/doc/correction.R, vignettes/batchelor/inst/doc/extension.R dependencyCount: 95 Package: BatchQC Version: 1.14.0 Depends: R (>= 3.3.0) Imports: utils, rmarkdown, knitr, pander, gplots, MCMCpack, shiny, sva, corpcor, moments, matrixStats, ggvis, d3heatmap, reshape2, limma, grDevices, graphics, stats, methods, Matrix Suggests: testthat License: GPL (>= 2) MD5sum: a98524e9f6171cb81b556d78070741ca NeedsCompilation: no Title: Batch Effects Quality Control Software Description: Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data, and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs, and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA. biocViews: BatchEffect, GraphAndNetwork, Microarray, PrincipalComponent, Sequencing, Software, Visualization, QualityControl, RNASeq, Preprocessing, DifferentialExpression, ImmunoOncology Author: Solaiappan Manimaran , W. Evan Johnson , Heather Selby , Claire Ruberman , Kwame Okrah , Hector Corrada Bravo Maintainer: Solaiappan Manimaran URL: https://github.com/mani2012/BatchQC SystemRequirements: pandoc (http://pandoc.org/installing.html) for generating reports from markdown files. VignetteBuilder: knitr BugReports: https://github.com/mani2012/BatchQC/issues git_url: https://git.bioconductor.org/packages/BatchQC git_branch: RELEASE_3_10 git_last_commit: 7a5a5dc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BatchQC_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BatchQC_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BatchQC_1.14.0.tgz vignettes: vignettes/BatchQC/inst/doc/BatchQC_usage_advanced.pdf, vignettes/BatchQC/inst/doc/BatchQC_examples.html, vignettes/BatchQC/inst/doc/BatchQCIntro.html vignetteTitles: BatchQC_usage_advanced, BatchQC_examples, BatchQCIntro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BatchQC/inst/doc/BatchQC_usage_advanced.R dependencyCount: 132 Package: BayesKnockdown Version: 1.12.0 Depends: R (>= 3.3) Imports: stats, Biobase License: GPL-3 MD5sum: 1e560e7a929e4ffe2f180f28ac3cb310 NeedsCompilation: no Title: BayesKnockdown: Posterior Probabilities for Edges from Knockdown Data Description: A simple, fast Bayesian method for computing posterior probabilities for relationships between a single predictor variable and multiple potential outcome variables, incorporating prior probabilities of relationships. In the context of knockdown experiments, the predictor variable is the knocked-down gene, while the other genes are potential targets. Can also be used for differential expression/2-class data. biocViews: NetworkInference, GeneExpression, GeneTarget, Network, Bayesian Author: William Chad Young Maintainer: William Chad Young git_url: https://git.bioconductor.org/packages/BayesKnockdown git_branch: RELEASE_3_10 git_last_commit: fa78a35 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BayesKnockdown_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BayesKnockdown_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BayesKnockdown_1.12.0.tgz vignettes: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.pdf vignetteTitles: BayesKnockdown.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.R dependencyCount: 7 Package: BayesPeak Version: 1.38.0 Depends: R (>= 2.14), IRanges Imports: IRanges, graphics Suggests: BiocStyle, parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: 6d7485cc7d30a77cb86d73b0d12d083a NeedsCompilation: yes Title: Bayesian Analysis of ChIP-seq Data Description: This package is an implementation of the BayesPeak algorithm for peak-calling in ChIP-seq data. biocViews: ChIPSeq Author: Christiana Spyrou, Jonathan Cairns, Rory Stark, Andy Lynch, Simon Tavar\\'{e}, Maintainer: Jonathan Cairns git_url: https://git.bioconductor.org/packages/BayesPeak git_branch: RELEASE_3_10 git_last_commit: edf7c38 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BayesPeak_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BayesPeak_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BayesPeak_1.38.0.tgz vignettes: vignettes/BayesPeak/inst/doc/BayesPeak.pdf vignetteTitles: BayesPeak Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BayesPeak/inst/doc/BayesPeak.R dependencyCount: 9 Package: bayNorm Version: 1.4.14 Depends: R (>= 3.5), Imports: Rcpp (>= 0.12.12), BB, foreach, iterators, doSNOW, Matrix, parallel, MASS, locfit, fitdistrplus, stats, methods, graphics, grDevices, SingleCellExperiment, SummarizedExperiment, BiocParallel, utils LinkingTo: Rcpp, RcppArmadillo,RcppProgress Suggests: knitr, rmarkdown, BiocStyle, devtools, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: 6d7561f5a0708e4400b505fea948af73 NeedsCompilation: yes Title: Single-cell RNA sequencing data normalization Description: bayNorm is used for normalizing single-cell RNA-seq data. biocViews: ImmunoOncology, Normalization, RNASeq, SingleCell,Sequencing Author: Wenhao Tang [aut, cre], Franois Bertaux [aut], Philipp Thomas [aut], Claire Stefanelli [aut], Malika Saint [aut], Samuel Marguerat [aut], Vahid Shahrezaei [aut] Maintainer: Wenhao Tang URL: https://github.com/WT215/bayNorm VignetteBuilder: knitr BugReports: https://github.com/WT215/bayNorm/issues git_url: https://git.bioconductor.org/packages/bayNorm git_branch: RELEASE_3_10 git_last_commit: cd5d5e4 git_last_commit_date: 2020-01-09 Date/Publication: 2020-01-09 source.ver: src/contrib/bayNorm_1.4.14.tar.gz win.binary.ver: bin/windows/contrib/3.6/bayNorm_1.4.14.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/bayNorm_1.4.14.tgz vignettes: vignettes/bayNorm/inst/doc/bayNorm.html vignetteTitles: Introduction to bayNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bayNorm/inst/doc/bayNorm.R dependencyCount: 49 Package: baySeq Version: 2.20.0 Depends: R (>= 2.3.0), methods, GenomicRanges, abind, parallel Imports: edgeR Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: d5d6c5815fdd0eb88a5cece36eceb3b6 NeedsCompilation: no Title: Empirical Bayesian analysis of patterns of differential expression in count data Description: This package identifies differential expression in high-throughput 'count' data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods. biocViews: Sequencing, DifferentialExpression, MultipleComparison, SAGE Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/baySeq git_branch: RELEASE_3_10 git_last_commit: f4f56a9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/baySeq_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/baySeq_2.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/baySeq_2.20.0.tgz vignettes: vignettes/baySeq/inst/doc/baySeq_generic.pdf, vignettes/baySeq/inst/doc/baySeq.pdf vignetteTitles: Advanced baySeq analyses, baySeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/baySeq/inst/doc/baySeq_generic.R, vignettes/baySeq/inst/doc/baySeq.R dependsOnMe: clusterSeq, Rcade, segmentSeq, TCC importsMe: EDDA, metaseqR, riboSeqR suggestsMe: compcodeR dependencyCount: 24 Package: BBCAnalyzer Version: 1.16.0 Imports: SummarizedExperiment, VariantAnnotation, Rsamtools, grDevices, GenomicRanges, IRanges, Biostrings Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 65a49f08041dda380c3abada6432a77c NeedsCompilation: no Title: BBCAnalyzer: an R/Bioconductor package for visualizing base counts Description: BBCAnalyzer is a package for visualizing the relative or absolute number of bases, deletions and insertions at defined positions in sequence alignment data available as bam files in comparison to the reference bases. Markers for the relative base frequencies, the mean quality of the detected bases, known mutations or polymorphisms and variants called in the data may additionally be included in the plots. biocViews: Sequencing, Alignment, Coverage, GeneticVariability, SNP Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/BBCAnalyzer git_branch: RELEASE_3_10 git_last_commit: e7d013a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BBCAnalyzer_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BBCAnalyzer_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BBCAnalyzer_1.16.0.tgz vignettes: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.pdf vignetteTitles: Using BBCAnalyzer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.R dependencyCount: 85 Package: BCRANK Version: 1.48.0 Depends: methods Imports: Biostrings Suggests: seqLogo License: GPL-2 Archs: i386, x64 MD5sum: 714d96756300dfb2f39749293fb21af6 NeedsCompilation: yes Title: Predicting binding site consensus from ranked DNA sequences Description: Functions and classes for de novo prediction of transcription factor binding consensus by heuristic search biocViews: MotifDiscovery, GeneRegulation Author: Adam Ameur Maintainer: Adam Ameur git_url: https://git.bioconductor.org/packages/BCRANK git_branch: RELEASE_3_10 git_last_commit: 466922c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BCRANK_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BCRANK_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BCRANK_1.48.0.tgz vignettes: vignettes/BCRANK/inst/doc/BCRANK.pdf vignetteTitles: BCRANK hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BCRANK/inst/doc/BCRANK.R dependencyCount: 12 Package: bcSeq Version: 1.8.0 Depends: R (>= 3.4.0) Imports: Rcpp (>= 0.12.12), Matrix, Biostrings LinkingTo: Rcpp, Matrix Suggests: knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: af1a970e4b1345e05fd5860ca9325870 NeedsCompilation: yes Title: Fast Sequence Mapping in High-Throughput shRNA and CRISPR Screens Description: This Rcpp-based package implements a highly efficient data structure and algorithm for performing alignment of short reads from CRISPR or shRNA screens to reference barcode library. Sequencing error are considered and matching qualities are evaluated based on Phred scores. A Bayes' classifier is employed to predict the originating barcode of a read. The package supports provision of user-defined probability models for evaluating matching qualities. The package also supports multi-threading. biocViews: ImmunoOncology, Alignment, CRISPR, Sequencing, SequenceMatching, MultipleSequenceAlignment, Software, ATACSeq Author: Jiaxing Lin [aut, cre], Jeremy Gresham [aut], Jichun Xie [aut], Kouros Owzar [aut], Tongrong Wang [ctb], So Young Kim [ctb], James Alvarez [ctb], Jeffrey S. Damrauer [ctb], Scott Floyd [ctb], Joshua Granek [ctb], Andrew Allen [ctb], Cliburn Chan [ctb] Maintainer: Jiaxing Lin URL: https://github.com/jl354/bcSeq VignetteBuilder: knitr BugReports: https://support.bioconductor.org git_url: https://git.bioconductor.org/packages/bcSeq git_branch: RELEASE_3_10 git_last_commit: f87331f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/bcSeq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/bcSeq_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/bcSeq_1.8.0.tgz vignettes: vignettes/bcSeq/inst/doc/bcSeq.pdf vignetteTitles: bcSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bcSeq/inst/doc/bcSeq.R dependencyCount: 17 Package: BDMMAcorrect Version: 1.4.0 Depends: R (>= 3.5), vegan, ellipse, ggplot2, ape, SummarizedExperiment Imports: Rcpp (>= 0.12.12), RcppArmadillo, RcppEigen, stats LinkingTo: Rcpp, RcppArmadillo, RcppEigen Suggests: knitr, rmarkdown, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 261ab5dac83358b318f39d42d88c80d8 NeedsCompilation: yes Title: Meta-analysis for the metagenomic read counts data from different cohorts Description: Metagenomic sequencing techniques enable quantitative analyses of the microbiome. However, combining the microbial data from these experiments is challenging due to the variations between experiments. The existing methods for correcting batch effects do not consider the interactions between variables—microbial taxa in microbial studies—and the overdispersion of the microbiome data. Therefore, they are not applicable to microbiome data. We develop a new method, Bayesian Dirichlet-multinomial regression meta-analysis (BDMMA), to simultaneously model the batch effects and detect the microbial taxa associated with phenotypes. BDMMA automatically models the dependence among microbial taxa and is robust to the high dimensionality of the microbiome and their association sparsity. biocViews: ImmunoOncology, BatchEffect, Microbiome, Bayesian Author: ZHENWEI DAI Maintainer: ZHENWEI DAI VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BDMMAcorrect git_branch: RELEASE_3_10 git_last_commit: 8359f9b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BDMMAcorrect_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BDMMAcorrect_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BDMMAcorrect_1.4.0.tgz vignettes: vignettes/BDMMAcorrect/inst/doc/Vignette.pdf vignetteTitles: BDMMAcorrect_user_guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BDMMAcorrect/inst/doc/Vignette.R dependencyCount: 84 Package: beachmat Version: 2.2.1 Imports: methods, DelayedArray, BiocGenerics, Matrix Suggests: testthat, BiocStyle, knitr, rmarkdown, devtools License: GPL-3 MD5sum: c653bfe93eda885aa1d664587b7cc76b NeedsCompilation: yes Title: Compiling Bioconductor to Handle Each Matrix Type Description: Provides a consistent C++ class interface for reading from and writing data to a variety of commonly used matrix types. Ordinary matrices and several sparse/dense Matrix classes are directly supported, third-party S4 classes may be supported by external linkage, while all other matrices are handled by DelayedArray block processing. biocViews: DataRepresentation, DataImport, Infrastructure Author: Aaron Lun [aut, cre], Hervé Pagès [aut], Mike Smith [aut] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/beachmat git_branch: RELEASE_3_10 git_last_commit: 1e85e4d git_last_commit_date: 2019-11-14 Date/Publication: 2019-11-15 source.ver: src/contrib/beachmat_2.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/beachmat_2.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/beachmat_2.2.1.tgz vignettes: vignettes/beachmat/inst/doc/external.html, vignettes/beachmat/inst/doc/input.html, vignettes/beachmat/inst/doc/linking.html, vignettes/beachmat/inst/doc/output.html vignetteTitles: 4. Supporting arbitrary matrix classes, 2. Reading data from R matrices in C++, 1. Linking to beachmat from another package, 3. Writing data into R matrix objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beachmat/inst/doc/external.R, vignettes/beachmat/inst/doc/input.R, vignettes/beachmat/inst/doc/linking.R, vignettes/beachmat/inst/doc/output.R suggestsMe: batchelor, BiocSingular, bsseq, DropletUtils, mbkmeans, PCAtools, scater, scran, SingleR linksToMe: batchelor, BiocSingular, bsseq, DropletUtils, mbkmeans, PCAtools, scater, scran, SingleR dependencyCount: 22 Package: beadarray Version: 2.36.1 Depends: R (>= 2.13.0), BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), hexbin Imports: BeadDataPackR, limma, AnnotationDbi, stats4, reshape2, GenomicRanges, IRanges, illuminaio, methods, ggplot2 Suggests: lumi, vsn, affy, hwriter, beadarrayExampleData, illuminaHumanv3.db, gridExtra, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, ggbio, Nozzle.R1, knitr License: MIT + file LICENSE Archs: i386, x64 MD5sum: c5cfae358596a197fe2d9377c7141c9e NeedsCompilation: yes Title: Quality assessment and low-level analysis for Illumina BeadArray data Description: The package is able to read bead-level data (raw TIFFs and text files) output by BeadScan as well as bead-summary data from BeadStudio. Methods for quality assessment and low-level analysis are provided. biocViews: Microarray, OneChannel, QualityControl, Preprocessing Author: Mark Dunning, Mike Smith, Jonathan Cairns, Andy Lynch, Matt Ritchie Maintainer: Mark Dunning VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/beadarray git_branch: RELEASE_3_10 git_last_commit: 1939559 git_last_commit_date: 2020-04-13 Date/Publication: 2020-04-14 source.ver: src/contrib/beadarray_2.36.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/beadarray_2.36.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/beadarray_2.36.1.tgz vignettes: vignettes/beadarray/inst/doc/beadarray.pdf, vignettes/beadarray/inst/doc/beadlevel.pdf, vignettes/beadarray/inst/doc/beadsummary.pdf, vignettes/beadarray/inst/doc/ImageProcessing.pdf vignetteTitles: beadarray.pdf, beadlevel.pdf, beadsummary.pdf, ImageProcessing.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/beadarray/inst/doc/beadarray.R, vignettes/beadarray/inst/doc/beadlevel.R, vignettes/beadarray/inst/doc/beadsummary.R, vignettes/beadarray/inst/doc/ImageProcessing.R importsMe: arrayQualityMetrics, blima, epigenomix suggestsMe: beadarraySNP, lumi dependencyCount: 88 Package: beadarraySNP Version: 1.52.0 Depends: methods, Biobase (>= 2.14), quantsmooth Suggests: aCGH, affy, limma, snapCGH, beadarray, DNAcopy License: GPL-2 MD5sum: 8f8313eea7e43163869a0f6747911a00 NeedsCompilation: no Title: Normalization and reporting of Illumina SNP bead arrays Description: Importing data from Illumina SNP experiments and performing copy number calculations and reports. biocViews: CopyNumberVariation, SNP, GeneticVariability, TwoChannel, Preprocessing, DataImport Author: Jan Oosting Maintainer: Jan Oosting git_url: https://git.bioconductor.org/packages/beadarraySNP git_branch: RELEASE_3_10 git_last_commit: 53b7e15 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/beadarraySNP_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/beadarraySNP_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/beadarraySNP_1.52.0.tgz vignettes: vignettes/beadarraySNP/inst/doc/beadarraySNP.pdf vignetteTitles: beadarraySNP.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beadarraySNP/inst/doc/beadarraySNP.R dependencyCount: 15 Package: BeadDataPackR Version: 1.38.0 Imports: stats, utils Suggests: BiocStyle, knitr License: GPL-2 Archs: i386, x64 MD5sum: b5e3dc184392ebc194ffa80bcc73ca69 NeedsCompilation: yes Title: Compression of Illumina BeadArray data Description: Provides functionality for the compression and decompression of raw bead-level data from the Illumina BeadArray platform. biocViews: Microarray Author: Mike Smith, Andy Lynch Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BeadDataPackR git_branch: RELEASE_3_10 git_last_commit: afe5ca9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BeadDataPackR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BeadDataPackR_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BeadDataPackR_1.38.0.tgz vignettes: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.pdf vignetteTitles: BeadDataPackR.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.R importsMe: beadarray dependencyCount: 2 Package: BEARscc Version: 1.6.0 Imports: ggplot2, SingleCellExperiment, data.table, stats, utils, graphics, compiler Suggests: testthat, cowplot, knitr, rmarkdown, BiocStyle, NMF License: GPL-3 MD5sum: f9b9cefbd313181ba352698a172c7aad NeedsCompilation: no Title: BEARscc (Bayesian ERCC Assesstment of Robustness of Single Cell Clusters) Description: BEARscc is a noise estimation and injection tool that is designed to assess putative single-cell RNA-seq clusters in the context of experimental noise estimated by ERCC spike-in controls. biocViews: ImmunoOncology, SingleCell, Clustering, Transcriptomics Author: David T. Severson Maintainer: Benjamin Schuster-Boeckler VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BEARscc git_branch: RELEASE_3_10 git_last_commit: 2cbd532 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BEARscc_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BEARscc_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BEARscc_1.6.0.tgz vignettes: vignettes/BEARscc/inst/doc/BEARscc.pdf vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BEARscc/inst/doc/BEARscc.R dependencyCount: 80 Package: BEAT Version: 1.24.0 Depends: R (>= 2.13.0) Imports: GenomicRanges, ShortRead, Biostrings, BSgenome License: LGPL (>= 3.0) MD5sum: 8fb651fdc384d34af0dad0a0b83c914b NeedsCompilation: no Title: BEAT - BS-Seq Epimutation Analysis Toolkit Description: Model-based analysis of single-cell methylation data biocViews: ImmunoOncology, Genetics, MethylSeq, Software, DNAMethylation, Epigenetics Author: Kemal Akman Maintainer: Kemal Akman git_url: https://git.bioconductor.org/packages/BEAT git_branch: RELEASE_3_10 git_last_commit: a4e37f9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BEAT_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BEAT_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BEAT_1.24.0.tgz vignettes: vignettes/BEAT/inst/doc/BEAT.pdf vignetteTitles: Analysing single-cell BS-Seq data with the "BEAT" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BEAT/inst/doc/BEAT.R dependencyCount: 45 Package: BEclear Version: 2.2.0 Depends: BiocParallel (>= 1.14.2) Imports: futile.logger, Rdpack, Matrix, data.table (>= 1.11.8), Rcpp, outliers, abind, stats, graphics, utils, methods LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, pander License: GPL-3 Archs: i386, x64 MD5sum: a3a1eb00a1569eda79c5da4f2bc66b89 NeedsCompilation: yes Title: Correction of batch effects in DNA methylation data Description: Provides functions to detect and correct for batch effects in DNA methylation data. The core function is based on latent factor models and can also be used to predict missing values in any other matrix containing real numbers. biocViews: BatchEffect, DNAMethylation, Software, Preprocessing, StatisticalMethod Author: David Rasp [aut, cre] (), Markus Merl [aut], Ruslan Akulenko [aut] Maintainer: David Rasp URL: https://github.com/David-J-R/BEclear SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/David-J-R/BEclear/issues git_url: https://git.bioconductor.org/packages/BEclear git_branch: RELEASE_3_10 git_last_commit: 0537966 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BEclear_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BEclear_2.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BEclear_2.2.0.tgz vignettes: vignettes/BEclear/inst/doc/BEclear.html vignetteTitles: BEclear tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BEclear/inst/doc/BEclear.R dependencyCount: 28 Package: bgafun Version: 1.48.0 Depends: made4, seqinr,ade4 License: Artistic-2.0 MD5sum: 83e9580e37b1777a6241e396c1cd9c67 NeedsCompilation: no Title: BGAfun A method to identify specifity determining residues in protein families Description: A method to identify specifity determining residues in protein families using Between Group Analysis biocViews: Classification Author: Iain Wallace Maintainer: Iain Wallace git_url: https://git.bioconductor.org/packages/bgafun git_branch: RELEASE_3_10 git_last_commit: dce9fdc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/bgafun_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/bgafun_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/bgafun_1.48.0.tgz vignettes: vignettes/bgafun/inst/doc/bgafun.pdf vignetteTitles: bgafun.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bgafun/inst/doc/bgafun.R dependencyCount: 22 Package: BgeeDB Version: 2.12.1 Depends: R (>= 3.3.0), topGO, tidyr Imports: data.table, RCurl, digest, methods, stats, utils, dplyr, graph, Biobase Suggests: knitr, BiocStyle, testthat, rmarkdown License: GPL-3 MD5sum: a59934aad9c596366ec80fa1947b8617 NeedsCompilation: no Title: Annotation and gene expression data retrieval from Bgee database. TopAnat, an anatomical entities Enrichment Analysis tool for UBERON ontology. Description: A package for the annotation and gene expression data download from Bgee database, and TopAnat analysis: GO-like enrichment of anatomical terms, mapped to genes by expression patterns. biocViews: Software, DataImport, Sequencing, GeneExpression, Microarray, GO, GeneSetEnrichment Author: Andrea Komljenovic [aut, cre], Julien Roux [aut, cre] Maintainer: Julien Wollbrett , Julien Roux , Andrea Komljenovic , Frederic Bastian URL: https://github.com/BgeeDB/BgeeDB_R VignetteBuilder: knitr BugReports: https://github.com/BgeeDB/BgeeDB_R/issues git_url: https://git.bioconductor.org/packages/BgeeDB git_branch: RELEASE_3_10 git_last_commit: e18ee59 git_last_commit_date: 2020-03-04 Date/Publication: 2020-03-04 source.ver: src/contrib/BgeeDB_2.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/BgeeDB_2.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BgeeDB_2.12.1.tgz vignettes: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.R importsMe: psygenet2r, RITAN dependencyCount: 53 Package: BGmix Version: 1.46.0 Depends: R (>= 2.3.1), KernSmooth License: GPL-2 MD5sum: 14718453ebd2713e0f3cc621f95f9bea NeedsCompilation: yes Title: Bayesian models for differential gene expression Description: Fully Bayesian mixture models for differential gene expression biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Alex Lewin, Natalia Bochkina Maintainer: Alex Lewin git_url: https://git.bioconductor.org/packages/BGmix git_branch: RELEASE_3_10 git_last_commit: d476b23 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BGmix_1.46.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BGmix_1.46.0.tgz vignettes: vignettes/BGmix/inst/doc/BGmix.pdf vignetteTitles: BGmix Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BGmix/inst/doc/BGmix.R dependencyCount: 2 Package: bgx Version: 1.52.0 Depends: R (>= 2.0.1), Biobase, affy (>= 1.5.0), gcrma (>= 2.4.1) Imports: Rcpp (>= 0.11.0) LinkingTo: Rcpp Suggests: affydata, hgu95av2cdf License: GPL-2 Archs: i386, x64 MD5sum: 7d7debe597e5ee808d6a4f286e915db3 NeedsCompilation: yes Title: Bayesian Gene eXpression Description: Bayesian integrated analysis of Affymetrix GeneChips biocViews: Microarray, DifferentialExpression Author: Ernest Turro, Graeme Ambler, Anne-Mette K Hein Maintainer: Ernest Turro git_url: https://git.bioconductor.org/packages/bgx git_branch: RELEASE_3_10 git_last_commit: d5748cf git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/bgx_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/bgx_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/bgx_1.52.0.tgz vignettes: vignettes/bgx/inst/doc/bgx.pdf vignetteTitles: HowTo BGX hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bgx/inst/doc/bgx.R dependencyCount: 21 Package: BHC Version: 1.38.0 License: GPL-3 Archs: i386, x64 MD5sum: 63d772dcddf8aa1c6d151c3a8d9f7c5a NeedsCompilation: yes Title: Bayesian Hierarchical Clustering Description: The method performs bottom-up hierarchical clustering, using a Dirichlet Process (infinite mixture) to model uncertainty in the data and Bayesian model selection to decide at each step which clusters to merge. This avoids several limitations of traditional methods, for example how many clusters there should be and how to choose a principled distance metric. This implementation accepts multinomial (i.e. discrete, with 2+ categories) or time-series data. This version also includes a randomised algorithm which is more efficient for larger data sets. biocViews: Microarray, Clustering Author: Rich Savage, Emma Cooke, Robert Darkins, Yang Xu Maintainer: Rich Savage git_url: https://git.bioconductor.org/packages/BHC git_branch: RELEASE_3_10 git_last_commit: 26e930e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BHC_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BHC_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BHC_1.38.0.tgz vignettes: vignettes/BHC/inst/doc/bhc.pdf vignetteTitles: Bayesian Hierarchical Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BHC/inst/doc/bhc.R dependencyCount: 0 Package: BicARE Version: 1.44.0 Depends: R (>= 1.8.0), Biobase (>= 2.5.5), multtest, GSEABase License: GPL-2 Archs: i386, x64 MD5sum: ba23aa3434494892f658b819fe62bd57 NeedsCompilation: yes Title: Biclustering Analysis and Results Exploration Description: Biclustering Analysis and Results Exploration biocViews: Microarray, Transcription, Clustering Author: Pierre Gestraud Maintainer: Pierre Gestraud URL: http://bioinfo.curie.fr git_url: https://git.bioconductor.org/packages/BicARE git_branch: RELEASE_3_10 git_last_commit: 41b10e4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BicARE_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BicARE_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BicARE_1.44.0.tgz vignettes: vignettes/BicARE/inst/doc/BicARE.pdf vignetteTitles: BicARE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BicARE/inst/doc/BicARE.R importsMe: miRSM dependencyCount: 41 Package: BiFET Version: 1.6.0 Imports: stats, poibin, GenomicRanges Suggests: testthat, knitr License: GPL-3 MD5sum: 5ab5d649e9eb00943a8b5ed125d53d12 NeedsCompilation: no Title: Bias-free Footprint Enrichment Test Description: BiFET identifies TFs whose footprints are over-represented in target regions compared to background regions after correcting for the bias arising from the imbalance in read counts and GC contents between the target and background regions. For a given TF k, BiFET tests the null hypothesis that the target regions have the same probability of having footprints for the TF k as the background regions while correcting for the read count and GC content bias. For this, we use the number of target regions with footprints for TF k, t_k as a test statistic and calculate the p-value as the probability of observing t_k or more target regions with footprints under the null hypothesis. biocViews: ImmunoOncology, Genetics, Epigenetics, Transcription, GeneRegulation, ATACSeq, DNaseSeq, RIPSeq, Software Author: Ahrim Youn [aut, cre], Eladio Marquez [aut], Nathan Lawlor [aut], Michael Stitzel [aut], Duygu Ucar [aut] Maintainer: Ahrim Youn VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiFET git_branch: RELEASE_3_10 git_last_commit: 7b7b60b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BiFET_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BiFET_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BiFET_1.6.0.tgz vignettes: vignettes/BiFET/inst/doc/BiFET.html vignetteTitles: "A Guide to using BiFET" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiFET/inst/doc/BiFET.R dependencyCount: 17 Package: BiGGR Version: 1.22.0 Depends: R (>= 2.14.0), rsbml, hyperdraw, LIM,stringr Imports: hypergraph, limSolve License: file LICENSE MD5sum: 7c0b87562afbeadaed1903b65f7ca3fb NeedsCompilation: no Title: Constraint based modeling in R using metabolic reconstruction databases Description: This package provides an interface to simulate metabolic reconstruction from the BiGG database(http://bigg.ucsd.edu/) and other metabolic reconstruction databases. The package facilitates flux balance analysis (FBA) and the sampling of feasible flux distributions. Metabolic networks and estimated fluxes can be visualized with hypergraphs. biocViews: Systems Biology,Pathway,Network,GraphAndNetwork, Visualization,Metabolomics Author: Anand K. Gavai, Hannes Hettling Maintainer: Anand K. Gavai , Hannes Hettling URL: http://www.bioconductor.org/ git_url: https://git.bioconductor.org/packages/BiGGR git_branch: RELEASE_3_10 git_last_commit: f34d300 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BiGGR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BiGGR_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BiGGR_1.22.0.tgz vignettes: vignettes/BiGGR/inst/doc/BiGGR.pdf vignetteTitles: BiGGR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiGGR/inst/doc/BiGGR.R dependencyCount: 26 Package: bigmelon Version: 1.12.0 Depends: R (>= 3.3), wateRmelon (>= 1.25.0), gdsfmt (>= 1.0.4), methods, minfi (>= 1.21.0), Biobase, methylumi Imports: stats, utils, GEOquery, graphics, BiocGenerics Suggests: BiocGenerics, BiocStyle, minfiData, parallel, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, bumphunter License: GPL-3 MD5sum: 7831164924735c3e9b1b16308adc367f NeedsCompilation: no Title: Illumina methylation array analysis for large experiments Description: Methods for working with Illumina arrays using gdsfmt. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl, MethylationArray, DataImport, CpGIsland Author: Tyler J. Gorrie-Stone [cre, aut], Ayden Saffari [aut], Karim Malki [aut], Leonard C. Schalkwyk [aut] Maintainer: Tyler J. Gorrie-Stone git_url: https://git.bioconductor.org/packages/bigmelon git_branch: RELEASE_3_10 git_last_commit: c9d2cfb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/bigmelon_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/bigmelon_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/bigmelon_1.12.0.tgz vignettes: vignettes/bigmelon/inst/doc/bigmelon.pdf vignetteTitles: The bigmelon Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bigmelon/inst/doc/bigmelon.R dependencyCount: 165 Package: bigmemoryExtras Version: 1.34.0 Depends: R (>= 2.12), bigmemory (>= 4.5.31) Imports: methods Suggests: testthat, BiocGenerics, BiocStyle, knitr License: Artistic-2.0 OS_type: unix MD5sum: 9f0a832ea9925319ae03dad6f896a43a NeedsCompilation: no Title: An extension of the bigmemory package with added safety, convenience, and a factor class Description: This package defines a "BigMatrix" ReferenceClass which adds safety and convenience features to the filebacked.big.matrix class from the bigmemory package. BigMatrix protects against segfaults by monitoring and gracefully restoring the connection to on-disk data and it also protects against accidental data modification with a filesystem-based permissions system. We provide utilities for using BigMatrix-derived classes as assayData matrices within the Biobase package's eSet family of classes. BigMatrix provides some optimizations related to attaching to, and indexing into, file-backed matrices with dimnames. Additionally, the package provides a "BigMatrixFactor" class, a file-backed matrix with factor properties. biocViews: Infrastructure, DataRepresentation Author: Peter M. Haverty Maintainer: Peter M. Haverty URL: https://github.com/phaverty/bigmemoryExtras VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bigmemoryExtras git_branch: RELEASE_3_10 git_last_commit: 5c86515 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/bigmemoryExtras_1.34.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/bigmemoryExtras_1.34.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: gCMAP dependencyCount: 6 Package: bigPint Version: 1.2.2 Depends: R (>= 3.6.0) Imports: dplyr (>= 0.7.2), GGally (>= 1.3.2), ggplot2 (>= 2.2.1), graphics (>= 3.5.0), grDevices (>= 3.5.0), grid (>= 3.5.0), gridExtra (>= 2.3), hexbin (>= 1.27.1), Hmisc (>= 4.0.3), htmlwidgets (>= 0.9), methods (>= 3.5.2), plotly (>= 4.7.1), plyr (>= 1.8.4), RColorBrewer (>= 1.1.2), reshape (>= 0.8.7), shiny (>= 1.0.5), shinycssloaders (>= 0.2.0), shinydashboard (>= 0.6.1), stats (>= 3.5.0), stringr (>= 1.3.1), tidyr (>= 0.7.0), utils (>= 3.5.0) Suggests: BiocGenerics (>= 0.29.1), data.table (>= 1.11.8), EDASeq (>= 2.14.0), edgeR (>= 3.22.2), gtools (>= 3.5.0), knitr (>= 1.13), matrixStats (>= 0.53.1), rmarkdown (>= 1.10), roxygen2 (>= 3.0.0), RUnit (>= 0.4.32), tibble (>= 1.4.2), License: GPL-3 MD5sum: 115530a0fbca8c076879a67df5491cac NeedsCompilation: no Title: Big multivariate data plotted interactively Description: Methods for visualizing large multivariate datasets using static and interactive scatterplot matrices, parallel coordinate plots, volcano plots, and litre plots. Includes examples for visualizing RNA-sequencing datasets and differentially expressed genes. biocViews: Clustering, DataImport, DifferentialExpression, GeneExpression, MultipleComparison, Normalization, Preprocessing, QualityControl, RNASeq, Sequencing, Software, Transcription, Visualization Author: Lindsay Rutter [aut, cre], Dianne Cook [aut] Maintainer: Lindsay Rutter URL: https://github.com/lindsayrutter/bigPint VignetteBuilder: knitr BugReports: https://github.com/lindsayrutter/bigPint/issues git_url: https://git.bioconductor.org/packages/bigPint git_branch: RELEASE_3_10 git_last_commit: b121faa git_last_commit_date: 2020-03-17 Date/Publication: 2020-03-18 source.ver: src/contrib/bigPint_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/bigPint_1.2.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/bigPint_1.2.2.tgz vignettes: vignettes/bigPint/inst/doc/bioconductor.html, vignettes/bigPint/inst/doc/clusters.html, vignettes/bigPint/inst/doc/createDataMetrics.html, vignettes/bigPint/inst/doc/data.html, vignettes/bigPint/inst/doc/dataMetrics.html, vignettes/bigPint/inst/doc/honeybee.html, vignettes/bigPint/inst/doc/installation.html, vignettes/bigPint/inst/doc/interactive.html, vignettes/bigPint/inst/doc/pipeline.html, vignettes/bigPint/inst/doc/plotIntro.html, vignettes/bigPint/inst/doc/static.html, vignettes/bigPint/inst/doc/superimposeData.html vignetteTitles: "bigPint Vignette", "Hierarchical clustering", "Creating data metrics object", "Data object", "Data metrics object", "Research paper", "Installation", "Producing interactive plots", "Recommended RNA-seq pipeline", "Introduction to bigPint plots", "Producing static plots", "Alternatives to data metrics object" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bigPint/inst/doc/bioconductor.R, vignettes/bigPint/inst/doc/clusters.R, vignettes/bigPint/inst/doc/createDataMetrics.R, vignettes/bigPint/inst/doc/data.R, vignettes/bigPint/inst/doc/dataMetrics.R, vignettes/bigPint/inst/doc/honeybee.R, vignettes/bigPint/inst/doc/installation.R, vignettes/bigPint/inst/doc/interactive.R, vignettes/bigPint/inst/doc/pipeline.R, vignettes/bigPint/inst/doc/plotIntro.R, vignettes/bigPint/inst/doc/static.R, vignettes/bigPint/inst/doc/superimposeData.R dependencyCount: 111 Package: bioassayR Version: 1.24.0 Depends: R (>= 3.5.0), DBI (>= 0.3.1), RSQLite (>= 1.0.0), methods, Matrix, rjson, BiocGenerics (>= 0.13.8) Imports: XML, ChemmineR Suggests: BiocStyle, RCurl, biomaRt, cellHTS2, knitr, knitcitations, knitrBootstrap, testthat, ggplot2, rmarkdown License: Artistic-2.0 MD5sum: 05cea7d2e5f6cd5bcf80ba87c6a3cae4 NeedsCompilation: no Title: Cross-target analysis of small molecule bioactivity Description: bioassayR is a computational tool that enables simultaneous analysis of thousands of bioassay experiments performed over a diverse set of compounds and biological targets. Unique features include support for large-scale cross-target analyses of both public and custom bioassays, generation of high throughput screening fingerprints (HTSFPs), and an optional preloaded database that provides access to a substantial portion of publicly available bioactivity data. biocViews: ImmunoOncology, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Bioinformatics, Proteomics, Metabolomics Author: Tyler Backman, Ronly Schlenk, Thomas Girke Maintainer: Tyler Backman URL: https://github.com/TylerBackman/bioassayR VignetteBuilder: knitr BugReports: https://github.com/TylerBackman/bioassayR/issues git_url: https://git.bioconductor.org/packages/bioassayR git_branch: RELEASE_3_10 git_last_commit: b81c97d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/bioassayR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/bioassayR_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/bioassayR_1.24.0.tgz vignettes: vignettes/bioassayR/inst/doc/bioassayR.html vignetteTitles: Introduction and Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioassayR/inst/doc/bioassayR.R dependencyCount: 82 Package: Biobase Version: 2.46.0 Depends: R (>= 2.10), BiocGenerics (>= 0.27.1), utils Imports: methods Suggests: tools, tkWidgets, ALL, RUnit, golubEsets License: Artistic-2.0 Archs: i386, x64 MD5sum: 961d26c6399e94de8c7c816b4f6967e9 NeedsCompilation: yes Title: Biobase: Base functions for Bioconductor Description: Functions that are needed by many other packages or which replace R functions. biocViews: Infrastructure Author: R. Gentleman, V. Carey, M. Morgan, S. Falcon Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/Biobase git_branch: RELEASE_3_10 git_last_commit: 153c5c6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Biobase_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Biobase_2.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Biobase_2.46.0.tgz vignettes: vignettes/Biobase/inst/doc/BiobaseDevelopment.pdf, vignettes/Biobase/inst/doc/esApply.pdf, vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf vignetteTitles: Notes for eSet developers, esApply Introduction, An introduction to Biobase and ExpressionSets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biobase/inst/doc/BiobaseDevelopment.R, vignettes/Biobase/inst/doc/esApply.R, vignettes/Biobase/inst/doc/ExpressionSetIntroduction.R dependsOnMe: a4Base, a4Core, ACME, affy, affycomp, affyContam, affycoretools, affyPLM, affyQCReport, AGDEX, AIMS, altcdfenvs, annaffy, AnnotationDbi, AnnotationForge, ArrayExpress, arrayMvout, ArrayTools, BAGS, beadarray, beadarraySNP, bgx, BicARE, bigmelon, BiocCaseStudies, BioMVCClass, BioQC, biosigner, birta, BLMA, BrainStars, CAMERA, cancerclass, casper, Category, categoryCompare, CCPROMISE, cellHTS2, CGHbase, CGHcall, CGHregions, chimera, chroGPS, clippda, clusterStab, CMA, cn.farms, codelink, convert, copa, covEB, covRNA, DESeq, DEXSeq, DFP, diggit, doppelgangR, DSS, dualKS, dyebias, EBarrays, EDASeq, edge, EGSEA, eisa, epigenomix, epivizrData, ExiMiR, ExpressionAtlas, fabia, factDesign, fastseg, flowBeads, frma, gaga, gCMAPWeb, GeneAnswers, GeneExpressionSignature, GeneMeta, geneplotter, geneRecommender, GeneRegionScan, GeneSelectMMD, geNetClassifier, GEOquery, GOexpress, GOFunction, goProfiles, GOstats, GSEABase, GSEABenchmarkeR, GSEAlm, GWASTools, hapFabia, HELP, hopach, HTqPCR, HybridMTest, iCheck, IdeoViz, idiogram, InPAS, INSPEcT, isobar, iterativeBMA, IVAS, LMGene, lumi, macat, mAPKL, massiR, MEAL, MergeMaid, metagenomeFeatures, metagenomeSeq, metavizr, MethPed, methyAnalysis, methylumi, Mfuzz, MiChip, mimager, MIMOSA, MineICA, MiRaGE, miRcomp, MLInterfaces, MmPalateMiRNA, monocle, MSnbase, Mulcom, MultiDataSet, multtest, NanoStringDiff, NOISeq, nondetects, normalize450K, NormqPCR, oligo, omicRexposome, OrderedList, OTUbase, OutlierD, pandaR, panp, pcaMethods, pcot2, pdInfoBuilder, pepStat, PGSEA, phenoTest, PLPE, plrs, prada, PREDA, pRolocGUI, PROMISE, qpcrNorm, qPLEXanalyzer, R453Plus1Toolbox, RbcBook1, rbsurv, rcellminer, ReadqPCR, reb, RefPlus, rexposome, Ringo, Risa, Rmagpie, RNAinteract, rnaSeqMap, Rnits, Roleswitch, ropls, RpsiXML, RTopper, RUVSeq, safe, SCAN.UPC, SeqGSEA, sigaR, SigCheck, siggenes, simpleaffy, simulatorZ, singleCellTK, SpeCond, SPEM, spkTools, splicegear, splineTimeR, STROMA4, SummarizedExperiment, TDARACNE, tigre, tilingArray, topGO, TPP, tRanslatome, tspair, twilight, UNDO, variancePartition, VegaMC, viper, vsn, wateRmelon, waveTiling, webbioc, xcms, XDE, XINA, yarn importsMe: ABarray, ACE, aCGH, adSplit, affyILM, affyQCReport, AgiMicroRna, AnalysisPageServer, ANF, annmap, annotate, AnnotationHubData, annotationTools, ArrayExpressHTS, arrayQualityMetrics, ArrayTools, attract, ballgown, BASiCS, BayesKnockdown, BgeeDB, biobroom, bioCancer, biocViews, BioNet, BioSeqClass, biosvd, biscuiteer, BiSeq, blima, BrainStars, bsseq, BubbleTree, CAFE, CAMTHC, canceR, Cardinal, CATALYST, CellScore, CellTrails, CGHnormaliter, ChIPpeakAnno, ChIPQC, ChIPXpress, ChromHeatMap, chromswitch, cicero, clipper, CluMSID, cn.mops, COCOA, coexnet, cogena, ConsensusClusterPlus, consensusDE, consensusOV, coRdon, crlmm, CrossICC, crossmeta, cummeRbund, cycle, cydar, CytoML, ddCt, debCAM, deco, DEGreport, DESeq2, destiny, diffloop, discordant, easyRNASeq, EBarrays, ecolitk, EGAD, ensembldb, erma, esetVis, ExiMiR, farms, ffpe, FindMyFriends, flowClust, flowCore, flowFP, flowMatch, flowMeans, flowSpy, flowStats, flowType, flowUtils, flowViz, flowWorkspace, FourCSeq, frma, frmaTools, FunciSNP, GAPGOM, gCMAP, gCrisprTools, gcrma, GCSscore, genbankr, geneClassifiers, genefilter, GeneMeta, geneRecommender, GeneRegionScan, GeneSelectMMD, GENESIS, GenomicFeatures, GenomicInteractions, GenomicScores, GEOsubmission, gep2pep, gespeR, GGBase, ggbio, GGtools, girafe, GISPA, GlobalAncova, globaltest, gmapR, GOFunction, gQTLstats, GSRI, GSVA, Gviz, Harshlight, HEM, HTqPCR, HTSFilter, IdMappingAnalysis, imageHTS, ImmuneSpaceR, ImpulseDE2, IsoGeneGUI, isomiRs, iterClust, JunctionSeq, kimod, kissDE, KnowSeq, lapmix, LINC, LiquidAssociation, LRBaseDbi, LVSmiRNA, maanova, MAGeCKFlute, makecdfenv, maSigPro, MAST, mBPCR, MCRestimate, MeSHDbi, metaArray, methyAnalysis, MethylAid, methylCC, methylumi, mfa, MiChip, MIGSA, minfi, MinimumDistance, MiPP, MIRA, miRSM, MLSeq, MMAPPR2, MmPalateMiRNA, MOFA, mogsa, MoonlightR, MoPS, MOSim, MSnID, MTseeker, MultiAssayExperiment, multiscan, mzR, NanoStringQCPro, NormalyzerDE, npGSEA, nucleR, OGSA, oligoClasses, ontoProc, oposSOM, oppar, OrderedList, OrganismDbi, OUTRIDER, panp, PathwaySplice, Pbase, PCpheno, phantasus, PharmacoGx, phemd, phyloseq, piano, plethy, plgem, plier, podkat, POST, PowerExplorer, ppiStats, prada, prebs, PrInCE, proBatch, proFIA, progeny, pRoloc, PROMISE, PROPS, ProteomicsAnnotationHubData, PSEA, psygenet2r, puma, pvac, pvca, pwOmics, qcmetrics, QDNAseq, qpgraph, quantro, QuasR, qusage, randPack, readat, RGalaxy, RIVER, Rmagpie, rols, ROTS, rqubic, rScudo, Rtreemix, RUVnormalize, SAGx, scmap, scTGIF, SeqVarTools, ShortRead, SigsPack, sigsquared, SimBindProfiles, simpleaffy, singscore, SLGI, SNPchip, SomaticSignatures, spkTools, splicegear, SPONGE, STATegRa, subSeq, synapter, TEQC, TFBSTools, timecourse, TMixClust, TnT, ToPASeq, topdownr, TTMap, twilight, uSORT, VanillaICE, VariantAnnotation, VariantFiltering, VariantTools, vidger, vulcan, wateRmelon, XBSeq, Xeva, birte suggestsMe: AUCell, BiocCaseStudies, BiocCheck, BiocGenerics, BiocOncoTK, BSgenome, CellMapper, cellTree, clustComp, coseq, CountClust, DAPAR, DART, dcanr, EpiDISH, epivizr, epivizrChart, epivizrStandalone, farms, genefu, GENIE3, GenomicRanges, GSAR, Heatplus, interactiveDisplay, kebabs, les, limma, Logolas, M3Drop, mCSEA, messina, msa, multiClust, nem, OSAT, PCAtools, pkgDepTools, RcisTarget, ReactomeGSA, ROC, RTCGA, scater, scmeth, scran, SeqArray, slinky, stageR, survcomp, TargetScore, TCGAbiolinks, TFutils, TimeSeriesExperiment, tkWidgets, TypeInfo, vbmp, widgetTools dependencyCount: 6 Package: biobroom Version: 1.18.0 Depends: R (>= 3.0.0), broom Imports: dplyr, tidyr, Biobase Suggests: limma, DESeq2, airway, ggplot2, plyr, GenomicRanges, testthat, magrittr, edgeR, qvalue, knitr, data.table, MSnbase, SummarizedExperiment License: LGPL MD5sum: 547beb7c999972eba98d582ce6fb8f18 NeedsCompilation: no Title: Turn Bioconductor objects into tidy data frames Description: This package contains methods for converting standard objects constructed by bioinformatics packages, especially those in Bioconductor, and converting them to tidy data. It thus serves as a complement to the broom package, and follows the same the tidy, augment, glance division of tidying methods. Tidying data makes it easy to recombine, reshape and visualize bioinformatics analyses. biocViews: MultipleComparison, DifferentialExpression, Regression, GeneExpression, Proteomics, DataImport Author: Andrew J. Bass, David G. Robinson, Steve Lianoglou, Emily Nelson, John D. Storey, with contributions from Laurent Gatto Maintainer: John D. Storey and Andrew J. Bass URL: https://github.com/StoreyLab/biobroom VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/biobroom/issues git_url: https://git.bioconductor.org/packages/biobroom git_branch: RELEASE_3_10 git_last_commit: 1e7d17b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/biobroom_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/biobroom_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/biobroom_1.18.0.tgz vignettes: vignettes/biobroom/inst/doc/biobroom_vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biobroom/inst/doc/biobroom_vignette.R importsMe: TPP dependencyCount: 42 Package: bioCancer Version: 1.14.02 Depends: R (>= 3.5.0), radiant.data (>= 1.0.6), cgdsr(>= 1.2.6), XML(>= 3.99) Imports: shiny (>= 1.0.5), AlgDesign (>= 1.1.7.3), import (>= 1.1.0), methods, shinythemes, Biobase, geNetClassifier, AnnotationFuncs, org.Hs.eg.db, DOSE, clusterProfiler, reactome.db, ReactomePA, DiagrammeR(>= 1.0.5), visNetwork, htmlwidgets, plyr, tibble, DT (>= 0.12), dplyr (>= 0.8.5) Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 0.10.0) License: AGPL-3 | file LICENSE MD5sum: 13f23673a3ad16ce59ff4dc7670bdeeb NeedsCompilation: no Title: Interactive Multi-Omics Cancers Data Visualization and Analysis Description: bioCancer is a Shiny App to visualize and analyse interactively Multi-Assays of Cancer Genomic Data. biocViews: GUI, DataRepresentation, Network, MultipleComparison, Pathways, Reactome, Visualization,GeneExpression,GeneTarget Author: Karim Mezhoud [aut, cre] Maintainer: Karim Mezhoud URL: http://kmezhoud.github.io/bioCancer VignetteBuilder: knitr BugReports: https://github.com/kmezhoud/bioCancer/issues git_url: https://git.bioconductor.org/packages/bioCancer git_branch: RELEASE_3_10 git_last_commit: 601af5c git_last_commit_date: 2020-03-16 Date/Publication: 2020-03-16 source.ver: src/contrib/bioCancer_1.14.02.tar.gz win.binary.ver: bin/windows/contrib/3.6/bioCancer_1.14.02.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/bioCancer_1.14.02.tgz vignettes: vignettes/bioCancer/inst/doc/bioCancer.html vignetteTitles: bioCancer: Interactive Multi-OMICS Cancers Data Visualization and Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/bioCancer/inst/doc/bioCancer.R dependencyCount: 210 Package: BiocCaseStudies Version: 1.48.0 Depends: tools, methods, utils, Biobase Suggests: affy (>= 1.17.3), affyPLM (>= 1.15.1), affyQCReport (>= 1.17.0), ALL (>= 1.4.3), annaffy (>= 1.11.1), annotate (>= 1.17.3), AnnotationDbi (>= 1.1.6), apComplex (>= 2.5.0), Biobase (>= 1.17.5), bioDist (>= 1.11.3), biocGraph (>= 1.1.1), biomaRt (>= 1.13.5), CCl4 (>= 1.0.6), CLL (>= 1.2.4), Category (>= 2.5.0), class (>= 7.2-38), cluster (>= 1.11.9), convert (>= 1.15.0), gcrma (>= 2.11.1), genefilter (>= 1.17.6), geneplotter (>= 1.17.2), GO.db (>= 2.0.2), GOstats (>= 2.5.0), graph (>= 1.17.4), GSEABase (>= 1.1.13), hgu133a.db (>= 2.0.2), hgu95av2.db, hgu95av2cdf (>= 2.0.0), hgu95av2probe (>= 2.0.0), hopach (>= 1.13.0), KEGG.db (>= 2.0.2), kohonen (>= 2.0.2), lattice (>= 0.17.2), latticeExtra (>= 0.3-1), limma (>= 2.13.1), MASS (>= 7.2-38), MLInterfaces (>= 1.13.17), multtest (>= 1.19.0), org.Hs.eg.db (>= 2.0.2), ppiStats (>= 1.5.4), randomForest (>= 4.5-20), RBGL (>= 1.15.6), RColorBrewer (>= 1.0-2), Rgraphviz (>= 1.17.11), vsn (>= 3.4.0), weaver (>= 1.5.0), xtable (>= 1.5-2), yeastExpData (>= 0.9.11) License: Artistic-2.0 MD5sum: 0ea6936a6ab5b8558190d3c12b1bbc41 NeedsCompilation: no Title: BiocCaseStudies: Support for the Case Studies Monograph Description: Software and data to support the case studies. biocViews: Infrastructure Author: R. Gentleman, W. Huber, F. Hahne, M. Morgan, S. Falcon Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/BiocCaseStudies git_branch: RELEASE_3_10 git_last_commit: 03e1cf0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BiocCaseStudies_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BiocCaseStudies_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BiocCaseStudies_1.48.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: BiocCheck Version: 1.22.0 Depends: R (>= 3.5.0) Imports: biocViews (>= 1.33.7), BiocManager, stringdist, graph, httr, tools, optparse, codetools, methods, utils, knitr Suggests: RUnit, BiocGenerics, Biobase, RJSONIO, rmarkdown, devtools (>= 1.4.1), usethis, BiocStyle Enhances: codetoolsBioC License: Artistic-2.0 MD5sum: 97eda55125dd5dd7e30f3df2a2d531dd NeedsCompilation: no Title: Bioconductor-specific package checks Description: Executes Bioconductor-specific package checks. biocViews: Infrastructure Author: Bioconductor Package Maintainer [aut, cre], Lori Shepherd [ctb], Daniel von Twisk [ctb], Kevin Rue [ctb] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/BiocCheck/issues VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocCheck git_branch: RELEASE_3_10 git_last_commit: bb71553 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BiocCheck_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BiocCheck_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BiocCheck_1.22.0.tgz vignettes: vignettes/BiocCheck/inst/doc/BiocCheck.html vignetteTitles: BiocCheck: Ensuring Bioconductor package guidelines hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocCheck/inst/doc/BiocCheck.R importsMe: ExperimentHubData suggestsMe: SpectralTAD dependencyCount: 40 Package: BiocFileCache Version: 1.10.2 Depends: R (>= 3.4.0), dbplyr (>= 1.0.0) Imports: methods, stats, utils, dplyr, RSQLite, DBI, rappdirs, curl, httr Suggests: testthat, knitr, BiocStyle, rmarkdown, rtracklayer License: Artistic-2.0 MD5sum: e136d5aa30d1c4dd12bbeb0d577f3035 NeedsCompilation: no Title: Manage Files Across Sessions Description: This package creates a persistent on-disk cache of files that the user can add, update, and retrieve. It is useful for managing resources (such as custom Txdb objects) that are costly or difficult to create, web resources, and data files used across sessions. biocViews: DataImport Author: Lori Shepherd [aut, cre], Martin Morgan [aut] Maintainer: Lori Shepherd VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocFileCache/issues git_url: https://git.bioconductor.org/packages/BiocFileCache git_branch: RELEASE_3_10 git_last_commit: e6d2a47 git_last_commit_date: 2019-11-08 Date/Publication: 2019-11-08 source.ver: src/contrib/BiocFileCache_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/BiocFileCache_1.10.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BiocFileCache_1.10.2.tgz vignettes: vignettes/BiocFileCache/inst/doc/BiocFileCache.html vignetteTitles: BiocFileCache: Managing File Resources Across Sessions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocFileCache/inst/doc/BiocFileCache.R dependsOnMe: AnnotationHub, ExperimentHub, RcwlPipelines importsMe: AMARETTO, atSNP, BiocPkgTools, biomaRt, brendaDb, cbaf, CellBench, CTDquerier, easyRNASeq, EnrichmentBrowser, GAPGOM, GSEABenchmarkeR, HCABrowser, MBQN, Organism.dplyr, psichomics, tximeta, UniProt.ws, waddR suggestsMe: BiocOncoTK, BiocSet, HumanTranscriptomeCompendium, progeny, scater, seqsetvis, TCGAutils, TimeSeriesExperiment dependencyCount: 42 Package: BiocGenerics Version: 0.32.0 Depends: R (>= 3.6.0), methods, utils, graphics, stats, parallel Imports: methods, utils, graphics, stats, parallel Suggests: Biobase, S4Vectors, IRanges, GenomicRanges, DelayedArray, Biostrings, Rsamtools, AnnotationDbi, oligoClasses, oligo, affyPLM, flowClust, affy, DESeq2, MSnbase, annotate, RUnit License: Artistic-2.0 MD5sum: f1430e164062897b72e3ef6477d4f93c NeedsCompilation: no Title: S4 generic functions used in Bioconductor Description: The package defines S4 generic functions used in Bioconductor. biocViews: Infrastructure Author: The Bioconductor Dev Team Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/BiocGenerics git_branch: RELEASE_3_10 git_last_commit: 22a01ac git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BiocGenerics_0.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BiocGenerics_0.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BiocGenerics_0.32.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ACME, affy, affyPLM, altcdfenvs, amplican, AnnotationDbi, AnnotationForge, AnnotationHub, ATACseqQC, beadarray, bioassayR, Biobase, Biostrings, bnbc, BSgenome, bsseq, Cardinal, Category, categoryCompare, chipseq, ChIPseqR, ChromHeatMap, cleanUpdTSeq, clusterExperiment, codelink, consensusDE, consensusSeekeR, copynumber, CRISPRseek, cummeRbund, DelayedArray, DESeq, dexus, ensembldb, ensemblVEP, ExperimentHub, ExperimentHubData, GDSArray, geneplotter, GenomeInfoDb, genomeIntervals, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges, GenomicScores, Genominator, genoset, ggbio, girafe, graph, GSEABase, GUIDEseq, HelloRanges, interactiveDisplay, interactiveDisplayBase, IRanges, MBASED, MeSHDbi, methyAnalysis, MIGSA, MineICA, minfi, MLInterfaces, MotifDb, MotIV, mpra, MSnbase, multtest, NADfinder, ngsReports, oligo, OrganismDbi, Pbase, PICS, plethy, plyranges, PoTRA, profileplyr, PSICQUIC, PWMEnrich, RareVariantVis, REDseq, Repitools, RNAprobR, RnBeads, RPA, rsbml, S4Vectors, scsR, shinyMethyl, ShortRead, simpleaffy, simulatorZ, soGGi, StructuralVariantAnnotation, SummarizedBenchmark, TEQC, tigre, topdownr, topGO, UNDO, UniProt.ws, VanillaICE, VariantAnnotation, VariantFiltering, VCFArray, XVector, yamss importsMe: affycoretools, affylmGUI, AllelicImbalance, AneuFinder, annmap, annotate, AnnotationHubData, ArrayExpressHTS, ASpli, AUCell, bamsignals, BASiCS, batchelor, beachmat, bigmelon, biocGraph, BiocSingular, biosvd, biotmle, biovizBase, biscuiteer, BiSeq, blima, breakpointR, BrowserViz, BSgenome, BubbleTree, bumphunter, CAGEfightR, CAGEr, casper, celaref, cellHTS2, CellMixS, CellTrails, cghMCR, ChemmineOB, ChemmineR, ChIC, chipenrich, ChIPpeakAnno, ChIPQC, ChIPseeker, chipseq, ChIPSeqSpike, chromstaR, chromVAR, cicero, clusterSeq, cn.mops, CNEr, cobindR, COCOA, cola, compEpiTools, contiBAIT, crlmm, crossmeta, csaw, cummeRbund, cydar, dada2, dagLogo, ddCt, decompTumor2Sig, DEGreport, DelayedDataFrame, derfinder, DEScan2, DESeq2, destiny, DEWSeq, DEXSeq, diffcoexp, diffHic, DirichletMultinomial, DiscoRhythm, DRIMSeq, DrugVsDisease, easyRNASeq, EBImage, EDASeq, eiR, eisa, enrichTF, epigenomix, epivizrChart, epivizrStandalone, erma, esATAC, FamAgg, fastseg, ffpe, FindMyFriends, flowBin, flowClust, flowCore, flowFP, FlowSOM, flowSpecs, flowStats, flowWorkspace, fmcsR, frma, FunciSNP, GA4GHclient, GA4GHshiny, gcapc, gCMAPWeb, genbankr, geneAttribution, geneClassifiers, genefilter, GENESIS, GenomicAlignments, GenomicInteractions, GenomicTuples, genotypeeval, GenVisR, GGBase, GGtools, gmapR, GOTHiC, gQTLBase, gQTLstats, GSVA, Gviz, gwascat, HDF5Array, heatmaps, HiLDA, hiReadsProcessor, hopach, HTSeqGenie, icetea, igvR, IHW, IMAS, infercnv, INSPEcT, intansv, InteractionSet, IntEREst, IONiseR, iSEE, isomiRs, IVAS, JunctionSeq, KCsmart, ldblock, LOLA, LVSmiRNA, M3D, maser, MAST, matter, MEAL, meshr, metaMS, methInheritSim, MethylAid, methylPipe, methylumi, methyvim, mimager, MinimumDistance, MIRA, MiRaGE, MMAPPR2, Modstrings, mogsa, monocle, motifbreakR, msa, MTseeker, MultiAssayExperiment, MultiDataSet, multiMiR, MutationalPatterns, mzR, NarrowPeaks, npGSEA, nucleR, oligoClasses, OmicsLonDA, openPrimeR, ORFik, OUTRIDER, parglms, PathwaySplice, pcaMethods, pdInfoBuilder, phemd, phyloseq, piano, PING, plrs, podkat, prada, pram, primirTSS, proDA, profileScoreDist, pRoloc, PureCN, pwOmics, qPLEXanalyzer, qsea, QuasR, R3CPET, R453Plus1Toolbox, RaggedExperiment, ramwas, Rariant, RCAS, RcisTarget, RCy3, RCyjs, recoup, REDseq, RefNet, REMP, ReportingTools, RGalaxy, RGMQL, RGSEA, RiboProfiling, Ringo, RJMCMCNucleosomes, RNAmodR, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, roar, rols, Rqc, rqubic, Rsamtools, rsbml, rScudo, RTCGAToolbox, rtracklayer, SC3, scater, scmap, SCnorm, scPipe, scran, scruff, sevenC, SGSeq, SharedObject, signeR, simpleaffy, SingleCellExperiment, SLGI, SNPhood, snpStats, SparseSignatures, splatter, SplicingGraphs, SQLDataFrame, sRACIPE, sscu, STAN, strandCheckR, Streamer, Structstrings, SummarizedExperiment, SynMut, systemPipeR, target, TarSeqQC, TCGAutils, TCseq, TFBSTools, TitanCNA, trackViewer, transcriptR, transite, TransView, triform, tRNA, tRNAdbImport, tRNAscanImport, TSRchitect, TVTB, Ularcirc, unifiedWMWqPCR, universalmotif, uSORT, VariantTools, wavClusteR, xcms, XDE, XVector, gpuMagic suggestsMe: acde, AIMS, ArrayTV, ASSET, BaalChIP, baySeq, BDMMAcorrect, bigmelon, bigmemoryExtras, bigPint, BiocCheck, BiocParallel, BiocStyle, biocViews, BioMM, biosigner, BiRewire, BLMA, BUScorrect, CAFE, CAMERA, CancerSubtypes, CAnD, CausalR, ccrepe, CexoR, ChIPanalyser, ChIPXpress, CHRONOS, CINdex, clipper, clonotypeR, clustComp, CNORfeeder, CNORfuzzy, CNVPanelizer, coexnet, coMET, consensus, cosmiq, COSNet, cpvSNP, DAPAR, DBChIP, DEsubs, DMRcaller, DMRcate, EnhancedVolcano, ENmix, epiNEM, EventPointer, fCCAC, fcScan, FGNet, flowCL, FlowRepositoryR, flowSpy, flowTime, focalCall, GateFinder, gCMAP, gCrisprTools, gdsfmt, GEM, GeneNetworkBuilder, GeneOverlap, geneplast, geneRxCluster, geNetClassifier, genomation, GEOquery, GMRP, GOstats, GraphPAC, GreyListChIP, GWASTools, h5vc, Harman, hiAnnotator, hierGWAS, HIREewas, hypergraph, iCARE, iClusterPlus, illuminaio, InPAS, INPower, IPO, kebabs, KEGGREST, LINC, LRBaseDbi, mAPKL, massiR, MatrixRider, MBttest, mCSEA, mdgsa, Mergeomics, Metab, MetaboSignal, metagene, metagene2, metagenomeSeq, metaseqR, MetCirc, methylCC, methylInheritance, MetNet, microbiome, miRBaseConverter, miRcomp, mirIntegrator, Mirsynergy, mnem, motifStack, MSnID, multiClust, MultiMed, multiOmicsViz, MWASTools, NBSplice, netbenchmark, NetSAM, nondetects, nucleoSim, OMICsPCA, OncoScore, PAA, panelcn.mops, Path2PPI, pathview, PCAtools, pepXMLTab, PGA, PhenStat, powerTCR, Prize, proBAMr, proFIA, pwrEWAS, qpgraph, quantro, QuartPAC, RBGL, rBiopaxParser, Rcade, rcellminer, rCGH, Rcpi, REBET, RGraph2js, Rgraphviz, rgsepd, riboSeqR, ROntoTools, ropls, RTN, RTNduals, RTNsurvival, rTRM, SAIGEgds, sangerseqR, SANTA, sapFinder, scmeth, segmentSeq, SeqArray, seqPattern, seqTools, SeqVarTools, SICtools, sigFeature, sigsquared, SIMAT, similaRpeak, SIMLR, SingleR, slingshot, SNPRelate, sojourner, SpacePAC, sparseDOSSA, specL, STATegRa, STRINGdb, TCC, TFEA.ChIP, TIN, transcriptogramer, traseR, trena, TRONCO, Uniquorn, variancePartition dependencyCount: 5 Package: biocGraph Version: 1.48.0 Depends: Rgraphviz, graph Imports: Rgraphviz, geneplotter, graph, BiocGenerics, methods Suggests: fibroEset, geneplotter, hgu95av2.db License: Artistic-2.0 MD5sum: e375cc69356cbc9ee38fae5dd5420561 NeedsCompilation: no Title: Graph examples and use cases in Bioinformatics Description: This package provides examples and code that make use of the different graph related packages produced by Bioconductor. biocViews: Visualization, GraphAndNetwork Author: Li Long , Robert Gentleman , Seth Falcon Florian Hahne Maintainer: Florian Hahne git_url: https://git.bioconductor.org/packages/biocGraph git_branch: RELEASE_3_10 git_last_commit: 2ce8d86 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/biocGraph_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/biocGraph_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/biocGraph_1.48.0.tgz vignettes: vignettes/biocGraph/inst/doc/biocGraph.pdf, vignettes/biocGraph/inst/doc/layingOutPathways.pdf vignetteTitles: Examples of plotting graphs Using Rgraphviz, HOWTO layout pathways hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocGraph/inst/doc/biocGraph.R, vignettes/biocGraph/inst/doc/layingOutPathways.R importsMe: EnrichmentBrowser suggestsMe: BiocCaseStudies dependencyCount: 38 Package: BiocNeighbors Version: 1.4.2 Imports: Rcpp, S4Vectors, BiocParallel, stats, methods, Matrix LinkingTo: Rcpp, RcppAnnoy, RcppHNSW Suggests: testthat, BiocStyle, knitr, rmarkdown, FNN, RcppAnnoy, RcppHNSW License: GPL-3 Archs: i386, x64 MD5sum: 04bc20f86783363beccd81d56d1fcefb NeedsCompilation: yes Title: Nearest Neighbor Detection for Bioconductor Packages Description: Implements exact and approximate methods for nearest neighbor detection, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Exact searches can be performed using the k-means for k-nearest neighbors algorithm or with vantage point trees. Approximate searches can be performed using the Annoy or HNSW libraries. Searching on either Euclidean or Manhattan distances is supported. Parallelization is achieved for all methods by using BiocParallel. Functions are also provided to search for all neighbors within a given distance. biocViews: Clustering, Classification Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocNeighbors git_branch: RELEASE_3_10 git_last_commit: 1fcb470 git_last_commit_date: 2020-02-29 Date/Publication: 2020-02-29 source.ver: src/contrib/BiocNeighbors_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/BiocNeighbors_1.4.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BiocNeighbors_1.4.2.tgz vignettes: vignettes/BiocNeighbors/inst/doc/approx.html, vignettes/BiocNeighbors/inst/doc/exact.html, vignettes/BiocNeighbors/inst/doc/range.html vignetteTitles: 2. Detecting approximate nearest neighbors, 1. Detecting exact nearest neighbors, 3. Detecting neighbors within range hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocNeighbors/inst/doc/approx.R, vignettes/BiocNeighbors/inst/doc/exact.R, vignettes/BiocNeighbors/inst/doc/range.R importsMe: batchelor, CellMixS, cydar, flowSpy, scater, scDblFinder, scran, SingleR dependencyCount: 22 Package: BiocOncoTK Version: 1.6.0 Depends: R (>= 3.6.0), methods Imports: ComplexHeatmap, S4Vectors, bigrquery, shiny, stats, httr, rjson, dplyr, magrittr, grid, utils, DT, GenomicRanges, IRanges, ggplot2, SummarizedExperiment, DBI, GenomicFeatures, curatedTCGAData, scales, ggpubr, plyr, car, graph, Rgraphviz Suggests: knitr, dbplyr, org.Hs.eg.db, MultiAssayExperiment, BiocStyle, ontoProc, ontologyPlot, pogos, GenomeInfoDb, restfulSE (>= 1.3.7), BiocFileCache, TxDb.Hsapiens.UCSC.hg19.knownGene, Biobase, TxDb.Hsapiens.UCSC.hg18.knownGene, reshape2, testthat, AnnotationDbi, FDb.InfiniumMethylation.hg19, EnsDb.Hsapiens.v75 License: Artistic-2.0 MD5sum: c216c01b120fae031805469dbc5f7e43 NeedsCompilation: no Title: Bioconductor components for general cancer genomics Description: Provide a central interface to various tools for genome-scale analysis of cancer studies. biocViews: CopyNumberVariation, CpGIsland, DNAMethylation, GeneExpression, GeneticVariability, SNP, Transcription, ImmunoOncology Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocOncoTK git_branch: RELEASE_3_10 git_last_commit: ca628a5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BiocOncoTK_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BiocOncoTK_1.5.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BiocOncoTK_1.6.0.tgz vignettes: vignettes/BiocOncoTK/inst/doc/BiocOncoTK.html, vignettes/BiocOncoTK/inst/doc/curatedMSIData.html, vignettes/BiocOncoTK/inst/doc/maptcga.html vignetteTitles: BiocOncoTK -- cancer oriented components for Bioconductor, curatedMSIData overview, "Mapping TCGA tumor codes to NCIT" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocOncoTK/inst/doc/BiocOncoTK.R, vignettes/BiocOncoTK/inst/doc/curatedMSIData.R, vignettes/BiocOncoTK/inst/doc/maptcga.R dependencyCount: 185 Package: BioCor Version: 1.10.0 Depends: R (>= 3.4.0) Imports: BiocParallel, Matrix, GSEABase Suggests: reactome.db, org.Hs.eg.db, WGCNA, methods, GOSemSim, testthat, knitr, rmarkdown, BiocStyle, airway, DESeq2, boot, targetscan.Hs.eg.db, Hmisc, spelling License: MIT + file LICENSE MD5sum: f85acc67ae93b10cb8052aa9c2b48d5e NeedsCompilation: no Title: Functional similarities Description: Calculates functional similarities based on the pathways described on KEGG and REACTOME or in gene sets. These similarities can be calculated for pathways or gene sets, genes, or clusters and combined with other similarities. They can be used to improve networks, gene selection, testing relationships... biocViews: StatisticalMethod, Clustering, GeneExpression, Network, Pathways, NetworkEnrichment, SystemsBiology Author: Lluís Revilla Sancho [aut, cre] (), Pau Sancho-Bru [ths] (), Juan José Salvatella Lozano [ths] () Maintainer: Lluís Revilla Sancho URL: https://llrs.github.io/BioCor/ VignetteBuilder: knitr BugReports: https://github.com/llrs/BioCor/issues git_url: https://git.bioconductor.org/packages/BioCor git_branch: RELEASE_3_10 git_last_commit: 1e7e870 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BioCor_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BioCor_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BioCor_1.10.0.tgz vignettes: vignettes/BioCor/inst/doc/BioCor_1_basics.html, vignettes/BioCor/inst/doc/BioCor_2_advanced.html vignetteTitles: About BioCor, Advanced usage of BioCor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioCor/inst/doc/BioCor_1_basics.R, vignettes/BioCor/inst/doc/BioCor_2_advanced.R dependencyCount: 43 Package: BiocParallel Version: 1.20.1 Depends: methods Imports: stats, utils, futile.logger, parallel, snow LinkingTo: BH Suggests: BiocGenerics, tools, foreach, BatchJobs, BBmisc, doParallel, Rmpi, GenomicRanges, RNAseqData.HNRNPC.bam.chr14, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, Rsamtools, GenomicAlignments, ShortRead, codetools, RUnit, BiocStyle, knitr, batchtools, data.table License: GPL-2 | GPL-3 Archs: i386, x64 MD5sum: 8ac7378d97047c57bbd2efaebec9f37a NeedsCompilation: yes Title: Bioconductor facilities for parallel evaluation Description: This package provides modified versions and novel implementation of functions for parallel evaluation, tailored to use with Bioconductor objects. biocViews: Infrastructure Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Valerie Obenchain [aut], Michel Lang [aut], Ryan Thompson [aut], Nitesh Turaga [aut] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/BiocParallel SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocParallel/issues git_url: https://git.bioconductor.org/packages/BiocParallel git_branch: RELEASE_3_10 git_last_commit: 374b87c git_last_commit_date: 2019-12-20 Date/Publication: 2019-12-21 source.ver: src/contrib/BiocParallel_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/BiocParallel_1.20.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BiocParallel_1.20.1.tgz vignettes: vignettes/BiocParallel/inst/doc/BiocParallel_BatchtoolsParam.pdf, vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.pdf, vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.pdf vignetteTitles: 2. Introduction to BatchtoolsParam, 3. Errors,, Logs and Debugging, 1. Introduction to BiocParallel hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocParallel/inst/doc/BiocParallel_BatchtoolsParam.R, vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.R, vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.R dependsOnMe: bacon, BEclear, Cardinal, ClassifyR, clusterSeq, consensusSeekeR, CopywriteR, cydar, deco, DelayedArray, DEWSeq, DEXSeq, DMCFB, DMCHMM, doppelgangR, GenomicFiles, hiReadsProcessor, INSPEcT, matter, MBASED, metagene, metagene2, ncGTW, Oscope, OUTRIDER, PCAN, pRoloc, Rqc, ShortRead, SigCheck, STROMA4, SummarizedBenchmark, sva, xcms importsMe: abseqR, AffiXcan, ALDEx2, AlphaBeta, ALPS, amplican, ASICS, ASpediaFI, atSNP, BANDITS, batchelor, bayNorm, BiocNeighbors, BioCor, BiocSingular, BioMM, BioNetStat, biotmle, brendaDb, bsseq, CAGEfightR, CAGEr, CAMTHC, cellbaseR, CellBench, CellMixS, ChIPexoQual, ChIPQC, chromswitch, chromVAR, CNVRanger, CoGAPS, consensusDE, contiBAIT, coseq, cpvSNP, CRISPRseek, CrispRVariants, csaw, dcGSA, debCAM, DEComplexDisease, DelayedMatrixStats, derfinder, DEScan2, DESeq2, DEsingle, DiffBind, dmrseq, DOSE, DRIMSeq, DropletUtils, easyRNASeq, EMDomics, erma, ERSSA, fgsea, FindMyFriends, flowcatchR, flowSpecs, GDCRNATools, GenoGAM, GenomicAlignments, genotypeeval, gmapR, gscreend, GSEABenchmarkeR, GUIDEseq, h5vc, HiCBricks, HiCcompare, HTSeqGenie, HTSFilter, iasva, icetea, ideal, IMAS, ImpulseDE2, InPAS, IntEREst, IONiseR, IPO, JunctionSeq, KinSwingR, LineagePulse, loci2path, LowMACA, MACPET, MCbiclust, metabomxtr, MethCP, MethylAid, methylGSA, methylInheritance, methyvim, MetNet, MIGSA, minfi, MMAPPR2, motifbreakR, MPRAnalyze, MSnbase, MSstatsSampleSize, multiHiCcompare, muscat, NBAMSeq, NBSplice, OmicsLonDA, ORFik, OVESEG, PAIRADISE, Pbase, PCAtools, PowerExplorer, pram, PrecisionTrialDrawer, proFIA, profileplyr, qpgraph, qsea, QuasR, Rcwl, recount, REMP, RJMCMCNucleosomes, RNAmodR, Rsamtools, RUVcorr, scater, scDblFinder, scDD, scde, scMerge, SCnorm, scone, scoreInvHap, scPCA, scran, scRecover, scruff, sesame, sigFeature, signatureSearch, SingleR, singscore, SNPhood, soGGi, SpectralTAD, splatter, SplicingGraphs, srnadiff, TarSeqQC, TFBSTools, TMixClust, tradeSeq, trena, Trendy, TSRchitect, TVTB, TxRegInfra, variancePartition, VariantFiltering, VariantTools, waddR, zinbwave suggestsMe: chimera, HDF5Array, netSmooth, omicsPrint, PureCN, RcisTarget, scGPS, SeqArray, systemPipeR, TFutils, tofsims, universalmotif dependencyCount: 10 Package: BiocPkgTools Version: 1.4.6 Depends: htmlwidgets Imports: BiocFileCache, BiocManager, biocViews, tibble, methods, rlang, tidyselect, stringr, rvest, rex, dplyr, xml2, rappdirs, readr, httr, htmltools, DT, tools, utils, igraph, tidyr, jsonlite, gh, RBGL, graph, magrittr Suggests: BiocStyle, knitr, rmarkdown, testthat, tm, SnowballC, pdftools, visNetwork, clipr, blastula, kableExtra License: MIT + file LICENSE MD5sum: 1ea0e9b159f13bba4becb2afa04a7dc0 NeedsCompilation: no Title: Collection of simple tools for learning about Bioc Packages Description: Bioconductor has a rich ecosystem of metadata around packages, usage, and build status. This package is a simple collection of functions to access that metadata from R. The goal is to expose metadata for data mining and value-added functionality such as package searching, text mining, and analytics on packages. biocViews: Software, Infrastructure Author: Shian Su [aut, ctb], Lori Shepherd [ctb], Marcel Ramos [ctb], Felix Ernst [ctb], Charlotte Soneson [ctb], Martin Morgan [ctb], Vince Carey [ctb], Sean Davis [aut, cre] Maintainer: Sean Davis URL: https://github.com/seandavi/BiocPkgTools SystemRequirements: mailsend-go VignetteBuilder: knitr BugReports: https://github.com/seandavi/BiocPkgTools/issues/new git_url: https://git.bioconductor.org/packages/BiocPkgTools git_branch: RELEASE_3_10 git_last_commit: 43e56c1 git_last_commit_date: 2020-03-16 Date/Publication: 2020-03-16 source.ver: src/contrib/BiocPkgTools_1.4.6.tar.gz win.binary.ver: bin/windows/contrib/3.6/BiocPkgTools_1.4.6.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BiocPkgTools_1.4.6.tgz vignettes: vignettes/BiocPkgTools/inst/doc/BiocPkgTools.html vignetteTitles: Overview of BiocPkgTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiocPkgTools/inst/doc/BiocPkgTools.R dependencyCount: 80 Package: BiocSet Version: 1.0.1 Depends: R (>= 3.6), dplyr Imports: methods, tibble, utils, rlang, plyr, rtracklayer, AnnotationDbi, KEGGREST Suggests: GSEABase, airway, org.Hs.eg.db, DESeq2, limma, BiocFileCache, GO.db, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: ecbeee7612259065f048788f35941d63 NeedsCompilation: no Title: Representing Different Biological Sets Description: BiocSet displays different biological sets in a triple tibble format. These three tibbles are `element`, `set`, and `elementset`. The user has the abilty to activate one of these three tibbles to perform common functions from the dplyr package. Mapping functionality and accessing web references for elements/sets are also available in BiocSet. biocViews: GeneExpression, GO, KEGG, Software Author: Kayla Morrell [aut, cre], Martin Morgan [aut], Kevin Rue-Albrecht [ctb], Lluís Revilla Sancho [ctb] Maintainer: Kayla Morrell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocSet git_branch: RELEASE_3_10 git_last_commit: 12d71b2 git_last_commit_date: 2019-11-06 Date/Publication: 2019-11-06 source.ver: src/contrib/BiocSet_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/BiocSet_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BiocSet_1.0.1.tgz vignettes: vignettes/BiocSet/inst/doc/BiocSet.html vignetteTitles: BiocSet: Representing Element Sets in the Tidyverse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocSet/inst/doc/BiocSet.R dependencyCount: 76 Package: BiocSingular Version: 1.2.2 Imports: BiocGenerics, S4Vectors, Matrix, methods, utils, DelayedArray, BiocParallel, irlba, rsvd, Rcpp LinkingTo: Rcpp, beachmat Suggests: testthat, BiocStyle, knitr, rmarkdown, beachmat License: GPL-3 Archs: i386, x64 MD5sum: 8a3523629f739098ebd17fc795e9c996 NeedsCompilation: yes Title: Singular Value Decomposition for Bioconductor Packages Description: Implements exact and approximate methods for singular value decomposition and principal components analysis, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Where possible, parallelization is achieved using the BiocParallel framework. biocViews: Software, DimensionReduction, PrincipalComponent Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/BiocSingular SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/LTLA/BiocSingular/issues git_url: https://git.bioconductor.org/packages/BiocSingular git_branch: RELEASE_3_10 git_last_commit: 1dd1697 git_last_commit_date: 2020-02-14 Date/Publication: 2020-02-14 source.ver: src/contrib/BiocSingular_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/BiocSingular_1.2.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BiocSingular_1.2.2.tgz vignettes: vignettes/BiocSingular/inst/doc/decomposition.html, vignettes/BiocSingular/inst/doc/representations.html vignetteTitles: 1. SVD and PCA, 2. Matrix classes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocSingular/inst/doc/decomposition.R, vignettes/BiocSingular/inst/doc/representations.R importsMe: batchelor, PCAtools, scater, scMerge, scran dependencyCount: 26 Package: BiocSklearn Version: 1.8.1 Depends: R (>= 3.5.0), reticulate, methods, SummarizedExperiment, knitr Imports: BBmisc Suggests: testthat, restfulSE, HDF5Array, BiocStyle License: Artistic-2.0 MD5sum: d415ffc431c2168a2eb56bbb3ba5f5c4 NeedsCompilation: no Title: interface to python sklearn via Rstudio reticulate Description: This package provides interfaces to selected sklearn elements, and demonstrates fault tolerant use of python modules requiring extensive iteration. biocViews: StatisticalMethod, DimensionReduction, Infrastructure Author: Vince Carey Maintainer: VJ Carey SystemRequirements: python (>= 2.7), sklearn, numpy, pandas, h5py VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocSklearn git_branch: RELEASE_3_10 git_last_commit: 891f07a git_last_commit_date: 2020-03-25 Date/Publication: 2020-03-26 source.ver: src/contrib/BiocSklearn_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/BiocSklearn_1.7.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BiocSklearn_1.8.1.tgz vignettes: vignettes/BiocSklearn/inst/doc/BiocSklearn.html vignetteTitles: BiocSklearn overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocSklearn/inst/doc/BiocSklearn.R dependencyCount: 50 Package: BiocStyle Version: 2.14.4 Imports: bookdown, knitr (>= 1.12), rmarkdown (>= 1.2), stats, utils, yaml, BiocManager Suggests: BiocGenerics, RUnit, htmltools License: Artistic-2.0 MD5sum: 8d51f9b1727a8e0997f82ba1a6af8349 NeedsCompilation: no Title: Standard styles for vignettes and other Bioconductor documents Description: Provides standard formatting styles for Bioconductor PDF and HTML documents. Package vignettes illustrate use and functionality. biocViews: Software Author: Andrzej Oleś, Martin Morgan, Wolfgang Huber Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/BiocStyle VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocStyle/issues git_url: https://git.bioconductor.org/packages/BiocStyle git_branch: RELEASE_3_10 git_last_commit: 1533469 git_last_commit_date: 2020-01-09 Date/Publication: 2020-01-09 source.ver: src/contrib/BiocStyle_2.14.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/BiocStyle_2.14.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BiocStyle_2.14.4.tgz vignettes: vignettes/BiocStyle/inst/doc/LatexStyle2.pdf, vignettes/BiocStyle/inst/doc/AuthoringRmdVignettes.html vignetteTitles: Bioconductor LaTeX Style 2.0, Authoring R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocStyle/inst/doc/AuthoringRmdVignettes.R, vignettes/BiocStyle/inst/doc/LatexStyle2.R importsMe: abseqR, ASpli, BiocWorkflowTools, BPRMeth, BubbleTree, chimeraviz, COMPASS, deco, DiscoRhythm, geneXtendeR, Melissa, PathoStat, PhyloProfile, regionReport, Rmmquant, Rqc, scTensor, scTGIF, srnadiff suggestsMe: ABAEnrichment, ACE, ADAMgui, adaptest, AffiXcan, affycoretools, ALDEx2, alevinQC, AllelicImbalance, AMOUNTAIN, amplican, AneuFinder, animalcules, AnnotationDbi, AnnotationFilter, AnnotationForge, AnnotationHub, AnnotationHubData, annotatr, APAlyzer, arrayQualityMetrics, artMS, ASGSCA, ASICS, AssessORF, ASSIGN, ATACseqQC, atSNP, AUCell, BaalChIP, bacon, bamsignals, BANDITS, basecallQC, BASiCS, batchelor, BayesPeak, bayNorm, baySeq, beachmat, beadarray, BeadDataPackR, BEARscc, BEclear, BgeeDB, bigmelon, bigmemoryExtras, bioassayR, bioCancer, BiocCheck, BiocFileCache, BiocNeighbors, BiocOncoTK, BioCor, BiocParallel, BiocPkgTools, BiocSet, BiocSingular, BiocSklearn, biomaRt, biomformat, BioMM, biosigner, biotmle, BitSeq, blacksheepr, blima, bnbc, brainflowprobes, branchpointer, breakpointR, brendaDb, BridgeDbR, BrowserViz, bsseq, BUMHMM, BUScorrect, BUSpaRse, CAFE, CAGEfightR, CAGEr, CAMTHC, CancerInSilico, CAnD, Cardinal, CATALYST, cbaf, ccfindR, ccrepe, celda, cellbaseR, CellBench, cellity, CellMapper, CellMixS, cellTree, CexoR, ChemmineOB, ChemmineR, Chicago, chipenrich, ChIPexoQual, ChIPpeakAnno, ChIPQC, ChIPSeqSpike, chromstaR, chromswitch, ClassifyR, cleanUpdTSeq, cleaver, clipper, clusterExperiment, clusterSeq, ClusterSignificance, CNEr, CNVRanger, COCOA, CoGAPS, coMET, compcodeR, CONFESS, consensusOV, consensusSeekeR, contiBAIT, conumee, CopyNumberPlots, CopywriteR, coRdon, CoRegNet, cosmiq, CountClust, covRNA, cpvSNP, CRISPRseek, CrispRVariants, csaw, CTDquerier, ctsGE, CVE, cydar, cytofast, dada2, dagLogo, DaMiRseq, DAPAR, dcanr, DChIPRep, ddPCRclust, debCAM, decompTumor2Sig, decontam, DEFormats, DEGreport, DelayedArray, DelayedMatrixStats, DEP, DepecheR, derfinder, derfinderHelper, derfinderPlot, DEScan2, DEWSeq, DEXSeq, DiffBind, diffcyt, diffuStats, discordant, dmrseq, DNABarcodeCompatibility, DNABarcodes, doppelgangR, Doscheda, doseR, drawProteins, DRIMSeq, DropletUtils, DSS, dupRadar, easyRNASeq, EBImage, EDASeq, EGSEA, eiR, ELMER, EmpiricalBrownsMethod, EnrichmentBrowser, ensembldb, EpiDISH, epivizr, epivizrChart, epivizrData, epivizrServer, epivizrStandalone, erma, ERSSA, evaluomeR, EventPointer, ExperimentHub, ExperimentHubData, FamAgg, FastqCleaner, fCCAC, fCI, fcScan, FELLA, FindMyFriends, flowcatchR, flowMap, FlowSOM, flowSpecs, flowSpy, fmcsR, FourCSeq, GA4GHclient, GA4GHshiny, GARS, gcapc, GDSArray, genbankr, GeneAccord, genefilter, GeneNetworkBuilder, GeneOverlap, geneplast, GENESIS, GeneStructureTools, GenoGAM, GenomeInfoDb, GenomicAlignments, GenomicDataCommons, GenomicFeatures, GenomicFiles, GenomicInteractions, GenomicRanges, GenomicScores, GenomicTuples, genoset, GenVisR, ggbio, GladiaTOX, Glimma, glmSparseNet, GMRP, GOexpress, GOfuncR, GOpro, goSTAG, gpart, gQTLBase, gQTLstats, graper, graphite, GreyListChIP, GRmetrics, groHMM, GSAR, GSEABase, GSEABenchmarkeR, GUIDEseq, Gviz, gwasurvivr, Harman, HCABrowser, HCAExplorer, HDF5Array, HelloRanges, HiCBricks, HiLDA, hipathia, HIREewas, HiTC, HPAanalyze, hpar, HTSFilter, HumanTranscriptomeCompendium, ideal, iGC, IgGeneUsage, igvR, IHW, illuminaio, imageHTS, IMAS, Imetagene, immunoClust, infercnv, InPAS, INSPEcT, InTAD, InteractionSet, InterMineR, IONiseR, IRanges, iSEE, isomiRs, IVAS, JunctionSeq, karyoploteR, kissDE, ldblock, LinkHD, Linnorm, lipidr, loci2path, Logolas, LOLA, LoomExperiment, LowMACA, lpsymphony, LRBaseDbi, M3D, MACPET, mAPKL, maser, MAST, MatrixRider, matter, MaxContrastProjection, MBASED, mbkmeans, MBttest, MCbiclust, mCSEA, mdgsa, MEAL, MEDIPS, messina, MetaboSignal, metagene, metagene2, metagenomeFeatures, metavizr, methimpute, methInheritSim, MethPed, MethylAid, methylCC, methylInheritance, MethylMix, methyvim, MetNet, mfa, microbiome, mimager, minfi, MIRA, miRcomp, miRmine, miRSM, miRspongeR, missMethyl, missRows, mixOmics, mlm4omics, MLSeq, MMAPPR2, MMDiff2, MMUPHin, MODA, Modstrings, mogsa, MoonlightR, MOSim, motifbreakR, motifStack, mpra, MSnbase, MSnID, msPurity, MSstats, MSstatsSampleSize, MSstatsTMT, MTseeker, MultiAssayExperiment, MultiDataSet, multiHiCcompare, multiMiR, muscat, MutationalPatterns, MWASTools, mygene, myvariant, mzR, NADfinder, NanoStringDiff, NanoStringQCPro, NarrowPeaks, ncGTW, ndexr, nethet, NetPathMiner, netprioR, netReg, netSmooth, ngsReports, nondetects, NormalyzerDE, normr, npGSEA, nucleoSim, nucleR, oligo, omicade4, omicRexposome, OmicsMarkeR, omicsPrint, OmnipathR, Onassis, OncoScore, OncoSimulR, ontoProc, OPWeight, ORFik, Organism.dplyr, Oscope, OUTRIDER, OVESEG, PAA, PAIRADISE, PanVizGenerator, parglms, Path2PPI, paxtoolsr, Pbase, pcaExplorer, PCAN, peakPantheR, PepsNMR, perturbatr, PGA, phantasus, phenopath, philr, phyloseq, Pi, piano, Pigengene, plethy, plotGrouper, plyranges, Polyfit, PoTRA, powerTCR, pqsfinder, pram, PrecisionTrialDrawer, PrInCE, profileplyr, profileScoreDist, projectR, pRoloc, pRolocGUI, Prostar, ProteomicsAnnotationHubData, ProteoMM, proteoQC, PSEA, PureCN, PWMEnrich, qcmetrics, QDNAseq, qpgraph, qsea, qsmooth, QSutils, Qtlizer, quantro, QuasR, R3CPET, RaggedExperiment, rain, ramwas, RandomWalkRestartMH, Rbowtie, Rcade, rcellminer, rCGH, RcisTarget, Rcwl, RcwlPipelines, RCy3, RCyjs, ReactomePA, recount, recoup, RedeR, RefNet, regioneR, regsplice, ReQON, restfulSE, rexposome, rfPred, RGMQL, RGraph2js, RGSEA, rhdf5, rhdf5client, Rhdf5lib, Rhisat2, Rhtslib, RiboProfiling, riboSeqR, RIVER, RJMCMCNucleosomes, RNAmodR, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RNAprobR, rnaseqcomp, RnaSeqSampleSize, Rnits, rols, ropls, rpx, rqt, Rsamtools, rScudo, RTCGAToolbox, RTN, RTNduals, RTNsurvival, RUVcorr, RUVSeq, RVS, rWikiPathways, S4Vectors, sampleClassifier, sangerseqR, sapFinder, scater, scDblFinder, scDD, scds, scMerge, SCnorm, scone, scoreInvHap, scPCA, scran, scruff, segmentSeq, seqCAT, seqPattern, seqplots, seqsetvis, SeqSQC, SeqVarTools, sesame, SEtools, sevenC, SGSeq, SharedObject, shinyMethyl, ShortRead, SIAMCAT, SigCheck, SigFuge, signatureSearch, signet, SigsPack, SIMD, similaRpeak, SIMLR, simulatorZ, sincell, SingleCellExperiment, singleCellTK, SingleR, sitePath, slingshot, slinky, SMAD, SNPediaR, SNPhood, soGGi, sojourner, sparseDOSSA, sparsenetgls, SparseSignatures, SpatialCPie, specL, SpectralTAD, SpidermiR, splatter, SPLINTER, sRACIPE, SSPA, stageR, STAN, StarBioTrek, STATegRa, statTarget, strandCheckR, Structstrings, SubCellBarCode, SummarizedBenchmark, SummarizedExperiment, sva, SVAPLSseq, swfdr, switchde, synapter, systemPipeR, TargetSearch, TCGAbiolinks, TCGAbiolinksGUI, TCGAutils, TFARM, TFBSTools, TFHAZ, TFutils, tigre, TMixClust, TOAST, ToPASeq, topdownr, TPP, tracktables, trackViewer, transcriptogramer, transcriptR, TreeSummarizedExperiment, Trendy, tRNA, tRNAdbImport, tRNAscanImport, TRONCO, TTMap, TurboNorm, TVTB, twoddpcr, Ularcirc, UniProt.ws, variancePartition, VariantAnnotation, VariantFiltering, VCFArray, vidger, ViSEAGO, vsn, wavClusteR, XBSeq, xcms, Xeva, yamss, YAPSA, zinbwave, dSimer, gpuMagic dependencyCount: 25 Package: BiocVersion Version: 3.10.1 Depends: R (>= 3.6.0) License: Artistic-2.0 MD5sum: 475ac89c1f90e254af4a58ac6bbcbf79 NeedsCompilation: no Title: Set the appropriate version of Bioconductor packages Description: This package provides repository information for the appropriate version of Bioconductor. biocViews: Infrastructure Author: Martin Morgan [aut], Marcel Ramos [ctb], Bioconductor Package Maintainer [ctb, cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/BiocVersion git_branch: master git_last_commit: 8c298c6 git_last_commit_date: 2019-06-05 Date/Publication: 2019-06-06 source.ver: src/contrib/BiocVersion_3.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/BiocVersion_3.10.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BiocVersion_3.10.1.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: AnnotationHub dependencyCount: 0 Package: biocViews Version: 1.54.0 Depends: R (>= 3.6.0) Imports: Biobase, graph (>= 1.9.26), methods, RBGL (>= 1.13.5), tools, utils, XML, RCurl, RUnit, BiocManager Suggests: BiocGenerics, knitr, commonmark License: Artistic-2.0 MD5sum: 7692afd7285bf72145543a3b116c31c6 NeedsCompilation: no Title: Categorized views of R package repositories Description: Infrastructure to support 'views' used to classify Bioconductor packages. 'biocViews' are directed acyclic graphs of terms from a controlled vocabulary. There are three major classifications, corresponding to 'software', 'annotation', and 'experiment data' packages. biocViews: Infrastructure Author: VJ Carey , BJ Harshfield , S Falcon , Sonali Arora, Lori Shepherd Maintainer: Bioconductor Package Maintainer URL: http://www.bioconductor.org/packages/release/BiocViews.html git_url: https://git.bioconductor.org/packages/biocViews git_branch: RELEASE_3_10 git_last_commit: a76f97c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/biocViews_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/biocViews_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/biocViews_1.54.0.tgz vignettes: vignettes/biocViews/inst/doc/createReposHtml.pdf, vignettes/biocViews/inst/doc/HOWTO-BCV.pdf vignetteTitles: biocViews-CreateRepositoryHTML, biocViews-HOWTO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocViews/inst/doc/createReposHtml.R, vignettes/biocViews/inst/doc/HOWTO-BCV.R dependsOnMe: Risa importsMe: AnnotationHubData, BiocCheck, BiocPkgTools, ExperimentHubData, monocle, sigFeature dependencyCount: 17 Package: BiocWorkflowTools Version: 1.12.1 Depends: R (>= 3.4) Imports: BiocStyle, bookdown, git2r, httr, knitr, rmarkdown, rstudioapi, stringr, tools, utils, usethis License: MIT + file LICENSE MD5sum: 2d5d140689085d30946a0f8eda900e6b NeedsCompilation: no Title: Tools to aid the development of Bioconductor Workflow packages Description: Provides functions to ease the transition between Rmarkdown and LaTeX documents when authoring a Bioconductor Workflow. biocViews: Software, ReportWriting Author: Mike Smith [aut, cre], Andrzej Oleś [aut] Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocWorkflowTools git_branch: RELEASE_3_10 git_last_commit: 370886f git_last_commit_date: 2020-03-27 Date/Publication: 2020-03-27 source.ver: src/contrib/BiocWorkflowTools_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/BiocWorkflowTools_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BiocWorkflowTools_1.12.1.tgz vignettes: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.html vignetteTitles: Converting Rmarkdown to F1000Research LaTeX Format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.R dependencyCount: 59 Package: bioDist Version: 1.58.0 Depends: R (>= 2.0), methods, Biobase,KernSmooth Suggests: locfit License: Artistic-2.0 MD5sum: 5f5a6127541569f1f3c8ce651edb4d34 NeedsCompilation: no Title: Different distance measures Description: A collection of software tools for calculating distance measures. biocViews: Clustering, Classification Author: B. Ding, R. Gentleman and Vincent Carey Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/bioDist git_branch: RELEASE_3_10 git_last_commit: 1f744e1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/bioDist_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/bioDist_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/bioDist_1.58.0.tgz vignettes: vignettes/bioDist/inst/doc/bioDist.pdf vignetteTitles: bioDist Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioDist/inst/doc/bioDist.R importsMe: CHETAH, PhyloProfile suggestsMe: BiocCaseStudies dependencyCount: 8 Package: biomaRt Version: 2.42.1 Depends: methods Imports: utils, XML, AnnotationDbi, progress, stringr, httr, openssl, BiocFileCache, rappdirs Suggests: annotate, BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: ce434693d772dcf02ab307a56c771271 NeedsCompilation: no Title: Interface to BioMart databases (i.e. Ensembl) Description: In recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis. biomaRt provides an interface to a growing collection of databases implementing the BioMart software suite (). The package enables retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas or write complex SQL queries. The most prominent examples of BioMart databases are maintain by Ensembl, which provides biomaRt users direct access to a diverse set of data and enables a wide range of powerful online queries from gene annotation to database mining. biocViews: Annotation Author: Steffen Durinck [aut], Wolfgang Huber [aut], Sean Davis [ctb], Francois Pepin [ctb], Vince S Buffalo [ctb], Mike Smith [ctb, cre] Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biomaRt git_branch: RELEASE_3_10 git_last_commit: 381229c git_last_commit_date: 2020-03-26 Date/Publication: 2020-03-26 source.ver: src/contrib/biomaRt_2.42.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/biomaRt_2.42.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/biomaRt_2.42.1.tgz vignettes: vignettes/biomaRt/inst/doc/biomaRt.html vignetteTitles: The biomaRt users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomaRt/inst/doc/biomaRt.R dependsOnMe: chromPlot, coMET, customProDB, dagLogo, DrugVsDisease, genefu, GenomeGraphs, GenomicOZone, MineICA, PPInfer, PSICQUIC, RepViz, Roleswitch, VegaMC importsMe: ArrayExpressHTS, artMS, ASpediaFI, BadRegionFinder, branchpointer, BUSpaRse, ChIPpeakAnno, CHRONOS, cobindR, cTRAP, DEXSeq, diffloop, DominoEffect, easyRNASeq, EDASeq, ELMER, GDCRNATools, GeneAccord, GenomicFeatures, GenVisR, gespeR, glmSparseNet, GOexpress, goSTAG, gpart, Gviz, HTSanalyzeR, IdMappingRetrieval, isobar, KnowSeq, MAGeCKFlute, mCSEA, MEDIPS, MetaboSignal, metaseqR, methyAnalysis, MGFR, OncoScore, oposSOM, Pbase, pcaExplorer, PGA, phenoTest, PrecisionTrialDrawer, pRoloc, ProteoMM, psygenet2r, pwOmics, R453Plus1Toolbox, ramwas, RCAS, recoup, rgsepd, RNAither, scPipe, seq2pathway, SeqGSEA, SPLINTER, SWATH2stats, TCGAbiolinks, TFEA.ChIP, transcriptogramer, trena, ViSEAGO, XCIR, yarn suggestsMe: AnnotationForge, bioassayR, BiocCaseStudies, celda, cellTree, chromstaR, ClusterJudge, FELLA, GeneAnswers, Genominator, h5vc, LINC, martini, massiR, MineICA, MiRaGE, MutationalPatterns, netSmooth, oligo, OrganismDbi, paxtoolsr, PCAtools, piano, Pigengene, progeny, R3CPET, Rcade, RIPSeeker, RnBeads, rTANDEM, rTRM, scater, ShortRead, SIM, sincell, SummarizedBenchmark, systemPipeR, trackViewer, wiggleplotr, zinbwave dependencyCount: 57 Package: biomformat Version: 1.14.0 Depends: R (>= 3.2), methods Imports: plyr (>= 1.8), jsonlite (>= 0.9.16), Matrix (>= 1.2), rhdf5 Suggests: testthat (>= 0.10), knitr (>= 1.10), BiocStyle (>= 1.6), rmarkdown (>= 0.7) License: GPL-2 MD5sum: 1a90d404e53ec8a87424c34ab0257e0c NeedsCompilation: no Title: An interface package for the BIOM file format Description: This is an R package for interfacing with the BIOM format. This package includes basic tools for reading biom-format files, accessing and subsetting data tables from a biom object (which is more complex than a single table), as well as limited support for writing a biom-object back to a biom-format file. The design of this API is intended to match the python API and other tools included with the biom-format project, but with a decidedly "R flavor" that should be familiar to R users. This includes S4 classes and methods, as well as extensions of common core functions/methods. biocViews: ImmunoOncology, DataImport, Metagenomics, Microbiome Author: Paul J. McMurdie and Joseph N Paulson Maintainer: Paul J. McMurdie URL: https://github.com/joey711/biomformat/, http://biom-format.org/ VignetteBuilder: knitr BugReports: https://github.com/joey711/biomformat/issues git_url: https://git.bioconductor.org/packages/biomformat git_branch: RELEASE_3_10 git_last_commit: f20af45 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/biomformat_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/biomformat_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/biomformat_1.14.0.tgz vignettes: vignettes/biomformat/inst/doc/biomformat.html vignetteTitles: The biomformat package Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomformat/inst/doc/biomformat.R importsMe: animalcules, phyloseq suggestsMe: metagenomeSeq dependencyCount: 13 Package: BioMM Version: 1.2.0 Depends: R (>= 3.6) Imports: stats, utils, grDevices, lattice, BiocParallel, glmnet, rms, nsprcomp, ranger, e1071, variancePartition, ggplot2, pROC, vioplot, CMplot Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 9e510eab5f4951d4157a1ff193f4e66b NeedsCompilation: no Title: BioMM: Biological-informed Multi-stage Machine learning framework for phenotype prediction using omics data Description: The identification of reproducible biological patterns from high-dimensional omics data is a key factor in understanding the biology of complex disease or traits. Incorporating prior biological knowledge into machine learning is an important step in advancing such research. We have proposed a biologically informed multi-stage machine learing framework termed BioMM specifically for phenotype prediction based on omics-scale data where we can evaluate different machine learning models with prior biological meta information. biocViews: Genetics, Classification, Regression, Pathways, GO, Software Author: Junfang Chen and Emanuel Schwarz Maintainer: Junfang Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BioMM git_branch: RELEASE_3_10 git_last_commit: b509122 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BioMM_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BioMM_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BioMM_1.2.0.tgz vignettes: vignettes/BioMM/inst/doc/BioMMtutorial.html vignetteTitles: BioMMtutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioMM/inst/doc/BioMMtutorial.R dependencyCount: 138 Package: BioMVCClass Version: 1.54.0 Depends: R (>= 2.1.0), methods, MVCClass, Biobase, graph, Rgraphviz License: LGPL MD5sum: 91e0f5dbbe61f5a1736e5bc410e34d86 NeedsCompilation: no Title: Model-View-Controller (MVC) Classes That Use Biobase Description: Creates classes used in model-view-controller (MVC) design biocViews: Visualization, Infrastructure, GraphAndNetwork Author: Elizabeth Whalen Maintainer: Elizabeth Whalen git_url: https://git.bioconductor.org/packages/BioMVCClass git_branch: RELEASE_3_10 git_last_commit: da6a665 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BioMVCClass_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BioMVCClass_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BioMVCClass_1.54.0.tgz vignettes: vignettes/BioMVCClass/inst/doc/BioMVCClass.pdf vignetteTitles: BioMVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 13 Package: biomvRCNS Version: 1.26.0 Depends: IRanges, GenomicRanges, Gviz Imports: methods, mvtnorm Suggests: cluster, parallel, GenomicFeatures, dynamicTreeCut, Rsamtools, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) Archs: i386, x64 MD5sum: 11a142b6e9b818670928b69b6726ec2d NeedsCompilation: yes Title: Copy Number study and Segmentation for multivariate biological data Description: In this package, a Hidden Semi Markov Model (HSMM) and one homogeneous segmentation model are designed and implemented for segmentation genomic data, with the aim of assisting in transcripts detection using high throughput technology like RNA-seq or tiling array, and copy number analysis using aCGH or sequencing. biocViews: aCGH, CopyNumberVariation, Microarray, Sequencing, Sequencing, Visualization, Genetics Author: Yang Du Maintainer: Yang Du git_url: https://git.bioconductor.org/packages/biomvRCNS git_branch: RELEASE_3_10 git_last_commit: b1a6d65 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/biomvRCNS_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/biomvRCNS_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/biomvRCNS_1.26.0.tgz vignettes: vignettes/biomvRCNS/inst/doc/biomvRCNS.pdf vignetteTitles: biomvRCNS package introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomvRCNS/inst/doc/biomvRCNS.R dependencyCount: 144 Package: BioNet Version: 1.46.0 Depends: R (>= 2.10.0), graph, RBGL Imports: igraph (>= 1.0.1), AnnotationDbi, Biobase Suggests: rgl, impute, DLBCL, genefilter, xtable, ALL, limma, hgu95av2.db, XML License: GPL (>= 2) MD5sum: 0451ed5f6ff7aba6b740625e67b84c6e NeedsCompilation: no Title: Routines for the functional analysis of biological networks Description: This package provides functions for the integrated analysis of protein-protein interaction networks and the detection of functional modules. Different datasets can be integrated into the network by assigning p-values of statistical tests to the nodes of the network. E.g. p-values obtained from the differential expression of the genes from an Affymetrix array are assigned to the nodes of the network. By fitting a beta-uniform mixture model and calculating scores from the p-values, overall scores of network regions can be calculated and an integer linear programming algorithm identifies the maximum scoring subnetwork. biocViews: Microarray, DataImport, GraphAndNetwork, Network, NetworkEnrichment, GeneExpression, DifferentialExpression Author: Marcus Dittrich and Daniela Beisser Maintainer: Marcus Dittrich URL: http://bionet.bioapps.biozentrum.uni-wuerzburg.de/ git_url: https://git.bioconductor.org/packages/BioNet git_branch: RELEASE_3_10 git_last_commit: b438d2c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BioNet_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BioNet_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BioNet_1.46.0.tgz vignettes: vignettes/BioNet/inst/doc/Tutorial.pdf vignetteTitles: BioNet Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioNet/inst/doc/Tutorial.R importsMe: HTSanalyzeR, SMITE suggestsMe: SANTA dependencyCount: 34 Package: BioNetStat Version: 1.6.0 Depends: R (>= 3.5), shiny, igraph, shinyBS, pathview Imports: BiocParallel, RJSONIO, whisker, yaml, pheatmap, ggplot2, plyr, utils, stats, RColorBrewer, Hmisc, psych, knitr License: GPL (>= 3) MD5sum: 2a9cdcaa0cc8295478917204b85b37da NeedsCompilation: no Title: Biological Network Analysis Description: A package to perform differential network analysis, differential node analysis (differential coexpression analysis), network and metabolic pathways view. The reference paper is avaible in https://doi.org/10.3389/fgene.2019.00594. biocViews: Network, NetworkInference, Pathways, GraphAndNetwork, Sequencing, Microarray, Metabolomics, Proteomics, GeneExpression, RNASeq, SystemsBiology, DifferentialExpression, GeneSetEnrichment, ImmunoOncology Author: Vinícius Jardim, Suzana Santos, André Fujita, and Marcos Buckeridge Maintainer: Vinicius Jardim URL: http://github.com/jardimViniciusC/BioNetStat VignetteBuilder: knitr BugReports: http://github.com/jardimViniciusC/BioNetStat/issues git_url: https://git.bioconductor.org/packages/BioNetStat git_branch: RELEASE_3_10 git_last_commit: 7ddaf2a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BioNetStat_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BioNetStat_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BioNetStat_1.6.0.tgz vignettes: vignettes/BioNetStat/inst/doc/BNS_tutorial_by_command_line.html, vignettes/BioNetStat/inst/doc/vignette.html vignetteTitles: 1. Line command tutorial, 2. Interface tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 135 Package: BioQC Version: 1.14.0 Depends: Biobase Imports: edgeR, Rcpp, methods, stats, utils LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, lattice, latticeExtra, rbenchmark, gplots, gridExtra, org.Hs.eg.db, ineq, covr License: GPL (>=3) Archs: i386, x64 MD5sum: 634216f2b4460f085892ce4a81d54341 NeedsCompilation: yes Title: Detect tissue heterogeneity in expression profiles with gene sets Description: BioQC performs quality control of high-throughput expression data based on tissue gene signatures. It can detect tissue heterogeneity in gene expression data. The core algorithm is a Wilcoxon-Mann-Whitney test that is optimised for high performance. biocViews: GeneExpression,QualityControl,StatisticalMethod, GeneSetEnrichment Author: Jitao David Zhang , Laura Badi, Gregor Sturm, Roland Ambs Maintainer: Jitao David Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BioQC git_branch: RELEASE_3_10 git_last_commit: c49a9cd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BioQC_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BioQC_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BioQC_1.14.0.tgz vignettes: vignettes/BioQC/inst/doc/bioqc-efficiency.html, vignettes/BioQC/inst/doc/bioqc-signedGenesets.html, vignettes/BioQC/inst/doc/bioqc.html vignetteTitles: BioQC Alogrithm: Speeding up the Wilcoxon-Mann-Whitney Test, Using BioQC with signed genesets, BioQC: Detect tissue heterogeneity in gene expression data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioQC/inst/doc/bioqc-efficiency.R, vignettes/BioQC/inst/doc/bioqc-signedGenesets.R, vignettes/BioQC/inst/doc/bioqc.R dependencyCount: 14 Package: BioSeqClass Version: 1.44.0 Depends: R (>= 2.10), scatterplot3d Imports: Biostrings, ipred, e1071, klaR, randomForest, class, tree, nnet, rpart, party, foreign, Biobase, utils, stats, grDevices Suggests: scatterplot3d License: LGPL (>= 2.0) MD5sum: 42518ee89c309f78ad753bf49a92ec0f NeedsCompilation: no Title: Classification for Biological Sequences Description: Extracting Features from Biological Sequences and Building Classification Model biocViews: Classification Author: Li Hong sysptm@gmail.com Maintainer: Li Hong git_url: https://git.bioconductor.org/packages/BioSeqClass git_branch: RELEASE_3_10 git_last_commit: adfa127 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BioSeqClass_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BioSeqClass_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BioSeqClass_1.44.0.tgz vignettes: vignettes/BioSeqClass/inst/doc/BioSeqClass.pdf vignetteTitles: Using the BioSeqClass Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioSeqClass/inst/doc/BioSeqClass.R dependencyCount: 92 Package: biosigner Version: 1.14.4 Depends: Biobase, ropls Imports: methods, e1071, MultiDataSet, randomForest Suggests: BioMark, BiocGenerics, BiocStyle, golubEsets, hu6800.db, knitr, omicade4, rmarkdown, testthat License: CeCILL MD5sum: efc6a52ff5b77c4ac77497f1179a41fc NeedsCompilation: no Title: Signature discovery from omics data Description: Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a new method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets. A Galaxy implementation of the package is available within the Workflow4metabolomics.org online infrastructure for computational metabolomics. biocViews: Classification, FeatureExtraction, Transcriptomics, Proteomics, Metabolomics, Lipidomics Author: Philippe Rinaudo , Etienne Thevenot Maintainer: Philippe Rinaudo , Etienne Thevenot VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biosigner git_branch: RELEASE_3_10 git_last_commit: 3776fad git_last_commit_date: 2020-03-18 Date/Publication: 2020-03-18 source.ver: src/contrib/biosigner_1.14.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/biosigner_1.14.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/biosigner_1.14.4.tgz vignettes: vignettes/biosigner/inst/doc/biosigner-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biosigner/inst/doc/biosigner-vignette.R dependencyCount: 86 Package: Biostrings Version: 2.54.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.31.5), S4Vectors (>= 0.21.13), IRanges, XVector (>= 0.23.2) Imports: graphics, methods, stats, utils LinkingTo: S4Vectors, IRanges, XVector Suggests: BSgenome (>= 1.13.14), BSgenome.Celegans.UCSC.ce2 (>= 1.3.11), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.11), BSgenome.Hsapiens.UCSC.hg18, drosophila2probe, hgu95av2probe, hgu133aprobe, GenomicFeatures (>= 1.3.14), hgu95av2cdf, affy (>= 1.41.3), affydata (>= 1.11.5), RUnit Enhances: Rmpi License: Artistic-2.0 Archs: i386, x64 MD5sum: 696435a75bd490ac62e52bb5327a72c5 NeedsCompilation: yes Title: Efficient manipulation of biological strings Description: Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences. biocViews: SequenceMatching, Alignment, Sequencing, Genetics, DataImport, DataRepresentation, Infrastructure Author: H. Pagès, P. Aboyoun, R. Gentleman, and S. DebRoy Maintainer: H. Pagès git_url: https://git.bioconductor.org/packages/Biostrings git_branch: RELEASE_3_10 git_last_commit: b8982e7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Biostrings_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Biostrings_2.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Biostrings_2.54.0.tgz vignettes: vignettes/Biostrings/inst/doc/Biostrings2Classes.pdf, vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf, vignettes/Biostrings/inst/doc/matchprobes.pdf, vignettes/Biostrings/inst/doc/MultipleAlignments.pdf, vignettes/Biostrings/inst/doc/PairwiseAlignments.pdf vignetteTitles: A short presentation of the basic classes defined in Biostrings 2, Biostrings Quick Overview, Handling probe sequence information, Multiple Alignments, Pairwise Sequence Alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biostrings/inst/doc/Biostrings2Classes.R, vignettes/Biostrings/inst/doc/matchprobes.R, vignettes/Biostrings/inst/doc/MultipleAlignments.R, vignettes/Biostrings/inst/doc/PairwiseAlignments.R dependsOnMe: altcdfenvs, amplican, Basic4Cseq, BRAIN, BSgenome, chimeraviz, ChIPanalyser, ChIPpeakAnno, ChIPsim, cleaver, CODEX, CRISPRseek, DECIPHER, deepSNV, GeneRegionScan, GenomicAlignments, GOTHiC, HelloRanges, hiReadsProcessor, iPAC, kebabs, MethTargetedNGS, methVisual, minfi, Modstrings, MotifDb, motifRG, motifStack, msa, muscle, oligo, pcaGoPromoter, PGA, pqsfinder, qrqc, QSutils, R453Plus1Toolbox, R4RNA, REDseq, rGADEM, RiboProfiling, Roleswitch, rRDP, Rsamtools, RSVSim, sangerseqR, SCAN.UPC, scsR, SELEX, seqbias, ShortRead, SICtools, Structstrings, systemPipeR, topdownr, triplex, waveTiling importsMe: AffyCompatible, AllelicImbalance, alpine, AneuFinder, AnnotationHubData, appreci8R, ArrayExpressHTS, AssessORF, ATACseqQC, BBCAnalyzer, BCRANK, bcSeq, BEAT, BioSeqClass, biovizBase, brainflowprobes, branchpointer, BSgenome, bsseq, BUMHMM, BUSpaRse, ChIPseqR, ChIPsim, chromVAR, circRNAprofiler, CNEr, CNVfilteR, cobindR, compEpiTools, consensusDE, coRdon, CrispRVariants, customProDB, dada2, dagLogo, decompTumor2Sig, diffHic, DNAshapeR, DominoEffect, easyRNASeq, EDASeq, ensembldb, ensemblVEP, esATAC, eudysbiome, EventPointer, FastqCleaner, FindMyFriends, FourCSeq, GA4GHclient, gcapc, gcrma, genbankr, GeneRegionScan, GenoGAM, genomation, GenomicAlignments, GenomicFeatures, GenomicScores, genphen, GenVisR, ggbio, GGtools, girafe, gmapR, GUIDEseq, Gviz, gwascat, h5vc, heatmaps, HiLDA, HiTC, HTSeqGenie, icetea, IMMAN, IntEREst, InterMineR, IONiseR, ipdDb, IsoformSwitchAnalyzeR, KEGGREST, Logolas, LowMACA, LymphoSeq, MACPET, MADSEQ, MatrixRider, MDTS, MEDIPS, MEDME, metagenomeFeatures, methimpute, methVisual, methylPipe, microRNA, MMDiff2, motifbreakR, motifcounter, motifmatchr, MotIV, MTseeker, MutationalPatterns, ngsReports, nucleR, oligoClasses, OmaDB, openPrimeR, ORFik, OTUbase, Pbase, pdInfoBuilder, PhyloProfile, phyloseq, pipeFrame, podkat, polyester, primirTSS, proBAMr, procoil, ProteomicsAnnotationHubData, PureCN, Pviz, qPLEXanalyzer, qrqc, qsea, QuasR, r3Cseq, ramwas, RCAS, Rcpi, REDseq, regioneR, REMP, Repitools, rGADEM, RNAmodR, RNAprobR, RNASeqR, Rqc, rtracklayer, scmeth, scoreInvHap, scruff, SeqArray, seqcombo, seqPattern, seqplots, SGSeq, signeR, SigsPack, SNPhood, soGGi, SomaticSignatures, SparseSignatures, SPLINTER, sscu, StructuralVariantAnnotation, synapter, SynMut, TarSeqQC, TFBSTools, transite, trena, tRNA, tRNAdbImport, tRNAscanImport, TVTB, tximeta, Ularcirc, universalmotif, VariantAnnotation, VariantExperiment, VariantFiltering, VariantTools, wavClusteR suggestsMe: annotate, AnnotationForge, AnnotationHub, BANDITS, BiocGenerics, CSAR, exomeCopy, GenomicFiles, GenomicRanges, genoset, GWASTools, maftools, methrix, methylumi, MiRaGE, nuCpos, RNAmodR.AlkAnilineSeq, rpx, rTRM, treeio, XVector linksToMe: DECIPHER, kebabs, MatrixRider, Rsamtools, ShortRead, triplex, VariantAnnotation, VariantFiltering dependencyCount: 11 Package: biosvd Version: 2.22.0 Depends: R (>= 3.1.0) Imports: BiocGenerics, Biobase, methods, grid, graphics, NMF License: Artistic-2.0 MD5sum: 18e4ccecfe2931661a55563c2f871fdc NeedsCompilation: no Title: Package for high-throughput data processing, outlier detection, noise removal and dynamic modeling Description: The biosvd package contains functions to reduce the input data set from the feature x assay space to the reduced diagonalized eigenfeature x eigenassay space, with the eigenfeatures and eigenassays unique orthonormal superpositions of the features and assays, respectively. Results of SVD applied to the data can subsequently be inspected based on generated graphs, such as a heatmap of the eigenfeature x assay matrix and a bar plot with the eigenexpression fractions of all eigenfeatures. These graphs aid in deciding which eigenfeatures and eigenassays to filter out (i.e., eigenfeatures representing steady state, noise, or experimental artifacts; or when applied to the variance in the data, eigenfeatures representing steady-scale variance). After possible removal of steady state expression, steady-scale variance, noise and experimental artifacts, and after re-applying SVD to the normalized data, a summary html report of the eigensystem is generated, containing among others polar plots of the assays and features, a table with the list of features sortable according to their coordinates, radius and phase in the polar plot, and a visualization of the data sorted according to the two selected eigenfeatures and eigenassays with colored feature/assay annotation information when provided. This gives a global picture of the dynamics of expression/intensity levels, in which individual features and assays are classified in groups of similar regulation and function or similar cellular state and biological phenotype. biocViews: TimeCourse, Visualization Author: Anneleen Daemen , Matthew Brauer Maintainer: Anneleen Daemen , Matthew Brauer git_url: https://git.bioconductor.org/packages/biosvd git_branch: RELEASE_3_10 git_last_commit: f85c6ea git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/biosvd_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/biosvd_2.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/biosvd_2.22.0.tgz vignettes: vignettes/biosvd/inst/doc/biosvd.pdf vignetteTitles: biosvd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biosvd/inst/doc/biosvd.R dependencyCount: 74 Package: BioTIP Version: 1.0.0 Depends: R (>= 3.6) Imports: igraph, cluster, psych, stringr, GenomicRanges Suggests: knitr, markdown, base, rmarkdown, ggplot2 License: GPL-2 MD5sum: 6fb4f171eb0e5833d05ebfcacaa4239c NeedsCompilation: no Title: BioTIP: An R package for characterization of Biological Tipping-Point Description: Adopting tipping-point theory to transcriptome profiles to unravel disease regulatory trajectory. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, Software Author: Zhezhen Wang, Biniam Feleke, Qier An, Antonio Feliciano y Pleyto and Xinan Yang Maintainer: Zhezhen Wang , X Holly Yang < xyang2@uchicago> and Yuxi (Jennifer) Sun URL: https://github.com/xyang2uchicago/BioTIP VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BioTIP git_branch: RELEASE_3_10 git_last_commit: cb9a58f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BioTIP_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BioTIP_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BioTIP_1.0.0.tgz vignettes: vignettes/BioTIP/inst/doc/BioTIP.html vignetteTitles: BioTIP- an R package for characterization of Biological Tipping-Point hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioTIP/inst/doc/BioTIP.R dependencyCount: 31 Package: biotmle Version: 1.10.0 Depends: R (>= 3.4) Imports: stats, methods, dplyr, tibble, ggplot2, ggsci, superheat, assertthat, future, doFuture, tmle (>= 1.4.0.1), S4Vectors, BiocGenerics, BiocParallel, SummarizedExperiment, limma Suggests: testthat, knitr, rmarkdown, BiocStyle, earth, glmnet, randomForest, SuperLearner, Matrix, DBI, biotmleData (>= 1.1.1) License: file LICENSE MD5sum: 8b08f07363a288e99b7e4bc952923f65 NeedsCompilation: no Title: Targeted Learning with Moderated Statistics for Biomarker Discovery Description: This package facilitates the discovery of biomarkers from biological sequencing data (e.g., microarrays, RNA-seq) based on the associations of potential biomarkers with exposure variables by implementing an inferential procedure that combines a generalization of moderated statistics with targeted minimum loss estimates of the average treatment effect whose estimator admits an asymptotically linear representations (in terms of an efficient influence function). biocViews: GeneExpression, DifferentialExpression, Sequencing, Microarray, RNASeq, ImmunoOncology Author: Nima Hejazi [aut, cre, cph] (), Alan Hubbard [aut, ths] (), Mark van der Laan [aut, ths] (), Weixin Cai [ctb] () Maintainer: Nima Hejazi URL: https://code.nimahejazi.org/biotmle VignetteBuilder: knitr BugReports: https://github.com/nhejazi/biotmle/issues git_url: https://git.bioconductor.org/packages/biotmle git_branch: RELEASE_3_10 git_last_commit: d9a0a1f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-30 source.ver: src/contrib/biotmle_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/biotmle_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/biotmle_1.10.0.tgz vignettes: vignettes/biotmle/inst/doc/exposureBiomarkers.html, vignettes/biotmle/inst/doc/rnaseqProcessing.html vignetteTitles: Identifying Biomarkers from an Exposure Variable, Processing and Analyzing RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/biotmle/inst/doc/exposureBiomarkers.R, vignettes/biotmle/inst/doc/rnaseqProcessing.R dependencyCount: 106 Package: biovizBase Version: 1.34.1 Depends: R (>= 3.5.0), methods Imports: grDevices, stats, scales, Hmisc, RColorBrewer, dichromat, BiocGenerics, S4Vectors (>= 0.23.19), IRanges (>= 1.99.28), GenomeInfoDb (>= 1.5.14), GenomicRanges (>= 1.23.21), SummarizedExperiment, Biostrings (>= 2.33.11), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), GenomicFeatures (>= 1.21.19), AnnotationDbi, VariantAnnotation (>= 1.11.4), ensembldb (>= 1.99.13), AnnotationFilter (>= 0.99.8), rlang Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome, rtracklayer, EnsDb.Hsapiens.v75, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: f65dd3201ecf6517e0b54f1eeb895630 NeedsCompilation: yes Title: Basic graphic utilities for visualization of genomic data. Description: The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and encourages consistency. biocViews: Infrastructure, Visualization, Preprocessing Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne Cook [aut, ths], Johannes Rainer [ctb] Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/biovizBase git_branch: RELEASE_3_10 git_last_commit: 98962be git_last_commit_date: 2019-12-03 Date/Publication: 2019-12-04 source.ver: src/contrib/biovizBase_1.34.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/biovizBase_1.34.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/biovizBase_1.34.1.tgz vignettes: vignettes/biovizBase/inst/doc/intro.pdf vignetteTitles: An Introduction to biovizBase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biovizBase/inst/doc/intro.R dependsOnMe: CAFE, qrqc importsMe: BubbleTree, ChIPexoQual, ggbio, Gviz, karyoploteR, Pviz, qrqc, RNAmodR, Rqc suggestsMe: CINdex, derfinder, derfinderPlot, R3CPET, regionReport, StructuralVariantAnnotation, TxRegInfra dependencyCount: 141 Package: BiRewire Version: 3.18.0 Depends: igraph, slam, tsne, Matrix Suggests: RUnit, BiocGenerics License: GPL-3 Archs: i386, x64 MD5sum: 1ea71feb96921f504a8238079de86550 NeedsCompilation: yes Title: High-performing routines for the randomization of a bipartite graph (or a binary event matrix), undirected and directed signed graph preserving degree distribution (or marginal totals) Description: Fast functions for bipartite network rewiring through N consecutive switching steps (See References) and for the computation of the minimal number of switching steps to be performed in order to maximise the dissimilarity with respect to the original network. Includes functions for the analysis of the introduced randomness across the switching steps and several other routines to analyse the resulting networks and their natural projections. Extension to undirected networks and directed signed networks is also provided. Starting from version 1.9.7 a more precise bound (especially for small network) has been implemented. Starting from version 2.2.0 the analysis routine is more complete and a visual montioring of the underlying Markov Chain has been implemented. Starting from 3.6.0 the library can handle also matrices with NA (not for the directed signed graphs). biocViews: Network Author: Andrea Gobbi [aut], Francesco Iorio [aut], Giuseppe Jurman [cbt], Davide Albanese [cbt], Julio Saez-Rodriguez [cbt]. Maintainer: Andrea Gobbi URL: http://www.ebi.ac.uk/~iorio/BiRewire git_url: https://git.bioconductor.org/packages/BiRewire git_branch: RELEASE_3_10 git_last_commit: ae684c9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BiRewire_3.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BiRewire_3.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BiRewire_3.18.0.tgz vignettes: vignettes/BiRewire/inst/doc/BiRewire.pdf vignetteTitles: BiRewire hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiRewire/inst/doc/BiRewire.R dependencyCount: 13 Package: birta Version: 1.30.0 Depends: limma, MASS, R(>= 2.10), Biobase, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 8c8df0673aa99041717b1df0969a4f66 NeedsCompilation: yes Title: Bayesian Inference of Regulation of Transcriptional Activity Description: Expression levels of mRNA molecules are regulated by different processes, comprising inhibition or activation by transcription factors and post-transcriptional degradation by microRNAs. birta (Bayesian Inference of Regulation of Transcriptional Activity) uses the regulatory networks of TFs and miRNAs together with mRNA and miRNA expression data to predict switches in regulatory activity between two conditions. A Bayesian network is used to model the regulatory structure and Markov-Chain-Monte-Carlo is applied to sample the activity states. biocViews: Microarray, Sequencing, GeneExpression, Transcription, GraphAndNetwork Author: Benedikt Zacher, Khalid Abnaof, Stephan Gade, Erfan Younesi, Achim Tresch, Holger Froehlich Maintainer: Benedikt Zacher , Holger Froehlich git_url: https://git.bioconductor.org/packages/birta git_branch: RELEASE_3_10 git_last_commit: 4921b50 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/birta_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/birta_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/birta_1.30.0.tgz vignettes: vignettes/birta/inst/doc/birta.pdf vignetteTitles: Bayesian Inference of Regulation of Transcriptional Activity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/birta/inst/doc/birta.R dependencyCount: 10 Package: biscuiteer Version: 1.0.0 Depends: R (>= 3.6.0), biscuiteerData, bsseq Imports: readr, qualV, Matrix, impute, HDF5Array, S4Vectors, Rsamtools, data.table, Biobase, GenomicRanges, BiocGenerics, VariantAnnotation, DelayedMatrixStats, SummarizedExperiment, GenomeInfoDb, Mus.musculus, Homo.sapiens, matrixStats, rtracklayer, QDNAseq, dmrseq, methods, utils, R.utils, gtools Suggests: DSS, covr, knitr, rlang, scmeth, pkgdown, roxygen2, testthat, QDNAseq.hg19, QDNAseq.mm10 License: GPL-3 Archs: i386, x64 MD5sum: f4431fcb2ac72bfdc4d1d4f1f1341092 NeedsCompilation: no Title: Convenience Functions for Biscuit Description: A test harness for bsseq loading of Biscuit output, summarization of WGBS data over defined regions and in mappable samples, with or without imputation, dropping of mostly-NA rows, age estimates, etc. biocViews: DataImport, MethylSeq, DNAMethylation Author: Tim Triche, Jr. [aut, cre], Wanding Zhou [aut], Ben Johnson [aut], Jacob Morrison [aut], Lyong Heo [aut] Maintainer: "Tim Triche, Jr." URL: https://github.com/trichelab/biscuiteer VignetteBuilder: knitr BugReports: https://github.com/trichelab/biscuiteer/issues git_url: https://git.bioconductor.org/packages/biscuiteer git_branch: RELEASE_3_10 git_last_commit: 4445919 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/biscuiteer_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/biscuiteer_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/biscuiteer_1.0.0.tgz vignettes: vignettes/biscuiteer/inst/doc/biscuiteer.html vignetteTitles: Biscuiteer User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biscuiteer/inst/doc/biscuiteer.R dependencyCount: 178 Package: BiSeq Version: 1.26.0 Depends: R (>= 2.15.2), methods, S4Vectors, IRanges (>= 1.17.24), GenomicRanges, SummarizedExperiment (>= 0.2.0), Formula Imports: methods, BiocGenerics, Biobase, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, rtracklayer, parallel, betareg, lokern, Formula, globaltest License: LGPL-3 MD5sum: 05fc2f9f868fa018fec99579d932bc6f NeedsCompilation: no Title: Processing and analyzing bisulfite sequencing data Description: The BiSeq package provides useful classes and functions to handle and analyze targeted bisulfite sequencing (BS) data such as reduced-representation bisulfite sequencing (RRBS) data. In particular, it implements an algorithm to detect differentially methylated regions (DMRs). The package takes already aligned BS data from one or multiple samples. biocViews: Genetics, Sequencing, MethylSeq, DNAMethylation Author: Katja Hebestreit, Hans-Ulrich Klein Maintainer: Katja Hebestreit git_url: https://git.bioconductor.org/packages/BiSeq git_branch: RELEASE_3_10 git_last_commit: 1e02352 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BiSeq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BiSeq_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BiSeq_1.26.0.tgz vignettes: vignettes/BiSeq/inst/doc/BiSeq.pdf vignetteTitles: An Introduction to BiSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiSeq/inst/doc/BiSeq.R importsMe: M3D dependencyCount: 68 Package: BitSeq Version: 1.30.1 Depends: Rsamtools (>= 1.99.3) Imports: S4Vectors, IRanges LinkingTo: Rhtslib (>= 1.15.5) Suggests: edgeR, DESeq, BiocStyle License: Artistic-2.0 + file LICENSE MD5sum: cd3b8ccc9a74e7376175d7a861613fc7 NeedsCompilation: yes Title: Transcript expression inference and differential expression analysis for RNA-seq data Description: The BitSeq package is targeted for transcript expression analysis and differential expression analysis of RNA-seq data in two stage process. In the first stage it uses Bayesian inference methodology to infer expression of individual transcripts from individual RNA-seq experiments. The second stage of BitSeq embraces the differential expression analysis of transcript expression. Providing expression estimates from replicates of multiple conditions, Log-Normal model of the estimates is used for inferring the condition mean transcript expression and ranking the transcripts based on the likelihood of differential expression. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Sequencing, RNASeq, Bayesian, AlternativeSplicing, DifferentialSplicing, Transcription Author: Peter Glaus, Antti Honkela and Magnus Rattray Maintainer: Antti Honkela , Panagiotis Papastamoulis SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/BitSeq git_branch: RELEASE_3_10 git_last_commit: cde1f1d git_last_commit_date: 2020-04-02 Date/Publication: 2020-04-02 source.ver: src/contrib/BitSeq_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/BitSeq_1.30.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BitSeq_1.30.1.tgz vignettes: vignettes/BitSeq/inst/doc/BitSeq.pdf vignetteTitles: BitSeq User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BitSeq/inst/doc/BitSeq.R dependencyCount: 26 Package: blacksheepr Version: 1.0.0 Depends: R (>= 3.6) Imports: grid, stats, grDevices, utils, circlize, viridis, RColorBrewer, ComplexHeatmap, SummarizedExperiment, pasilla Suggests: testthat (>= 2.1.0), knitr, BiocStyle, rmarkdown, curl License: MIT + file LICENSE Archs: i386, x64 MD5sum: 54adf0f79dd7335d2255d5f1ec0439b5 NeedsCompilation: no Title: Outlier Analysis for pairwise differential comparison Description: Blacksheep is a tool designed for outlier analysis in the context of pairwise comparisons in an effort to find distinguishing characteristics from two groups. This tool was designed to be applied for biological applications such as phosphoproteomics or transcriptomics, but it can be used for any data that can be represented by a 2D table, and has two sub populations within the table to compare. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, DifferentialExpression, Transcriptomics Author: MacIntosh Cornwell [aut], RugglesLab [cre] Maintainer: RugglesLab VignetteBuilder: knitr BugReports: https://github.com/ruggleslab/blackSheepR/issues git_url: https://git.bioconductor.org/packages/blacksheepr git_branch: RELEASE_3_10 git_last_commit: 6baf5fb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/blacksheepr_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/blacksheepr_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/blacksheepr_1.0.0.tgz vignettes: vignettes/blacksheepr/inst/doc/blacksheepr_vignette.html vignetteTitles: Outlier Analysis using blacksheepr - Phosphoprotein hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/blacksheepr/inst/doc/blacksheepr_vignette.R dependencyCount: 89 Package: blima Version: 1.20.0 Depends: R(>= 3.3) Imports: beadarray(>= 2.0.0), Biobase(>= 2.0.0), Rcpp (>= 0.12.8), BiocGenerics, grDevices, stats, graphics LinkingTo: Rcpp Suggests: xtable, blimaTestingData, BiocStyle, illuminaHumanv4.db, lumi, knitr License: GPL-3 MD5sum: 782e26e37212d1d87bc91add66a98bb4 NeedsCompilation: yes Title: Tools for the preprocessing and analysis of the Illumina microarrays on the detector (bead) level Description: Package blima includes several algorithms for the preprocessing of Illumina microarray data. It focuses to the bead level analysis and provides novel approach to the quantile normalization of the vectors of unequal lengths. It provides variety of the methods for background correction including background subtraction, RMA like convolution and background outlier removal. It also implements variance stabilizing transformation on the bead level. There are also implemented methods for data summarization. It also provides the methods for performing T-tests on the detector (bead) level and on the probe level for differential expression testing. biocViews: Microarray, Preprocessing, Normalization, DifferentialExpression, GeneRegulation, GeneExpression Author: Vojtěch Kulvait Maintainer: Vojtěch Kulvait URL: https://bitbucket.org/kulvait/blima VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/blima git_branch: RELEASE_3_10 git_last_commit: 914fab7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/blima_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/blima_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/blima_1.20.0.tgz vignettes: vignettes/blima/inst/doc/blima.pdf vignetteTitles: blima.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/blima/inst/doc/blima.R dependencyCount: 89 Package: BLMA Version: 1.10.0 Depends: ROntoTools, GSA, PADOG, limma, graph, stats, utils, parallel, Biobase Suggests: RUnit, BiocGenerics License: GPL (>=2) Archs: i386, x64 MD5sum: 98a53789318812b0320c6f3da98ba090 NeedsCompilation: no Title: BLMA: A package for bi-level meta-analysis Description: Suit of tools for bi-level meta-analysis. The package can be used in a wide range of applications, including general hypothesis testings, differential expression analysis, functional analysis, and pathway analysis. biocViews: GeneSetEnrichment, Pathways, DifferentialExpression, Microarray Author: Tin Nguyen and Sorin Draghici Maintainer: Tin Nguyen git_url: https://git.bioconductor.org/packages/BLMA git_branch: RELEASE_3_10 git_last_commit: defcde0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BLMA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BLMA_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BLMA_1.10.0.tgz vignettes: vignettes/BLMA/inst/doc/BLMA.pdf vignetteTitles: BLMA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BLMA/inst/doc/BLMA.R importsMe: multiHiCcompare dependencyCount: 65 Package: bnbc Version: 1.8.0 Depends: R (>= 3.4.0), methods, BiocGenerics, SummarizedExperiment, GenomicRanges Imports: Rcpp (>= 0.12.12), IRanges, GenomeInfoDb, S4Vectors, matrixStats, preprocessCore, sva, parallel, EBImage, utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: f9deb7c0504c9ea90ef6b3887a0047c7 NeedsCompilation: yes Title: Bandwise normalization and batch correction of Hi-C data Description: Tools to normalize (several) Hi-C data from replicates. biocViews: HiC, Preprocessing, Normalization, Software Author: Kipper Fletez-Brant [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Kipper Fletez-Brant URL: https://github.com/hansenlab/bnbc VignetteBuilder: knitr BugReports: https://github.com/hansenlab/bnbc/issues git_url: https://git.bioconductor.org/packages/bnbc git_branch: RELEASE_3_10 git_last_commit: 4f16142 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/bnbc_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/bnbc_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/bnbc_1.8.0.tgz vignettes: vignettes/bnbc/inst/doc/bnbc.html vignetteTitles: bnbc User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bnbc/inst/doc/bnbc.R dependencyCount: 69 Package: BPRMeth Version: 1.12.0 Depends: R (>= 3.5.0), GenomicRanges Imports: assertthat, methods, MASS, doParallel, parallel, e1071, earth, foreach, randomForest, stats, IRanges, S4Vectors, data.table, graphics, truncnorm, mvtnorm, Rcpp (>= 0.12.14), matrixcalc, magrittr, kernlab, ggplot2, cowplot, BiocStyle LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown License: GPL-3 | file LICENSE Archs: i386, x64 MD5sum: 272a951d85025b392ff2102cb1d09417 NeedsCompilation: yes Title: Model higher-order methylation profiles Description: The BPRMeth package is a probabilistic method to quantify explicit features of methylation profiles, in a way that would make it easier to formally use such profiles in downstream modelling efforts, such as predicting gene expression levels or clustering genomic regions or cells according to their methylation profiles. biocViews: ImmunoOncology, DNAMethylation, GeneExpression, GeneRegulation, Epigenetics, Genetics, Clustering, FeatureExtraction, Regression, RNASeq, Bayesian, KEGG, Sequencing, Coverage, SingleCell Author: Chantriolnt-Andreas Kapourani [aut, cre] Maintainer: Chantriolnt-Andreas Kapourani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BPRMeth git_branch: RELEASE_3_10 git_last_commit: ddd4beb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BPRMeth_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BPRMeth_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BPRMeth_1.12.0.tgz vignettes: vignettes/BPRMeth/inst/doc/BPRMeth_vignette.html vignetteTitles: BPRMeth: Model higher-order methylation profiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BPRMeth/inst/doc/BPRMeth_vignette.R dependsOnMe: Melissa dependencyCount: 101 Package: BRAIN Version: 1.32.0 Depends: R (>= 2.8.1), PolynomF, Biostrings, lattice License: GPL-2 Archs: i386, x64 MD5sum: 5b3f1aa663c925216cf1d841560b5840 NeedsCompilation: no Title: Baffling Recursive Algorithm for Isotope distributioN calculations Description: Package for calculating aggregated isotopic distribution and exact center-masses for chemical substances (in this version composed of C, H, N, O and S). This is an implementation of the BRAIN algorithm described in the paper by J. Claesen, P. Dittwald, T. Burzykowski and D. Valkenborg. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Piotr Dittwald, with contributions of Dirk Valkenborg and Jurgen Claesen Maintainer: Piotr Dittwald git_url: https://git.bioconductor.org/packages/BRAIN git_branch: RELEASE_3_10 git_last_commit: 3eb6869 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BRAIN_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BRAIN_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BRAIN_1.32.0.tgz vignettes: vignettes/BRAIN/inst/doc/BRAIN-vignette.pdf vignetteTitles: BRAIN Usage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BRAIN/inst/doc/BRAIN-vignette.R suggestsMe: cleaver, synapter dependencyCount: 17 Package: brainflowprobes Version: 1.0.0 Depends: R (>= 3.6.0) Imports: Biostrings (>= 2.52.0), BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), bumphunter (>= 1.26.0), cowplot (>= 1.0.0), derfinder (>= 1.18.1), derfinderPlot (>= 1.18.1), GenomicRanges (>= 1.36.0), ggplot2 (>= 3.1.1), RColorBrewer (>= 1.1), utils, grDevices, GenomicState (>= 0.99.7) Suggests: BiocStyle, knitcitations, knitr, rmarkdown, sessioninfo, testthat (>= 2.1.0) License: Artistic-2.0 MD5sum: af9495affe5ec1e0e7ce0f269434bb8b NeedsCompilation: no Title: Plots and annotation for choosing BrainFlow target probe sequence Description: Use these functions to characterize genomic regions for BrainFlow target probe design. biocViews: Coverage, Visualization, ExperimentalDesign, Transcriptomics, FlowCytometry, GeneTarget Author: Amanda Price [aut, cre] (), Leonardo Collado-Torres [ctb] () Maintainer: Amanda Price URL: https://github.com/LieberInstitute/brainflowprobes VignetteBuilder: knitr BugReports: https://github.com/LieberInstitute/brainflowprobes/issues git_url: https://git.bioconductor.org/packages/brainflowprobes git_branch: RELEASE_3_10 git_last_commit: 6c379c8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-11-01 source.ver: src/contrib/brainflowprobes_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/brainflowprobes_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/brainflowprobes_1.0.0.tgz vignettes: vignettes/brainflowprobes/inst/doc/brainflowprobes-vignette.html vignetteTitles: brainflowprobes users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/brainflowprobes/inst/doc/brainflowprobes-vignette.R dependencyCount: 178 Package: BrainStars Version: 1.30.0 Depends: RCurl, Biobase, methods Imports: RJSONIO, Biobase License: Artistic-2.0 MD5sum: 13f1b53116c5b14e3ee9ac50600c61e5 NeedsCompilation: no Title: query gene expression data and plots from BrainStars (B*) Description: This package can search and get gene expression data and plots from BrainStars (B*). BrainStars is a quantitative expression database of the adult mouse brain. The database has genome-wide expression profile at 51 adult mouse CNS regions. biocViews: Microarray, OneChannel, DataImport Author: Itoshi NIKAIDO Maintainer: Itoshi NIKAIDO git_url: https://git.bioconductor.org/packages/BrainStars git_branch: RELEASE_3_10 git_last_commit: 0231587 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BrainStars_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BrainStars_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BrainStars_1.30.0.tgz vignettes: vignettes/BrainStars/inst/doc/BrainStars.pdf vignetteTitles: BrainStars hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrainStars/inst/doc/BrainStars.R dependencyCount: 10 Package: branchpointer Version: 1.12.0 Depends: caret, R(>= 3.4) Imports: plyr, kernlab, gbm, stringr, cowplot, ggplot2, biomaRt, Biostrings, parallel, utils, stats, BSgenome.Hsapiens.UCSC.hg38, rtracklayer, GenomicRanges, GenomeInfoDb, IRanges, S4Vectors, data.table Suggests: knitr, BiocStyle License: BSD_3_clause + file LICENSE MD5sum: 887c1f12b56f0f134fc6e5825cfd003a NeedsCompilation: no Title: Prediction of intronic splicing branchpoints Description: Predicts branchpoint probability for sites in intronic branchpoint windows. Queries can be supplied as intronic regions; or to evaluate the effects of mutations, SNPs. biocViews: Software, GenomeAnnotation, GenomicVariation, MotifAnnotation Author: Beth Signal Maintainer: Beth Signal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/branchpointer git_branch: RELEASE_3_10 git_last_commit: e00ca01 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/branchpointer_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/branchpointer_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/branchpointer_1.12.0.tgz vignettes: vignettes/branchpointer/inst/doc/branchpointer.pdf vignetteTitles: Using Branchpointer for annotation of intronic human splicing branchpoints hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/branchpointer/inst/doc/branchpointer.R dependencyCount: 140 Package: breakpointR Version: 1.4.0 Depends: R (>= 3.6), GenomicRanges, cowplot, breakpointRdata Imports: methods, utils, grDevices, stats, S4Vectors, GenomeInfoDb (>= 1.12.3), IRanges, Rsamtools, GenomicAlignments, ggplot2, BiocGenerics, gtools, doParallel, foreach Suggests: knitr, BiocStyle, testthat License: file LICENSE MD5sum: 219aa6d0c223e73c3e3b95b9a9a83532 NeedsCompilation: no Title: Find breakpoints in Strand-seq data Description: This package implements functions for finding breakpoints, plotting and export of Strand-seq data. biocViews: Software, Sequencing, DNASeq, SingleCell, Coverage Author: David Porubsky, Ashley Sanders, Aaron Taudt Maintainer: David Porubsky URL: https://github.com/daewoooo/BreakPointR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/breakpointR git_branch: RELEASE_3_10 git_last_commit: 3101874 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/breakpointR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/breakpointR_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/breakpointR_1.4.0.tgz vignettes: vignettes/breakpointR/inst/doc/breakpointR.pdf vignetteTitles: How to use breakpointR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/breakpointR/inst/doc/breakpointR.R dependencyCount: 88 Package: brendaDb Version: 1.0.0 Imports: dplyr, Rcpp, tibble, stringr, magrittr, purrr, BiocParallel, crayon, utils, tidyr, curl, xml2, grDevices, rlang, BiocFileCache, rappdirs LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, devtools License: MIT + file LICENSE MD5sum: 0a02333aba17abe0c930a2541f1530cd NeedsCompilation: yes Title: The BRENDA Enzyme Database Description: R interface for importing and analyzing enzyme information from the BRENDA database. biocViews: ThirdPartyClient, Annotation, DataImport Author: Yi Zhou [aut, cre] () Maintainer: Yi Zhou URL: https://github.com/y1zhou/brendaDb SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/y1zhou/brendaDb/issues git_url: https://git.bioconductor.org/packages/brendaDb git_branch: RELEASE_3_10 git_last_commit: a20aedb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/brendaDb_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/brendaDb_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/brendaDb_1.0.0.tgz vignettes: vignettes/brendaDb/inst/doc/brendaDb.html vignetteTitles: brendaDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/brendaDb/inst/doc/brendaDb.R dependencyCount: 54 Package: bridge Version: 1.50.0 Depends: R (>= 1.9.0), rama License: GPL (>= 2) Archs: i386, x64 MD5sum: 6ec7c1aacee8efdca19bd6b0706d8dc4 NeedsCompilation: yes Title: Bayesian Robust Inference for Differential Gene Expression Description: Test for differentially expressed genes with microarray data. This package can be used with both cDNA microarrays or Affymetrix chip. The packge fits a robust Bayesian hierarchical model for testing for differential expression. Outliers are modeled explicitly using a $t$-distribution. The model includes an exchangeable prior for the variances which allow different variances for the genes but still shrink extreme empirical variances. Our model can be used for testing for differentially expressed genes among multiple samples, and can distinguish between the different possible patterns of differential expression when there are three or more samples. Parameter estimation is carried out using a novel version of Markov Chain Monte Carlo that is appropriate when the model puts mass on subspaces of the full parameter space. biocViews: Microarray,OneChannel,TwoChannel,DifferentialExpression Author: Raphael Gottardo Maintainer: Raphael Gottardo git_url: https://git.bioconductor.org/packages/bridge git_branch: RELEASE_3_10 git_last_commit: 47833c4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/bridge_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/bridge_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/bridge_1.50.0.tgz vignettes: vignettes/bridge/inst/doc/bridge.pdf vignetteTitles: bridge Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bridge/inst/doc/bridge.R dependencyCount: 1 Package: BridgeDbR Version: 1.20.3 Depends: R (>= 3.3.0), rJava Imports: RCurl Suggests: BiocStyle, knitr, rmarkdown, testthat License: AGPL-3 Archs: i386, x64 MD5sum: 6b3e7717a6adb461f73db80d3b766c3b NeedsCompilation: no Title: Code for using BridgeDb identifier mapping framework from within R Description: Use BridgeDb functions and load identifier mapping databases in R. biocViews: Software, Annotation, Metabolomics, Cheminformatics Author: Christ Leemans , Egon Willighagen , Anwesha Bohler , Lars Eijssen Maintainer: Egon Willighagen URL: https://github.com/bridgedb/BridgeDbR VignetteBuilder: knitr BugReports: https://github.com/bridgedb/BridgeDbR/issues git_url: https://git.bioconductor.org/packages/BridgeDbR git_branch: RELEASE_3_10 git_last_commit: 4673e45 git_last_commit_date: 2020-02-14 Date/Publication: 2020-02-14 source.ver: src/contrib/BridgeDbR_1.20.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/BridgeDbR_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BridgeDbR_1.20.3.tgz vignettes: vignettes/BridgeDbR/inst/doc/tutorial.html vignetteTitles: Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BridgeDbR/inst/doc/tutorial.R dependencyCount: 4 Package: BrowserViz Version: 2.8.0 Depends: R (>= 3.5.0), jsonlite (>= 1.5), httpuv(>= 1.5.0) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 8d0d15724b06dd026cf674ba85e0e7c9 NeedsCompilation: no Title: BrowserViz: interactive R/browser graphics using websockets and JSON Description: Interactvive graphics in a web browser from R, using websockets and JSON. biocViews: Visualization, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon URL: https://paul-shannon.github.io/BrowserViz/ VignetteBuilder: knitr BugReports: https://github.com/paul-shannon/BrowserViz/issues git_url: https://git.bioconductor.org/packages/BrowserViz git_branch: RELEASE_3_10 git_last_commit: 04b22ca git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BrowserViz_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BrowserViz_2.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BrowserViz_2.8.0.tgz vignettes: vignettes/BrowserViz/inst/doc/BrowserViz.html vignetteTitles: "BrowserViz: support programmatic access to javascript apps running in your web browser" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrowserViz/inst/doc/BrowserViz.R dependsOnMe: igvR, RCyjs dependencyCount: 15 Package: BSgenome Version: 1.54.0 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.17.28), IRanges (>= 2.13.16), GenomeInfoDb (>= 1.15.2), GenomicRanges (>= 1.31.10), Biostrings (>= 2.47.6), rtracklayer (>= 1.39.7) Imports: methods, utils, stats, BiocGenerics, S4Vectors, IRanges, XVector, GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, rtracklayer Suggests: BiocManager, Biobase, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Scerevisiae.UCSC.sacCer1, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, hgu95av2probe, RUnit License: Artistic-2.0 MD5sum: ece15f4fb466a5161461cccc2b10858a NeedsCompilation: no Title: Software infrastructure for efficient representation of full genomes and their SNPs Description: Infrastructure shared by all the Biostrings-based genome data packages. biocViews: Genetics, Infrastructure, DataRepresentation, SequenceMatching, Annotation, SNP Author: Hervé Pagès Maintainer: H. Pagès git_url: https://git.bioconductor.org/packages/BSgenome git_branch: RELEASE_3_10 git_last_commit: 58524c9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BSgenome_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BSgenome_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BSgenome_1.54.0.tgz vignettes: vignettes/BSgenome/inst/doc/BSgenomeForge.pdf, vignettes/BSgenome/inst/doc/GenomeSearching.pdf vignetteTitles: How to forge a BSgenome data package, Efficient genome searching with Biostrings and the BSgenome data packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BSgenome/inst/doc/BSgenomeForge.R, vignettes/BSgenome/inst/doc/GenomeSearching.R dependsOnMe: ChIPanalyser, GOTHiC, HelloRanges, MEDIPS, motifRG, REDseq, rGADEM importsMe: AllelicImbalance, appreci8R, ATACseqQC, atSNP, BEAT, bsseq, BUSpaRse, CAGEr, ChIPpeakAnno, chromVAR, circRNAprofiler, cleanUpdTSeq, cobindR, CRISPRseek, crisprseekplus, diffHic, enrichTF, esATAC, EventPointer, gcapc, genomation, GenomicScores, GenVisR, ggbio, gmapR, GreyListChIP, GUIDEseq, Gviz, hiAnnotator, InPAS, IsoformSwitchAnalyzeR, MADSEQ, methrix, MethylSeekR, MMDiff2, motifbreakR, motifmatchr, msgbsR, PING, pipeFrame, podkat, qsea, QuasR, R453Plus1Toolbox, RareVariantVis, regioneR, REMP, Repitools, RNAmodR, scmeth, seqplots, SigsPack, SparseSignatures, TFBSTools, trena, tRNAscanImport, Ularcirc, VariantAnnotation, VariantFiltering, VariantTools suggestsMe: Biostrings, biovizBase, chipseq, easyRNASeq, GeneRegionScan, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, genoset, maftools, metaseqR, MiRaGE, MutationalPatterns, ORFik, QDNAseq, recoup, rtracklayer, waveTiling dependencyCount: 38 Package: bsseq Version: 1.22.0 Depends: R (>= 3.5), methods, BiocGenerics, GenomicRanges (>= 1.29.14), SummarizedExperiment (>= 1.9.18) Imports: IRanges (>= 2.11.16), GenomeInfoDb, scales, stats, graphics, Biobase, locfit, gtools, data.table (>= 1.11.8), S4Vectors (>= 0.23.11), R.utils (>= 2.0.0), DelayedMatrixStats (>= 1.5.2), permute, limma, DelayedArray (>= 0.9.8), Rcpp, BiocParallel, BSgenome, Biostrings, utils, HDF5Array (>= 1.11.9), rhdf5 LinkingTo: Rcpp, beachmat Suggests: testthat, bsseqData, BiocStyle, rmarkdown, knitr, Matrix, doParallel, rtracklayer, BSgenome.Hsapiens.UCSC.hg38, beachmat (>= 1.5.2), BatchJobs License: Artistic-2.0 MD5sum: c67d2b77063d360f3973d826017ece4b NeedsCompilation: yes Title: Analyze, manage and store bisulfite sequencing data Description: A collection of tools for analyzing and visualizing bisulfite sequencing data. biocViews: DNAMethylation Author: Kasper Daniel Hansen [aut, cre], Peter Hickey [aut] Maintainer: Kasper Daniel Hansen URL: https://github.com/kasperdanielhansen/bsseq SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/kasperdanielhansen/bsseq/issues git_url: https://git.bioconductor.org/packages/bsseq git_branch: RELEASE_3_10 git_last_commit: d4f7301 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/bsseq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/bsseq_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/bsseq_1.22.0.tgz vignettes: vignettes/bsseq/inst/doc/bsseq_analysis.html, vignettes/bsseq/inst/doc/bsseq.html vignetteTitles: Analyzing WGBS data with bsseq, bsseq User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bsseq/inst/doc/bsseq_analysis.R, vignettes/bsseq/inst/doc/bsseq.R dependsOnMe: biscuiteer, dmrseq, DSS importsMe: DMRcate, MethCP, methylCC, MIRA, scmeth suggestsMe: methrix dependencyCount: 64 Package: BubbleTree Version: 2.16.0 Depends: R (>= 3.5), IRanges, GenomicRanges, plyr, dplyr, magrittr Imports: BiocGenerics (>= 0.31.6), BiocStyle, Biobase, ggplot2, WriteXLS, gtools, RColorBrewer, limma, grid, gtable, gridExtra, biovizBase, e1071, methods, grDevices, stats, utils Suggests: knitr, rmarkdown License: LGPL (>= 3) Archs: i386, x64 MD5sum: 238800334b0db73c258ceb9589d60809 NeedsCompilation: no Title: BubbleTree: an intuitive visualization to elucidate tumoral aneuploidy and clonality in somatic mosaicism using next generation sequencing data Description: CNV analysis in groups of tumor samples. biocViews: CopyNumberVariation, Software, Sequencing, Coverage Author: Wei Zhu , Michael Kuziora , Todd Creasy , Brandon Higgs Maintainer: Todd Creasy , Wei Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BubbleTree git_branch: RELEASE_3_10 git_last_commit: 4929476 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BubbleTree_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BubbleTree_2.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BubbleTree_2.16.0.tgz vignettes: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.html vignetteTitles: BubbleTree Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.R dependencyCount: 153 Package: BufferedMatrix Version: 1.50.0 Depends: R (>= 2.6.0), methods License: LGPL (>= 2) MD5sum: 88546bcc93c5f4291744bebaae20f6d9 NeedsCompilation: yes Title: A matrix data storage object held in temporary files Description: A tabular style data object where most data is stored outside main memory. A buffer is used to speed up access to data. biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/BufferedMatrix git_url: https://git.bioconductor.org/packages/BufferedMatrix git_branch: RELEASE_3_10 git_last_commit: c7a0fa5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BufferedMatrix_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BufferedMatrix_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BufferedMatrix_1.50.0.tgz vignettes: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.pdf vignetteTitles: BufferedMatrix: Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.R dependsOnMe: BufferedMatrixMethods linksToMe: BufferedMatrixMethods dependencyCount: 1 Package: BufferedMatrixMethods Version: 1.50.0 Depends: R (>= 2.6.0), BufferedMatrix (>= 1.3.0), methods LinkingTo: BufferedMatrix Suggests: affyio, affy License: GPL (>= 2) Archs: i386, x64 MD5sum: a38eeee1af90bb6d6498e5c54acd54f5 NeedsCompilation: yes Title: Microarray Data related methods that utlize BufferedMatrix objects Description: Microarray analysis methods that use BufferedMatrix objects biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.bom/bmbolstad/BufferedMatrixMethods git_url: https://git.bioconductor.org/packages/BufferedMatrixMethods git_branch: RELEASE_3_10 git_last_commit: c88e3a1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BufferedMatrixMethods_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BufferedMatrixMethods_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BufferedMatrixMethods_1.50.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 2 Package: BUMHMM Version: 1.10.0 Depends: R (>= 3.4) Imports: devtools, stringi, gtools, stats, utils, SummarizedExperiment, Biostrings, IRanges Suggests: testthat, knitr, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 37da9f42d0cde0ff7422cd1c7991f4f9 NeedsCompilation: no Title: Computational pipeline for computing probability of modification from structure probing experiment data Description: This is a probabilistic modelling pipeline for computing per- nucleotide posterior probabilities of modification from the data collected in structure probing experiments. The model supports multiple experimental replicates and empirically corrects coverage- and sequence-dependent biases. The model utilises the measure of a "drop-off rate" for each nucleotide, which is compared between replicates through a log-ratio (LDR). The LDRs between control replicates define a null distribution of variability in drop-off rate observed by chance and LDRs between treatment and control replicates gets compared to this distribution. Resulting empirical p-values (probability of being "drawn" from the null distribution) are used as observations in a Hidden Markov Model with a Beta-Uniform Mixture model used as an emission model. The resulting posterior probabilities indicate the probability of a nucleotide of having being modified in a structure probing experiment. biocViews: ImmunoOncology, GeneticVariability, Transcription, GeneExpression, GeneRegulation, Coverage, Genetics, StructuralPrediction, Transcriptomics, Bayesian, Classification, FeatureExtraction, HiddenMarkovModel, Regression, RNASeq, Sequencing Author: Alina Selega (alina.selega@gmail.com), Sander Granneman, Guido Sanguinetti Maintainer: Alina Selega VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BUMHMM git_branch: RELEASE_3_10 git_last_commit: de55cdd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BUMHMM_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BUMHMM_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BUMHMM_1.10.0.tgz vignettes: vignettes/BUMHMM/inst/doc/BUMHMM.pdf vignetteTitles: An Introduction to the BUMHMM pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUMHMM/inst/doc/BUMHMM.R dependencyCount: 108 Package: bumphunter Version: 1.28.0 Depends: R (>= 3.5), S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), GenomeInfoDb, GenomicRanges, foreach, iterators, methods, parallel, locfit Imports: matrixStats, limma, doRNG, BiocGenerics, utils, GenomicFeatures, AnnotationDbi, stats Suggests: testthat, RUnit, doParallel, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 7835e7795ea94c6fa352e13509ac4db0 NeedsCompilation: no Title: Bump Hunter Description: Tools for finding bumps in genomic data biocViews: DNAMethylation, Epigenetics, Infrastructure, MultipleComparison, ImmunoOncology Author: Rafael A. Irizarry [cre, aut], Martin Aryee [aut], Kasper Daniel Hansen [aut], Hector Corrada Bravo [aut], Shan Andrews [ctb], Andrew E. Jaffe [ctb], Harris Jaffee [ctb], Leonardo Collado-Torres [ctb] Maintainer: Rafael A. Irizarry URL: https://github.com/ririzarr/bumphunter git_url: https://git.bioconductor.org/packages/bumphunter git_branch: RELEASE_3_10 git_last_commit: 308ed33 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/bumphunter_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/bumphunter_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/bumphunter_1.28.0.tgz vignettes: vignettes/bumphunter/inst/doc/bumphunter.pdf vignetteTitles: The bumphunter user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bumphunter/inst/doc/bumphunter.R dependsOnMe: minfi importsMe: brainflowprobes, derfinder, dmrseq, methylCC, methyvim suggestsMe: bigmelon, derfinderPlot, epivizrData, regionReport dependencyCount: 90 Package: BUS Version: 1.42.0 Depends: R (>= 2.3.0), minet Imports: stats, infotheo License: GPL-3 MD5sum: c514b63cb11fe4d645ffe09848e2ee88 NeedsCompilation: yes Title: Gene network reconstruction Description: This package can be used to compute associations among genes (gene-networks) or between genes and some external traits (i.e. clinical). biocViews: Preprocessing Author: Yin Jin, Hesen Peng, Lei Wang, Raffaele Fronza, Yuanhua Liu and Christine Nardini Maintainer: Yuanhua Liu git_url: https://git.bioconductor.org/packages/BUS git_branch: RELEASE_3_10 git_last_commit: 1cac661 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BUS_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BUS_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BUS_1.42.0.tgz vignettes: vignettes/BUS/inst/doc/bus.pdf vignetteTitles: bus.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUS/inst/doc/bus.R dependencyCount: 3 Package: BUScorrect Version: 1.4.0 Depends: R (>= 3.5.0) Imports: gplots, methods, grDevices, stats, SummarizedExperiment Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: aa24f153cdde2d6fc35ae9f23f11e015 NeedsCompilation: yes Title: Batch Effects Correction with Unknown Subtypes Description: High-throughput experimental data are accumulating exponentially in public databases. However, mining valid scientific discoveries from these abundant resources is hampered by technical artifacts and inherent biological heterogeneity. The former are usually termed "batch effects," and the latter is often modelled by "subtypes." The R package BUScorrect fits a Bayesian hierarchical model, the Batch-effects-correction-with-Unknown-Subtypes model (BUS), to correct batch effects in the presence of unknown subtypes. BUS is capable of (a) correcting batch effects explicitly, (b) grouping samples that share similar characteristics into subtypes, (c) identifying features that distinguish subtypes, and (d) enjoying a linear-order computation complexity. biocViews: GeneExpression, StatisticalMethod, Bayesian, Clustering, FeatureExtraction, BatchEffect Author: Xiangyu Luo , Yingying Wei Maintainer: Xiangyu Luo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BUScorrect git_branch: RELEASE_3_10 git_last_commit: 2b44d47 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BUScorrect_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BUScorrect_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BUScorrect_1.4.0.tgz vignettes: vignettes/BUScorrect/inst/doc/BUScorrect_user_guide.pdf vignetteTitles: BUScorrect_user_guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUScorrect/inst/doc/BUScorrect_user_guide.R dependencyCount: 37 Package: BUSpaRse Version: 1.0.0 Imports: AnnotationDbi, AnnotationFilter, biomaRt, Biostrings, BSgenome, data.table, dplyr, ensembldb, GenomeInfoDb, GenomicFeatures, GenomicRanges, magrittr, Matrix, methods, plyranges, Rcpp, RcppParallel, S4Vectors, stats, stringr, tibble, tidyr, zeallot LinkingTo: Rcpp, RcppArmadillo, RcppProgress, BH, RcppParallel Suggests: knitr, rmarkdown, testthat, BiocStyle, TENxBUSData, DropletUtils, ggplot2, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86 License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: 4e17fcc6b4b80a717db767daef9262af NeedsCompilation: yes Title: kallisto | bustools R utilities Description: The kallisto | bustools pipeline is a fast and modular set of tools to convert single cell RNA-seq reads in fastq files into gene count or transcript compatibility counts (TCC) matrices for downstream analysis. Central to this pipeline is the barcode, UMI, and set (BUS) file format. This package serves the following purposes: First, this package allows users to manipulate BUS format files as data frames in R and then convert them into gene count or TCC matrices. Furthermore, since R and Rcpp code is easier to handle than pure C++ code, users are encouraged to tweak the source code of this package to experiment with new uses of BUS format and different ways to convert the BUS file into gene count matrix. Second, this package can conveniently generate files required to generate gene count matrices for spliced and unspliced transcripts for RNA velocity. Third, this package implements utility functions to get transcripts and associated genes required to convert BUS files to gene count matrices, to write the transcript to gene information in the format required by bustools, and to read output of bustools into R as sparses matrices. biocViews: SingleCell, RNASeq, WorkflowStep Author: Lambda Moses [aut, cre] (), Lior Pachter [aut, ths] () Maintainer: Lambda Moses URL: https://github.com/BUStools/BUSpaRse SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/BUStools/BUSpaRse/issues git_url: https://git.bioconductor.org/packages/BUSpaRse git_branch: RELEASE_3_10 git_last_commit: 9b33e52 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/BUSpaRse_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/BUSpaRse_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/BUSpaRse_1.0.0.tgz vignettes: vignettes/BUSpaRse/inst/doc/sparse-matrix.html, vignettes/BUSpaRse/inst/doc/tr2g.html vignetteTitles: Converting BUS format into sparse matrix, tr2g hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BUSpaRse/inst/doc/sparse-matrix.R, vignettes/BUSpaRse/inst/doc/tr2g.R dependencyCount: 95 Package: CAFE Version: 1.22.0 Depends: R (>= 2.10), biovizBase, GenomicRanges, IRanges, ggbio Imports: affy, ggplot2, annotate, grid, gridExtra, tcltk, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: f98829e00358bca542f4d2033c906b2d NeedsCompilation: no Title: Chromosmal Aberrations Finder in Expression data Description: Detection and visualizations of gross chromosomal aberrations using Affymetrix expression microarrays as input biocViews: GeneExpression, Microarray, OneChannel, GeneSetEnrichment Author: Sander Bollen Maintainer: Sander Bollen git_url: https://git.bioconductor.org/packages/CAFE git_branch: RELEASE_3_10 git_last_commit: 6e2d9e7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CAFE_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CAFE_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CAFE_1.22.0.tgz vignettes: vignettes/CAFE/inst/doc/CAFE-manual.pdf vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAFE/inst/doc/CAFE-manual.R dependencyCount: 157 Package: CAGEfightR Version: 1.6.0 Depends: R (>= 3.5), GenomicRanges (>= 1.30.1), rtracklayer (>= 1.38.2), SummarizedExperiment (>= 1.8.1) Imports: pryr(>= 0.1.3), assertthat(>= 0.2.0), methods, Matrix(>= 1.2-12), Matrix.utils(>= 0.9.6), grr(>= 0.9.5), BiocGenerics(>= 0.24.0), S4Vectors(>= 0.16.0), IRanges(>= 2.12.0), GenomeInfoDb(>= 1.14.0), GenomicFeatures(>= 1.29.11), BiocParallel(>= 1.12.0), GenomicFiles(>= 1.14.0), Gviz(>= 1.22.2), InteractionSet(>= 1.9.4), GenomicInteractions(>= 1.15.1) Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm9.knownGene License: GPL-3 + file LICENSE MD5sum: a6df43e83b50390bfa2b3263166961df NeedsCompilation: no Title: Analysis of Cap Analysis of Gene Expression (CAGE) data using Bioconductor Description: CAGE is a widely used high throughput assay for measuring transcription start site (TSS) activity. CAGEfightR is an R/Bioconductor package for performing a wide range of common data analysis tasks for CAGE and 5'-end data in general. Core functionality includes: import of CAGE TSSs (CTSSs), tag (or unidirectional) clustering for TSS identification, bidirectional clustering for enhancer identification, annotation with transcript and gene models, correlation of TSS and enhancer expression, calculation of TSS shapes, quantification of CAGE expression as expression matrices and genome brower visualization. biocViews: Software, Transcription, Coverage, GeneExpression, GeneRegulation, PeakDetection, DataImport, DataRepresentation, Transcriptomics, Sequencing, Annotation, GenomeBrowsers, Normalization, Preprocessing, Visualization Author: Malte Thodberg Maintainer: Malte Thodberg URL: https://github.com/MalteThodberg/CAGEfightR VignetteBuilder: knitr BugReports: https://github.com/MalteThodberg/CAGEfightR/issues git_url: https://git.bioconductor.org/packages/CAGEfightR git_branch: RELEASE_3_10 git_last_commit: a293b9a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CAGEfightR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CAGEfightR_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CAGEfightR_1.6.0.tgz vignettes: vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.html vignetteTitles: Introduction to CAGEfightR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.R dependencyCount: 151 Package: CAGEr Version: 1.28.0 Depends: methods, MultiAssayExperiment, R (>= 3.5.0) Imports: beanplot, BiocGenerics, BiocParallel, BSgenome, data.table, DelayedArray, formula.tools, GenomeInfoDb, GenomicAlignments, GenomicRanges (>= 1.37.16), ggplot2 (>= 2.2.0), gtools, IRanges (>= 2.18.0), KernSmooth, memoise, plyr, Rsamtools, reshape, rtracklayer, S4Vectors, som, stringdist, stringi, SummarizedExperiment, utils, vegan, VGAM Suggests: BSgenome.Drerio.UCSC.danRer7, DESeq2, FANTOM3and4CAGE, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: f107e79ce98cd3bcba4945ac2869e57c NeedsCompilation: no Title: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing data for precise mapping of transcription start sites and promoterome mining Description: Preprocessing of CAGE sequencing data, identification and normalization of transcription start sites and downstream analysis of transcription start sites clusters (promoters). biocViews: Preprocessing, Sequencing, Normalization, FunctionalGenomics, Transcription, GeneExpression, Clustering, Visualization Author: Vanja Haberle Maintainer: Vanja Haberle , Charles Plessy , Damir Baranasic , Sarvesh Nikumbh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CAGEr git_branch: RELEASE_3_10 git_last_commit: de5899e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CAGEr_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CAGEr_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CAGEr_1.28.0.tgz vignettes: vignettes/CAGEr/inst/doc/CAGE_Resources.html, vignettes/CAGEr/inst/doc/CAGEexp.html vignetteTitles: Use of CAGE resources with CAGEr, CAGEr: an R package for CAGE data analysis and promoterome mining hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAGEr/inst/doc/CAGE_Resources.R, vignettes/CAGEr/inst/doc/CAGEexp.R suggestsMe: seqPattern dependencyCount: 106 Package: CALIB Version: 1.52.0 Depends: R (>= 2.10), limma, methods Imports: limma, methods, graphics, stats, utils License: LGPL MD5sum: 22317b1f897cff3d5f231437fdeb4030 NeedsCompilation: yes Title: Calibration model for estimating absolute expression levels from microarray data Description: This package contains functions for normalizing spotted microarray data, based on a physically motivated calibration model. The model parameters and error distributions are estimated from external control spikes. biocViews: Microarray,TwoChannel,Preprocessing Author: Hui Zhao, Kristof Engelen, Bart De Moor and Kathleen Marchal Maintainer: Hui Zhao git_url: https://git.bioconductor.org/packages/CALIB git_branch: RELEASE_3_10 git_last_commit: c32140a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CALIB_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CALIB_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CALIB_1.52.0.tgz vignettes: vignettes/CALIB/inst/doc/quickstart.pdf vignetteTitles: CALIB Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CALIB/inst/doc/quickstart.R dependencyCount: 6 Package: calm Version: 1.0.0 Imports: mgcv, stats, graphics Suggests: knitr, rmarkdown License: GPL (>=2) Archs: i386, x64 MD5sum: fed048370e2b5b59eee2d62d9e6fd124 NeedsCompilation: no Title: Covariate Assisted Large-scale Multiple testing Description: Statistical methods for multiple testing with covariate information. Traditional multiple testing methods only consider a list of test statistics, such as p-values. Our methods incorporate the auxiliary information, such as the lengths of gene coding regions or the minor allele frequencies of SNPs, to improve power. biocViews: Bayesian, DifferentialExpression, GeneExpression, Regression, Microarray, Sequencing, RNASeq, MultipleComparison, Genetics, ImmunoOncology, Metabolomics, Proteomics, Transcriptomics Author: Kun Liang [aut, cre] Maintainer: Kun Liang VignetteBuilder: knitr BugReports: https://github.com/k22liang/calm/issues git_url: https://git.bioconductor.org/packages/calm git_branch: RELEASE_3_10 git_last_commit: 67501d2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/calm_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/calm_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/calm_1.0.0.tgz vignettes: vignettes/calm/inst/doc/calm_intro.html vignetteTitles: Userguide for calm package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/calm/inst/doc/calm_intro.R dependencyCount: 11 Package: CAMERA Version: 1.42.0 Depends: R (>= 2.1.0), methods, Biobase, xcms (>= 1.13.5) Imports: methods, xcms, RBGL, graph, graphics, grDevices, stats, utils, Hmisc, igraph Suggests: faahKO, RUnit, BiocGenerics Enhances: Rmpi, snow License: GPL (>= 2) MD5sum: 0b65201e5fc7182b80994caea23043c9 NeedsCompilation: yes Title: Collection of annotation related methods for mass spectrometry data Description: Annotation of peaklists generated by xcms, rule based annotation of isotopes and adducts, isotope validation, EIC correlation based tagging of unknown adducts and fragments biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Carsten Kuhl, Ralf Tautenhahn, Hendrik Treutler, Steffen Neumann {ckuhl|htreutle|sneumann}@ipb-halle.de, rtautenh@scripps.edu Maintainer: Steffen Neumann URL: http://msbi.ipb-halle.de/msbi/CAMERA/ BugReports: https://github.com/sneumann/CAMERA/issues/new git_url: https://git.bioconductor.org/packages/CAMERA git_branch: RELEASE_3_10 git_last_commit: c99e379 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CAMERA_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CAMERA_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CAMERA_1.42.0.tgz vignettes: vignettes/CAMERA/inst/doc/CAMERA.pdf, vignettes/CAMERA/inst/doc/compoundQuantilesVignette.pdf, vignettes/CAMERA/inst/doc/IsotopeDetectionVignette.pdf vignetteTitles: Molecule Identification with CAMERA, Atom count expectations with compoundQuantiles, Isotope pattern validation with CAMERA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAMERA/inst/doc/CAMERA.R dependsOnMe: flagme, IPO, LOBSTAHS, MAIT, metaMS suggestsMe: cliqueMS, msPurity, RMassBank dependencyCount: 127 Package: CAMTHC Version: 1.4.0 Depends: R (>= 3.5) Imports: methods, rJava, BiocParallel, stats, Biobase, SummarizedExperiment, corpcor, geometry, NMF, nnls, DMwR, pcaPP, apcluster, graphics Suggests: knitr, rmarkdown, BiocStyle, testthat, GEOquery, rgl License: GPL-2 Archs: i386, x64 MD5sum: 2f44e0d65707afb6e7c218cf6f7be80c NeedsCompilation: no Title: Convex Analysis of Mixtures for Tissue Heterogeneity Characterization Description: An R package for tissue heterogeneity characterization by convex analysis of mixtures (CAM). It provides basic functions to perform unsupervised deconvolution on mixture expression profiles by CAM and some auxiliary functions to help understand the subpopulation-specific results. It also implements functions to perform supervised deconvolution based on prior knowledge of molecular markers, S matrix or A matrix. Combining molecular markers from CAM and from prior knowledge can achieve semi-supervised deconvolution of mixtures. biocViews: Software, CellBiology, GeneExpression Author: Lulu Chen Maintainer: Lulu Chen SystemRequirements: Java (>= 1.8) VignetteBuilder: knitr BugReports: https://github.com/Lululuella/CAMTHC/issues PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/CAMTHC git_branch: RELEASE_3_10 git_last_commit: 306688f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CAMTHC_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CAMTHC_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CAMTHC_1.4.0.tgz vignettes: vignettes/CAMTHC/inst/doc/camthc.html vignetteTitles: CAMTHC User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAMTHC/inst/doc/camthc.R dependencyCount: 120 Package: canceR Version: 1.20.0 Depends: R (>= 3.4), tcltk, tcltk2, cgdsr Imports: GSEABase, tkrplot, geNetClassifier, RUnit, Formula, rpart, survival, Biobase, phenoTest, circlize, plyr, graphics, stats, utils, grDevices Suggests: testthat (>= 0.10.0), R.rsp License: GPL-2 MD5sum: 2abbafbffb38d21ec3df70f748418773 NeedsCompilation: no Title: A Graphical User Interface for accessing and modeling the Cancer Genomics Data of MSKCC Description: The package is user friendly interface based on the cgdsr and other modeling packages to explore, compare, and analyse all available Cancer Data (Clinical data, Gene Mutation, Gene Methylation, Gene Expression, Protein Phosphorylation, Copy Number Alteration) hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC). biocViews: GUI, GeneExpression, Software Author: Karim Mezhoud. Nuclear Safety & Security Department. Nuclear Science Center of Tunisia. Maintainer: Karim Mezhoud VignetteBuilder: R.rsp git_url: https://git.bioconductor.org/packages/canceR git_branch: RELEASE_3_10 git_last_commit: f1b52a7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/canceR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/canceR_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/canceR_1.20.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 195 Package: cancerclass Version: 1.30.0 Depends: R (>= 2.14.0), Biobase, binom, methods, stats Suggests: cancerdata License: GPL 3 MD5sum: 33df9e945c882b1a663d082a94c28b5a NeedsCompilation: yes Title: Development and validation of diagnostic tests from high-dimensional molecular data Description: The classification protocol starts with a feature selection step and continues with nearest-centroid classification. The accurarcy of the predictor can be evaluated using training and test set validation, leave-one-out cross-validation or in a multiple random validation protocol. Methods for calculation and visualization of continuous prediction scores allow to balance sensitivity and specificity and define a cutoff value according to clinical requirements. biocViews: Cancer, Microarray, Classification, Visualization Author: Jan Budczies, Daniel Kosztyla Maintainer: Daniel Kosztyla git_url: https://git.bioconductor.org/packages/cancerclass git_branch: RELEASE_3_10 git_last_commit: c4208b1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cancerclass_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cancerclass_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cancerclass_1.30.0.tgz vignettes: vignettes/cancerclass/inst/doc/vignette_cancerclass.pdf vignetteTitles: Cancerclass: An R package for development and validation of diagnostic tests from high-dimensional molecular data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cancerclass/inst/doc/vignette_cancerclass.R dependencyCount: 8 Package: CancerInSilico Version: 2.6.0 Depends: R (>= 3.4), Rcpp Imports: methods, utils, graphics, stats LinkingTo: Rcpp, BH Suggests: testthat, knitr, rmarkdown, BiocStyle, Rtsne, viridis, rgl, gplots License: GPL-2 Archs: i386, x64 MD5sum: 9f80e928cb1205f8eddeec2180f08126 NeedsCompilation: yes Title: An R interface for computational modeling of tumor progression Description: The CancerInSilico package provides an R interface for running mathematical models of tumor progresson and generating gene expression data from the results. This package has the underlying models implemented in C++ and the output and analysis features implemented in R. biocViews: ImmunoOncology, MathematicalBiology, SystemsBiology, CellBiology, BiomedicalInformatics, GeneExpression, RNASeq, SingleCell Author: Thomas D. Sherman, Raymond Cheng, Elana J. Fertig Maintainer: Thomas D. Sherman , Elana J. Fertig VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CancerInSilico git_branch: RELEASE_3_10 git_last_commit: 96c0a6f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CancerInSilico_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CancerInSilico_2.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CancerInSilico_2.6.0.tgz vignettes: vignettes/CancerInSilico/inst/doc/CancerInSilico.html vignetteTitles: The CancerInSilico Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CancerInSilico/inst/doc/CancerInSilico.R dependencyCount: 6 Package: CancerMutationAnalysis Version: 1.28.0 Depends: R (>= 2.10.0), qvalue Imports: AnnotationDbi, limma, methods, stats Suggests: KEGG.db License: GPL (>= 2) + file LICENSE Archs: i386, x64 MD5sum: 15f6e46fd529774ede04a3dd7ad0911d NeedsCompilation: yes Title: Cancer mutation analysis Description: This package implements gene and gene-set level analysis methods for somatic mutation studies of cancer. The gene-level methods distinguish between driver genes (which play an active role in tumorigenesis) and passenger genes (which are mutated in tumor samples, but have no role in tumorigenesis) and incorporate a two-stage study design. The gene-set methods implement a patient-oriented approach, which calculates gene-set scores for each sample, then combines them across samples; a gene-oriented approach which uses the Wilcoxon test is also provided for comparison. biocViews: Genetics, Software Author: Giovanni Parmigiani, Simina M. Boca Maintainer: Simina M. Boca git_url: https://git.bioconductor.org/packages/CancerMutationAnalysis git_branch: RELEASE_3_10 git_last_commit: 4973cff git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CancerMutationAnalysis_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CancerMutationAnalysis_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CancerMutationAnalysis_1.28.0.tgz vignettes: vignettes/CancerMutationAnalysis/inst/doc/CancerMutationAnalysis.pdf vignetteTitles: CancerMutationAnalysisTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CancerMutationAnalysis/inst/doc/CancerMutationAnalysis.R dependencyCount: 75 Package: CancerSubtypes Version: 1.12.1 Depends: R (>= 3.4), sigclust, NMF Imports: SNFtool, iCluster, cluster, impute, limma, ConsensusClusterPlus, grDevices, survival Suggests: BiocGenerics, RUnit, knitr, RTCGA.mRNA, RTCGA.clinical License: GPL (>= 2) Archs: i386, x64 MD5sum: 9453555a9cea165db5b45f912bf1ef3c NeedsCompilation: no Title: Cancer subtypes identification, validation and visualization based on multiple genomic data sets Description: CancerSubtypes integrates the current common computational biology methods for cancer subtypes identification and provides a standardized framework for cancer subtype analysis based multi-omics data, such as gene expression, miRNA expression, DNA methylation and others. biocViews: Clustering, Software, Visualization, GeneExpression Author: Taosheng Xu, Thuc Le Maintainer: Taosheng Xu URL: https://github.com/taoshengxu/CancerSubtypes VignetteBuilder: knitr BugReports: https://github.com/taoshengxu/CancerSubtypes/issues git_url: https://git.bioconductor.org/packages/CancerSubtypes git_branch: RELEASE_3_10 git_last_commit: 68df12d git_last_commit_date: 2020-04-04 Date/Publication: 2020-04-04 source.ver: src/contrib/CancerSubtypes_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/CancerSubtypes_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CancerSubtypes_1.12.1.tgz vignettes: vignettes/CancerSubtypes/inst/doc/CancerSubtypes-vignette.html vignetteTitles: CancerSubtypes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CancerSubtypes/inst/doc/CancerSubtypes-vignette.R dependencyCount: 92 Package: CAnD Version: 1.18.0 Imports: methods, ggplot2, reshape Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 MD5sum: 53f59502f4894990b40483ad10377fb7 NeedsCompilation: no Title: Perform Chromosomal Ancestry Differences (CAnD) Analyses Description: Functions to perform the CAnD test on a set of ancestry proportions. For a particular ancestral subpopulation, a user will supply the estimated ancestry proportion for each sample, and each chromosome or chromosomal segment of interest. A p-value for each chromosome as well as an overall CAnD p-value will be returned for each test. Plotting functions are also available. biocViews: Genetics, StatisticalMethod, GeneticVariability, SNP Author: Caitlin McHugh, Timothy Thornton Maintainer: Caitlin McHugh git_url: https://git.bioconductor.org/packages/CAnD git_branch: RELEASE_3_10 git_last_commit: 4962096 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CAnD_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CAnD_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CAnD_1.18.0.tgz vignettes: vignettes/CAnD/inst/doc/CAnD.pdf vignetteTitles: Detecting heterogenity in population structure across chromosomes with the "CAnD" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAnD/inst/doc/CAnD.R dependencyCount: 56 Package: caOmicsV Version: 1.16.0 Depends: R (>= 3.2), igraph (>= 0.7.1), bc3net (>= 1.0.2) License: GPL (>=2.0) MD5sum: f91450ed5f5fc400d6911cd16af85a75 NeedsCompilation: no Title: Visualization of multi-dimentional cancer genomics data Description: caOmicsV package provides methods to visualize multi-dimentional cancer genomics data including of patient information, gene expressions, DNA methylations, DNA copy number variations, and SNP/mutations in matrix layout or network layout. biocViews: ImmunoOncology, Visualization, Network, RNASeq Author: Henry Zhang Maintainer: Henry Zhang git_url: https://git.bioconductor.org/packages/caOmicsV git_branch: RELEASE_3_10 git_last_commit: f404be7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/caOmicsV_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/caOmicsV_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/caOmicsV_1.16.0.tgz vignettes: vignettes/caOmicsV/inst/doc/Introduction_to_caOmicsV.pdf vignetteTitles: Intrudoction_to_caOmicsV hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/caOmicsV/inst/doc/Introduction_to_caOmicsV.R dependencyCount: 14 Package: Cardinal Version: 2.4.0 Depends: BiocGenerics, BiocParallel, EBImage, graphics, methods, S4Vectors (>= 0.23.18), stats, ProtGenerics Imports: Biobase, dplyr, irlba, lattice, Matrix, matter, magrittr, mclust, nlme, parallel, signal, sp, stats4, utils, viridisLite Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: 413eb50c3e9e66d5f9b566a4f0123388 NeedsCompilation: yes Title: A mass spectrometry imaging toolbox for statistical analysis Description: Implements statistical & computational tools for analyzing mass spectrometry imaging datasets, including methods for efficient pre-processing, spatial segmentation, and classification. biocViews: Software, Infrastructure, Proteomics, Lipidomics, MassSpectrometry, ImagingMassSpectrometry, ImmunoOncology, Normalization, Clustering, Classification, Regression Author: Kylie A. Bemis Maintainer: Kylie A. Bemis URL: http://www.cardinalmsi.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Cardinal git_branch: RELEASE_3_10 git_last_commit: 5f5c0c9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Cardinal_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Cardinal_2.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Cardinal_2.4.0.tgz vignettes: vignettes/Cardinal/inst/doc/Cardinal-2-guide.html, vignettes/Cardinal/inst/doc/Cardinal-2-stats.html vignetteTitles: 1. Cardinal 2: User guide for mass spectrometry imaging analysis, 2. Cardinal 2: Statistical methods for mass spectrometry imaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cardinal/inst/doc/Cardinal-2-guide.R, vignettes/Cardinal/inst/doc/Cardinal-2-stats.R dependencyCount: 66 Package: casper Version: 2.20.0 Depends: R (>= 3.6.0), Biobase, IRanges, methods, GenomicRanges Imports: BiocGenerics (>= 0.31.6), coda, EBarrays, gaga, gtools, GenomeInfoDb, GenomicFeatures, limma, mgcv, Rsamtools, rtracklayer, S4Vectors (>= 0.9.25), sqldf, survival, VGAM Enhances: parallel License: GPL (>=2) Archs: i386, x64 MD5sum: 64f042826657031d7767c37f47cf25a9 NeedsCompilation: yes Title: Characterization of Alternative Splicing based on Paired-End Reads Description: Infer alternative splicing from paired-end RNA-seq data. The model is based on counting paths across exons, rather than pairwise exon connections, and estimates the fragment size and start distributions non-parametrically, which improves estimation precision. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Transcription, RNASeq, Sequencing Author: David Rossell, Camille Stephan-Otto, Manuel Kroiss, Miranda Stobbe, Victor Pena Maintainer: David Rossell git_url: https://git.bioconductor.org/packages/casper git_branch: RELEASE_3_10 git_last_commit: a1c170a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/casper_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/casper_2.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/casper_2.20.0.tgz vignettes: vignettes/casper/inst/doc/casper.pdf, vignettes/casper/inst/doc/DesignRNASeq.pdf vignetteTitles: Manual for the casper library, DesignRNASeq.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/casper/inst/doc/casper.R dependencyCount: 98 Package: CATALYST Version: 1.10.3 Depends: R (>= 3.6) Imports: Biobase, circlize, ComplexHeatmap, ConsensusClusterPlus, cowplot, data.table, dplyr, drc, DT, flowCore, FlowSOM, ggplot2, ggrepel, ggridges, graphics, grDevices, grid, gridExtra, htmltools, limma, magrittr, Matrix, matrixStats, methods, nnls, plotly, purrr, RColorBrewer, reshape2, Rtsne, SingleCellExperiment, SummarizedExperiment, S4Vectors, scales, scater, shiny, shinydashboard, shinyBS, shinyjs, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, diffcyt License: GPL (>=2) Archs: i386, x64 MD5sum: 5aa75cb5e68edf8e5e14708c455be03e NeedsCompilation: no Title: Cytometry dATa anALYSis Tools Description: Mass cytometry (CyTOF) uses heavy metal isotopes rather than fluorescent tags as reporters to label antibodies, thereby substantially decreasing spectral overlap and allowing for examination of over 50 parameters at the single cell level. While spectral overlap is significantly less pronounced in CyTOF than flow cytometry, spillover due to detection sensitivity, isotopic impurities, and oxide formation can impede data interpretability. We designed CATALYST (Cytometry dATa anALYSis Tools) to provide a pipeline for preprocessing of cytometry data, including i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. biocViews: Clustering, DifferentialExpression, ExperimentalDesign, FlowCytometry, ImmunoOncology, MassSpectrometry, Normalization, Preprocessing, SingleCell, Software, StatisticalMethod, Visualization Author: Helena L. Crowell [aut, cre], Vito R.T. Zanotelli [aut], Stéphane Chevrier [aut, dtc], Mark D. Robinson [aut, fnd], Bernd Bodenmiller [fnd] Maintainer: Helena L. Crowell URL: https://github.com/HelenaLC/CATALYST VignetteBuilder: knitr BugReports: https://github.com/HelenaLC/CATALYST/issues git_url: https://git.bioconductor.org/packages/CATALYST git_branch: RELEASE_3_10 git_last_commit: d2e724c git_last_commit_date: 2020-04-02 Date/Publication: 2020-04-02 source.ver: src/contrib/CATALYST_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/CATALYST_1.10.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CATALYST_1.10.3.tgz vignettes: vignettes/CATALYST/inst/doc/differential_analysis.html, vignettes/CATALYST/inst/doc/preprocessing.html vignetteTitles: "Differential analysis with CATALYST", "Preprocessing with CATALYST" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CATALYST/inst/doc/differential_analysis.R, vignettes/CATALYST/inst/doc/preprocessing.R suggestsMe: diffcyt dependencyCount: 228 Package: Category Version: 2.52.1 Depends: methods, stats4, BiocGenerics, AnnotationDbi, Biobase, Matrix Imports: utils, stats, graph, RBGL, GSEABase, genefilter, annotate, DBI Suggests: EBarrays, ALL, Rgraphviz, RColorBrewer, xtable (>= 1.4-6), hgu95av2.db, KEGG.db, karyoploteR, geneplotter, limma, lattice, RUnit, org.Sc.sgd.db, GOstats, GO.db License: Artistic-2.0 MD5sum: 4d836e5f9bbdebc83ab4a7b7c08ae3fe NeedsCompilation: no Title: Category Analysis Description: A collection of tools for performing category (gene set enrichment) analysis. biocViews: Annotation, GO, Pathways, GeneSetEnrichment Author: Robert Gentleman [aut], Seth Falcon [ctb], Deepayan Sarkar [ctb], Robert Castelo [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/Category git_branch: RELEASE_3_10 git_last_commit: 71e3a7a git_last_commit_date: 2019-11-08 Date/Publication: 2019-11-08 source.ver: src/contrib/Category_2.52.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/Category_2.52.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Category_2.52.1.tgz vignettes: vignettes/Category/inst/doc/Category.pdf, vignettes/Category/inst/doc/ChromBand.pdf vignetteTitles: Using Categories to Analyze Microarray Data, Using Chromosome Bands as Categories hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Category/inst/doc/Category.R, vignettes/Category/inst/doc/ChromBand.R dependsOnMe: GOstats, PCpheno importsMe: categoryCompare, cellHTS2, eisa, gCMAP, GmicR, interactiveDisplay, meshr, miRLAB, PCpheno, phenoTest, ppiStats, RDAVIDWebService, scTensor suggestsMe: BiocCaseStudies, MmPalateMiRNA, qpgraph, RnBeads dependencyCount: 41 Package: categoryCompare Version: 1.30.0 Depends: R (>= 2.10), Biobase, BiocGenerics (>= 0.13.8), Imports: AnnotationDbi, hwriter, GSEABase, Category (>= 2.33.1), GOstats, annotate, colorspace, graph, RCy3 (>= 1.99.29), methods, grDevices, utils Suggests: knitr, GO.db, KEGG.db, estrogen, org.Hs.eg.db, hgu95av2.db, limma, affy, genefilter License: GPL-2 MD5sum: 11d4b0080c5a96944c6cf3746243a652 NeedsCompilation: no Title: Meta-analysis of high-throughput experiments using feature annotations Description: Calculates significant annotations (categories) in each of two (or more) feature (i.e. gene) lists, determines the overlap between the annotations, and returns graphical and tabular data about the significant annotations and which combinations of feature lists the annotations were found to be significant. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested). biocViews: Annotation, GO, MultipleComparison, Pathways, GeneExpression Author: Robert M. Flight Maintainer: Robert M. Flight URL: https://github.com/rmflight/categoryCompare SystemRequirements: Cytoscape (>= 3.6.1) (if used for visualization of results, heavily suggested) VignetteBuilder: knitr BugReports: https://github.com/rmflight/categoryCompare/issues git_url: https://git.bioconductor.org/packages/categoryCompare git_branch: RELEASE_3_10 git_last_commit: 9cfd52a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/categoryCompare_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/categoryCompare_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/categoryCompare_1.30.0.tgz vignettes: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.html vignetteTitles: categoryCompare: High-throughput data meta-analysis using gene annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.R dependencyCount: 64 Package: CausalR Version: 1.18.0 Depends: R (>= 3.2.0) Imports: igraph Suggests: knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 9746eed22121f99728f8ca97ce7bfe60 NeedsCompilation: no Title: Causal network analysis methods Description: Causal network analysis methods for regulator prediction and network reconstruction from genome scale data. biocViews: ImmunoOncology, SystemsBiology, Network, GraphAndNetwork, Network Inference, Transcriptomics, Proteomics, DifferentialExpression, RNASeq, Microarray Author: Glyn Bradley, Steven Barrett, Chirag Mistry, Mark Pipe, David Wille, David Riley, Bhushan Bonde, Peter Woollard Maintainer: Glyn Bradley , Steven Barrett VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CausalR git_branch: RELEASE_3_10 git_last_commit: f2d6c69 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CausalR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CausalR_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CausalR_1.18.0.tgz vignettes: vignettes/CausalR/inst/doc/CausalR.pdf vignetteTitles: CausalR.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CausalR/inst/doc/CausalR.R dependencyCount: 11 Package: cbaf Version: 1.8.1 Imports: BiocFileCache, RColorBrewer, cgdsr, genefilter, gplots, grDevices, stats, utils, xlsx Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 5bc5220de9604573a0167793246c1a37 NeedsCompilation: no Title: Automated functions for comparing various data from cbioportal.org Description: This package contains functions that allow analysing and comparing various gene groups from different cancers/cancer subgroups easily. So far, it is compatible with RNA-seq, microRNA-seq, microarray and methylation datasets that are stored on cbioportal.org. biocViews: Software, AssayDomain, DNAMethylation, GeneExpression, Transcription, ResearchField, BiomedicalInformatics, ComparativeGenomics, Epigenetics, Genetics, Transcriptomics Author: Arman Shahrisa [aut, cre, cph], Maryam Tahmasebi Birgani [aut] Maintainer: Arman Shahrisa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cbaf git_branch: RELEASE_3_10 git_last_commit: 93365f3 git_last_commit_date: 2019-12-31 Date/Publication: 2020-01-01 source.ver: src/contrib/cbaf_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/cbaf_1.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cbaf_1.8.1.tgz vignettes: vignettes/cbaf/inst/doc/cbaf.html vignetteTitles: cbaf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cbaf/inst/doc/cbaf.R dependencyCount: 74 Package: ccfindR Version: 1.6.0 Depends: R (>= 3.6.0) Imports: stats, S4Vectors, utils, methods, Matrix, SummarizedExperiment, SingleCellExperiment, Rtsne, graphics, grDevices, gtools, RColorBrewer, ape, Rmpi, irlba, Rcpp, Rdpack (>= 0.7) LinkingTo: Rcpp, RcppEigen Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 74cdb48734d844e0d2a0ac90ac0c770f NeedsCompilation: yes Title: Cancer Clone Finder Description: A collection of tools for cancer genomic data clustering analyses, including those for single cell RNA-seq. Cell clustering and feature gene selection analysis employ Bayesian (and maximum likelihood) non-negative matrix factorization (NMF) algorithm. Input data set consists of RNA count matrix, gene, and cell bar code annotations. Analysis outputs are factor matrices for multiple ranks and marginal likelihood values for each rank. The package includes utilities for downstream analyses, including meta-gene identification, visualization, and construction of rank-based trees for clusters. biocViews: Transcriptomics, SingleCell, ImmunoOncology, Bayesian, Clustering Author: Jun Woo [aut, cre], Jinhua Wang [aut] Maintainer: Jun Woo URL: http://dx.doi.org/10.26508/lsa.201900443 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccfindR git_branch: RELEASE_3_10 git_last_commit: 8b5b604 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ccfindR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ccfindR_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ccfindR_1.6.0.tgz vignettes: vignettes/ccfindR/inst/doc/ccfindR.html vignetteTitles: ccfindR: single-cell RNA-seq analysis using Bayesian non-negative matrix factorization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ccfindR/inst/doc/ccfindR.R dependencyCount: 49 Package: ccmap Version: 1.12.0 Imports: AnnotationDbi (>= 1.36.2), BiocManager (>= 1.24.0), ccdata (>= 1.1.2), doParallel (>= 1.0.10), data.table (>= 1.10.4), foreach (>= 1.4.3), parallel (>= 3.3.3), xgboost (>= 0.6.4), lsa (>= 0.73.1) Suggests: crossmeta, knitr, rmarkdown, testthat, lydata License: MIT + file LICENSE Archs: i386, x64 MD5sum: df700bd09648b2ed38a9f4fcf229f0f5 NeedsCompilation: no Title: Combination Connectivity Mapping Description: Finds drugs and drug combinations that are predicted to reverse or mimic gene expression signatures. These drugs might reverse diseases or mimic healthy lifestyles. biocViews: GeneExpression, Transcription, Microarray, DifferentialExpression Author: Alex Pickering Maintainer: Alex Pickering VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccmap git_branch: RELEASE_3_10 git_last_commit: 1770f4b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ccmap_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ccmap_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ccmap_1.12.0.tgz vignettes: vignettes/ccmap/inst/doc/ccmap-vignette.html vignetteTitles: ccmap vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ccmap/inst/doc/ccmap-vignette.R importsMe: crossmeta dependencyCount: 43 Package: CCPROMISE Version: 1.12.0 Depends: R (>= 3.3.0), stats, methods, CCP, PROMISE, Biobase, GSEABase, utils License: GPL (>= 2) MD5sum: 43b8aff213c38a2e17e266b5936b8576 NeedsCompilation: no Title: PROMISE analysis with Canonical Correlation for Two Forms of High Dimensional Genetic Data Description: Perform Canonical correlation between two forms of high demensional genetic data, and associate the first compoent of each form of data with a specific biologically interesting pattern of associations with multiple endpoints. A probe level analysis is also implemented. biocViews: Microarray, GeneExpression Author: Xueyuan Cao and Stanley.pounds Maintainer: Xueyuan Cao git_url: https://git.bioconductor.org/packages/CCPROMISE git_branch: RELEASE_3_10 git_last_commit: 7ac4968 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CCPROMISE_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CCPROMISE_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CCPROMISE_1.12.0.tgz vignettes: vignettes/CCPROMISE/inst/doc/CCPROMISE.pdf vignetteTitles: An introduction to CCPROMISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CCPROMISE/inst/doc/CCPROMISE.R dependencyCount: 35 Package: ccrepe Version: 1.22.0 Imports: infotheo (>= 1.1) Suggests: knitr, BiocStyle, BiocGenerics, testthat License: MIT + file LICENSE MD5sum: f25d69f92f8f05a53d17a5b1131170dc NeedsCompilation: no Title: ccrepe_and_nc.score Description: The CCREPE (Compositionality Corrected by REnormalizaion and PErmutation) package is designed to assess the significance of general similarity measures in compositional datasets. In microbial abundance data, for example, the total abundances of all microbes sum to one; CCREPE is designed to take this constraint into account when assigning p-values to similarity measures between the microbes. The package has two functions: ccrepe: Calculates similarity measures, p-values and q-values for relative abundances of bugs in one or two body sites using bootstrap and permutation matrices of the data. nc.score: Calculates species-level co-variation and co-exclusion patterns based on an extension of the checkerboard score to ordinal data. biocViews: ImmunoOncology, Statistics, Metagenomics, Bioinformatics, Software Author: Emma Schwager ,Craig Bielski, George Weingart Maintainer: Emma Schwager ,Craig Bielski, George Weingart VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccrepe git_branch: RELEASE_3_10 git_last_commit: 1c05f01 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ccrepe_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ccrepe_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ccrepe_1.22.0.tgz vignettes: vignettes/ccrepe/inst/doc/ccrepe.pdf vignetteTitles: ccrepe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ccrepe/inst/doc/ccrepe.R dependencyCount: 1 Package: celaref Version: 1.4.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: MAST, ggplot2, Matrix, dplyr, magrittr, stats, utils, rlang, BiocGenerics, S4Vectors, readr, tibble, DelayedArray Suggests: limma, parallel, knitr, rmarkdown, ExperimentHub, testthat License: GPL-3 MD5sum: 6bac0da7258c8d7be13cfd12d13c1860 NeedsCompilation: no Title: Single-cell RNAseq cell cluster labelling by reference Description: After the clustering step of a single-cell RNAseq experiment, this package aims to suggest labels/cell types for the clusters, on the basis of similarity to a reference dataset. It requires a table of read counts per cell per gene, and a list of the cells belonging to each of the clusters, (for both test and reference data). biocViews: SingleCell Author: Sarah Williams [aut, cre] Maintainer: Sarah Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/celaref git_branch: RELEASE_3_10 git_last_commit: 89082dd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/celaref_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/celaref_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/celaref_1.4.0.tgz vignettes: vignettes/celaref/inst/doc/celaref_doco.html vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/celaref/inst/doc/celaref_doco.R dependencyCount: 93 Package: celda Version: 1.2.4 Depends: R (>= 3.6) Imports: stats, plyr, foreach, ggplot2, RColorBrewer, grid, scales, gtable, grDevices, graphics, matrixStats, doParallel, digest, gridExtra, methods, reshape2, MAST, S4Vectors, data.table, Rcpp, RcppEigen, uwot, enrichR, stringi, SummarizedExperiment, MCMCprecision, ggrepel, Rtsne, withr, dendextend, ggdendro, pROC, magrittr LinkingTo: Rcpp, RcppEigen Suggests: testthat, knitr, roxygen2, rmarkdown, corrplot, Matrix, biomaRt, covr, M3DExampleData, BiocManager, BiocStyle License: MIT + file LICENSE MD5sum: 9271b458009d8f6d7c428ebfa499a761 NeedsCompilation: yes Title: CEllular Latent Dirichlet Allocation Description: celda is a Bayesian hierarchical model that can co-cluster features and cells in single cell sequencing data. biocViews: SingleCell, GeneExpression, Clustering, Sequencing, Bayesian Author: Joshua Campbell [aut, cre], Sean Corbett [aut], Yusuke Koga [aut], Shiyi Yang [aut], Eric Reed [aut], Zhe Wang [aut] Maintainer: Joshua Campbell VignetteBuilder: knitr BugReports: https://github.com/campbio/celda/issues git_url: https://git.bioconductor.org/packages/celda git_branch: RELEASE_3_10 git_last_commit: d12778d git_last_commit_date: 2020-01-21 Date/Publication: 2020-01-22 source.ver: src/contrib/celda_1.2.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/celda_1.2.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/celda_1.2.4.tgz vignettes: vignettes/celda/inst/doc/celda-analysis.html, vignettes/celda/inst/doc/DecontX-analysis.html, vignettes/celda/inst/doc/FindMarkers-analysis.html vignetteTitles: Analyzing single-cell RNA-seq count data with celda, Estimate and remove cross-contamination from ambient RNA for scRNA-seq data with DecontX, Sorting and annotation of single-cell clustering results with multiclass decision trees hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/celda/inst/doc/celda-analysis.R, vignettes/celda/inst/doc/DecontX-analysis.R, vignettes/celda/inst/doc/FindMarkers-analysis.R importsMe: singleCellTK dependencyCount: 120 Package: cellbaseR Version: 1.10.0 Depends: R(>= 3.4) Imports: methods, jsonlite, httr, data.table, pbapply, tidyr, R.utils, Rsamtools, BiocParallel, foreach, utils, parallel, doParallel Suggests: BiocStyle, knitr, rmarkdown, Gviz, VariantAnnotation License: Apache License (== 2.0) Archs: i386, x64 MD5sum: ab48f4df2f1034702a08ee43e6fcdf19 NeedsCompilation: no Title: Querying annotation data from the high performance Cellbase web Description: This R package makes use of the exhaustive RESTful Web service API that has been implemented for the Cellabase database. It enable researchers to query and obtain a wealth of biological information from a single database saving a lot of time. Another benefit is that researchers can easily make queries about different biological topics and link all this information together as all information is integrated. biocViews: Annotation, VariantAnnotation Author: Mohammed OE Abdallah Maintainer: Mohammed OE Abdallah URL: https://github.com/melsiddieg/cellbaseR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellbaseR git_branch: RELEASE_3_10 git_last_commit: 3cf038b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cellbaseR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cellbaseR_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cellbaseR_1.10.0.tgz vignettes: vignettes/cellbaseR/inst/doc/cellbaseR.html vignetteTitles: "Simplifying Genomic Annotations in R" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellbaseR/inst/doc/cellbaseR.R dependencyCount: 67 Package: CellBench Version: 1.2.0 Depends: R (>= 3.6), SingleCellExperiment, magrittr, methods, stats, tibble, utils Imports: BiocFileCache, BiocParallel, dplyr, rlang, glue, memoise, purrr (>= 0.3.0), rappdirs, tidyr, lubridate Suggests: BiocStyle, covr, knitr, rmarkdown, testthat, limma, ggplot2 License: GPL-3 MD5sum: cf719ea3c2c7363a56abcecc8672783a NeedsCompilation: no Title: Construct Benchmarks for Single Cell Analysis Methods Description: This package contains infrastructure for benchmarking analysis methods and access to single cell mixture benchmarking data. It provides a framework for organising analysis methods and testing combinations of methods in a pipeline without explicitly laying out each combination. It also provides utilities for sampling and filtering SingleCellExperiment objects, constructing lists of functions with varying parameters, and multithreaded evaluation of analysis methods. biocViews: Software, Infrastructure Author: Shian Su [cre, aut], Saskia Freytag [aut], Luyi Tian [aut], Xueyi Dong [aut], Matthew Ritchie [aut], Peter Hickey [ctb], Stuart Lee [ctb] Maintainer: Shian Su URL: https://github.com/shians/cellbench VignetteBuilder: knitr BugReports: https://github.com/Shians/CellBench/issues git_url: https://git.bioconductor.org/packages/CellBench git_branch: RELEASE_3_10 git_last_commit: c725e05 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CellBench_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CellBench_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CellBench_1.2.0.tgz vignettes: vignettes/CellBench/inst/doc/DataManipulation.pdf, vignettes/CellBench/inst/doc/TidyversePatterns.pdf, vignettes/CellBench/inst/doc/Introduction.html, vignettes/CellBench/inst/doc/Timing.html, vignettes/CellBench/inst/doc/WritingWrappers.html vignetteTitles: Data Manipulation, Tidyverse Patterns, Introduction, Timing, Writing Wrappers hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellBench/inst/doc/DataManipulation.R, vignettes/CellBench/inst/doc/Introduction.R, vignettes/CellBench/inst/doc/TidyversePatterns.R, vignettes/CellBench/inst/doc/Timing.R, vignettes/CellBench/inst/doc/WritingWrappers.R dependencyCount: 74 Package: cellGrowth Version: 1.30.0 Depends: R (>= 2.12.0), locfit (>= 1.5-4) Imports: lattice License: Artistic-2.0 MD5sum: 2722643809b0b29a45a0ae31846dd2c6 NeedsCompilation: no Title: Fitting cell population growth models Description: This package provides functionalities for the fitting of cell population growth models on experimental OD curves. biocViews: ImmunoOncology, CellBasedAssays, MicrotitrePlateAssay, DataImport, Visualization, TimeCourse Author: Julien Gagneur , Andreas Neudecker Maintainer: Julien Gagneur git_url: https://git.bioconductor.org/packages/cellGrowth git_branch: RELEASE_3_10 git_last_commit: 8775667 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cellGrowth_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cellGrowth_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cellGrowth_1.30.0.tgz vignettes: vignettes/cellGrowth/inst/doc/cellGrowth.pdf vignetteTitles: Overview of the cellGrowth package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellGrowth/inst/doc/cellGrowth.R dependencyCount: 7 Package: cellHTS2 Version: 2.50.0 Depends: R (>= 2.10), RColorBrewer, Biobase, methods, genefilter, splots, vsn, hwriter, locfit, grid Imports: prada, GSEABase, Category, stats4, BiocGenerics Suggests: ggplot2 License: Artistic-2.0 MD5sum: b0478e85c23db7123113f27f76395b41 NeedsCompilation: no Title: Analysis of cell-based screens - revised version of cellHTS Description: This package provides tools for the analysis of high-throughput assays that were performed in microtitre plate formats (including but not limited to 384-well plates). The functionality includes data import and management, normalisation, quality assessment, replicate summarisation and statistical scoring. A webpage that provides a detailed graphical overview over the data and analysis results is produced. In our work, we have applied the package to RNAi screens on fly and human cells, and for screens of yeast libraries. See ?cellHTS2 for a brief introduction. biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, Visualization Author: Ligia Bras, Wolfgang Huber , Michael Boutros , Gregoire Pau , Florian Hahne Maintainer: Joseph Barry URL: http://www.dkfz.de/signaling, http://www.ebi.ac.uk/huber git_url: https://git.bioconductor.org/packages/cellHTS2 git_branch: RELEASE_3_10 git_last_commit: 05030fc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cellHTS2_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cellHTS2_2.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cellHTS2_2.50.0.tgz vignettes: vignettes/cellHTS2/inst/doc/cellhts2.pdf, vignettes/cellHTS2/inst/doc/cellhts2Complete.pdf, vignettes/cellHTS2/inst/doc/twoChannels.pdf, vignettes/cellHTS2/inst/doc/twoWay.pdf vignetteTitles: Main vignette: End-to-end analysis of cell-based screens, Main vignette (complete version): End-to-end analysis of cell-based screens, Supplement: multi-channel assays, Supplement: enhancer-suppressor screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellHTS2/inst/doc/cellhts2.R, vignettes/cellHTS2/inst/doc/cellhts2Complete.R, vignettes/cellHTS2/inst/doc/twoChannels.R, vignettes/cellHTS2/inst/doc/twoWay.R dependsOnMe: imageHTS, staRank importsMe: gespeR, HTSanalyzeR, RNAinteract suggestsMe: bioassayR, prada dependencyCount: 96 Package: cellity Version: 1.14.0 Depends: R (>= 3.3) Imports: AnnotationDbi, e1071, ggplot2, graphics, grDevices, grid, mvoutlier, org.Hs.eg.db, org.Mm.eg.db, robustbase, stats, topGO, utils Suggests: BiocStyle, caret, knitr, testthat, rmarkdown License: GPL (>= 2) MD5sum: 9bfbf972a360bfc8364ef4ccf78b7248 NeedsCompilation: no Title: Quality Control for Single-Cell RNA-seq Data Description: A support vector machine approach to identifying and filtering low quality cells from single-cell RNA-seq datasets. biocViews: ImmunoOncology, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, SupportVectorMachine Author: Tomislav Illicic, Davis McCarthy Maintainer: Tomislav Ilicic VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellity git_branch: RELEASE_3_10 git_last_commit: cf77bd9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cellity_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cellity_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cellity_1.14.0.tgz vignettes: vignettes/cellity/inst/doc/cellity_vignette.html vignetteTitles: An introduction to the cellity package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellity/inst/doc/cellity_vignette.R dependencyCount: 147 Package: CellMapper Version: 1.12.0 Depends: S4Vectors, methods Imports: stats, utils Suggests: CellMapperData, Biobase, HumanAffyData, ALL, BiocStyle, ExperimentHub License: Artistic-2.0 MD5sum: 12e8cfde7c5019876336d683aa307fba NeedsCompilation: no Title: Predict genes expressed selectively in specific cell types Description: Infers cell type-specific expression based on co-expression similarity with known cell type marker genes. Can make accurate predictions using publicly available expression data, even when a cell type has not been isolated before. biocViews: Microarray, Software, GeneExpression Author: Brad Nelms Maintainer: Brad Nelms git_url: https://git.bioconductor.org/packages/CellMapper git_branch: RELEASE_3_10 git_last_commit: ee2dbf5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CellMapper_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CellMapper_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CellMapper_1.12.0.tgz vignettes: vignettes/CellMapper/inst/doc/CellMapper.pdf vignetteTitles: CellMapper Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellMapper/inst/doc/CellMapper.R dependencyCount: 8 Package: CellMixS Version: 1.2.6 Depends: kSamples, R (>= 3.6) Imports: BiocNeighbors, ggplot2, scater, viridis, cowplot, SummarizedExperiment, SingleCellExperiment, tidyr, magrittr, dplyr, ggridges, stats, purrr, listarrays, methods, BiocParallel, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown, testthat, limma License: GPL (>=2) MD5sum: 3187de721abd7efca5ace8a3f3c32aac NeedsCompilation: no Title: Evaluate Cellspecific Mixing Description: Evaluate Cellspecific Mixing Scores (CMS) for different batches/groups in scRNA-seq data. biocViews: SingleCell, Transcriptomics, GeneExpression, BatchEffect Author: Almut Lütge Maintainer: Almut Lütge URL: https://github.com/almutlue/CellMixS VignetteBuilder: knitr BugReports: https://github.com/almutlue/CellMixS/issues git_url: https://git.bioconductor.org/packages/CellMixS git_branch: RELEASE_3_10 git_last_commit: 146a532 git_last_commit_date: 2020-04-09 Date/Publication: 2020-04-09 source.ver: src/contrib/CellMixS_1.2.6.tar.gz win.binary.ver: bin/windows/contrib/3.6/CellMixS_1.2.6.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CellMixS_1.2.6.tgz vignettes: vignettes/CellMixS/inst/doc/CellMixS.html vignetteTitles: Explore data integration and batch effects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellMixS/inst/doc/CellMixS.R dependencyCount: 107 Package: CellNOptR Version: 1.32.0 Depends: R (>= 2.15.0), RBGL, graph, methods, hash, RCurl, Rgraphviz, XML, ggplot2 Imports: igraph, stringi, stringr, Suggests: data.table, plyr, dplyr, tidyr, readr, RUnit, BiocGenerics, License: GPL-3 MD5sum: ad42ee74aa650594b789070ceee85006 NeedsCompilation: yes Title: Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data Description: This package does optimisation of boolean logic networks of signalling pathways based on a previous knowledge network and a set of data upon perturbation of the nodes in the network. biocViews: CellBasedAssays, CellBiology, Proteomics, Bioinformatics, TimeCourse, ImmunoOncology Author: T.Cokelaer, F.Eduati, A.MacNamara, S.Schrier, C.Terfve Maintainer: A.Gabor SystemRequirements: Graphviz version >= 2.2 git_url: https://git.bioconductor.org/packages/CellNOptR git_branch: RELEASE_3_10 git_last_commit: 44da697 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CellNOptR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CellNOptR_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CellNOptR_1.32.0.tgz vignettes: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.pdf, vignettes/CellNOptR/inst/doc/CellNOptR0_1flowchart.pdf, vignettes/CellNOptR/inst/doc/Fig2.pdf, vignettes/CellNOptR/inst/doc/Fig3.pdf, vignettes/CellNOptR/inst/doc/Fig4.pdf, vignettes/CellNOptR/inst/doc/Fig6.pdf, vignettes/CellNOptR/inst/doc/Fig7.pdf, vignettes/CellNOptR/inst/doc/Fig8.pdf vignetteTitles: Main vignette:Playing with networks using CellNOptR, CellNOptR0_1flowchart.pdf, Fig2.pdf, Fig3.pdf, Fig4.pdf, Fig6.pdf, Fig7.pdf, Fig8.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellNOptR/inst/doc/CellNOptR-examples.R, vignettes/CellNOptR/inst/doc/CellNOptR-vignette.R dependsOnMe: CNORdt, CNORfeeder, CNORfuzzy, CNORode suggestsMe: MEIGOR dependencyCount: 68 Package: cellscape Version: 1.10.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), reshape2 (>= 1.4.1), stringr (>= 1.0.0), plyr (>= 1.8.3), dplyr (>= 0.4.3), gtools (>= 3.5.0) Suggests: knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 55d2952901405f4581ac4967961fe459 NeedsCompilation: no Title: Explores single cell copy number profiles in the context of a single cell tree Description: CellScape facilitates interactive browsing of single cell clonal evolution datasets. The tool requires two main inputs: (i) the genomic content of each single cell in the form of either copy number segments or targeted mutation values, and (ii) a single cell phylogeny. Phylogenetic formats can vary from dendrogram-like phylogenies with leaf nodes to evolutionary model-derived phylogenies with observed or latent internal nodes. The CellScape phylogeny is flexibly input as a table of source-target edges to support arbitrary representations, where each node may or may not have associated genomic data. The output of CellScape is an interactive interface displaying a single cell phylogeny and a cell-by-locus genomic heatmap representing the mutation status in each cell for each locus. biocViews: Visualization Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellscape git_branch: RELEASE_3_10 git_last_commit: acf3bd2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cellscape_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cellscape_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cellscape_1.10.0.tgz vignettes: vignettes/cellscape/inst/doc/cellscape_vignette.html vignetteTitles: CellScape vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellscape/inst/doc/cellscape_vignette.R dependencyCount: 36 Package: CellScore Version: 1.6.0 Depends: R (>= 3.5.0) Imports: Biobase (>= 2.39.1), graphics (>= 3.5.0), grDevices (>= 3.5.0), gplots (>= 3.0.1), lsa (>= 0.73.1), methods (>= 3.5.0), RColorBrewer(>= 1.1-2), squash (>= 1.0.8), stats (>= 3.5.0), utils(>= 3.5.0) Suggests: hgu133plus2CellScore, knitr License: GPL-3 MD5sum: 1f206cf2de9c67d98dbad6e003e00a80 NeedsCompilation: no Title: Tool for Evaluation of Cell Identity from Transcription Profiles Description: The CellScore package contains functions to evaluate the cell identity of a test sample, given a cell transition defined with a starting (donor) cell type and a desired target cell type. The evaluation is based upon a scoring system, which uses a set of standard samples of known cell types, as the reference set. The functions have been carried out on a large set of microarray data from one platform (Affymetrix Human Genome U133 Plus 2.0). In principle, the method could be applied to any expression dataset, provided that there are a sufficient number of standard samples and that the data are normalized. biocViews: GeneExpression, Transcription, Microarray, MultipleComparison, ReportWriting, DataImport, Visualization Author: Nancy Mah, Katerina Taskova Maintainer: Nancy Mah VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellScore git_branch: RELEASE_3_10 git_last_commit: e3ee348 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CellScore_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CellScore_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CellScore_1.6.0.tgz vignettes: vignettes/CellScore/inst/doc/CellScoreVignette.pdf vignetteTitles: R packages: CellScore hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellScore/inst/doc/CellScoreVignette.R dependencyCount: 18 Package: CellTrails Version: 1.4.0 Depends: R (>= 3.5), SingleCellExperiment Imports: BiocGenerics, Biobase, cba, dendextend, dtw, EnvStats, ggplot2, ggrepel, grDevices, igraph, maptree, methods, mgcv, reshape2, Rtsne, stats, splines, SummarizedExperiment, utils Suggests: AnnotationDbi, destiny, RUnit, scater, scran, knitr, org.Mm.eg.db, rmarkdown License: Artistic-2.0 MD5sum: a8c20e5011c4ea80f210020fae7cca38 NeedsCompilation: no Title: Reconstruction, visualization and analysis of branching trajectories Description: CellTrails is an unsupervised algorithm for the de novo chronological ordering, visualization and analysis of single-cell expression data. CellTrails makes use of a geometrically motivated concept of lower-dimensional manifold learning, which exhibits a multitude of virtues that counteract intrinsic noise of single cell data caused by drop-outs, technical variance, and redundancy of predictive variables. CellTrails enables the reconstruction of branching trajectories and provides an intuitive graphical representation of expression patterns along all branches simultaneously. It allows the user to define and infer the expression dynamics of individual and multiple pathways towards distinct phenotypes. biocViews: ImmunoOncology, Clustering, DataRepresentation, DifferentialExpression, DimensionReduction, GeneExpression, Sequencing, SingleCell, Software, TimeCourse Author: Daniel Ellwanger [aut, cre, cph] Maintainer: Daniel Ellwanger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellTrails git_branch: RELEASE_3_10 git_last_commit: 71d24e0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CellTrails_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CellTrails_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CellTrails_1.4.0.tgz vignettes: vignettes/CellTrails/inst/doc/vignette.pdf vignetteTitles: CellTrails: Reconstruction,, visualization,, and analysis of branching trajectories from single-cell expression data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellTrails/inst/doc/vignette.R dependencyCount: 97 Package: cellTree Version: 1.16.0 Depends: R (>= 3.3), topGO Imports: topicmodels, slam, maptpx, igraph, xtable, gplots Suggests: BiocStyle, knitr, HSMMSingleCell, biomaRt, org.Hs.eg.db, Biobase, tools License: Artistic-2.0 MD5sum: 51ef04461c053d6b47113e8760a8e132 NeedsCompilation: no Title: Inference and visualisation of Single-Cell RNA-seq data as a hierarchical tree structure Description: This packages computes a Latent Dirichlet Allocation (LDA) model of single-cell RNA-seq data and builds a compact tree modelling the relationship between individual cells over time or space. biocViews: ImmunoOncology, Sequencing, RNASeq, Clustering, GraphAndNetwork, Visualization, GeneExpression, GeneSetEnrichment, BiomedicalInformatics, CellBiology, FunctionalGenomics, SystemsBiology, GO, TimeCourse, Microarray Author: David duVerle [aut, cre], Koji Tsuda [aut] Maintainer: David duVerle URL: http://tsudalab.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellTree git_branch: RELEASE_3_10 git_last_commit: 180a4a2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cellTree_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cellTree_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cellTree_1.16.0.tgz vignettes: vignettes/cellTree/inst/doc/cellTree-vignette.pdf vignetteTitles: Inference and visualisation of Single-Cell RNA-seq Data data as a hierarchical tree structure hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellTree/inst/doc/cellTree-vignette.R dependencyCount: 52 Package: CEMiTool Version: 1.10.2 Depends: R (>= 3.6) Imports: methods, scales, dplyr, data.table (>= 1.9.4), WGCNA, grid, ggplot2, ggpmisc, ggthemes, ggrepel, sna, clusterProfiler, fgsea, stringr, knitr, rmarkdown, igraph, DT, htmltools, pracma, intergraph, grDevices, utils, network, matrixStats, ggdendro, gridExtra, gtable Suggests: testthat, BiocManager License: GPL-3 MD5sum: f3b3aeb298a3fc7010390904b4fa95b1 NeedsCompilation: no Title: Co-expression Modules identification Tool Description: The CEMiTool package unifies the discovery and the analysis of coexpression gene modules in a fully automatic manner, while providing a user-friendly html report with high quality graphs. Our tool evaluates if modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group. Additionally, CEMiTool is able to integrate transcriptomic data with interactome information, identifying the potential hubs on each network. biocViews: GeneExpression, Transcriptomics, GraphAndNetwork, mRNAMicroarray, RNASeq, Network, NetworkEnrichment, Pathways, ImmunoOncology Author: Pedro Russo [aut], Gustavo Ferreira [aut], Matheus Bürger [aut], Lucas Cardozo [aut], Diogenes Lima [aut], Thiago Hirata [aut], Melissa Lever [aut], Helder Nakaya [aut, cre] Maintainer: Helder Nakaya VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CEMiTool git_branch: RELEASE_3_10 git_last_commit: b2830d1 git_last_commit_date: 2020-03-27 Date/Publication: 2020-03-27 source.ver: src/contrib/CEMiTool_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/CEMiTool_1.10.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CEMiTool_1.10.2.tgz vignettes: vignettes/CEMiTool/inst/doc/CEMiTool.html vignetteTitles: CEMiTool: Co-expression Modules Identification Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CEMiTool/inst/doc/CEMiTool.R dependencyCount: 178 Package: CexoR Version: 1.24.0 Depends: R (>= 2.10.0), S4Vectors, IRanges Imports: Rsamtools, GenomeInfoDb, GenomicRanges, rtracklayer, idr, RColorBrewer, genomation Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 | GPL-2 + file LICENSE MD5sum: bcbb2ac38e4566e531f304957ccad35f NeedsCompilation: no Title: An R package to uncover high-resolution protein-DNA interactions in ChIP-exo replicates Description: Strand specific peak-pair calling in ChIP-exo replicates. The cumulative Skellam distribution function (package 'skellam') is used to detect significant normalised count differences of opposed sign at each DNA strand (peak-pairs). Irreproducible discovery rate for overlapping peak-pairs across biological replicates is estimated using the package 'idr'. biocViews: Transcription, Genetics, Sequencing Author: Pedro Madrigal Maintainer: Pedro Madrigal git_url: https://git.bioconductor.org/packages/CexoR git_branch: RELEASE_3_10 git_last_commit: 9749fa7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CexoR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CexoR_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CexoR_1.24.0.tgz vignettes: vignettes/CexoR/inst/doc/CexoR.pdf vignetteTitles: CexoR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CexoR/inst/doc/CexoR.R dependencyCount: 100 Package: CFAssay Version: 1.20.0 Depends: R (>= 2.10.0) License: LGPL MD5sum: b79730851b55a55ec908fd4119067232 NeedsCompilation: no Title: Statistical analysis for the Colony Formation Assay Description: The package provides functions for calculation of linear-quadratic cell survival curves and for ANOVA of experimental 2-way designs along with the colony formation assay. biocViews: CellBasedAssays, CellBiology, ImmunoOncology, Regression, Survival Author: Herbert Braselmann Maintainer: Herbert Braselmann git_url: https://git.bioconductor.org/packages/CFAssay git_branch: RELEASE_3_10 git_last_commit: c8d98fc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CFAssay_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CFAssay_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CFAssay_1.20.0.tgz vignettes: vignettes/CFAssay/inst/doc/cfassay.pdf vignetteTitles: CFAssay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CFAssay/inst/doc/cfassay.R dependencyCount: 0 Package: CGEN Version: 3.22.0 Depends: R (>= 2.10.1), survival, mvtnorm Suggests: cluster License: GPL-2 + file LICENSE MD5sum: 721454cdb615f3175f89e2abdd6a8524 NeedsCompilation: yes Title: An R package for analysis of case-control studies in genetic epidemiology Description: An R package for analysis of case-control studies in genetic epidemiology. biocViews: SNP, MultipleComparisons, Clustering Author: Samsiddhi Bhattacharjee, Nilanjan Chatterjee, Summer Han, Minsun Song and William Wheeler Maintainer: William Wheeler git_url: https://git.bioconductor.org/packages/CGEN git_branch: RELEASE_3_10 git_last_commit: 4975526 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CGEN_3.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CGEN_3.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CGEN_3.22.0.tgz vignettes: vignettes/CGEN/inst/doc/vignette_GxE.pdf, vignettes/CGEN/inst/doc/vignette.pdf vignetteTitles: CGEN Scan Vignette, CGEN Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CGEN/inst/doc/vignette_GxE.R, vignettes/CGEN/inst/doc/vignette.R dependencyCount: 11 Package: CGHbase Version: 1.46.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), marray License: GPL Archs: i386, x64 MD5sum: f9b81419cd3190e65b251c0ea6f27915 NeedsCompilation: no Title: CGHbase: Base functions and classes for arrayCGH data analysis. Description: Contains functions and classes that are needed by arrayCGH packages. biocViews: Infrastructure, Microarray, CopyNumberVariation Author: Sjoerd Vosse, Mark van de Wiel Maintainer: Mark van de Wiel URL: https://github.com/tgac-vumc/CGHbase BugReports: https://github.com/tgac-vumc/CGHbase/issues git_url: https://git.bioconductor.org/packages/CGHbase git_branch: RELEASE_3_10 git_last_commit: 0a0ad49 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CGHbase_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CGHbase_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CGHbase_1.46.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, CGHnormaliter, CGHregions, GeneBreak, sigaR importsMe: CGHnormaliter, plrs, QDNAseq dependencyCount: 10 Package: CGHcall Version: 2.48.0 Depends: R (>= 2.0.0), impute(>= 1.8.0), DNAcopy (>= 1.6.0), methods, Biobase, CGHbase (>= 1.15.1), snowfall License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: a30897dc841ecd97b4e77994f4fa8e8d NeedsCompilation: no Title: Calling aberrations for array CGH tumor profiles. Description: Calls aberrations for array CGH data using a six state mixture model as well as several biological concepts that are ignored by existing algorithms. Visualization of profiles is also provided. biocViews: Microarray,Preprocessing,Visualization Author: Mark van de Wiel, Sjoerd Vosse Maintainer: Mark van de Wiel git_url: https://git.bioconductor.org/packages/CGHcall git_branch: RELEASE_3_10 git_last_commit: fbfc366 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CGHcall_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CGHcall_2.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CGHcall_2.48.0.tgz vignettes: vignettes/CGHcall/inst/doc/CGHcall.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHcall/inst/doc/CGHcall.R dependsOnMe: CGHnormaliter, focalCall, GeneBreak importsMe: CGHnormaliter, QDNAseq suggestsMe: sigaR dependencyCount: 15 Package: cghMCR Version: 1.44.0 Depends: methods, DNAcopy, CNTools, limma Imports: BiocGenerics (>= 0.1.6), stats4 License: LGPL MD5sum: e4c48ba4c17fe9cb6229565bca6ed321 NeedsCompilation: no Title: Find chromosome regions showing common gains/losses Description: This package provides functions to identify genomic regions of interests based on segmented copy number data from multiple samples. biocViews: Microarray, CopyNumberVariation Author: J. Zhang and B. Feng Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/cghMCR git_branch: RELEASE_3_10 git_last_commit: 9d1306e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cghMCR_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cghMCR_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cghMCR_1.44.0.tgz vignettes: vignettes/cghMCR/inst/doc/findMCR.pdf vignetteTitles: cghMCR findMCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cghMCR/inst/doc/findMCR.R dependencyCount: 42 Package: CGHnormaliter Version: 1.40.0 Depends: CGHcall (>= 2.17.0), CGHbase (>= 1.15.0) Imports: Biobase, CGHbase, CGHcall, methods, stats, utils License: GPL (>= 3) MD5sum: ebe1d671afd4055e0695cd431de4b17c NeedsCompilation: no Title: Normalization of array CGH data with imbalanced aberrations. Description: Normalization and centralization of array comparative genomic hybridization (aCGH) data. The algorithm uses an iterative procedure that effectively eliminates the influence of imbalanced copy numbers. This leads to a more reliable assessment of copy number alterations (CNAs). biocViews: Microarray, Preprocessing Author: Bart P.P. van Houte, Thomas W. Binsl, Hannes Hettling Maintainer: Bart P.P. van Houte git_url: https://git.bioconductor.org/packages/CGHnormaliter git_branch: RELEASE_3_10 git_last_commit: 7f45642 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CGHnormaliter_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CGHnormaliter_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CGHnormaliter_1.40.0.tgz vignettes: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.pdf vignetteTitles: CGHnormaliter hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.R dependencyCount: 16 Package: CGHregions Version: 1.44.0 Depends: R (>= 2.0.0), methods, Biobase, CGHbase License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: 0ea40733756f1325e1470128b103fce8 NeedsCompilation: no Title: Dimension Reduction for Array CGH Data with Minimal Information Loss. Description: Dimension Reduction for Array CGH Data with Minimal Information Loss biocViews: Microarray, CopyNumberVariation, Visualization Author: Sjoerd Vosse & Mark van de Wiel Maintainer: Sjoerd Vosse git_url: https://git.bioconductor.org/packages/CGHregions git_branch: RELEASE_3_10 git_last_commit: 6319eb8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CGHregions_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CGHregions_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CGHregions_1.44.0.tgz vignettes: vignettes/CGHregions/inst/doc/CGHregions.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHregions/inst/doc/CGHregions.R suggestsMe: ADaCGH2 dependencyCount: 11 Package: ChAMP Version: 2.16.2 Depends: R (>= 3.3), minfi, ChAMPdata (>= 2.6.0), FEM (>= 3.1),DMRcate, Illumina450ProbeVariants.db,IlluminaHumanMethylationEPICmanifest, DT Imports: prettydoc,Hmisc,globaltest,sva,illuminaio,rmarkdown,IlluminaHumanMethylation450kmanifest,IlluminaHumanMethylationEPICanno.ilm10b2.hg19, limma,RPMM, DNAcopy, preprocessCore,impute, marray, wateRmelon, plyr,goseq,missMethyl,kpmt,ggplot2, GenomicRanges,qvalue,isva,doParallel,bumphunter,quadprog,shiny,shinythemes,plotly (>= 4.5.6),RColorBrewer,dendextend, matrixStats,combinat Suggests: knitr,rmarkdown License: GPL-3 MD5sum: a9c3a19488858f8dfde5f703ace2d7b0 NeedsCompilation: no Title: Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 and EPIC Description: The package includes quality control metrics, a selection of normalization methods and novel methods to identify differentially methylated regions and to highlight copy number alterations. biocViews: Microarray, MethylationArray, Normalization, TwoChannel, CopyNumber, DNAMethylation Author: Yuan Tian [cre,aut], Tiffany Morris [ctb], Lee Stirling [ctb], Andrew Feber [ctb], Andrew Teschendorff [ctb], Ankur Chakravarthy [ctb] Maintainer: Yuan Tian VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChAMP git_branch: RELEASE_3_10 git_last_commit: b8b9ff8 git_last_commit_date: 2020-04-12 Date/Publication: 2020-04-12 source.ver: src/contrib/ChAMP_2.16.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/ChAMP_2.16.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ChAMP_2.16.2.tgz vignettes: vignettes/ChAMP/inst/doc/ChAMP.html vignetteTitles: ChAMP: The Chip Analysis Methylation Pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChAMP/inst/doc/ChAMP.R dependencyCount: 251 Package: CHARGE Version: 1.6.0 Depends: R (>= 3.5), GenomicRanges, methods Imports: SummarizedExperiment, FactoMineR, factoextra, IRanges, graphics, modes, parallel, plyr, cluster, diptest, stats, matrixStats Suggests: roxygen2, EnsDb.Hsapiens.v86 License: GPL-2 MD5sum: 532412017f984f9655653a1182cf616e NeedsCompilation: no Title: CHARGE: CHromosome Assessment in R from Gene Expression data Description: Identifies genomic duplications or deletions from gene expression data. biocViews: GeneExpression, Clustering Author: Benjamin Mayne Maintainer: Benjamin Mayne git_url: https://git.bioconductor.org/packages/CHARGE git_branch: RELEASE_3_10 git_last_commit: 0d8eb0f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CHARGE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CHARGE_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CHARGE_1.6.0.tgz vignettes: vignettes/CHARGE/inst/doc/CHARGE_Vignette.pdf vignetteTitles: CHARGE_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CHARGE/inst/doc/CHARGE_Vignette.R dependencyCount: 136 Package: ChemmineOB Version: 1.24.0 Depends: R (>= 2.15.1), methods Imports: BiocGenerics, zlibbioc, Rcpp (>= 0.11.0) LinkingTo: BH, Rcpp Suggests: ChemmineR, BiocStyle, knitr, knitcitations, knitrBootstrap, BiocManager Enhances: ChemmineR (>= 2.13.0) License: file LICENSE MD5sum: 7df66062e96ac419d02fd866160c8848 NeedsCompilation: yes Title: R interface to a subset of OpenBabel functionalities Description: ChemmineOB provides an R interface to a subset of cheminformatics functionalities implemented by the OpelBabel C++ project. OpenBabel is an open source cheminformatics toolbox that includes utilities for structure format interconversions, descriptor calculations, compound similarity searching and more. ChemineOB aims to make a subset of these utilities available from within R. For non-developers, ChemineOB is primarily intended to be used from ChemmineR as an add-on package rather than used directly. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineOB SystemRequirements: OpenBabel (>= 2.3.1) with headers (http://openbabel.org). VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChemmineOB git_branch: RELEASE_3_10 git_last_commit: cd2f82f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ChemmineOB_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ChemmineOB_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ChemmineOB_1.24.0.tgz vignettes: vignettes/ChemmineOB/inst/doc/ChemmineOB.html vignetteTitles: ChemmineOB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/ChemmineOB/inst/doc/ChemmineOB.R suggestsMe: ChemmineR dependencyCount: 9 Package: ChemmineR Version: 3.38.0 Depends: R (>= 2.10.0), methods Imports: rjson, graphics, stats, RCurl, DBI, digest, BiocGenerics, Rcpp (>= 0.11.0), ggplot2,grid,gridExtra, png,base64enc,DT,rsvg LinkingTo: Rcpp, BH Suggests: RSQLite, scatterplot3d, gplots, fmcsR, snow, RPostgreSQL, BiocStyle, knitr, knitcitations, knitrBootstrap, ChemmineOB (>= 1.16.1), ChemmineDrugs, png,rmarkdown, BiocManager Enhances: ChemmineOB License: Artistic-2.0 Archs: i386, x64 MD5sum: d8edea9845cd6d0a97f8a5e3627d6ba4 NeedsCompilation: yes Title: Cheminformatics Toolkit for R Description: ChemmineR is a cheminformatics package for analyzing drug-like small molecule data in R. Its latest version contains functions for efficient processing of large numbers of molecules, physicochemical/structural property predictions, structural similarity searching, classification and clustering of compound libraries with a wide spectrum of algorithms. In addition, it offers visualization functions for compound clustering results and chemical structures. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics,Metabolomics Author: Y. Eddie Cao, Kevin Horan, Tyler Backman, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineR SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChemmineR git_branch: RELEASE_3_10 git_last_commit: 1cbe310 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ChemmineR_3.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ChemmineR_3.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ChemmineR_3.38.0.tgz vignettes: vignettes/ChemmineR/inst/doc/ChemmineR.html vignetteTitles: ChemmineR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChemmineR/inst/doc/ChemmineR.R dependsOnMe: eiR, fmcsR importsMe: bioassayR, eiR, fmcsR, MetID, Rcpi, Rchemcpp suggestsMe: ChemmineOB dependencyCount: 74 Package: CHETAH Version: 1.2.0 Depends: R (>= 3.6), ggplot2, SingleCellExperiment Imports: gplots, shiny, plotly, pheatmap, bioDist, dendextend, cowplot, corrplot, grDevices, stats, graphics, reshape2, S4Vectors, SummarizedExperiment Suggests: knitr, rmarkdown, Matrix, testthat, vdiffr License: file LICENSE Archs: i386, x64 MD5sum: c632f374b008d57643952c0f12505259 NeedsCompilation: no Title: Fast and accurate scRNA-seq cell type identification Description: CHETAH (CHaracterization of cEll Types Aided by Hierarchical classification) is an accurate, selective and fast scRNA-seq classifier. Classification is guided by a reference dataset, preferentially also a scRNA-seq dataset. By hierarchical clustering of the reference data, CHETAH creates a classification tree that enables a step-wise, top-to-bottom classification. Using a novel stopping rule, CHETAH classifies the input cells to the cell types of the references and to "intermediate types": more general classifications that ended in an intermediate node of the tree. biocViews: Classification, RNASeq, SingleCell, Clustering Author: Jurrian de Kanter [aut, cre], Philip Lijnzaad [aut] Maintainer: Jurrian de Kanter URL: https://github.com/jdekanter/CHETAH VignetteBuilder: knitr BugReports: https://github.com/jdekanter/CHETAH git_url: https://git.bioconductor.org/packages/CHETAH git_branch: RELEASE_3_10 git_last_commit: 063de2d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CHETAH_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CHETAH_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CHETAH_1.2.0.tgz vignettes: vignettes/CHETAH/inst/doc/CHETAH_introduction.html vignetteTitles: Introduction to the CHETAH package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CHETAH/inst/doc/CHETAH_introduction.R dependencyCount: 123 Package: ChIC Version: 1.6.0 Depends: spp, R (>= 3.6) Imports: ChIC.data (>= 1.3.3), caTools, methods,GenomicRanges, IRanges, parallel, progress, caret, grDevices, stats, utils, graphics, S4Vectors, BiocGenerics License: GPL-2 MD5sum: cf55133f5d7d1f4949280ed6a9aca278 NeedsCompilation: no Title: Quality Control Pipeline for ChIP-Seq Data Description: Quality control (QC) pipeline for ChIP-seq data using a comprehensive set of QC metrics, including previously proposed metrics as well as novel ones, based on local characteristics of the enrichment profile. The package provides functions to calculate a set of QC metrics, a compendium with reference values and machine learning models to score sample quality. biocViews: ChIPSeq, QualityControl Author: Carmen Maria Livi Maintainer: Carmen Maria Livi git_url: https://git.bioconductor.org/packages/ChIC git_branch: RELEASE_3_10 git_last_commit: 209fad0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ChIC_1.6.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ChIC_1.6.0.tgz vignettes: vignettes/ChIC/inst/doc/ChIC-Vignette.pdf vignetteTitles: ChIC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIC/inst/doc/ChIC-Vignette.R dependencyCount: 112 Package: Chicago Version: 1.14.0 Depends: R (>= 3.2), data.table Imports: matrixStats, MASS, Hmisc, Delaporte, methods, grDevices, graphics, stats, utils Suggests: argparser, BiocStyle, knitr, rmarkdown, PCHiCdata, testthat, Rsamtools, GenomicInteractions, GenomicRanges, IRanges, AnnotationHub License: Artistic-2.0 MD5sum: ea2dd4ffebeff4443a74cb31a2f06ddb NeedsCompilation: no Title: CHiCAGO: Capture Hi-C Analysis of Genomic Organization Description: A pipeline for analysing Capture Hi-C data. biocViews: Epigenetics, HiC, Sequencing, Software Author: Jonathan Cairns, Paula Freire Pritchett, Steven Wingett, Mikhail Spivakov Maintainer: Mikhail Spivakov VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Chicago git_branch: RELEASE_3_10 git_last_commit: a79cb8e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Chicago_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Chicago_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Chicago_1.14.0.tgz vignettes: vignettes/Chicago/inst/doc/Chicago.html vignetteTitles: CHiCAGO Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Chicago/inst/doc/Chicago.R dependencyCount: 84 Package: chimera Version: 1.28.0 Depends: Biobase, GenomicRanges (>= 1.13.3), Rsamtools (>= 1.13.1), GenomicAlignments, methods, AnnotationDbi, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, Homo.sapiens Suggests: BiocParallel, geneplotter Enhances: Rsubread, BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, BSgenome.Mmusculus.UCSC.mm10, TxDb.Mmusculus.UCSC.mm10.knownGene, Mus.musculus, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene License: Artistic-2.0 MD5sum: 50d8ae69a11e019639e8d575100e89fa NeedsCompilation: yes Title: A package for secondary analysis of fusion products Description: This package facilitates the characterisation of fusion products events. It allows to import fusion data results from the following fusion finders: chimeraScan, bellerophontes, deFuse, FusionFinder, FusionHunter, mapSplice, tophat-fusion, FusionMap, STAR, Rsubread, fusionCatcher. biocViews: Infrastructure Author: Raffaele A Calogero, Matteo Carrara, Marco Beccuti, Francesca Cordero Maintainer: Raffaele A Calogero SystemRequirements: STAR, TopHat, bowtie and samtools are required for some functionalities git_url: https://git.bioconductor.org/packages/chimera git_branch: RELEASE_3_10 git_last_commit: ddedb49 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/chimera_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/chimera_1.27.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/chimera_1.28.0.tgz vignettes: vignettes/chimera/inst/doc/chimera.pdf vignetteTitles: chimera hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chimera/inst/doc/chimera.R dependencyCount: 93 Package: chimeraviz Version: 1.12.3 Depends: Biostrings, GenomicRanges, IRanges, Gviz, S4Vectors, ensembldb, AnnotationFilter, data.table Imports: methods, grid, Rsamtools, GenomeInfoDb, GenomicAlignments, RColorBrewer, graphics, AnnotationDbi, RCircos, org.Hs.eg.db, org.Mm.eg.db, rmarkdown, graph, Rgraphviz, DT, plyr, dplyr, BiocStyle, ArgumentCheck, gtools, magick Suggests: testthat, roxygen2, devtools, knitr, lintr License: Artistic-2.0 Archs: i386, x64 MD5sum: e30ec76ea1ca61bf06033945e94583cd NeedsCompilation: no Title: Visualization tools for gene fusions Description: chimeraviz manages data from fusion gene finders and provides useful visualization tools. biocViews: Infrastructure, Alignment Author: Stian Lågstad [aut, cre], Sen Zhao [ctb], Andreas M. Hoff [ctb], Bjarne Johannessen [ctb], Ole Christian Lingjærde [ctb], Rolf Skotheim [ctb] Maintainer: Stian Lågstad URL: https://github.com/stianlagstad/chimeraviz SystemRequirements: bowtie, samtools, and egrep are required for some functionalities VignetteBuilder: knitr BugReports: https://github.com/stianlagstad/chimeraviz/issues git_url: https://git.bioconductor.org/packages/chimeraviz git_branch: RELEASE_3_10 git_last_commit: 18940b3 git_last_commit_date: 2020-03-02 Date/Publication: 2020-03-02 source.ver: src/contrib/chimeraviz_1.12.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/chimeraviz_1.12.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/chimeraviz_1.12.3.tgz vignettes: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.html vignetteTitles: chimeraviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.R dependencyCount: 161 Package: ChIPanalyser Version: 1.8.0 Depends: R (>= 3.5.0),GenomicRanges, Biostrings, BSgenome, RcppRoll, parallel Imports: methods, IRanges, S4Vectors,grDevices,graphics,stats,utils,rtracklayer,ROCR, BiocManager,GenomeInfoDb Suggests: BSgenome.Dmelanogaster.UCSC.dm3,knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 7ece95d8cf170575491b5d5b8af00239 NeedsCompilation: no Title: ChIPanalyser: Predicting Transcription Factor Binding Sites Description: Based on a statistical thermodynamic framework, ChIPanalyser tries to produce ChIP-seq like profile. The model relies on four consideration: TF binding sites can be scored using a Position weight Matrix, DNA accessibility plays a role in Transcription Factor binding, binding profiles are dependant on the number of transcription factors bound to DNA and finally binding energy (another way of describing PWM's) or binding specificity should be modulated (hence the introduction of a binding specificity modulator). The end result of ChIPanalyser is to produce profiles simulating real ChIP-seq profile and provide accuracy measurements of these predicted profiles after being compared to real ChIP-seq data. The ultimate goal is to produce ChIP-seq like profiles predicting ChIP-seq like profile to circumvent the need to produce costly ChIP-seq experiments. biocViews: Software, BiologicalQuestion, WorkflowStep, Transcription, Sequencing, ChipOnChip, Coverage, Alignment, ChIPSeq, SequenceMatching, DataImport ,PeakDetection Author: Patrick C.N.Martin & Nicolae Radu Zabet Maintainer: Patrick C.N. Martin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPanalyser git_branch: RELEASE_3_10 git_last_commit: acf14fb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ChIPanalyser_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ChIPanalyser_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ChIPanalyser_1.8.0.tgz vignettes: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.pdf vignetteTitles: ChIPanalyser User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.R dependencyCount: 48 Package: ChIPComp Version: 1.16.0 Depends: R (>= 3.2.0),GenomicRanges,IRanges,rtracklayer,GenomeInfoDb,S4Vectors Imports: Rsamtools,limma,BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm9,BiocGenerics Suggests: BiocStyle,RUnit License: GPL MD5sum: af76f8a380294347ba197b8a4ed8b909 NeedsCompilation: yes Title: Quantitative comparison of multiple ChIP-seq datasets Description: ChIPComp detects differentially bound sharp binding sites across multiple conditions considering matching control. biocViews: ChIPSeq, Sequencing, Transcription, Genetics,Coverage, MultipleComparison, DataImport Author: Hao Wu, Li Chen, Zhaohui S.Qin, Chi Wang Maintainer: Li Chen git_url: https://git.bioconductor.org/packages/ChIPComp git_branch: RELEASE_3_10 git_last_commit: e589412 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ChIPComp_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ChIPComp_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ChIPComp_1.16.0.tgz vignettes: vignettes/ChIPComp/inst/doc/ChIPComp.pdf vignetteTitles: ChIPComp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPComp/inst/doc/ChIPComp.R dependencyCount: 42 Package: chipenrich Version: 2.10.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, BiocGenerics, chipenrich.data, GenomeInfoDb, GenomicRanges, grDevices, grid, IRanges, lattice, latticeExtra, MASS, methods, mgcv, org.Dm.eg.db, org.Dr.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, parallel, plyr, rms, rtracklayer, S4Vectors (>= 0.23.10), stats, stringr, utils Suggests: BiocStyle, devtools, knitr, rmarkdown, roxygen2, testthat License: GPL-3 Archs: i386, x64 MD5sum: 561e3d55be16bee9230536893b96052a NeedsCompilation: no Title: Gene Set Enrichment For ChIP-seq Peak Data Description: ChIP-Enrich and Poly-Enrich perform gene set enrichment testing using peaks called from a ChIP-seq experiment. The method empirically corrects for confounding factors such as the length of genes, and the mappability of the sequence surrounding genes. biocViews: ImmunoOncology, ChIPSeq, Epigenetics, FunctionalGenomics, GeneSetEnrichment, HistoneModification, Regression Author: Ryan P. Welch [aut, cph], Chee Lee [aut], Raymond G. Cavalcante [aut, cre], Chris Lee [aut], Laura J. Scott [ths], Maureen A. Sartor [ths] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chipenrich git_branch: RELEASE_3_10 git_last_commit: 38187ca git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/chipenrich_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/chipenrich_2.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/chipenrich_2.10.0.tgz vignettes: vignettes/chipenrich/inst/doc/chipenrich-vignette.html vignetteTitles: chipenrich_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipenrich/inst/doc/chipenrich-vignette.R dependencyCount: 140 Package: ChIPexoQual Version: 1.10.0 Depends: R (>= 3.4.0), GenomicAlignments (>= 1.0.1) Imports: methods, utils, GenomeInfoDb, stats, BiocParallel, GenomicRanges (>= 1.14.4), ggplot2 (>= 1.0), data.table (>= 1.9.6), Rsamtools (>= 1.16.1), IRanges (>= 1.6), S4Vectors (>= 0.8), biovizBase (>= 1.18), broom (>= 0.4), RColorBrewer (>= 1.1), dplyr (>= 0.5), scales (>= 0.4.0), viridis (>= 0.3), hexbin (>= 1.27), rmarkdown Suggests: ChIPexoQualExample (>= 0.99.1), knitr (>= 1.10), BiocStyle, gridExtra (>= 2.2), testthat License: GPL (>=2) MD5sum: 3a4bed04ff754d67393b897cdf39c125 NeedsCompilation: no Title: ChIPexoQual Description: Package with a quality control pipeline for ChIP-exo/nexus data. biocViews: ChIPSeq, Sequencing, Transcription, Visualization, QualityControl, Coverage, Alignment Author: Rene Welch, Dongjun Chung, Sunduz Keles Maintainer: Rene Welch URL: https:github.com/keleslab/ChIPexoQual VignetteBuilder: knitr BugReports: https://github.com/welch16/ChIPexoQual/issues git_url: https://git.bioconductor.org/packages/ChIPexoQual git_branch: RELEASE_3_10 git_last_commit: 74a2848 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ChIPexoQual_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ChIPexoQual_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ChIPexoQual_1.10.0.tgz vignettes: vignettes/ChIPexoQual/inst/doc/vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPexoQual/inst/doc/vignette.R dependencyCount: 150 Package: ChIPpeakAnno Version: 3.20.1 Depends: R (>= 3.2), methods, grid, IRanges (>= 2.13.12), Biostrings (>= 2.47.6), GenomicRanges (>= 1.31.8), S4Vectors (>= 0.17.25), VennDiagram Imports: BiocGenerics (>= 0.1.0), GO.db, biomaRt, BSgenome, GenomicFeatures, GenomeInfoDb, matrixStats, AnnotationDbi, limma, multtest, RBGL, graph, BiocManager, stats, regioneR, DBI, ensembldb, Biobase, seqinr, idr, GenomicAlignments, DelayedArray, SummarizedExperiment, rtracklayer, Rsamtools Suggests: reactome.db, BSgenome.Ecoli.NCBI.20080805, BSgenome.Hsapiens.UCSC.hg19, org.Ce.eg.db, org.Hs.eg.db, BSgenome.Celegans.UCSC.ce10, BSgenome.Drerio.UCSC.danRer7, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v79, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, gplots, BiocStyle, knitr, rmarkdown, testthat, trackViewer, motifStack, OrganismDbi License: GPL (>= 2) MD5sum: 33edc00789df4797fddc70bbfe0de276 NeedsCompilation: no Title: Batch annotation of the peaks identified from either ChIP-seq, ChIP-chip experiments or any experiments resulted in large number of chromosome ranges Description: The package includes functions to retrieve the sequences around the peak, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. Starting 2.0.5, new functions have been added for finding the peaks with bi-directional promoters with summary statistics (peaksNearBDP), for summarizing the occurrence of motifs in peaks (summarizePatternInPeaks) and for adding other IDs to annotated peaks or enrichedGO (addGeneIDs). This package leverages the biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest and stat packages. biocViews: Annotation, ChIPSeq, ChIPchip Author: Lihua Julie Zhu, Jianhong Ou, Jun Yu, Hervé Pagès, Claude Gazin, Nathan Lawson, Ryan Thompson, Simon Lin, David Lapointe and Michael Green Maintainer: Lihua Julie Zhu , Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPpeakAnno git_branch: RELEASE_3_10 git_last_commit: dcea315 git_last_commit_date: 2020-02-24 Date/Publication: 2020-02-24 source.ver: src/contrib/ChIPpeakAnno_3.20.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/ChIPpeakAnno_3.20.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ChIPpeakAnno_3.20.1.tgz vignettes: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.html, vignettes/ChIPpeakAnno/inst/doc/FAQs.html, vignettes/ChIPpeakAnno/inst/doc/pipeline.html, vignettes/ChIPpeakAnno/inst/doc/quickStart.html vignetteTitles: ChIPpeakAnno Vignette, ChIPpeakAnno FAQs, ChIPpeakAnno Annotation Pipeline, ChIPpeakAnno Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.R, vignettes/ChIPpeakAnno/inst/doc/FAQs.R, vignettes/ChIPpeakAnno/inst/doc/pipeline.R, vignettes/ChIPpeakAnno/inst/doc/quickStart.R dependsOnMe: REDseq importsMe: ATACseqQC, DChIPRep, DEScan2, FunciSNP, GUIDEseq, REDseq suggestsMe: R3CPET, RIPSeeker, seqsetvis dependencyCount: 105 Package: ChIPQC Version: 1.22.0 Depends: R (>= 3.0.0), ggplot2, DiffBind, GenomicRanges (>= 1.17.19) Imports: BiocGenerics (>= 0.11.3), S4Vectors (>= 0.1.0), IRanges (>= 1.99.17), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), chipseq (>= 1.12.0), gtools, BiocParallel, methods, reshape2, Nozzle.R1, Biobase, grDevices, stats, utils, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene Suggests: BiocStyle License: GPL (>= 3) MD5sum: a1ae40ab86dd35e64e9023bf6ab7ab9e NeedsCompilation: no Title: Quality metrics for ChIPseq data Description: Quality metrics for ChIPseq data. biocViews: Sequencing, ChIPSeq, QualityControl, ReportWriting Author: Tom Carroll, Wei Liu, Ines de Santiago, Rory Stark Maintainer: Tom Carroll , Rory Stark git_url: https://git.bioconductor.org/packages/ChIPQC git_branch: RELEASE_3_10 git_last_commit: 5f110b0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ChIPQC_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ChIPQC_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ChIPQC_1.22.0.tgz vignettes: vignettes/ChIPQC/inst/doc/ChIPQC.pdf, vignettes/ChIPQC/inst/doc/ChIPQCSampleReport.pdf vignetteTitles: Assessing ChIP-seq sample quality with ChIPQC, ChIPQCSampleReport.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPQC/inst/doc/ChIPQC.R dependencyCount: 181 Package: ChIPseeker Version: 1.22.1 Depends: R (>= 3.5.0) Imports: AnnotationDbi, BiocGenerics, boot, enrichplot, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, ggplot2, gplots, graphics, grDevices, gtools, methods, plotrix, dplyr, parallel, magrittr, RColorBrewer, rtracklayer, S4Vectors, stats, TxDb.Hsapiens.UCSC.hg19.knownGene, utils Suggests: clusterProfiler, ggimage, ggplotify, ggupset, ReactomePA, org.Hs.eg.db, knitr, rmarkdown, testthat, tibble License: Artistic-2.0 MD5sum: 0e3cbab05294e0864f034f8defe1ca8a NeedsCompilation: no Title: ChIPseeker for ChIP peak Annotation, Comparison, and Visualization Description: This package implements functions to retrieve the nearest genes around the peak, annotate genomic region of the peak, statstical methods for estimate the significance of overlap among ChIP peak data sets, and incorporate GEO database for user to compare the own dataset with those deposited in database. The comparison can be used to infer cooperative regulation and thus can be used to generate hypotheses. Several visualization functions are implemented to summarize the coverage of the peak experiment, average profile and heatmap of peaks binding to TSS regions, genomic annotation, distance to TSS, and overlap of peaks or genes. biocViews: Annotation, ChIPSeq, Software, Visualization, MultipleComparison Author: Guangchuang Yu [aut, cre] (), Yun Yan [ctb], Hervé Pagès [ctb], Michael Kluge [ctb], Thomas Schwarzl [ctb], Zhougeng Xu [ctb] Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/software/ChIPseeker VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ChIPseeker/issues git_url: https://git.bioconductor.org/packages/ChIPseeker git_branch: RELEASE_3_10 git_last_commit: 2a44099 git_last_commit_date: 2019-12-20 Date/Publication: 2019-12-23 source.ver: src/contrib/ChIPseeker_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/ChIPseeker_1.22.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ChIPseeker_1.22.1.tgz vignettes: vignettes/ChIPseeker/inst/doc/ChIPseeker.html vignetteTitles: ChIPseeker: an R package for ChIP peak Annotation,, Comparison and Visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseeker/inst/doc/ChIPseeker.R importsMe: ALPS, esATAC dependencyCount: 152 Package: chipseq Version: 1.36.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), ShortRead Imports: methods, stats, lattice, BiocGenerics, IRanges, GenomicRanges, ShortRead Suggests: BSgenome, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene License: Artistic-2.0 MD5sum: aef5f33c483a568d307549073ad2b557 NeedsCompilation: yes Title: chipseq: A package for analyzing chipseq data Description: Tools for helping process short read data for chipseq experiments biocViews: ChIPSeq, Sequencing, Coverage, QualityControl, DataImport Author: Deepayan Sarkar, Robert Gentleman, Michael Lawrence, Zizhen Yao Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/chipseq git_branch: RELEASE_3_10 git_last_commit: 6e8fec5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/chipseq_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/chipseq_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/chipseq_1.36.0.tgz vignettes: vignettes/chipseq/inst/doc/Workflow.pdf vignetteTitles: A Sample ChIP-Seq analysis workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipseq/inst/doc/Workflow.R dependsOnMe: PING importsMe: ChIPQC, CopywriteR, HTSeqGenie, soGGi, transcriptR suggestsMe: GenoGAM, ggbio dependencyCount: 42 Package: ChIPseqR Version: 1.40.0 Depends: R (>= 2.10.0), methods, BiocGenerics, S4Vectors (>= 0.9.25) Imports: Biostrings, fBasics, GenomicRanges, IRanges (>= 2.5.14), graphics, grDevices, HilbertVis, ShortRead, stats, timsac, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 03f5de12246e95589e432f030fc31d5b NeedsCompilation: yes Title: Identifying Protein Binding Sites in High-Throughput Sequencing Data Description: ChIPseqR identifies protein binding sites from ChIP-seq and nucleosome positioning experiments. The model used to describe binding events was developed to locate nucleosomes but should flexible enough to handle other types of experiments as well. biocViews: ChIPSeq, Infrastructure Author: Peter Humburg Maintainer: Peter Humburg git_url: https://git.bioconductor.org/packages/ChIPseqR git_branch: RELEASE_3_10 git_last_commit: 5619e8b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ChIPseqR_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ChIPseqR_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ChIPseqR_1.40.0.tgz vignettes: vignettes/ChIPseqR/inst/doc/Introduction.pdf vignetteTitles: Introduction to ChIPseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseqR/inst/doc/Introduction.R dependencyCount: 51 Package: ChIPSeqSpike Version: 1.6.0 Depends: R (>= 3.5), rtracklayer (>= 1.37.6) Imports: tools, stringr, Rsamtools, GenomicRanges, IRanges, seqplots, ggplot2, LSD, corrplot, methods, stats, grDevices, graphics, utils, BiocGenerics, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: f104f15907945051c92953af77f0841a NeedsCompilation: no Title: ChIP-Seq data scaling according to spike-in control Description: Chromatin Immuno-Precipitation followed by Sequencing (ChIP-Seq) is used to determine the binding sites of any protein of interest, such as transcription factors or histones with or without a specific modification, at a genome scale. The many steps of the protocol can introduce biases that make ChIP-Seq more qualitative than quantitative. For instance, it was shown that global histone modification differences are not caught by traditional downstream data normalization techniques. A case study reported no differences in histone H3 lysine-27 trimethyl (H3K27me3) upon Ezh2 inhibitor treatment. To tackle this problem, external spike-in control were used to keep track of technical biases between conditions. Exogenous DNA from a different non-closely related species was inserted during the protocol to infer scaling factors that enabled an accurate normalization, thus revealing the inhibitor effect. ChIPSeqSpike offers tools for ChIP-Seq spike-in normalization. Ready to use scaled bigwig files and scaling factors values are obtained as output. ChIPSeqSpike also provides tools for ChIP-Seq spike-in assessment and analysis through a versatile collection of graphical functions. biocViews: ImmunoOncology, ChIPSeq, Sequencing, Normalization, Transcription, Coverage, DifferentialMethylation, Epigenetics, DataImport, HistoneModification Author: Nicolas Descostes Maintainer: Nicolas Descostes VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPSeqSpike git_branch: RELEASE_3_10 git_last_commit: 3e5d3f8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ChIPSeqSpike_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ChIPSeqSpike_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ChIPSeqSpike_1.6.0.tgz vignettes: vignettes/ChIPSeqSpike/inst/doc/ChIPSeqSpike.pdf vignetteTitles: ChIPSeqSpike hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPSeqSpike/inst/doc/ChIPSeqSpike.R dependencyCount: 122 Package: ChIPsim Version: 1.40.0 Depends: Biostrings (>= 2.29.2) Imports: IRanges, XVector, Biostrings, ShortRead, graphics, methods, stats, utils Suggests: actuar, zoo License: GPL (>= 2) MD5sum: 651136d886701d58ce6d740f1b5d2155 NeedsCompilation: no Title: Simulation of ChIP-seq experiments Description: A general framework for the simulation of ChIP-seq data. Although currently focused on nucleosome positioning the package is designed to support different types of experiments. biocViews: Infrastructure, ChIPSeq Author: Peter Humburg Maintainer: Peter Humburg git_url: https://git.bioconductor.org/packages/ChIPsim git_branch: RELEASE_3_10 git_last_commit: 3fad75e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ChIPsim_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ChIPsim_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ChIPsim_1.40.0.tgz vignettes: vignettes/ChIPsim/inst/doc/ChIPsimIntro.pdf vignetteTitles: Simulating ChIP-seq experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPsim/inst/doc/ChIPsimIntro.R dependencyCount: 42 Package: ChIPXpress Version: 1.30.0 Depends: R (>= 2.10), ChIPXpressData Imports: Biobase, GEOquery, frma, affy, bigmemory, biganalytics Suggests: mouse4302frmavecs, mouse4302.db, mouse4302cdf, RUnit, BiocGenerics License: GPL(>=2) MD5sum: 0e99079e935df9d6f66e4ade44828f43 NeedsCompilation: no Title: ChIPXpress: enhanced transcription factor target gene identification from ChIP-seq and ChIP-chip data using publicly available gene expression profiles Description: ChIPXpress takes as input predicted TF bound genes from ChIPx data and uses a corresponding database of gene expression profiles downloaded from NCBI GEO to rank the TF bound targets in order of which gene is most likely to be functional TF target. biocViews: ChIPchip, ChIPSeq Author: George Wu Maintainer: George Wu git_url: https://git.bioconductor.org/packages/ChIPXpress git_branch: RELEASE_3_10 git_last_commit: a44e3c5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ChIPXpress_1.30.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ChIPXpress_1.30.0.tgz vignettes: vignettes/ChIPXpress/inst/doc/ChIPXpress.pdf vignetteTitles: ChIPXpress hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPXpress/inst/doc/ChIPXpress.R dependencyCount: 94 Package: chopsticks Version: 1.52.0 Imports: graphics, stats, utils, methods, survival Suggests: hexbin License: GPL-3 MD5sum: eb123a59b69209926940050097120a78 NeedsCompilation: yes Title: The 'snp.matrix' and 'X.snp.matrix' Classes Description: Implements classes and methods for large-scale SNP association studies biocViews: Microarray, SNPsAndGeneticVariability, SNP, GeneticVariability Author: Hin-Tak Leung Maintainer: Hin-Tak Leung URL: http://outmodedbonsai.sourceforge.net/ git_url: https://git.bioconductor.org/packages/chopsticks git_branch: RELEASE_3_10 git_last_commit: de9a9f4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/chopsticks_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/chopsticks_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/chopsticks_1.52.0.tgz vignettes: vignettes/chopsticks/inst/doc/chopsticks-vignette.pdf vignetteTitles: snpMatrix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chopsticks/inst/doc/chopsticks-vignette.R dependencyCount: 10 Package: chroGPS Version: 2.4.0 Depends: R (>= 3.2.0), GenomicRanges, IRanges, methods, Biobase, MASS, graphics, stats, changepoint Imports: cluster, DPpackage, ICSNP, ellipse, vegan Enhances: parallel, XML, rgl, gplots, pheatmap, ChIPpeakAnno, org.Dm.eg.db, caTools, plotly License: GPL (>=2.14) Archs: i386, x64 MD5sum: d5b65b8dc6dae3c97a2b631ba8b447f9 NeedsCompilation: no Title: chroGPS2: Generation, visualization and differential analysis of epigenome maps Description: We provide intuitive maps to visualize, analyze and compare the association between genetic elements based on their epigenetic profiles. The approach is based on Multi-Dimensional Scaling, and includes a parallelized implementation for handling high dimensional datasets. We provide several sensible distance metrics, and adjustment procedures to remove systematic biases typically observed when merging data obtained under different technologies or genetic backgrounds. We also provide functions and methods to perform differential analysis of epigenome maps at factor and gene level. biocViews: Epigenetics, Clustering, ChIPchip, ChIPSeq, HistoneModification, Visualization, DataRepresentation, ImmunoOncology Author: Oscar Reina, David Rossell Maintainer: Oscar Reina git_url: https://git.bioconductor.org/packages/chroGPS git_branch: RELEASE_3_10 git_last_commit: 3f20425 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/chroGPS_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/chroGPS_2.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/chroGPS_2.4.0.tgz vignettes: vignettes/chroGPS/inst/doc/chroGPS.pdf vignetteTitles: Manual for the chroGPS library hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chroGPS/inst/doc/chroGPS.R dependencyCount: 42 Package: chromDraw Version: 2.16.0 Depends: R (>= 3.0.0) Imports: Rcpp (>= 0.11.1), GenomicRanges (>= 1.17.46) LinkingTo: Rcpp License: GPL-3 MD5sum: faa3fd64f6e24474ff8bbb192ae0fa78 NeedsCompilation: yes Title: chromDraw is a R package for drawing the schemes of karyotypes in the linear and circular fashion. Description: ChromDraw is a R package for drawing the schemes of karyotype(s) in the linear and circular fashion. It is possible to visualized cytogenetic marsk on the chromosomes. This tool has own input data format. Input data can be imported from the GenomicRanges data structure. This package can visualized the data in the BED file format. Here is requirement on to the first nine fields of the BED format. Output files format are *.eps and *.svg. biocViews: Software Author: Jan Janecka, Ing., Mgr. CEITEC Masaryk University Maintainer: Jan Janecka URL: www.plantcytogenomics.org/chromDraw SystemRequirements: Rtools (>= 3.1) git_url: https://git.bioconductor.org/packages/chromDraw git_branch: RELEASE_3_10 git_last_commit: e23ea9a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/chromDraw_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/chromDraw_2.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/chromDraw_2.16.0.tgz vignettes: vignettes/chromDraw/inst/doc/chromDraw.pdf vignetteTitles: chromDraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromDraw/inst/doc/chromDraw.R dependencyCount: 17 Package: ChromHeatMap Version: 1.40.0 Depends: R (>= 2.9.0), BiocGenerics (>= 0.3.2), annotate (>= 1.20.0), AnnotationDbi (>= 1.4.0) Imports: Biobase (>= 2.17.8), graphics, grDevices, methods, stats, IRanges, rtracklayer, GenomicRanges Suggests: ALL, hgu95av2.db License: Artistic-2.0 Archs: i386, x64 MD5sum: 13bae503e48a2f7e691d8efc7f2df41a NeedsCompilation: no Title: Heat map plotting by genome coordinate Description: The ChromHeatMap package can be used to plot genome-wide data (e.g. expression, CGH, SNP) along each strand of a given chromosome as a heat map. The generated heat map can be used to interactively identify probes and genes of interest. biocViews: Visualization Author: Tim F. Rayner Maintainer: Tim F. Rayner git_url: https://git.bioconductor.org/packages/ChromHeatMap git_branch: RELEASE_3_10 git_last_commit: 755b7fe git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ChromHeatMap_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ChromHeatMap_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ChromHeatMap_1.40.0.tgz vignettes: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.pdf vignetteTitles: Plotting expression data with ChromHeatMap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.R dependencyCount: 55 Package: chromPlot Version: 1.14.0 Depends: stats, utils, graphics, grDevices, datasets, base, biomaRt, GenomicRanges, R (>= 3.1.0) Suggests: qtl, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: ea946f8446978a979f6b936e478119e9 NeedsCompilation: no Title: Global visualization tool of genomic data Description: Package designed to visualize genomic data along the chromosomes, where the vertical chromosomes are sorted by number, with sex chromosomes at the end. biocViews: DataRepresentation, FunctionalGenomics, Genetics, Sequencing, Annotation, Visualization Author: Ricardo A. Verdugo and Karen Y. Orostica Maintainer: Karen Y. Orostica git_url: https://git.bioconductor.org/packages/chromPlot git_branch: RELEASE_3_10 git_last_commit: 7e00e31 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/chromPlot_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/chromPlot_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/chromPlot_1.14.0.tgz vignettes: vignettes/chromPlot/inst/doc/chromPlot.pdf vignetteTitles: General Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromPlot/inst/doc/chromPlot.R dependencyCount: 67 Package: chromstaR Version: 1.12.0 Depends: R (>= 3.3), GenomicRanges, ggplot2, chromstaRData Imports: methods, utils, grDevices, graphics, stats, foreach, doParallel, BiocGenerics (>= 0.31.6), S4Vectors, GenomeInfoDb, IRanges, reshape2, Rsamtools, GenomicAlignments, bamsignals, mvtnorm Suggests: knitr, BiocStyle, testthat, biomaRt License: Artistic-2.0 MD5sum: c557372e8c2de6cc2e08479c76ea0e89 NeedsCompilation: yes Title: Combinatorial and Differential Chromatin State Analysis for ChIP-Seq Data Description: This package implements functions for combinatorial and differential analysis of ChIP-seq data. It includes uni- and multivariate peak-calling, export to genome browser viewable files, and functions for enrichment analyses. biocViews: ImmunoOncology, Software, DifferentialPeakCalling, HiddenMarkovModel, ChIPSeq, HistoneModification, MultipleComparison, Sequencing, PeakDetection, ATACSeq Author: Aaron Taudt, Maria Colome Tatche, Matthias Heinig, Minh Anh Nguyen Maintainer: Aaron Taudt URL: https://github.com/ataudt/chromstaR VignetteBuilder: knitr BugReports: https://github.com/ataudt/chromstaR/issues git_url: https://git.bioconductor.org/packages/chromstaR git_branch: RELEASE_3_10 git_last_commit: b37e9bf git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/chromstaR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/chromstaR_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/chromstaR_1.12.0.tgz vignettes: vignettes/chromstaR/inst/doc/chromstaR.pdf vignetteTitles: The chromstaR user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromstaR/inst/doc/chromstaR.R dependencyCount: 92 Package: chromswitch Version: 1.8.0 Depends: R (>= 3.5.0), GenomicRanges (>= 1.26.4) Imports: cluster (>= 2.0.6), Biobase (>= 2.36.2), BiocParallel (>= 1.8.2), dplyr (>= 0.5.0), gplots(>= 3.0.1), graphics, grDevices, IRanges (>= 2.4.8), lazyeval (>= 0.2.0), matrixStats (>= 0.52), magrittr (>= 1.5), methods, NMF (>= 0.20.6), rtracklayer (>= 1.36.4), S4Vectors (>= 0.23.19), stats, tidyr (>= 0.6.3) Suggests: BiocStyle, DescTools (>= 0.99.19), devtools (>= 1.13.3), GenomeInfoDb (>= 1.16.0), knitr, rmarkdown, mclust (>= 5.3), testthat License: MIT + file LICENSE Archs: i386, x64 MD5sum: 77fca00f1f40f187146efc2af41e4127 NeedsCompilation: no Title: An R package to detect chromatin state switches from epigenomic data Description: Chromswitch implements a flexible method to detect chromatin state switches between samples in two biological conditions in a specific genomic region of interest given peaks or chromatin state calls from ChIP-seq data. biocViews: ImmunoOncology, MultipleComparison, Transcription, GeneExpression, DifferentialPeakCalling, HistoneModification, Epigenetics, FunctionalGenomics, Clustering Author: Selin Jessa [aut, cre], Claudia L. Kleinman [aut] Maintainer: Selin Jessa URL: https://github.com/sjessa/chromswitch VignetteBuilder: knitr BugReports: https://github.com/sjessa/chromswitch/issues git_url: https://git.bioconductor.org/packages/chromswitch git_branch: RELEASE_3_10 git_last_commit: e6aab90 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/chromswitch_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/chromswitch_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/chromswitch_1.8.0.tgz vignettes: vignettes/chromswitch/inst/doc/chromswitch_intro.html vignetteTitles: An introduction to `chromswitch` for detecting chromatin state switches hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chromswitch/inst/doc/chromswitch_intro.R dependencyCount: 111 Package: chromVAR Version: 1.8.0 Depends: R (>= 3.4) Imports: IRanges, GenomeInfoDb, GenomicRanges, ggplot2, nabor, BiocParallel, BiocGenerics, Biostrings, TFBSTools, Rsamtools, S4Vectors, methods, Rcpp, grid, plotly, shiny, miniUI, stats, utils, graphics, DT, Rtsne, Matrix, SummarizedExperiment, RColorBrewer, BSgenome LinkingTo: Rcpp, RcppArmadillo Suggests: JASPAR2016, BSgenome.Hsapiens.UCSC.hg19, readr, testthat, knitr, rmarkdown, pheatmap, motifmatchr License: MIT + file LICENSE MD5sum: 6501a2e685409be3588f56df67cbf71e NeedsCompilation: yes Title: Chromatin Variation Across Regions Description: Determine variation in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility data, e.g. from scATAC-seq or sparse bulk ATAC or DNAse-seq experiments. biocViews: SingleCell, Sequencing, GeneRegulation, ImmunoOncology Author: Alicia Schep [aut, cre], Jason Buenrostro [ctb], Caleb Lareau [ctb], William Greenleaf [ths], Stanford University [cph] Maintainer: Alicia Schep SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chromVAR git_branch: RELEASE_3_10 git_last_commit: 7a74fdd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/chromVAR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/chromVAR_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/chromVAR_1.8.0.tgz vignettes: vignettes/chromVAR/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chromVAR/inst/doc/Introduction.R dependencyCount: 148 Package: CHRONOS Version: 1.14.0 Depends: R (>= 3.5) Imports: XML, RCurl, RBGL, parallel, foreach, doParallel, openxlsx, igraph, circlize, graph, stats, utils, grDevices, graphics, methods, biomaRt Suggests: RUnit, BiocGenerics, knitr License: GPL-2 Archs: i386, x64 MD5sum: ceeb450c54b6bc0a5b08d174497a7967 NeedsCompilation: no Title: CHRONOS: A time-varying method for microRNA-mediated sub-pathway enrichment analysis Description: A package used for efficient unraveling of the inherent dynamic properties of pathways. MicroRNA-mediated subpathway topologies are extracted and evaluated by exploiting the temporal transition and the fold change activity of the linked genes/microRNAs. biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG Author: Aristidis G. Vrahatis, Konstantina Dimitrakopoulou, Panos Balomenos Maintainer: Panos Balomenos SystemRequirements: Java version >= 1.7, Pandoc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CHRONOS git_branch: RELEASE_3_10 git_last_commit: 897db9a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CHRONOS_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CHRONOS_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CHRONOS_1.14.0.tgz vignettes: vignettes/CHRONOS/inst/doc/CHRONOS.pdf vignetteTitles: CHRONOS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CHRONOS/inst/doc/CHRONOS.R dependencyCount: 76 Package: cicero Version: 1.4.4 Depends: R (>= 3.5.0), monocle, Gviz (>= 1.22.3) Imports: assertthat (>= 0.2.0), Biobase (>= 2.37.2), BiocGenerics (>= 0.23.0), data.table (>= 1.10.4), dplyr (>= 0.7.4), FNN (>= 1.1), GenomicRanges (>= 1.30.3), ggplot2 (>= 2.2.1), glasso (>= 1.8), grDevices, igraph (>= 1.1.0), IRanges (>= 2.10.5), Matrix (>= 1.2-12), methods, parallel, plyr (>= 1.8.4), reshape2 (>= 1.4.3), S4Vectors (>= 0.14.7), stats, stringr (>= 1.2.0), tibble (>= 1.4.2), tidyr, VGAM (>= 1.0-5), utils Suggests: AnnotationDbi (>= 1.38.2), knitr, rmarkdown, rtracklayer (>= 1.36.6), testthat, vdiffr (>= 0.2.3), covr License: MIT + file LICENSE MD5sum: 89fd0e2a52eb2e62cde951755a973f9e NeedsCompilation: no Title: Precict cis-co-accessibility from single-cell chromatin accessibility data Description: Cicero computes putative cis-regulatory maps from single-cell chromatin accessibility data. It also extends monocle 2 for use in chromatin accessibility data. biocViews: Sequencing, Clustering, CellBasedAssays, ImmunoOncology GeneRegulation, GeneTarget, Epigenetics, ATACSeq, SingleCell Author: Hannah Pliner [aut, cre], Cole Trapnell [aut] Maintainer: Hannah Pliner VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cicero git_branch: RELEASE_3_10 git_last_commit: 1bf63f3 git_last_commit_date: 2020-03-10 Date/Publication: 2020-03-10 source.ver: src/contrib/cicero_1.4.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/cicero_1.4.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cicero_1.4.4.tgz vignettes: vignettes/cicero/inst/doc/website.html vignetteTitles: Vignette from Cicero Website hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cicero/inst/doc/website.R dependencyCount: 173 Package: CINdex Version: 1.14.0 Depends: R (>= 3.3), GenomicRanges Imports: bitops,gplots,grDevices,som, dplyr,gridExtra,png,stringr,S4Vectors, IRanges, GenomeInfoDb,graphics, stats, utils Suggests: knitr, testthat, ReactomePA, RUnit, BiocGenerics, AnnotationHub, rtracklayer, pd.genomewidesnp.6, org.Hs.eg.db, biovizBase, TxDb.Hsapiens.UCSC.hg18.knownGene, methods, Biostrings,Homo.sapiens License: GPL (>= 2) MD5sum: ebdb4c0b76952a9ce7760ca70313ed12 NeedsCompilation: no Title: Chromosome Instability Index Description: The CINdex package addresses important area of high-throughput genomic analysis. It allows the automated processing and analysis of the experimental DNA copy number data generated by Affymetrix SNP 6.0 arrays or similar high throughput technologies. It calculates the chromosome instability (CIN) index that allows to quantitatively characterize genome-wide DNA copy number alterations as a measure of chromosomal instability. This package calculates not only overall genomic instability, but also instability in terms of copy number gains and losses separately at the chromosome and cytoband level. biocViews: Software, CopyNumberVariation, GenomicVariation, aCGH, Microarray, Genetics, Sequencing Author: Lei Song, Krithika Bhuvaneshwar, Yue Wang, Yuanjian Feng, Ie-Ming Shih, Subha Madhavan, Yuriy Gusev Maintainer: Yuriy Gusev VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CINdex git_branch: RELEASE_3_10 git_last_commit: 0ae2a81 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CINdex_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CINdex_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CINdex_1.14.0.tgz vignettes: vignettes/CINdex/inst/doc/CINdex.pdf, vignettes/CINdex/inst/doc/PrepareInputData.pdf vignetteTitles: CINdex Tutorial, Prepare input data for CINdex hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CINdex/inst/doc/CINdex.R, vignettes/CINdex/inst/doc/PrepareInputData.R dependencyCount: 52 Package: circRNAprofiler Version: 1.0.0 Depends: R(>= 3.6.0), gwascat Imports: dplyr, magrittr, readr, rtracklayer, stringr, Biostrings, BSgenome, stringi, DESeq2, edgeR, GenomicRanges, IRanges, seqinr, R.utils, reshape2, ggplot2, utils, rlang, S4Vectors, stats, AnnotationHub, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19, Suggests: testthat, knitr, roxygen2, rmarkdown, citr, devtools, gridExtra, ggpubr, VennDiagram, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, License: GPL-3 MD5sum: dd36fec121a1f7a2fc221bcc29b8705e NeedsCompilation: no Title: circRNAprofiler: An R-Based Computational Framework for the Downstream Analysis of Circular RNAs Description: R-based computational framework for a comprehensive in silico analysis of circRNAs. This computational framework allows to combine and analyze circRNAs previously detected by multiple publicly available annotation-based circRNA detection tools. It covers different aspects of circRNAs analysis from differential expression analysis, evolutionary conservation, biogenesis to functional analysis. biocViews: Annotation, StructuralPrediction, FunctionalPrediction, GenePrediction, GenomeAssembly, DifferentialExpression Author: Simona Aufiero Maintainer: Simona Aufiero URL: https://github.com/Aufiero/circRNAprofiler VignetteBuilder: knitr BugReports: https://github.com/Aufiero/circRNAprofiler/issues git_url: https://git.bioconductor.org/packages/circRNAprofiler git_branch: RELEASE_3_10 git_last_commit: 9167757 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/circRNAprofiler_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/circRNAprofiler_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/circRNAprofiler_1.0.0.tgz vignettes: vignettes/circRNAprofiler/inst/doc/circRNAprofiler.html vignetteTitles: circRNAprofiler hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/circRNAprofiler/inst/doc/circRNAprofiler.R dependencyCount: 175 Package: cisPath Version: 1.26.0 Depends: R (>= 2.10.0) Imports: methods, utils License: GPL (>= 3) MD5sum: 9371004bea1f90ef559e2dfc49153d27 NeedsCompilation: yes Title: Visualization and management of the protein-protein interaction networks. Description: cisPath is an R package that uses web browsers to visualize and manage protein-protein interaction networks. biocViews: Proteomics Author: Likun Wang Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/cisPath git_branch: RELEASE_3_10 git_last_commit: bde430e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cisPath_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cisPath_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cisPath_1.26.0.tgz vignettes: vignettes/cisPath/inst/doc/cisPath.pdf vignetteTitles: cisPath hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cisPath/inst/doc/cisPath.R dependencyCount: 2 Package: ClassifyR Version: 2.6.1 Depends: R (>= 3.5.0), methods, S4Vectors (>= 0.18.0), MultiAssayExperiment (>= 1.6.0), BiocParallel Imports: locfit, grid, utils, plyr Suggests: limma, genefilter, edgeR, car, Rmixmod, ggplot2 (>= 3.0.0), gridExtra (>= 2.0.0), cowplot, BiocStyle, pamr, PoiClaClu, parathyroidSE, knitr, htmltools, gtable, scales, e1071, rmarkdown, IRanges, randomForest, robustbase, glmnet, class License: GPL-3 Archs: i386, x64 MD5sum: 3bccbb395529a60e1e6e6ec486eda8c3 NeedsCompilation: no Title: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing Description: The software formalises a framework for classification in R. There are four stages; Data transformation, feature selection, classifier training, and prediction. The requirements of variable types and names are fixed, but specialised variables for functions can also be provided. The classification framework is wrapped in a driver loop, that reproducibly carries out a number of cross-validation schemes. Functions for differential expression, differential variability, and differential distribution are included. Additional functions may be developed by the user, by creating an interface to the framework. biocViews: Classification, Survival Author: Dario Strbenac, John Ormerod, Graham Mann, Jean Yang Maintainer: Dario Strbenac VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClassifyR git_branch: RELEASE_3_10 git_last_commit: 1faa6ba git_last_commit_date: 2020-01-22 Date/Publication: 2020-01-23 source.ver: src/contrib/ClassifyR_2.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/ClassifyR_2.5.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ClassifyR_2.6.1.tgz vignettes: vignettes/ClassifyR/inst/doc/ClassifyR.html, vignettes/ClassifyR/inst/doc/wrapper.html vignetteTitles: An Introduction to the ClassifyR Package, Example: Creating a Wrapper Function for the k-NN Classifier hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClassifyR/inst/doc/ClassifyR.R, vignettes/ClassifyR/inst/doc/wrapper.R dependencyCount: 58 Package: cleanUpdTSeq Version: 1.24.0 Depends: R (>= 2.15), BiocGenerics (>= 0.1.0), methods, stats Imports: BSgenome, GenomicRanges, seqinr, e1071, GenomeInfoDb, IRanges, utils, BSgenome.Drerio.UCSC.danRer7 Suggests: BiocStyle, knitr, RUnit License: GPL-2 MD5sum: 3b0727cb5a1100112d37d21bb667e002 NeedsCompilation: no Title: This package classifies putative polyadenylation sites as true or false/internally oligodT primed Description: This package implements a Naive Bayes classifier for accurate identification of polyadenylation sites (pA sites) from oligodT based 3 prime end sequencing such as PAS-Seq, PolyA-Seq and RNA-Seq. The classifer is highly accurate and outperforms heuristic methods. biocViews: Sequencing, SequenceMatching, Genetics, GeneRegulation Author: Sarah Sheppard, Jianhong Ou, Nathan Lawson, Lihua Julie Zhu Maintainer: Jianhong Ou ; Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cleanUpdTSeq git_branch: RELEASE_3_10 git_last_commit: 0443777 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cleanUpdTSeq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cleanUpdTSeq_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cleanUpdTSeq_1.24.0.tgz vignettes: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.html vignetteTitles: cleanUpdTSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.R importsMe: InPAS dependencyCount: 48 Package: cleaver Version: 1.24.0 Depends: R (>= 3.0.0), methods, Biostrings (>= 1.29.8) Imports: S4Vectors, IRanges Suggests: testthat (>= 0.8), knitr, BiocStyle (>= 0.0.14), rmarkdown, BRAIN, UniProt.ws (>= 2.1.4) License: GPL (>= 3) MD5sum: 0ed8d1c82721bd64a54d017f80714b33 NeedsCompilation: no Title: Cleavage of Polypeptide Sequences Description: In-silico cleavage of polypeptide sequences. The cleavage rules are taken from: http://web.expasy.org/peptide_cutter/peptidecutter_enzymes.html biocViews: Proteomics Author: Sebastian Gibb [aut, cre] () Maintainer: Sebastian Gibb URL: https://github.com/sgibb/cleaver/ VignetteBuilder: knitr BugReports: https://github.com/sgibb/cleaver/issues/ git_url: https://git.bioconductor.org/packages/cleaver git_branch: RELEASE_3_10 git_last_commit: 57cef7d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cleaver_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cleaver_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cleaver_1.24.0.tgz vignettes: vignettes/cleaver/inst/doc/cleaver.html vignetteTitles: In-silico cleavage of polypeptides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleaver/inst/doc/cleaver.R importsMe: Pbase, synapter dependencyCount: 12 Package: clippda Version: 1.36.0 Depends: R (>= 2.13.1),limma, statmod, rgl, lattice, scatterplot3d, graphics, grDevices, stats, utils, Biobase, tools, methods License: GPL (>=2) MD5sum: b1fb01545c2e5ea30f55e29f005b746c NeedsCompilation: no Title: A package for the clinical proteomic profiling data analysis Description: Methods for the nalysis of data from clinical proteomic profiling studies. The focus is on the studies of human subjects, which are often observational case-control by design and have technical replicates. A method for sample size determination for planning these studies is proposed. It incorporates routines for adjusting for the expected heterogeneities and imbalances in the data and the within-sample replicate correlations. biocViews: Proteomics, OneChannel, Preprocessing, DifferentialExpression, MultipleComparison Author: Stephen Nyangoma Maintainer: Stephen Nyangoma URL: http://www.cancerstudies.bham.ac.uk/crctu/CLIPPDA.shtml git_url: https://git.bioconductor.org/packages/clippda git_branch: RELEASE_3_10 git_last_commit: efc3264 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/clippda_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/clippda_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/clippda_1.36.0.tgz vignettes: vignettes/clippda/inst/doc/clippda.pdf vignetteTitles: Sample Size Calculation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clippda/inst/doc/clippda.R dependencyCount: 52 Package: clipper Version: 1.26.0 Depends: R (>= 2.15.0), Matrix, graph Imports: methods, Biobase, Rcpp, igraph, gRbase (>= 1.6.6), qpgraph, KEGGgraph, corpcor, RBGL Suggests: RUnit, BiocGenerics, graphite, ALL, hgu95av2.db, MASS, BiocStyle Enhances: RCy3 License: AGPL-3 MD5sum: e21c044202d10fb044411f367cb13966 NeedsCompilation: no Title: Gene Set Analysis Exploiting Pathway Topology Description: Implements topological gene set analysis using a two-step empirical approach. It exploits graph decomposition theory to create a junction tree and reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype. Author: Paolo Martini , Gabriele Sales , Chiara Romualdi Maintainer: Paolo Martini git_url: https://git.bioconductor.org/packages/clipper git_branch: RELEASE_3_10 git_last_commit: ae4ab82 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/clipper_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/clipper_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/clipper_1.26.0.tgz vignettes: vignettes/clipper/inst/doc/clipper.pdf vignetteTitles: clipper hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clipper/inst/doc/clipper.R suggestsMe: graphite dependencyCount: 97 Package: cliqueMS Version: 1.0.2 Depends: R (>= 3.6.0) Imports: Rcpp (>= 0.12.15), xcms(>= 3.0.0), MSnbase, igraph, qlcMatrix, matrixStats, methods LinkingTo: Rcpp, BH, RcppArmadillo Suggests: knitr, rmarkdown, testthat, CAMERA License: GPL (>= 2) MD5sum: 93be2171c10ef71587755dc9f0930d1d NeedsCompilation: yes Title: Annotation of Isotopes, Adducts and Fragmentation Adducts for in-Source LC/MS Metabolomics Data Description: Annotates data from liquid chromatography coupled to mass spectrometry (LC/MS) metabolomics experiments. Based on a network algorithm (O.Senan, A. Aguilar- Mogas, M. Navarro, O. Yanes, R.Guimerà and M. Sales-Pardo, Metabolomics Conference (2016), Dublin), 'CliqueMS' builds a weighted similarity network where nodes are features and edges are weighted according to the similarity of this features. Then it searches for the most plausible division of the similarity network into cliques (fully connected components). Finally it annotates metabolites within each clique, obtaining for each annotated metabolite the neutral mass and their features, corresponding to isotopes, ionization adducts and fragmentation adducts of that metabolite. biocViews: Metabolomics, MassSpectrometry, Network, NetworkInference Author: Oriol Senan Campos [aut, cre], Antoni Aguilar-Mogas [aut], Jordi Capellades [aut], Miriam Navarro [aut], Oscar Yanes [aut], Roger Guimera [aut], Marta Sales-Pardo [aut] Maintainer: Oriol Senan Campos URL: http://cliquems.seeslab.net SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/osenan/cliqueMS/issues git_url: https://git.bioconductor.org/packages/cliqueMS git_branch: RELEASE_3_10 git_last_commit: 8480f11 git_last_commit_date: 2020-03-26 Date/Publication: 2020-03-26 source.ver: src/contrib/cliqueMS_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/cliqueMS_1.0.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cliqueMS_1.0.2.tgz vignettes: vignettes/cliqueMS/inst/doc/annotate_features.html vignetteTitles: Annotating LC/MS data with cliqueMS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cliqueMS/inst/doc/annotate_features.R dependencyCount: 104 Package: Clomial Version: 1.22.0 Depends: R (>= 2.10), matrixStats Imports: methods, permute License: GPL (>= 2) Archs: i386, x64 MD5sum: 630e47911fcfc58ecb37f60c90517a44 NeedsCompilation: no Title: Infers clonal composition of a tumor Description: Clomial fits binomial distributions to counts obtained from Next Gen Sequencing data of multiple samples of the same tumor. The trained parameters can be interpreted to infer the clonal structure of the tumor. biocViews: Genetics, GeneticVariability, Sequencing, Clustering, MultipleComparison, Bayesian, DNASeq, ExomeSeq, TargetedResequencing, ImmunoOncology Author: Habil Zare and Alex Hu Maintainer: Habil Zare git_url: https://git.bioconductor.org/packages/Clomial git_branch: RELEASE_3_10 git_last_commit: 348a018 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Clomial_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Clomial_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Clomial_1.22.0.tgz vignettes: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.pdf vignetteTitles: A likelihood maximization approach to infer the clonal structure of a cancer using multiple tumor samples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.R dependencyCount: 4 Package: Clonality Version: 1.34.0 Depends: R (>= 2.12.2), DNAcopy Imports: grDevices, graphics, stats, utils Suggests: gdata License: GPL-3 MD5sum: 9c684eca472ca9021fb6c4d1f49d3693 NeedsCompilation: no Title: Clonality testing Description: Statistical tests for clonality versus independence of tumors from the same patient based on their LOH or genomewide copy number profiles biocViews: CopyNumber, Classification, aCGH, Mutations, Diagnosis, metastasis Author: Irina Ostrovnaya Maintainer: Irina Ostrovnaya git_url: https://git.bioconductor.org/packages/Clonality git_branch: RELEASE_3_10 git_last_commit: 681c6d1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Clonality_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Clonality_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Clonality_1.34.0.tgz vignettes: vignettes/Clonality/inst/doc/Clonality.pdf vignetteTitles: Clonality hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Clonality/inst/doc/Clonality.R dependencyCount: 5 Package: clonotypeR Version: 1.24.0 Imports: methods Suggests: BiocGenerics, edgeR, knitr, pvclust, RUnit, vegan License: file LICENSE MD5sum: 6d280eebf362476ee2c74c4b6d388aa1 NeedsCompilation: no Title: High throughput analysis of T cell antigen receptor sequences Description: High throughput analysis of T cell antigen receptor sequences The genes encoding T cell receptors are created by somatic recombination, generating an immense combination of V, (D) and J segments. Additional processes during the recombination create extra sequence diversity between the V an J segments. Collectively, this hyper-variable region is called the CDR3 loop. The purpose of this package is to process and quantitatively analyse millions of V-CDR3-J combination, called clonotypes, from multiple sequence libraries. biocViews: Sequencing Author: Charles Plessy Maintainer: Charles Plessy URL: http://clonotyper.branchable.com/ VignetteBuilder: knitr BugReports: http://clonotyper.branchable.com/Bugs/ git_url: https://git.bioconductor.org/packages/clonotypeR git_branch: RELEASE_3_10 git_last_commit: a749642 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/clonotypeR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/clonotypeR_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/clonotypeR_1.24.0.tgz vignettes: vignettes/clonotypeR/inst/doc/clonotypeR.html vignetteTitles: clonotypeR User's Guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/clonotypeR/inst/doc/clonotypeR.R dependencyCount: 1 Package: clst Version: 1.34.0 Depends: R (>= 2.10) Imports: ROC, lattice Suggests: RUnit License: GPL-3 MD5sum: 6fc79e8dbb09e141256e6df053eac971 NeedsCompilation: no Title: Classification by local similarity threshold Description: Package for modified nearest-neighbor classification based on calculation of a similarity threshold distinguishing within-group from between-group comparisons. biocViews: Classification Author: Noah Hoffman Maintainer: Noah Hoffman git_url: https://git.bioconductor.org/packages/clst git_branch: RELEASE_3_10 git_last_commit: 20b988d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/clst_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/clst_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/clst_1.34.0.tgz vignettes: vignettes/clst/inst/doc/clstDemo.pdf vignetteTitles: clst hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clst/inst/doc/clstDemo.R dependsOnMe: clstutils dependencyCount: 20 Package: clstutils Version: 1.34.0 Depends: R (>= 2.10), clst, rjson, ape Imports: lattice, RSQLite Suggests: RUnit, RSVGTipsDevice License: GPL-3 MD5sum: 78dc5bbed86431e17daa270b269abe44 NeedsCompilation: no Title: Tools for performing taxonomic assignment. Description: Tools for performing taxonomic assignment based on phylogeny using pplacer and clst. biocViews: Sequencing, Classification, Visualization, QualityControl Author: Noah Hoffman Maintainer: Noah Hoffman git_url: https://git.bioconductor.org/packages/clstutils git_branch: RELEASE_3_10 git_last_commit: 781cf94 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/clstutils_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/clstutils_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/clstutils_1.34.0.tgz vignettes: vignettes/clstutils/inst/doc/pplacerDemo.pdf, vignettes/clstutils/inst/doc/refSet.pdf vignetteTitles: clst, clstutils hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clstutils/inst/doc/pplacerDemo.R, vignettes/clstutils/inst/doc/refSet.R dependencyCount: 39 Package: CluMSID Version: 1.2.1 Depends: R (>= 3.6) Imports: mzR, S4Vectors, dbscan, RColorBrewer, ape, network, GGally, ggplot2, plotly, methods, utils, stats, sna, grDevices, graphics, Biobase, gplots, MSnbase Suggests: knitr, rmarkdown, testthat, dplyr, readr, stringr, magrittr, CluMSIDdata, metaMS, metaMSdata, xcms License: MIT + file LICENSE MD5sum: 1cd10aab686db9b3b0627e8a5266f9c3 NeedsCompilation: no Title: Clustering of MS2 Spectra for Metabolite Identification Description: CluMSID is a tool that aids the identification of features in untargeted LC-MS/MS analysis by the use of MS2 spectra similarity and unsupervised statistical methods. It offers functions for a complete and customisable workflow from raw data to visualisations and is interfaceable with the xmcs family of preprocessing packages. biocViews: Metabolomics, Preprocessing, Clustering Author: Tobias Depke [aut, cre], Raimo Franke [ctb], Mark Broenstrup [ths] Maintainer: Tobias Depke URL: https://github.com/tdepke/CluMSID VignetteBuilder: knitr BugReports: https://github.com/tdepke/CluMSID/issues git_url: https://git.bioconductor.org/packages/CluMSID git_branch: RELEASE_3_10 git_last_commit: 6f4e417 git_last_commit_date: 2020-04-14 Date/Publication: 2020-04-14 source.ver: src/contrib/CluMSID_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/CluMSID_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CluMSID_1.2.1.tgz vignettes: vignettes/CluMSID/inst/doc/CluMSID_DI-MSMS.html, vignettes/CluMSID/inst/doc/CluMSID_GC-EI-MS.html, vignettes/CluMSID/inst/doc/CluMSID_lowres-LC-MSMS.html, vignettes/CluMSID/inst/doc/CluMSID_MTBLS.html, vignettes/CluMSID/inst/doc/CluMSID_tutorial.html vignetteTitles: CluMSID DI-MS/MS Tutorial, CluMSID GC-EI-MS Tutorial, CluMSID LowRes Tutorial, CluMSID MTBLS Tutorial, CluMSID Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CluMSID/inst/doc/CluMSID_DI-MSMS.R, vignettes/CluMSID/inst/doc/CluMSID_GC-EI-MS.R, vignettes/CluMSID/inst/doc/CluMSID_lowres-LC-MSMS.R, vignettes/CluMSID/inst/doc/CluMSID_MTBLS.R, vignettes/CluMSID/inst/doc/CluMSID_tutorial.R dependencyCount: 129 Package: clustComp Version: 1.14.0 Depends: R (>= 3.3) Imports: sm, stats, graphics, grDevices Suggests: Biobase, colonCA, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 1b2fb64d1c500db97cf3ae7cd0ab203c NeedsCompilation: no Title: Clustering Comparison Package Description: clustComp is a package that implements several techniques for the comparison and visualisation of relationships between different clustering results, either flat versus flat or hierarchical versus flat. These relationships among clusters are displayed using a weighted bi-graph, in which the nodes represent the clusters and the edges connect pairs of nodes with non-empty intersection; the weight of each edge is the number of elements in that intersection and is displayed through the edge thickness. The best layout of the bi-graph is provided by the barycentre algorithm, which minimises the weighted number of crossings. In the case of comparing a hierarchical and a non-hierarchical clustering, the dendrogram is pruned at different heights, selected by exploring the tree by depth-first search, starting at the root. Branches are decided to be split according to the value of a scoring function, that can be based either on the aesthetics of the bi-graph or on the mutual information between the hierarchical and the flat clusterings. A mapping between groups of clusters from each side is constructed with a greedy algorithm, and can be additionally visualised. biocViews: GeneExpression, Clustering, Visualization Author: Aurora Torrente and Alvis Brazma. Maintainer: Aurora Torrente git_url: https://git.bioconductor.org/packages/clustComp git_branch: RELEASE_3_10 git_last_commit: b2a47c5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/clustComp_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/clustComp_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/clustComp_1.14.0.tgz vignettes: vignettes/clustComp/inst/doc/clustComp.pdf vignetteTitles: The clustComp Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clustComp/inst/doc/clustComp.R dependencyCount: 4 Package: clusterExperiment Version: 2.6.1 Depends: R (>= 3.5.0), SingleCellExperiment, SummarizedExperiment, BiocGenerics Imports: methods, NMF, RColorBrewer, ape (>= 5.0), cluster, stats, limma, howmany, locfdr, matrixStats, graphics, parallel, RSpectra, kernlab, stringr, S4Vectors, grDevices, DelayedArray (>= 0.7.48), HDF5Array (>= 1.7.10), Matrix, Rcpp, edgeR, scales, zinbwave, phylobase, pracma LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat, scRNAseq, MAST, Rtsne, scran, igraph License: Artistic-2.0 MD5sum: 3230217acf4493860045ebe104a8977e NeedsCompilation: yes Title: Compare Clusterings for Single-Cell Sequencing Description: Provides functionality for running and comparing many different clusterings of single-cell sequencing data or other large mRNA Expression data sets. biocViews: Clustering, RNASeq, Sequencing, Software, SingleCell Author: Elizabeth Purdom [aut, cre, cph], Davide Risso [aut] Maintainer: Elizabeth Purdom VignetteBuilder: knitr BugReports: https://github.com/epurdom/clusterExperiment/issues git_url: https://git.bioconductor.org/packages/clusterExperiment git_branch: RELEASE_3_10 git_last_commit: 96e2103 git_last_commit_date: 2019-11-05 Date/Publication: 2019-11-06 source.ver: src/contrib/clusterExperiment_2.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/clusterExperiment_2.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/clusterExperiment_2.6.1.tgz vignettes: vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.html vignetteTitles: clusterExperiment Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.R dependsOnMe: netSmooth importsMe: tradeSeq suggestsMe: slingshot dependencyCount: 144 Package: ClusterJudge Version: 1.8.0 Depends: R (>= 3.4), stats, utils, graphics, infotheo, lattice, latticeExtra, httr, jsonlite Suggests: yeastExpData, knitr, rmarkdown, devtools, testthat, biomaRt License: Artistic-2.0 Archs: i386, x64 MD5sum: a4fe7478b5b17c61ed8aeeeb79cdf673 NeedsCompilation: no Title: Judging Quality of Clustering Methods using Mutual Information Description: ClusterJudge implements the functions, examples and other software published as an algorithm by Gibbons, FD and Roth FP. The article is called "Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation" and it appeared in Genome Research, vol. 12, pp1574-1581 (2002). See package?ClusterJudge for an overview. biocViews: Software, StatisticalMethod, Clustering, GeneExpression, GO Author: Adrian Pasculescu Maintainer: Adrian Pasculescu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClusterJudge git_branch: RELEASE_3_10 git_last_commit: d806ac3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ClusterJudge_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ClusterJudge_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ClusterJudge_1.8.0.tgz vignettes: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.R dependencyCount: 21 Package: clusterProfiler Version: 3.14.3 Depends: R (>= 3.4.0) Imports: AnnotationDbi, DOSE (>= 3.5.1), enrichplot (>= 0.99.7), ggplot2, GO.db, GOSemSim, magrittr, methods, plyr, qvalue, rvcheck, stats, tidyr, utils Suggests: AnnotationHub, dplyr, KEGG.db, knitr, org.Hs.eg.db, prettydoc, ReactomePA, testthat License: Artistic-2.0 MD5sum: 01c51c57396b21694d5bbab2f83ac80b NeedsCompilation: no Title: statistical analysis and visualization of functional profiles for genes and gene clusters Description: This package implements methods to analyze and visualize functional profiles (GO and KEGG) of gene and gene clusters. biocViews: Annotation, Clustering, GeneSetEnrichment, GO, KEGG, MultipleComparison, Pathways, Reactome, Visualization Author: Guangchuang Yu [aut, cre, cph] (), Li-Gen Wang [ctb], Giovanni Dall'Olio [ctb] (formula interface of compareCluster) Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/software/clusterProfiler VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/clusterProfiler/issues git_url: https://git.bioconductor.org/packages/clusterProfiler git_branch: RELEASE_3_10 git_last_commit: d9752bc git_last_commit_date: 2020-01-08 Date/Publication: 2020-01-08 source.ver: src/contrib/clusterProfiler_3.14.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/clusterProfiler_3.14.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/clusterProfiler_3.14.3.tgz vignettes: vignettes/clusterProfiler/inst/doc/clusterProfiler.html vignetteTitles: Statistical analysis and visualization of functional profiles for genes and gene clusters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterProfiler/inst/doc/clusterProfiler.R importsMe: bioCancer, CEMiTool, DAPAR, debrowser, eegc, enrichTF, esATAC, fcoex, GDCRNATools, LINC, MAGeCKFlute, methylGSA, miRspongeR, MoonlightR, RNASeqR, signatureSearch, TCGAbiolinksGUI suggestsMe: ChIPseeker, cola, DOSE, enrichplot, epihet, GOSemSim, ReactomePA, scGPS, TCGAbiolinks dependencyCount: 123 Package: clusterSeq Version: 1.10.0 Depends: R (>= 3.0.0), methods, BiocParallel, baySeq, graphics, stats, utils Imports: BiocGenerics Suggests: BiocStyle License: GPL-3 MD5sum: 9f8f90a08a652e05201905a52ca96e80 NeedsCompilation: no Title: Clustering of high-throughput sequencing data by identifying co-expression patterns Description: Identification of clusters of co-expressed genes based on their expression across multiple (replicated) biological samples. biocViews: Sequencing, DifferentialExpression, MultipleComparison, Clustering, GeneExpression Author: Thomas J. Hardcastle & Irene Papatheodorou Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/clusterSeq git_branch: RELEASE_3_10 git_last_commit: 7835833 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/clusterSeq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/clusterSeq_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/clusterSeq_1.10.0.tgz vignettes: vignettes/clusterSeq/inst/doc/clusterSeq.pdf vignetteTitles: Advanced baySeq analyses hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterSeq/inst/doc/clusterSeq.R dependencyCount: 32 Package: ClusterSignificance Version: 1.14.0 Depends: R (>= 3.3.0) Imports: methods, pracma, princurve (>= 2.0.5), scatterplot3d, RColorBrewer, grDevices, graphics, utils, stats Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, plsgenomics, covr License: GPL-3 MD5sum: 756271dedacaa3f5bae7d75580efb0a4 NeedsCompilation: no Title: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data Description: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data. The term class clusters here refers to, clusters of points representing known classes in the data. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. ClusterSignificance accomplishes this by, projecting all points onto a one dimensional line. Cluster separations are then scored and the probability of the seen separation being due to chance is evaluated using a permutation method. biocViews: Clustering, Classification, PrincipalComponent, StatisticalMethod Author: Jason T. Serviss [aut, cre], Jesper R. Gadin [aut] Maintainer: Jason T Serviss URL: https://github.com/jasonserviss/ClusterSignificance/ VignetteBuilder: knitr BugReports: https://github.com/jasonserviss/ClusterSignificance/issues git_url: https://git.bioconductor.org/packages/ClusterSignificance git_branch: RELEASE_3_10 git_last_commit: 0aa7f45 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ClusterSignificance_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ClusterSignificance_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ClusterSignificance_1.14.0.tgz vignettes: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.html vignetteTitles: ClusterSignificance Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.R dependencyCount: 10 Package: clusterStab Version: 1.58.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), methods Suggests: fibroEset, genefilter License: Artistic-2.0 MD5sum: 83ef23beda3eb7c3bb7d7874160ffc57 NeedsCompilation: no Title: Compute cluster stability scores for microarray data Description: This package can be used to estimate the number of clusters in a set of microarray data, as well as test the stability of these clusters. biocViews: Clustering Author: James W. MacDonald, Debashis Ghosh, Mark Smolkin Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/clusterStab git_branch: RELEASE_3_10 git_last_commit: 485ddd6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/clusterStab_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/clusterStab_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/clusterStab_1.58.0.tgz vignettes: vignettes/clusterStab/inst/doc/clusterStab.pdf vignetteTitles: clusterStab Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterStab/inst/doc/clusterStab.R dependencyCount: 7 Package: CMA Version: 1.44.0 Depends: R (>= 2.10), methods, stats, Biobase Suggests: MASS, class, nnet, glmnet, e1071, randomForest, plsgenomics, gbm, mgcv, corpcor, limma, st, mvtnorm License: GPL (>= 2) MD5sum: 8dfdd503db1d69cfe6a4c69005c3e2ed NeedsCompilation: no Title: Synthesis of microarray-based classification Description: This package provides a comprehensive collection of various microarray-based classification algorithms both from Machine Learning and Statistics. Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed combined or stepwise in a user-friendly environment. biocViews: Classification, DecisionTree Author: Martin Slawski , Anne-Laure Boulesteix , Christoph Bernau . Maintainer: Roman Hornung git_url: https://git.bioconductor.org/packages/CMA git_branch: RELEASE_3_10 git_last_commit: 0908cc0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CMA_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CMA_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CMA_1.44.0.tgz vignettes: vignettes/CMA/inst/doc/CMA_vignette.pdf vignetteTitles: CMA_vignette.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CMA/inst/doc/CMA_vignette.R dependencyCount: 7 Package: cn.farms Version: 1.34.0 Depends: R (>= 3.0), Biobase, methods, ff, oligoClasses, snow Imports: DBI, affxparser, oligo, DNAcopy, preprocessCore, lattice Suggests: pd.mapping250k.sty, pd.mapping250k.nsp, pd.genomewidesnp.5, pd.genomewidesnp.6 License: LGPL (>= 2.0) MD5sum: 2675c2f0767733091a728afca6308ed4 NeedsCompilation: yes Title: cn.FARMS - factor analysis for copy number estimation Description: This package implements the cn.FARMS algorithm for copy number variation (CNV) analysis. cn.FARMS allows to analyze the most common Affymetrix (250K-SNP6.0) array types, supports high-performance computing using snow and ff. biocViews: Microarray, CopyNumberVariation Author: Andreas Mitterecker, Djork-Arne Clevert Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/cnfarms/cnfarms.html git_url: https://git.bioconductor.org/packages/cn.farms git_branch: RELEASE_3_10 git_last_commit: 47595d2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cn.farms_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cn.farms_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cn.farms_1.34.0.tgz vignettes: vignettes/cn.farms/inst/doc/cn.farms.pdf vignetteTitles: cn.farms: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cn.farms/inst/doc/cn.farms.R dependencyCount: 59 Package: cn.mops Version: 1.32.0 Depends: R (>= 2.12), methods, utils, stats, graphics, parallel, GenomicRanges Imports: BiocGenerics, Biobase, IRanges, Rsamtools, GenomeInfoDb, S4Vectors, exomeCopy Suggests: DNAcopy License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 7b34f8f0122a772aab01780983064f63 NeedsCompilation: yes Title: cn.mops - Mixture of Poissons for CNV detection in NGS data Description: cn.mops (Copy Number estimation by a Mixture Of PoissonS) is a data processing pipeline for copy number variations and aberrations (CNVs and CNAs) from next generation sequencing (NGS) data. The package supplies functions to convert BAM files into read count matrices or genomic ranges objects, which are the input objects for cn.mops. cn.mops models the depths of coverage across samples at each genomic position. Therefore, it does not suffer from read count biases along chromosomes. Using a Bayesian approach, cn.mops decomposes read variations across samples into integer copy numbers and noise by its mixture components and Poisson distributions, respectively. cn.mops guarantees a low FDR because wrong detections are indicated by high noise and filtered out. cn.mops is very fast and written in C++. biocViews: Sequencing, CopyNumberVariation, Homo_sapiens, CellBiology, HapMap, Genetics Author: Guenter Klambauer Maintainer: Guenter Klambauer URL: http://www.bioinf.jku.at/software/cnmops/cnmops.html git_url: https://git.bioconductor.org/packages/cn.mops git_branch: RELEASE_3_10 git_last_commit: df38eb7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cn.mops_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cn.mops_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cn.mops_1.32.0.tgz vignettes: vignettes/cn.mops/inst/doc/cn.mops.pdf vignetteTitles: cn.mops: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cn.mops/inst/doc/cn.mops.R dependsOnMe: panelcn.mops importsMe: CopyNumberPlots dependencyCount: 28 Package: CNAnorm Version: 1.32.0 Depends: R (>= 2.10.1), methods Imports: DNAcopy License: GPL-2 Archs: i386, x64 MD5sum: 408ecbbf0665fdd9ccd75ce90f205aa6 NeedsCompilation: yes Title: A normalization method for Copy Number Aberration in cancer samples Description: Performs ratio, GC content correction and normalization of data obtained using low coverage (one read every 100-10,000 bp) high troughput sequencing. It performs a "discrete" normalization looking for the ploidy of the genome. It will also provide tumour content if at least two ploidy states can be found. biocViews: CopyNumberVariation, Sequencing, Coverage, Normalization, WholeGenome, DNASeq, GenomicVariation Author: Stefano Berri , Henry M. Wood , Arief Gusnanto Maintainer: Stefano Berri URL: http://www.r-project.org, git_url: https://git.bioconductor.org/packages/CNAnorm git_branch: RELEASE_3_10 git_last_commit: ea79e48 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CNAnorm_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CNAnorm_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CNAnorm_1.32.0.tgz vignettes: vignettes/CNAnorm/inst/doc/CNAnorm.pdf vignetteTitles: CNAnorm.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNAnorm/inst/doc/CNAnorm.R dependencyCount: 2 Package: CNEr Version: 1.22.0 Depends: R (>= 3.4) Imports: Biostrings (>= 2.33.4), DBI (>= 0.7), RSQLite (>= 0.11.4), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.23.16), rtracklayer (>= 1.25.5), XVector (>= 0.5.4), GenomicAlignments (>= 1.1.9), methods, S4Vectors (>= 0.13.13), IRanges (>= 2.5.27), readr (>= 0.2.2), BiocGenerics, tools, parallel, reshape2 (>= 1.4.1), ggplot2 (>= 2.1.0), poweRlaw (>= 0.60.3), annotate (>= 1.50.0), GO.db (>= 3.3.0), R.utils (>= 2.3.0), KEGGREST (>= 1.14.0) LinkingTo: S4Vectors, IRanges, XVector Suggests: Gviz (>= 1.7.4), BiocStyle, knitr, rmarkdown, testthat, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, TxDb.Drerio.UCSC.danRer10.refGene, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Ggallus.UCSC.galGal3 License: GPL-2 | file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: 77d590d1b5d4348a8c0cad059e113217 NeedsCompilation: yes Title: CNE Detection and Visualization Description: Large-scale identification and advanced visualization of sets of conserved noncoding elements. biocViews: GeneRegulation, Visualization, DataImport Author: Ge Tan Maintainer: Ge Tan URL: https://github.com/ge11232002/CNEr VignetteBuilder: knitr BugReports: https://github.com/ge11232002/CNEr/issues git_url: https://git.bioconductor.org/packages/CNEr git_branch: RELEASE_3_10 git_last_commit: fcde299 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CNEr_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CNEr_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CNEr_1.22.0.tgz vignettes: vignettes/CNEr/inst/doc/CNEr.html, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.html vignetteTitles: CNE identification and visualisation, Pairwise whole genome alignment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CNEr/inst/doc/CNEr.R, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.R importsMe: TFBSTools dependencyCount: 115 Package: CNORdt Version: 1.28.0 Depends: R (>= 1.8.0), CellNOptR (>= 0.99), abind License: GPL-2 Archs: i386, x64 MD5sum: 3de00ea9b83d0dbc089626777c2f070c NeedsCompilation: yes Title: Add-on to CellNOptR: Discretized time treatments Description: This add-on to the package CellNOptR handles time-course data, as opposed to steady state data in CellNOptR. It scales the simulation step to allow comparison and model fitting for time-course data. Future versions will optimize delays and strengths for each edge. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics, TimeCourse Author: A. MacNamara Maintainer: A. MacNamara git_url: https://git.bioconductor.org/packages/CNORdt git_branch: RELEASE_3_10 git_last_commit: 991bece git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CNORdt_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CNORdt_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CNORdt_1.28.0.tgz vignettes: vignettes/CNORdt/inst/doc/CNORdt-vignette.pdf vignetteTitles: Using multiple time points to train logic models to data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORdt/inst/doc/CNORdt-vignette-example.R, vignettes/CNORdt/inst/doc/CNORdt-vignette.R dependencyCount: 70 Package: CNORfeeder Version: 1.26.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), graph Suggests: minet, catnet, Rgraphviz, RUnit, BiocGenerics, igraph License: GPL-3 Archs: i386, x64 MD5sum: cf14aaa41b02b32df22116a4e8fd1179 NeedsCompilation: no Title: Integration of CellNOptR to add missing links Description: This package integrates literature-constrained and data-driven methods to infer signalling networks from perturbation experiments. It permits to extends a given network with links derived from the data via various inference methods and uses information on physical interactions of proteins to guide and validate the integration of links. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics, Bioinformatics, NetworkInference Author: F.Eduati Maintainer: F.Eduati git_url: https://git.bioconductor.org/packages/CNORfeeder git_branch: RELEASE_3_10 git_last_commit: 17df295 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CNORfeeder_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CNORfeeder_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CNORfeeder_1.26.0.tgz vignettes: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORfeeder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.R dependencyCount: 69 Package: CNORfuzzy Version: 1.28.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), nloptr (>= 0.8.5) Suggests: xtable, Rgraphviz, RUnit, BiocGenerics License: GPL-2 MD5sum: 0313b32dd3b3626cf192883d2474b4fc NeedsCompilation: yes Title: Addon to CellNOptR: Fuzzy Logic Description: This package is an extension to CellNOptR. It contains additional functionality needed to simulate and train a prior knowledge network to experimental data using constrained fuzzy logic (cFL, rather than Boolean logic as is the case in CellNOptR). Additionally, this package will contain functions to use for the compilation of multiple optimization results (either Boolean or cFL). biocViews: Network Author: M. Morris, T. Cokelaer Maintainer: T. Cokelaer git_url: https://git.bioconductor.org/packages/CNORfuzzy git_branch: RELEASE_3_10 git_last_commit: 6ab97e9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CNORfuzzy_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CNORfuzzy_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CNORfuzzy_1.28.0.tgz vignettes: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORfuzzyl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.R dependencyCount: 70 Package: CNORode Version: 1.28.0 Depends: CellNOptR (>= 1.5.14), genalg Enhances: MEIGOR License: GPL-2 Archs: i386, x64 MD5sum: 391d4160ee200bd5a3f5c74de6d45a6d NeedsCompilation: yes Title: ODE add-on to CellNOptR Description: ODE add-on to CellNOptR biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics, Bioinformatics, TimeCourse Author: David Henriques, Thomas Cokelaer Maintainer: David Henriques git_url: https://git.bioconductor.org/packages/CNORode git_branch: RELEASE_3_10 git_last_commit: ad2b07e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CNORode_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CNORode_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CNORode_1.28.0.tgz vignettes: vignettes/CNORode/inst/doc/CNORode-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORode hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORode/inst/doc/CNORode-vignette.R dependsOnMe: MEIGOR dependencyCount: 70 Package: CNTools Version: 1.42.0 Depends: R (>= 2.10), methods, tools, stats, genefilter License: LGPL Archs: i386, x64 MD5sum: 0ec0bc66c851f9c9a3d67f207f53ae0c NeedsCompilation: yes Title: Convert segment data into a region by sample matrix to allow for other high level computational analyses. Description: This package provides tools to convert the output of segmentation analysis using DNAcopy to a matrix structure with overlapping segments as rows and samples as columns so that other computational analyses can be applied to segmented data biocViews: Microarray, CopyNumberVariation Author: Jianhua Zhang Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/CNTools git_branch: RELEASE_3_10 git_last_commit: 1864273 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CNTools_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CNTools_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CNTools_1.42.0.tgz vignettes: vignettes/CNTools/inst/doc/HowTo.pdf vignetteTitles: NCTools HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNTools/inst/doc/HowTo.R dependsOnMe: cghMCR dependencyCount: 39 Package: CNVfilteR Version: 1.0.4 Depends: R (>= 3.6) Imports: IRanges, GenomicRanges, SummarizedExperiment, pracma, regioneR, assertthat, karyoploteR, CopyNumberPlots, graphics, utils, VariantAnnotation, Rsamtools, GenomeInfoDb, Biostrings, methods Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked License: Artistic-2.0 Archs: i386, x64 MD5sum: d4cd85cf8c3d6c045c98f95fe4857f21 NeedsCompilation: no Title: Identifies false positives of CNV calling tools by using SNV calls Description: CNVfilteR identifies those CNVs that can be discarded by using the single nucleotide variant (SNV) calls that are usually obtained in common NGS pipelines. biocViews: CopyNumberVariation, Sequencing, DNASeq, Visualization, DataImport Author: Jose Marcos Moreno-Cabrera and Bernat Gel Maintainer: Jose Marcos Moreno-Cabrera URL: https://github.com/jpuntomarcos/CNVfilteR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNVfilteR git_branch: RELEASE_3_10 git_last_commit: 1c21535 git_last_commit_date: 2020-01-30 Date/Publication: 2020-01-30 source.ver: src/contrib/CNVfilteR_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/CNVfilteR_1.0.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CNVfilteR_1.0.4.tgz vignettes: vignettes/CNVfilteR/inst/doc/CNVfilteR.html vignetteTitles: CNVfilteR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVfilteR/inst/doc/CNVfilteR.R dependencyCount: 152 Package: cnvGSA Version: 1.30.0 Depends: brglm, doParallel, foreach, GenomicRanges, methods, splitstackshape Suggests: cnvGSAdata, org.Hs.eg.db License: LGPL MD5sum: a5cb6ea46355c3768d75c9fc466a83e2 NeedsCompilation: no Title: Gene Set Analysis of (Rare) Copy Number Variants Description: This package is intended to facilitate gene-set association with rare CNVs in case-control studies. biocViews: MultipleComparison Author: Daniele Merico , Robert Ziman ; packaged by Joseph Lugo Maintainer: Joseph Lugo git_url: https://git.bioconductor.org/packages/cnvGSA git_branch: RELEASE_3_10 git_last_commit: 80623b2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cnvGSA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cnvGSA_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cnvGSA_1.30.0.tgz vignettes: vignettes/cnvGSA/inst/doc/cnvGSA-vignette.pdf, vignettes/cnvGSA/inst/doc/cnvGSAUsersGuide.pdf vignetteTitles: cnvGSA - Gene-Set Analysis of Rare Copy Number Variants, cnvGSAUsersGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 24 Package: CNVPanelizer Version: 1.18.0 Depends: R (>= 3.2.0), GenomicRanges Imports: S4Vectors, grDevices, stats, utils, NOISeq, IRanges, Rsamtools, exomeCopy, foreach, ggplot2, plyr, GenomeInfoDb, gplots, reshape2, stringr, testthat, graphics, methods, shiny, shinyFiles, shinyjs, grid, openxlsx Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 3d7687cfc776de43543b09be3f92209b NeedsCompilation: no Title: Reliable CNV detection in targeted sequencing applications Description: A method that allows for the use of a collection of non-matched normal tissue samples. Our approach uses a non-parametric bootstrap subsampling of the available reference samples to estimate the distribution of read counts from targeted sequencing. As inspired by random forest, this is combined with a procedure that subsamples the amplicons associated with each of the targeted genes. The obtained information allows us to reliably classify the copy number aberrations on the gene level. biocViews: Classification, Sequencing, Normalization, CopyNumberVariation, Coverage Author: Cristiano Oliveira [aut], Thomas Wolf [aut, cre], Albrecht Stenzinger [ctb], Volker Endris [ctb], Nicole Pfarr [ctb], Benedikt Brors [ths], Wilko Weichert [ths] Maintainer: Thomas Wolf VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNVPanelizer git_branch: RELEASE_3_10 git_last_commit: c34b82b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CNVPanelizer_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CNVPanelizer_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CNVPanelizer_1.18.0.tgz vignettes: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.pdf vignetteTitles: CNVPanelizer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.R dependencyCount: 106 Package: CNVRanger Version: 1.2.2 Depends: GenomicRanges, RaggedExperiment Imports: BiocParallel, GDSArray, GenomeInfoDb, IRanges, S4Vectors, SNPRelate, SummarizedExperiment, data.table, edgeR, gdsfmt, grDevices, lattice, limma, methods, plyr, qqman, rappdirs, reshape2, stats, utils Suggests: AnnotationHub, BSgenome.Btaurus.UCSC.bosTau6.masked, BiocStyle, Gviz, MultiAssayExperiment, TCGAutils, curatedTCGAData, ensembldb, knitr, regioneR, rmarkdown License: Artistic-2.0 MD5sum: 8f4965e4838bc761ad2ed849db6096f7 NeedsCompilation: no Title: Summarization and expression/phenotype association of CNV ranges Description: The CNVRanger package implements a comprehensive tool suite for CNV analysis. This includes functionality for summarizing individual CNV calls across a population, assessing overlap with functional genomic regions, and association analysis with gene expression and quantitative phenotypes. biocViews: CopyNumberVariation, DifferentialExpression, GeneExpression, GenomeWideAssociation, GenomicVariation, Microarray, RNASeq, SNP Author: Ludwig Geistlinger [aut, cre], Vinicius Henrique da Silva [aut], Marcel Ramos [ctb], Levi Waldron [ctb] Maintainer: Ludwig Geistlinger VignetteBuilder: knitr BugReports: https://github.com/waldronlab/CNVRanger/issues git_url: https://git.bioconductor.org/packages/CNVRanger git_branch: RELEASE_3_10 git_last_commit: 556ffbf git_last_commit_date: 2019-11-13 Date/Publication: 2019-11-14 source.ver: src/contrib/CNVRanger_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/CNVRanger_1.2.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CNVRanger_1.2.2.tgz vignettes: vignettes/CNVRanger/inst/doc/CNVRanger.html vignetteTitles: Summarization and quantitative trait analysis of CNV ranges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVRanger/inst/doc/CNVRanger.R dependencyCount: 53 Package: CNVrd2 Version: 1.24.0 Depends: R (>= 3.0.0), methods, VariantAnnotation, parallel, rjags, ggplot2, gridExtra Imports: DNAcopy, IRanges, Rsamtools Suggests: knitr License: GPL-2 MD5sum: fa8f2ddc500c15db3687ff6774a9243c NeedsCompilation: no Title: CNVrd2: a read depth-based method to detect and genotype complex common copy number variants from next generation sequencing data. Description: CNVrd2 uses next-generation sequencing data to measure human gene copy number for multiple samples, indentify SNPs tagging copy number variants and detect copy number polymorphic genomic regions. biocViews: CopyNumberVariation, SNP, Sequencing, Software, Coverage, LinkageDisequilibrium, Clustering. Author: Hoang Tan Nguyen, Tony R Merriman and Mik Black Maintainer: Hoang Tan Nguyen URL: https://github.com/hoangtn/CNVrd2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNVrd2 git_branch: RELEASE_3_10 git_last_commit: 28d55c2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CNVrd2_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CNVrd2_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CNVrd2_1.24.0.tgz vignettes: vignettes/CNVrd2/inst/doc/CNVrd2.pdf vignetteTitles: A Markdown Vignette with knitr hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVrd2/inst/doc/CNVrd2.R dependencyCount: 116 Package: CNVtools Version: 1.80.0 Depends: R (>= 2.10), survival License: GPL-3 MD5sum: a1a65edad04aa5dbb2964f0e880bcf4f NeedsCompilation: yes Title: A package to test genetic association with CNV data Description: This package is meant to facilitate the testing of Copy Number Variant data for genetic association, typically in case-control studies. biocViews: GeneticVariability Author: Chris Barnes and Vincent Plagnol Maintainer: Chris Barnes git_url: https://git.bioconductor.org/packages/CNVtools git_branch: RELEASE_3_10 git_last_commit: bf3f720 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CNVtools_1.80.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CNVtools_1.80.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CNVtools_1.80.0.tgz vignettes: vignettes/CNVtools/inst/doc/CNVtools-vignette.pdf vignetteTitles: Copy Number Variation Tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVtools/inst/doc/CNVtools-vignette.R dependencyCount: 10 Package: cobindR Version: 1.24.0 Imports: methods, seqinr, yaml, rtfbs, gplots, mclust, gmp, BiocGenerics (>= 0.13.8), IRanges, Biostrings, BSgenome, biomaRt Suggests: RUnit Enhances: rGADEM, seqLogo, genoPlotR, parallel, VennDiagram, RColorBrewer, vcd, MotifDb, snowfall License: Artistic-2.0 Archs: i386, x64 MD5sum: 3f576d5d3d29ef129f76baf4d33d400d NeedsCompilation: no Title: Finding Co-occuring motifs of transcription factor binding sites Description: Finding and analysing co-occuring motifs of transcription factor binding sites in groups of genes biocViews: ChIPSeq, CellBiology, MultipleComparison, SequenceMatching Author: Manuela Benary, Stefan Kroeger, Yuehien Lee, Robert Lehmann Maintainer: Manuela Benary git_url: https://git.bioconductor.org/packages/cobindR git_branch: RELEASE_3_10 git_last_commit: 8add2bd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cobindR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cobindR_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cobindR_1.24.0.tgz vignettes: vignettes/cobindR/inst/doc/cobindR.pdf vignetteTitles: Using cobindR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cobindR/inst/doc/cobindR.R dependencyCount: 98 Package: CoCiteStats Version: 1.58.0 Depends: R (>= 2.0), org.Hs.eg.db Imports: AnnotationDbi License: CPL MD5sum: 49a1bfcde706f75630bb701c64af1fc4 NeedsCompilation: no Title: Different test statistics based on co-citation. Description: A collection of software tools for dealing with co-citation data. biocViews: Software Author: B. Ding and R. Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/CoCiteStats git_branch: RELEASE_3_10 git_last_commit: e4d4e67 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CoCiteStats_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CoCiteStats_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CoCiteStats_1.58.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 27 Package: COCOA Version: 2.0.0 Depends: R (>= 3.5), GenomicRanges Imports: BiocGenerics, S4Vectors, IRanges, data.table, ggplot2, Biobase, stats, methods, ComplexHeatmap, MIRA, tidyr, grid, grDevices, simpleCache, fitdistrplus Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown, AnnotationHub, LOLA License: GPL-3 MD5sum: 606eae22383b6657f933745d4a7f8ba0 NeedsCompilation: no Title: Coordinate Covariation Analysis Description: COCOA is a method for understanding epigenetic variation among samples. COCOA can be used with epigenetic data that includes genomic coordinates and an epigenetic signal, such as DNA methylation and chromatin accessibility data. To describe the method on a high level, COCOA quantifies inter-sample variation with either a supervised or unsupervised technique then uses a database of "region sets" to annotate the variation among samples. A region set is a set of genomic regions that share a biological annotation, for instance transcription factor (TF) binding regions, histone modification regions, or open chromatin regions. COCOA can identify region sets that are associated with epigenetic variation between samples and increase understanding of variation in your data. biocViews: Epigenetics, DNAMethylation, ATACSeq, DNaseSeq, MethylSeq, MethylationArray, PrincipalComponent, GenomicVariation, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, Sequencing, ImmunoOncology Author: John Lawson [aut, cre], Nathan Sheffield [aut] (http://www.databio.org), Jason Smith [ctb] Maintainer: John Lawson URL: http://code.databio.org/COCOA/ VignetteBuilder: knitr BugReports: https://github.com/databio/COCOA git_url: https://git.bioconductor.org/packages/COCOA git_branch: RELEASE_3_10 git_last_commit: 2a0bc89 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/COCOA_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/COCOA_2.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/COCOA_2.0.0.tgz vignettes: vignettes/COCOA/inst/doc/IntroToCOCOA.html vignetteTitles: Introduction to Coordinate Covariation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COCOA/inst/doc/IntroToCOCOA.R dependencyCount: 119 Package: codelink Version: 1.54.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), methods, Biobase (>= 2.17.8), limma Imports: annotate Suggests: genefilter, parallel, knitr License: GPL-2 MD5sum: d641dd4f8b41d4fa46b9f721d16cfbc8 NeedsCompilation: no Title: Manipulation of Codelink microarray data Description: This package facilitates reading, preprocessing and manipulating Codelink microarray data. The raw data must be exported as text file using the Codelink software. biocViews: Microarray, OneChannel, DataImport, Preprocessing Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/codelink VignetteBuilder: knitr BugReports: https://github.com/ddiez/codelink/issues git_url: https://git.bioconductor.org/packages/codelink git_branch: RELEASE_3_10 git_last_commit: ea4fead git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/codelink_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/codelink_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/codelink_1.54.0.tgz vignettes: vignettes/codelink/inst/doc/Codelink_Introduction.pdf, vignettes/codelink/inst/doc/Codelink_Legacy.pdf vignetteTitles: Codelink Intruction, Codelink Legacy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/codelink/inst/doc/Codelink_Introduction.R, vignettes/codelink/inst/doc/Codelink_Legacy.R dependencyCount: 33 Package: CODEX Version: 1.18.0 Depends: R (>= 3.2.3), Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19, IRanges, Biostrings, S4Vectors Suggests: WES.1KG.WUGSC License: GPL-2 MD5sum: 58ebbc73b6497addb33d1568e84508db NeedsCompilation: no Title: A Normalization and Copy Number Variation Detection Method for Whole Exome Sequencing Description: A normalization and copy number variation calling procedure for whole exome DNA sequencing data. CODEX relies on the availability of multiple samples processed using the same sequencing pipeline for normalization, and does not require matched controls. The normalization model in CODEX includes terms that specifically remove biases due to GC content, exon length and targeting and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based exome sequencing data. biocViews: ImmunoOncology, ExomeSeq, Normalization, QualityControl, CopyNumberVariation Author: Yuchao Jiang, Nancy R. Zhang Maintainer: Yuchao Jiang git_url: https://git.bioconductor.org/packages/CODEX git_branch: RELEASE_3_10 git_last_commit: 9a95ccc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CODEX_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CODEX_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CODEX_1.18.0.tgz vignettes: vignettes/CODEX/inst/doc/CODEX_vignettes.pdf vignetteTitles: Using CODEX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CODEX/inst/doc/CODEX_vignettes.R dependsOnMe: iCNV dependencyCount: 40 Package: coexnet Version: 1.8.0 Depends: R (>= 3.4) Imports: affy, siggenes, GEOquery, vsn, igraph, acde, Biobase, limma, graphics, stats, utils, STRINGdb, SummarizedExperiment, minet, rmarkdown Suggests: RUnit, BiocGenerics, knitr License: LGPL MD5sum: f7a3d7940240da58417abb30d63af455 NeedsCompilation: no Title: coexnet: An R package to build CO-EXpression NETworks from Microarray Data Description: Extracts the gene expression matrix from GEO DataSets (.CEL files) as a AffyBatch object. Additionally, can make the normalization process using two different methods (vsn and rma). The summarization (pass from multi-probe to one gene) uses two different criteria (Maximum value and Median of the samples expression data) and the process of gene differentially expressed analisys using two methods (sam and acde). The construction of the co-expression network can be conduced using two different methods, Pearson Correlation Coefficient (PCC) or Mutual Information (MI) and choosing a threshold value using a graph theory approach. biocViews: GeneExpression, Microarray, DifferentialExpression, GraphAndNetwork, NetworkInference, SystemsBiology, Normalization, Network Author: Juan David Henao [aut,cre], Liliana Lopez-Kleine [aut], Andres Pinzon-Velasco [aut] Maintainer: Juan David Henao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/coexnet git_branch: RELEASE_3_10 git_last_commit: 03b5355 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/coexnet_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/coexnet_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/coexnet_1.8.0.tgz vignettes: vignettes/coexnet/inst/doc/coexnet.pdf vignetteTitles: The title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coexnet/inst/doc/coexnet.R dependencyCount: 140 Package: CoGAPS Version: 3.6.0 Depends: R (>= 3.5.0) Imports: BiocParallel, cluster, methods, gplots, graphics, grDevices, RColorBrewer, Rcpp, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, tools, utils, rhdf5 LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL (==2) MD5sum: e804c00af14a3ca27a2bdcb8e7465e23 NeedsCompilation: yes Title: Coordinated Gene Activity in Pattern Sets Description: Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis. biocViews: GeneExpression, Transcription, GeneSetEnrichment, DifferentialExpression, Bayesian, Clustering, TimeCourse, RNASeq, Microarray, MultipleComparison, DimensionReduction, ImmunoOncology Author: Thomas Sherman, Wai-shing Lee, Conor Kelton, Ondrej Maxian, Jacob Carey, Genevieve Stein-O'Brien, Michael Considine, Maggie Wodicka, John Stansfield, Shawn Sivy, Carlo Colantuoni, Alexander Favorov, Mike Ochs, Elana Fertig Maintainer: Elana J. Fertig , Thomas D. Sherman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoGAPS git_branch: RELEASE_3_10 git_last_commit: 97325f7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CoGAPS_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CoGAPS_3.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CoGAPS_3.6.0.tgz vignettes: vignettes/CoGAPS/inst/doc/CoGAPS.html vignetteTitles: CoGAPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoGAPS/inst/doc/CoGAPS.R importsMe: projectR dependencyCount: 43 Package: cogena Version: 1.20.0 Depends: R (>= 3.5.0), cluster, ggplot2, kohonen Imports: methods, class, gplots, mclust, amap, apcluster, foreach, parallel, doParallel, fastcluster, corrplot, biwt, Biobase, reshape2, dplyr, devtools Suggests: knitr, rmarkdown License: LGPL-3 Archs: i386, x64 MD5sum: 7da7e33bfc35e7b617cd501b9ba83fcc NeedsCompilation: no Title: co-expressed gene-set enrichment analysis Description: cogena is a workflow for co-expressed gene-set enrichment analysis. It aims to discovery smaller scale, but highly correlated cellular events that may be of great biological relevance. A novel pipeline for drug discovery and drug repositioning based on the cogena workflow is proposed. Particularly, candidate drugs can be predicted based on the gene expression of disease-related data, or other similar drugs can be identified based on the gene expression of drug-related data. Moreover, the drug mode of action can be disclosed by the associated pathway analysis. In summary, cogena is a flexible workflow for various gene set enrichment analysis for co-expressed genes, with a focus on pathway/GO analysis and drug repositioning. biocViews: Clustering, GeneSetEnrichment, GeneExpression, Visualization, Pathways, KEGG, GO, Microarray, Sequencing, SystemsBiology, DataRepresentation, DataImport Author: Zhilong Jia [aut, cre], Michael Barnes [aut] Maintainer: Zhilong Jia URL: https://github.com/zhilongjia/cogena VignetteBuilder: knitr BugReports: https://github.com/zhilongjia/cogena/issues git_url: https://git.bioconductor.org/packages/cogena git_branch: RELEASE_3_10 git_last_commit: 6d9ab97 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cogena_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cogena_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cogena_1.20.0.tgz vignettes: vignettes/cogena/inst/doc/cogena-vignette_pdf.pdf, vignettes/cogena/inst/doc/cogena-vignette_html.html vignetteTitles: cogena,, a workflow for co-expressed gene-set enrichment analysis, cogena: a workflow for co-expressed gene-set enrichment analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cogena/inst/doc/cogena-vignette_html.R, vignettes/cogena/inst/doc/cogena-vignette_pdf.R dependencyCount: 131 Package: coGPS Version: 1.30.0 Depends: R (>= 2.13.0) Imports: graphics, grDevices Suggests: limma License: GPL-2 MD5sum: 734e2c0faacce1cd6540cb2bbd83e026 NeedsCompilation: no Title: cancer outlier Gene Profile Sets Description: Gene Set Enrichment Analysis of P-value based statistics for outlier gene detection in dataset merged from multiple studies biocViews: Microarray, DifferentialExpression Author: Yingying Wei, Michael Ochs Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/coGPS git_branch: RELEASE_3_10 git_last_commit: eba3594 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/coGPS_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/coGPS_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/coGPS_1.30.0.tgz vignettes: vignettes/coGPS/inst/doc/coGPS.pdf vignetteTitles: coGPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coGPS/inst/doc/coGPS.R dependencyCount: 2 Package: COHCAP Version: 1.32.0 Depends: WriteXLS, COHCAPanno, RColorBrewer, gplots Imports: Rcpp, RcppArmadillo, BH LinkingTo: Rcpp, BH License: GPL-3 MD5sum: 825fddb8ff20f8ff2bcdfe5248e30ce0 NeedsCompilation: yes Title: CpG Island Analysis Pipeline for Illumina Methylation Array and Targeted BS-Seq Data Description: COHCAP (pronounced "co-cap") provides a pipeline to analyze single-nucleotide resolution methylation data (Illumina 450k/EPIC methylation array, targeted BS-Seq, etc.). It provides differential methylation for CpG Sites, differential methylation for CpG Islands, integration with gene expression data, with visualizaton options. Discussion Group: https://sourceforge.net/p/cohcap/discussion/bioconductor/ biocViews: DNAMethylation, Microarray, MethylSeq, Epigenetics, DifferentialMethylation Author: Charles Warden , Yate-Ching Yuan , Xiwei Wu Maintainer: Charles Warden SystemRequirements: Perl git_url: https://git.bioconductor.org/packages/COHCAP git_branch: RELEASE_3_10 git_last_commit: ba8bdbb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/COHCAP_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/COHCAP_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/COHCAP_1.32.0.tgz vignettes: vignettes/COHCAP/inst/doc/COHCAP.pdf vignetteTitles: COHCAP Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COHCAP/inst/doc/COHCAP.R dependencyCount: 15 Package: cola Version: 1.2.1 Depends: R (>= 3.6.0) Imports: grDevices, graphics, grid, stats, utils, ComplexHeatmap (>= 2.0.0), matrixStats, GetoptLong, circlize (>= 0.4.7), GlobalOptions (>= 0.1.0), clue, parallel, RColorBrewer, cluster, skmeans, png, mclust, crayon, methods, xml2, microbenchmark, httr, knitr, markdown, digest, impute, brew, Rcpp (>= 0.11.0), BiocGenerics, eulerr LinkingTo: Rcpp Suggests: genefilter, mvtnorm, testthat (>= 0.3), data.tree, dendextend, samr, pamr, kohonen, NMF, WGCNA, Rtsne, umap, clusterProfiler, ReactomePA, DOSE, AnnotationDbi, gplots, hu6800.db License: MIT + file LICENSE Archs: i386, x64 MD5sum: d083b003bdd83e0b077a2a01ef80b5d6 NeedsCompilation: yes Title: A Framework for Consensus Partitioning Description: Subgroup classification is a basic task in genomic data analysis, especially for gene expression and DNA methylation data analysis. It can also be used to test the agreement to known clinical annotations, or to test whether there exist significant batch effects. The cola package provides a general framework for subgroup classification by consensus partitioning. It has the following features: 1. It modularizes the consensus partitioning processes that various methods can be easily integrated. 2. It provides rich visualizations for interpreting the results. 3. It allows running multiple methods at the same time and provides functionalities to compare results straightforwardly. 4. It provides a new method to extract features which are more efficient to separate subgroups. 5. It generates detailed reports for the complete analysis. biocViews: Clustering, GeneExpression, Classification, Software Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/cola, https://jokergoo.github.io/cola_collection/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cola git_branch: RELEASE_3_10 git_last_commit: ce9b863 git_last_commit_date: 2019-12-31 Date/Publication: 2019-12-31 source.ver: src/contrib/cola_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/cola_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cola_1.2.1.tgz vignettes: vignettes/cola/inst/doc/a_quick_start.html, vignettes/cola/inst/doc/cola.html, vignettes/cola/inst/doc/functional_enrichment.html vignetteTitles: 1. A Quick Start of cola Package, 2. cola: A Framework for Consensus Partitioning, 3. Automatic Functional Enrichment on Signature Genes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cola/inst/doc/a_quick_start.R, vignettes/cola/inst/doc/cola.R, vignettes/cola/inst/doc/functional_enrichment.R dependencyCount: 54 Package: coMET Version: 1.18.0 Depends: R (>= 3.6.0), grid, utils, biomaRt, Gviz, psych Imports: colortools, hash,grDevices, gridExtra, rtracklayer, IRanges, S4Vectors, GenomicRanges, stats, corrplot Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: aa23609080690392beba735845319e04 NeedsCompilation: no Title: coMET: visualisation of regional epigenome-wide association scan (EWAS) results and DNA co-methylation patterns Description: Visualisation of EWAS results in a genomic region. In addition to phenotype-association P-values, coMET also generates plots of co-methylation patterns and provides a series of annotation tracks. It can be used to other omic-wide association scans as long as the data can be translated to genomic level and for any species. biocViews: Software, DifferentialMethylation, Visualization, Sequencing, Genetics, FunctionalGenomics, Microarray, MethylationArray, MethylSeq, ChIPSeq, DNASeq, RiboSeq, RNASeq, ExomeSeq, DNAMethylation, GenomeWideAssociation, MotifAnnotation Author: Tiphaine C. Martin [aut,cre], Thomas Hardiman [aut], Idil Yet [aut], Pei-Chien Tsai [aut], Jordana T. Bell [aut] Maintainer: Tiphaine Martin URL: http://epigen.kcl.ac.uk/comet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/coMET git_branch: RELEASE_3_10 git_last_commit: 9eef045 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/coMET_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/coMET_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/coMET_1.18.0.tgz vignettes: vignettes/coMET/inst/doc/coMET.pdf vignetteTitles: coMET users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/coMET/inst/doc/coMET.R dependencyCount: 148 Package: compartmap Version: 1.4.0 Depends: R (>= 3.5.0), minfi, Homo.sapiens, mixOmics Imports: SummarizedExperiment, GenomicRanges, gtools, parallel Suggests: covr, testthat, knitr License: GPL-3 + file LICENSE MD5sum: b109f4d18841366808899984a846fb82 NeedsCompilation: no Title: A/B compartment inference from ATAC-seq and methylation array data Description: Compartmap performs shrunken A/B compartment inference from ATAC-seq and methylation arrays. biocViews: ImmunoOncology, Genetics, Epigenetics, ATACSeq, MethylSeq, MethylationArray Author: Benjamin Johnson [aut, cre], Tim Triche [aut], Kasper Hansen [aut], Jean-Philippe Fortin [aut] Maintainer: Benjamin Johnson URL: https://github.com/biobenkj/compartmap VignetteBuilder: knitr BugReports: https://github.com/biobenkj/compartmap/issues git_url: https://git.bioconductor.org/packages/compartmap git_branch: RELEASE_3_10 git_last_commit: ed1dcdf git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/compartmap_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/compartmap_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/compartmap_1.4.0.tgz vignettes: vignettes/compartmap/inst/doc/compartmap_vignette.html vignetteTitles: A/B compartment inference with compartmap hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/compartmap/inst/doc/compartmap_vignette.R dependencyCount: 163 Package: COMPASS Version: 1.24.1 Depends: R (>= 3.0.3) Imports: methods, Rcpp, data.table, RColorBrewer, scales, grid, plyr, knitr, abind, clue, grDevices, utils, pdist, magrittr, reshape2, dplyr, tidyr, rlang, BiocStyle, rmarkdown LinkingTo: Rcpp (>= 0.11.0) Suggests: flowWorkspace (>= 3.33.1), flowCore, ncdfFlow, shiny, testthat, devtools, flowWorkspaceData, ggplot2 License: Artistic-2.0 MD5sum: 51dbfe91c678334785d9a6dd0c14a1a7 NeedsCompilation: yes Title: Combinatorial Polyfunctionality Analysis of Single Cells Description: COMPASS is a statistical framework that enables unbiased analysis of antigen-specific T-cell subsets. COMPASS uses a Bayesian hierarchical framework to model all observed cell-subsets and select the most likely to be antigen-specific while regularizing the small cell counts that often arise in multi-parameter space. The model provides a posterior probability of specificity for each cell subset and each sample, which can be used to profile a subject's immune response to external stimuli such as infection or vaccination. biocViews: ImmunoOncology, FlowCytometry Author: Lynn Lin, Kevin Ushey, Greg Finak, Ravio Kolde (pheatmap) Maintainer: Greg Finak VignetteBuilder: knitr BugReports: https://github.com/RGLab/COMPASS/issues git_url: https://git.bioconductor.org/packages/COMPASS git_branch: RELEASE_3_10 git_last_commit: 4b605d3 git_last_commit_date: 2019-12-04 Date/Publication: 2019-12-04 source.ver: src/contrib/COMPASS_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/COMPASS_1.24.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/COMPASS_1.24.1.tgz vignettes: vignettes/COMPASS/inst/doc/SimpleCOMPASS.pdf, vignettes/COMPASS/inst/doc/COMPASS.html vignetteTitles: SimpleCOMPASS, COMPASS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COMPASS/inst/doc/COMPASS.R, vignettes/COMPASS/inst/doc/SimpleCOMPASS.R dependencyCount: 61 Package: compcodeR Version: 1.22.0 Depends: R (>= 3.0.2), sm Imports: tcltk, knitr (>= 1.2), markdown, ROCR, lattice (>= 0.16), gplots, gtools, gdata, caTools, grid, KernSmooth, MASS, ggplot2, stringr, modeest, edgeR, limma, vioplot, methods Suggests: BiocStyle, EBSeq, DESeq, DESeq2 (>= 1.1.31), baySeq (>= 2.2.0), genefilter, NOISeq, TCC, NBPSeq (>= 0.3.0) Enhances: rpanel, DSS License: GPL (>= 2) Archs: i386, x64 MD5sum: 9de8f9c7c444473f007e0583cce8d78c NeedsCompilation: no Title: RNAseq data simulation, differential expression analysis and performance comparison of differential expression methods Description: This package provides extensive functionality for comparing results obtained by different methods for differential expression analysis of RNAseq data. It also contains functions for simulating count data and interfaces to several packages for performing the differential expression analysis. biocViews: ImmunoOncology, RNASeq, DifferentialExpression Author: Charlotte Soneson [aut, cre] () Maintainer: Charlotte Soneson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/compcodeR git_branch: RELEASE_3_10 git_last_commit: 39f87fe git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/compcodeR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/compcodeR_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/compcodeR_1.22.0.tgz vignettes: vignettes/compcodeR/inst/doc/compcodeR.pdf vignetteTitles: compcodeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compcodeR/inst/doc/compcodeR.R dependencyCount: 89 Package: compEpiTools Version: 1.20.0 Depends: R (>= 3.1.1), methods, topGO, GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, Rsamtools, parallel, grDevices, gplots, IRanges, GenomicFeatures, XVector, methylPipe, GO.db, S4Vectors, GenomeInfoDb Suggests: BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, knitr, rtracklayer License: GPL MD5sum: b38c55202cbf3dc423e90d1730ac6bcf NeedsCompilation: no Title: Tools for computational epigenomics Description: Tools for computational epigenomics developed for the analysis, integration and simultaneous visualization of various (epi)genomics data types across multiple genomic regions in multiple samples. biocViews: GeneExpression, Sequencing, Visualization, GenomeAnnotation, Coverage Author: Mattia Pelizzola [aut], Kamal Kishore [aut, cre] Maintainer: Kamal Kishore VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/compEpiTools git_branch: RELEASE_3_10 git_last_commit: 10eba89 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/compEpiTools_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/compEpiTools_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/compEpiTools_1.20.0.tgz vignettes: vignettes/compEpiTools/inst/doc/compEpiTools.pdf vignetteTitles: compEpiTools.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compEpiTools/inst/doc/compEpiTools.R dependencyCount: 155 Package: CompGO Version: 1.22.0 Depends: RDAVIDWebService Imports: rtracklayer, Rgraphviz, ggplot2, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene, pcaMethods, reshape2, pathview License: GPL-2 MD5sum: 029213f4cda5de62e79cb93f93003deb NeedsCompilation: no Title: An R pipeline for .bed file annotation, comparing GO term enrichment between gene sets and data visualisation Description: This package contains functions to accomplish several tasks. It is able to download full genome databases from UCSC, import .bed files easily, annotate these .bed file regions with genes (plus distance) from aforementioned database dumps, interface with DAVID to create functional annotation and gene ontology enrichment charts based on gene lists (such as those generated from input .bed files) and finally visualise and compare these enrichments using either directed acyclic graphs or scatterplots. biocViews: GeneSetEnrichment, MultipleComparison, GO, Visualization Author: Sam D. Bassett [aut], Ashley J. Waardenberg [aut, cre] Maintainer: Ashley J. Waardenberg git_url: https://git.bioconductor.org/packages/CompGO git_branch: RELEASE_3_10 git_last_commit: 35e22d7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CompGO_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CompGO_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CompGO_1.22.0.tgz vignettes: vignettes/CompGO/inst/doc/CompGO-Intro.pdf vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CompGO/inst/doc/CompGO-Intro.R dependencyCount: 133 Package: ComplexHeatmap Version: 2.2.0 Depends: R (>= 3.1.2), methods, grid, graphics, stats, grDevices Imports: circlize (>= 0.4.5), GetoptLong, colorspace, clue, RColorBrewer, GlobalOptions (>= 0.1.0), parallel, png Suggests: testthat (>= 1.0.0), knitr, markdown, dendsort, Cairo, jpeg, tiff, fastcluster, dendextend (>= 1.0.1), grImport, grImport2, glue, GenomicRanges License: MIT + file LICENSE MD5sum: fbd83bdf32647e4c3d161fd9114b3c02 NeedsCompilation: no Title: Make Complex Heatmaps Description: Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports various annotation graphics. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/ComplexHeatmap, https://jokergoo.github.io/ComplexHeatmap-reference/book/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ComplexHeatmap git_branch: RELEASE_3_10 git_last_commit: 8ef3ec8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ComplexHeatmap_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ComplexHeatmap_2.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ComplexHeatmap_2.2.0.tgz vignettes: vignettes/ComplexHeatmap/inst/doc/complex_heatmap.html, vignettes/ComplexHeatmap/inst/doc/most_probably_asked_questions.html vignetteTitles: complex_heatmap.html, Most probably asked questions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ComplexHeatmap/inst/doc/most_probably_asked_questions.R dependsOnMe: AMARETTO, EnrichedHeatmap, recoup importsMe: artMS, BiocOncoTK, blacksheepr, CATALYST, COCOA, cola, DEComplexDisease, DEGreport, DEP, diffcyt, ELMER, EnrichmentBrowser, fCCAC, ImpulseDE2, LineagePulse, muscat, MWASTools, PathoStat, profileplyr, SEtools, singleCellTK, Xeva, YAPSA suggestsMe: ALPS, gtrellis, HilbertCurve, projectR, TCGAbiolinks, TCGAutils, TimeSeriesExperiment dependencyCount: 17 Package: CONFESS Version: 1.14.0 Depends: R (>= 3.3),grDevices,utils,stats,graphics Imports: methods,changepoint,cluster,contrast,data.table(>= 1.9.7),ecp,EBImage,flexmix,flowCore,flowClust,flowMeans,flowMerge,flowPeaks,foreach,ggplot2,grid,limma,MASS,moments,outliers,parallel,plotrix,raster,readbitmap,reshape2,SamSPECTRAL,waveslim,wavethresh,zoo Suggests: BiocStyle, knitr, rmarkdown, CONFESSdata License: GPL-2 MD5sum: 6e9600398489df5b3b6bd75a858f2f20 NeedsCompilation: no Title: Cell OrderiNg by FluorEScence Signal Description: Single Cell Fluidigm Spot Detector. biocViews: ImmunoOncology, GeneExpression,DataImport,CellBiology,Clustering,RNASeq,QualityControl,Visualization,TimeCourse,Regression,Classification Author: Diana LOW and Efthimios MOTAKIS Maintainer: Diana LOW VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CONFESS git_branch: RELEASE_3_10 git_last_commit: db95857 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CONFESS_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CONFESS_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CONFESS_1.14.0.tgz vignettes: vignettes/CONFESS/inst/doc/vignette_tex.pdf, vignettes/CONFESS/inst/doc/vignette.html vignetteTitles: CONFESS, CONFESS Walkthrough hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CONFESS/inst/doc/vignette_tex.R, vignettes/CONFESS/inst/doc/vignette.R dependencyCount: 168 Package: consensus Version: 1.4.0 Depends: R (>= 3.5), RColorBrewer Imports: matrixStats, gplots, grDevices, methods, graphics, stats, utils Suggests: knitr, RUnit, rmarkdown, BiocGenerics License: BSD_3_clause + file LICENSE MD5sum: 2afd8101db6afb0c7b4991be55e09b94 NeedsCompilation: no Title: Cross-platform consensus analysis of genomic measurements via interlaboratory testing method Description: An implementation of the American Society for Testing and Materials (ASTM) Standard E691 for interlaboratory testing procedures, designed for cross-platform genomic measurements. Given three (3) or more genomic platforms or laboratory protocols, this package provides interlaboratory testing procedures giving per-locus comparisons for sensitivity and precision between platforms. biocViews: ImmunoOncology, QualityControl, Regression, DataRepresentation, GeneExpression, Microarray, RNASeq Author: Tim Peters Maintainer: Tim Peters VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/consensus git_branch: RELEASE_3_10 git_last_commit: fe97947 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/consensus_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/consensus_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/consensus_1.4.0.tgz vignettes: vignettes/consensus/inst/doc/consensus.pdf vignetteTitles: Fitting and visualising row-linear models with \texttt{consensus} hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/consensus/inst/doc/consensus.R dependencyCount: 13 Package: ConsensusClusterPlus Version: 1.50.0 Imports: Biobase, ALL, graphics, stats, utils, cluster License: GPL version 2 MD5sum: bd7746024bddc494684190c8bd031728 NeedsCompilation: no Title: ConsensusClusterPlus Description: algorithm for determining cluster count and membership by stability evidence in unsupervised analysis biocViews: Software, Clustering Author: Matt Wilkerson , Peter Waltman Maintainer: Matt Wilkerson git_url: https://git.bioconductor.org/packages/ConsensusClusterPlus git_branch: RELEASE_3_10 git_last_commit: 4ef9c79 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ConsensusClusterPlus_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ConsensusClusterPlus_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ConsensusClusterPlus_1.50.0.tgz vignettes: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.pdf vignetteTitles: ConsensusClusterPlus Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.R importsMe: CancerSubtypes, CATALYST, CrossICC, CVE, DEGreport, FlowSOM suggestsMe: TCGAbiolinks dependencyCount: 10 Package: consensusDE Version: 1.4.0 Depends: R (>= 3.5), BiocGenerics Imports: airway, AnnotationDbi, BiocParallel, Biobase, Biostrings, data.table, dendextend, DESeq2 (>= 1.20.0), EDASeq, ensembldb, edgeR, EnsDb.Hsapiens.v86, GenomicAlignments, GenomicFeatures, limma, org.Hs.eg.db, pcaMethods, RColorBrewer, Rsamtools, RUVSeq, S4Vectors, stats, SummarizedExperiment, TxDb.Dmelanogaster.UCSC.dm3.ensGene, utils Suggests: knitr, rmarkdown License: GPL-3 MD5sum: c8afba0ce014aa8577b3043fb2318aab NeedsCompilation: no Title: RNA-seq analysis using multiple algorithms Description: This package allows users to perform DE analysis using multiple algorithms. It seeks consensus from multiple methods. Currently it supports "Voom", "EdgeR" and "DESeq". It uses RUV-seq (optional) to remove unwanted sources of variation. biocViews: Transcriptomics, MultipleComparison, Clustering, Sequencing, Software Author: Ashley J. Waardenberg Maintainer: Ashley J. Waardenberg VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/consensusDE git_branch: RELEASE_3_10 git_last_commit: faa0c14 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/consensusDE_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/consensusDE_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/consensusDE_1.4.0.tgz vignettes: vignettes/consensusDE/inst/doc/consensusDE.html vignetteTitles: consensusDE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusDE/inst/doc/consensusDE.R dependencyCount: 164 Package: consensusOV Version: 1.8.1 Depends: R (>= 3.6) Imports: Biobase, GSVA, gdata, genefu, limma, matrixStats, randomForest, stats, utils, methods Suggests: BiocStyle, ggplot2, knitr, rmarkdown License: Artistic-2.0 MD5sum: 280b8ac30d957218dd085a6561d1545d NeedsCompilation: no Title: Gene expression-based subtype classification for high-grade serous ovarian cancer Description: This package implements four major subtype classifiers for high-grade serous (HGS) ovarian cancer as described by Helland et al. (PLoS One, 2011), Bentink et al. (PLoS One, 2012), Verhaak et al. (J Clin Invest, 2013), and Konecny et al. (J Natl Cancer Inst, 2014). In addition, the package implements a consensus classifier, which consolidates and improves on the robustness of the proposed subtype classifiers, thereby providing reliable stratification of patients with HGS ovarian tumors of clearly defined subtype. biocViews: Classification, Clustering, DifferentialExpression, GeneExpression, Microarray, Transcriptomics Author: Gregory M Chen, Lavanya Kannan, Ludwig Geistlinger, Victor Kofia, Levi Waldron, Benjamin Haibe-Kains Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/software/consensusOV VignetteBuilder: knitr BugReports: https://github.com/bhklab/consensusOV/issues git_url: https://git.bioconductor.org/packages/consensusOV git_branch: RELEASE_3_10 git_last_commit: 7f8f2d6 git_last_commit_date: 2019-12-05 Date/Publication: 2019-12-05 source.ver: src/contrib/consensusOV_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/consensusOV_1.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/consensusOV_1.8.1.tgz vignettes: vignettes/consensusOV/inst/doc/consensusOV.html vignetteTitles: Molecular subtyping for ovarian cancer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusOV/inst/doc/consensusOV.R dependencyCount: 110 Package: consensusSeekeR Version: 1.14.0 Depends: R (>= 2.10), BiocGenerics, IRanges, GenomicRanges, BiocParallel Imports: GenomeInfoDb, rtracklayer, stringr, S4Vectors, methods Suggests: BiocStyle, ggplot2, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: f609c72352d13a38fa76b0895f9c622d NeedsCompilation: no Title: Detection of consensus regions inside a group of experiences using genomic positions and genomic ranges Description: This package compares genomic positions and genomic ranges from multiple experiments to extract common regions. The size of the analyzed region is adjustable as well as the number of experiences in which a feature must be present in a potential region to tag this region as a consensus region. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, Transcription, PeakDetection, Sequencing, Coverage Author: Astrid Deschenes [cre, aut], Fabien Claude Lamaze [ctb], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschenes URL: https://github.com/ArnaudDroitLab/consensusSeekeR VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/consensusSeekeR/issues git_url: https://git.bioconductor.org/packages/consensusSeekeR git_branch: RELEASE_3_10 git_last_commit: e6b8b32 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/consensusSeekeR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/consensusSeekeR_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/consensusSeekeR_1.14.0.tgz vignettes: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.html vignetteTitles: Detection of consensus regions inside a group of experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.R importsMe: RJMCMCNucleosomes dependencyCount: 42 Package: contiBAIT Version: 1.14.0 Depends: BH (>= 1.51.0-3), Rsamtools (>= 1.21) Imports: data.table, grDevices, clue, cluster, gplots, BiocGenerics (>= 0.31.6), S4Vectors, IRanges, GenomicRanges, Rcpp, TSP, GenomicFiles, gtools, rtracklayer, BiocParallel, DNAcopy, colorspace, reshape2, ggplot2, methods, exomeCopy, GenomicAlignments, diagram LinkingTo: Rcpp, BH Suggests: BiocStyle License: BSD_2_clause + file LICENSE MD5sum: 8285f3c2467175adf6ed40dc1119064b NeedsCompilation: yes Title: Improves Early Build Genome Assemblies using Strand-Seq Data Description: Using strand inheritance data from multiple single cells from the organism whose genome is to be assembled, contiBAIT can cluster unbridged contigs together into putative chromosomes, and order the contigs within those chromosomes. biocViews: ImmunoOncology, CellBasedAssays, QualityControl, WholeGenome, Genetics, GenomeAssembly Author: Kieran O'Neill, Mark Hills, Mike Gottlieb Maintainer: Kieran O'Neill git_url: https://git.bioconductor.org/packages/contiBAIT git_branch: RELEASE_3_10 git_last_commit: 362c9cb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/contiBAIT_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/contiBAIT_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/contiBAIT_1.14.0.tgz vignettes: vignettes/contiBAIT/inst/doc/contiBAIT.pdf vignetteTitles: flowBi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/contiBAIT/inst/doc/contiBAIT.R dependencyCount: 131 Package: conumee Version: 1.20.0 Depends: R (>= 3.0), minfi, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICmanifest Imports: methods, stats, DNAcopy, rtracklayer, GenomicRanges, IRanges, GenomeInfoDb Suggests: BiocStyle, knitr, rmarkdown, minfiData, RCurl License: GPL (>= 2) MD5sum: 82b1070f14d6c39114ccaca5b8f63885 NeedsCompilation: no Title: Enhanced copy-number variation analysis using Illumina DNA methylation arrays Description: This package contains a set of processing and plotting methods for performing copy-number variation (CNV) analysis using Illumina 450k or EPIC methylation arrays. biocViews: CopyNumberVariation, DNAMethylation, MethylationArray, Microarray, Normalization, Preprocessing, QualityControl, Software Author: Volker Hovestadt, Marc Zapatka Maintainer: Volker Hovestadt VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/conumee git_branch: RELEASE_3_10 git_last_commit: 009a7fd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/conumee_1.20.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/conumee_1.20.0.tgz vignettes: vignettes/conumee/inst/doc/conumee.html vignetteTitles: conumee hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/conumee/inst/doc/conumee.R dependencyCount: 127 Package: convert Version: 1.62.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.33), limma (>= 1.7.0), marray, utils, methods License: LGPL Archs: i386, x64 MD5sum: e1bf41fbff315cec76fd73037209440d NeedsCompilation: no Title: Convert Microarray Data Objects Description: Define coerce methods for microarray data objects. biocViews: Infrastructure, Microarray, TwoChannel Author: Gordon Smyth , James Wettenhall , Yee Hwa (Jean Yang) , Martin Morgan Maintainer: Yee Hwa (Jean) Yang URL: http://bioinf.wehi.edu.au/limma/convert.html git_url: https://git.bioconductor.org/packages/convert git_branch: RELEASE_3_10 git_last_commit: d3496e3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/convert_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/convert_1.62.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/convert_1.62.0.tgz vignettes: vignettes/convert/inst/doc/convert.pdf vignetteTitles: Converting Between Microarray Data Classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: maigesPack, TurboNorm suggestsMe: BiocCaseStudies, dyebias, OLIN dependencyCount: 10 Package: copa Version: 1.54.0 Depends: Biobase, methods Suggests: colonCA License: Artistic-2.0 MD5sum: ddc144b6b7270ce8806a589f893faa3e NeedsCompilation: yes Title: Functions to perform cancer outlier profile analysis. Description: COPA is a method to find genes that undergo recurrent fusion in a given cancer type by finding pairs of genes that have mutually exclusive outlier profiles. biocViews: OneChannel, TwoChannel, DifferentialExpression, Visualization Author: James W. MacDonald Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/copa git_branch: RELEASE_3_10 git_last_commit: 8d7944f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/copa_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/copa_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/copa_1.54.0.tgz vignettes: vignettes/copa/inst/doc/copa.pdf vignetteTitles: copa Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/copa/inst/doc/copa.R dependencyCount: 7 Package: copynumber Version: 1.26.0 Depends: R (>= 2.10), BiocGenerics Imports: S4Vectors, IRanges, GenomicRanges License: Artistic-2.0 Archs: i386, x64 MD5sum: 8cc12495b6980d5d41590195aa28ca76 NeedsCompilation: no Title: Segmentation of single- and multi-track copy number data by penalized least squares regression. Description: Penalized least squares regression is applied to fit piecewise constant curves to copy number data to locate genomic regions of constant copy number. Procedures are available for individual segmentation of each sample, joint segmentation of several samples and joint segmentation of the two data tracks from SNP-arrays. Several plotting functions are available for visualization of the data and the segmentation results. biocViews: aCGH, SNP, CopyNumberVariation, Genetics, Visualization Author: Gro Nilsen, Knut Liestoel and Ole Christian Lingjaerde. Maintainer: Gro Nilsen git_url: https://git.bioconductor.org/packages/copynumber git_branch: RELEASE_3_10 git_last_commit: c3a9594 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/copynumber_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/copynumber_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/copynumber_1.26.0.tgz vignettes: vignettes/copynumber/inst/doc/copynumber.pdf vignetteTitles: copynumber.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/copynumber/inst/doc/copynumber.R suggestsMe: PureCN dependencyCount: 16 Package: CopyNumberPlots Version: 1.2.3 Depends: R (>= 3.6), karyoploteR Imports: regioneR, IRanges, Rsamtools, SummarizedExperiment, VariantAnnotation, methods, stats, GenomeInfoDb, GenomicRanges, cn.mops, rhdf5, utils Suggests: BiocStyle, knitr, panelcn.mops, BSgenome.Hsapiens.UCSC.hg19.masked, DNAcopy, testthat License: Artistic-2.0 MD5sum: ec3d7e15a103b66d538608313e80b181 NeedsCompilation: no Title: Create Copy-Number Plots using karyoploteR functionality Description: CopyNumberPlots have a set of functions extending karyoploteRs functionality to create beautiful, customizable and flexible plots of copy-number related data. biocViews: Visualization, CopyNumberVariation, Coverage, OneChannel, DataImport, Sequencing, DNASeq Author: Bernat Gel and Miriam Magallon Maintainer: Bernat Gel URL: https://github.com/bernatgel/CopyNumberPlots VignetteBuilder: knitr BugReports: https://github.com/bernatgel/CopyNumberPlots/issues git_url: https://git.bioconductor.org/packages/CopyNumberPlots git_branch: RELEASE_3_10 git_last_commit: f27c5fc git_last_commit_date: 2019-12-23 Date/Publication: 2019-12-23 source.ver: src/contrib/CopyNumberPlots_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/CopyNumberPlots_1.2.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CopyNumberPlots_1.2.3.tgz vignettes: vignettes/CopyNumberPlots/inst/doc/CopyNumberPlots.html vignetteTitles: CopyNumberPlots vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CopyNumberPlots/inst/doc/CopyNumberPlots.R importsMe: CNVfilteR dependencyCount: 150 Package: CopywriteR Version: 2.18.0 Depends: R(>= 3.2), BiocParallel Imports: matrixStats, gtools, data.table, S4Vectors, chipseq, IRanges, Rsamtools, DNAcopy, GenomicAlignments, GenomicRanges, CopyhelpeR, GenomeInfoDb, futile.logger Suggests: BiocStyle, SCLCBam, snow License: GPL-2 MD5sum: 8a54aa2d8233c74778c1f9d18e005590 NeedsCompilation: no Title: Copy number information from targeted sequencing using off-target reads Description: CopywriteR extracts DNA copy number information from targeted sequencing by utiizing off-target reads. It allows for extracting uniformly distributed copy number information, can be used without reference, and can be applied to sequencing data obtained from various techniques including chromatin immunoprecipitation and target enrichment on small gene panels. Thereby, CopywriteR constitutes a widely applicable alternative to available copy number detection tools. biocViews: ImmunoOncology, TargetedResequencing, ExomeSeq, CopyNumberVariation, Preprocessing, Visualization, Coverage Author: Thomas Kuilman Maintainer: Oscar Krijgsman URL: https://github.com/PeeperLab/CopywriteR git_url: https://git.bioconductor.org/packages/CopywriteR git_branch: RELEASE_3_10 git_last_commit: 393d611 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CopywriteR_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CopywriteR_2.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CopywriteR_2.18.0.tgz vignettes: vignettes/CopywriteR/inst/doc/CopywriteR.pdf vignetteTitles: CopywriteR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CopywriteR/inst/doc/CopywriteR.R dependencyCount: 47 Package: coRdon Version: 1.4.0 Depends: R (>= 3.5) Imports: methods, stats, utils, Biostrings, Biobase, dplyr, stringr, purrr, ggplot2, data.table Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: a760e21b336efb916315aea91907f7cb NeedsCompilation: no Title: Codon Usage Analysis and Prediction of Gene Expressivity Description: Tool for analysis of codon usage in various unannotated or KEGG/COG annotated DNA sequences. Calculates different measures of CU bias and CU-based predictors of gene expressivity, and performs gene set enrichment analysis for annotated sequences. Implements several methods for visualization of CU and enrichment analysis results. biocViews: Software, Metagenomics, GeneExpression, GeneSetEnrichment, GenePrediction, Visualization, KEGG, Pathways, Genetics CellBiology, BiomedicalInformatics, ImmunoOncology Author: Anamaria Elek [cre, aut], Maja Kuzman [aut], Kristian Vlahovicek [aut] Maintainer: Anamaria Elek URL: https://github.com/BioinfoHR/coRdon VignetteBuilder: knitr BugReports: https://github.com/BioinfoHR/coRdon/issues git_url: https://git.bioconductor.org/packages/coRdon git_branch: RELEASE_3_10 git_last_commit: e2fafd9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/coRdon_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/coRdon_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/coRdon_1.4.0.tgz vignettes: vignettes/coRdon/inst/doc/coRdon.html vignetteTitles: coRdon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coRdon/inst/doc/coRdon.R dependencyCount: 71 Package: CoRegFlux Version: 1.2.0 Depends: R (>= 3.6) Imports: CoRegNet, sybil Suggests: glpkAPI, testthat, knitr, rmarkdown, digest, R.cache, ggplot2, plyr, igraph, methods, latex2exp, rBayesianOptimization License: GPL-3 MD5sum: fcf15007ce589628ac896680a13aa98f NeedsCompilation: no Title: CoRegFlux Description: CoRegFlux aims at providing tools to integrate reverse engineered gene regulatory networks and gene-expression into metabolic models to improve prediction of phenotypes, both for metabolic engineering, through transcription factor or gene (TF) knock-out or overexpression in various conditions as well as to improve our understanding of the interactions and cell inner-working. biocViews: GeneRegulation,Network,SystemsBiology,GeneExpression,Transcription,GenePrediction Author: Pauline Trébulle, Daniel Trejo-Banos, Mohamed Elati Maintainer: Pauline Trébulle and Mohamed Elati SystemRequirements: GLPK (>= 4.42) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoRegFlux git_branch: RELEASE_3_10 git_last_commit: d8b52b7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CoRegFlux_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CoRegFlux_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CoRegFlux_1.2.0.tgz vignettes: vignettes/CoRegFlux/inst/doc/coregflux.html vignetteTitles: CoRegFlux hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoRegFlux/inst/doc/coregflux.R dependencyCount: 31 Package: CoRegNet Version: 1.24.0 Depends: R (>= 2.14), igraph, shiny, arules, methods Suggests: RColorBrewer, gplots, BiocStyle, knitr License: GPL-3 MD5sum: 3622b26a0ba8eba870c993b8aa4ec63f NeedsCompilation: yes Title: CoRegNet : reconstruction and integrated analysis of co-regulatory networks Description: This package provides methods to identify active transcriptional programs. Methods and classes are provided to import or infer large scale co-regulatory network from transcriptomic data. The specificity of the encoded networks is to model Transcription Factor cooperation. External regulation evidences (TFBS, ChIP,...) can be integrated to assess the inferred network and refine it if necessary. Transcriptional activity of the regulators in the network can be estimated using an measure of their influence in a given sample. Finally, an interactive UI can be used to navigate through the network of cooperative regulators and to visualize their activity in a specific sample or subgroup sample. The proposed visualization tool can be used to integrate gene expression, transcriptional activity, copy number status, sample classification and a transcriptional network including co-regulation information. biocViews: NetworkInference, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork,SystemsBiology, Network, Visualization, Transcription Author: Remy Nicolle, Thibault Venzac and Mohamed Elati Maintainer: Remy Nicolle VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoRegNet git_branch: RELEASE_3_10 git_last_commit: ed79b8a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CoRegNet_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CoRegNet_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CoRegNet_1.24.0.tgz vignettes: vignettes/CoRegNet/inst/doc/CoRegNet.html vignetteTitles: Custom Print Methods hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoRegNet/inst/doc/CoRegNet.R importsMe: CoRegFlux dependencyCount: 29 Package: Cormotif Version: 1.32.0 Depends: R (>= 2.12.0), affy, limma Imports: affy, graphics, grDevices License: GPL-2 Archs: i386, x64 MD5sum: 916c98ceb09379b2adeb7dad093fd9cb NeedsCompilation: no Title: Correlation Motif Fit Description: It fits correlation motif model to multiple studies to detect study specific differential expression patterns. biocViews: Microarray, DifferentialExpression Author: Hongkai Ji, Yingying Wei Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/Cormotif git_branch: RELEASE_3_10 git_last_commit: 9a21281 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Cormotif_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Cormotif_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Cormotif_1.32.0.tgz vignettes: vignettes/Cormotif/inst/doc/CormotifVignette.pdf vignetteTitles: Cormotif Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cormotif/inst/doc/CormotifVignette.R dependencyCount: 14 Package: CorMut Version: 1.28.0 Depends: methods,seqinr,igraph License: GPL-2 MD5sum: bac25f92c77e4255a761e6e38f27ab67 NeedsCompilation: no Title: Detect the correlated mutations based on selection pressure Description: CorMut provides functions for computing kaks for individual sites or specific amino acids and detecting correlated mutations among them. Three methods are provided for detecting correlated mutations ,including conditional selection pressure, mutual information and Jaccard index. The computation consists of two steps: First, the positive selection sites are detected; Second, the mutation correlations are computed among the positive selection sites. Note that the first step is optional. Meanwhile, CorMut facilitates the comparison of the correlated mutations between two conditions by the means of correlated mutation network. The reference sequence should be the first sequence of the sequence file, and does not allow the presence of gap. biocViews: Sequencing Author: Zhenpeng Li Maintainer: Zhenpeng Li git_url: https://git.bioconductor.org/packages/CorMut git_branch: RELEASE_3_10 git_last_commit: 97cddf9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CorMut_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CorMut_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CorMut_1.28.0.tgz vignettes: vignettes/CorMut/inst/doc/CorMut.pdf vignetteTitles: CorMut hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CorMut/inst/doc/CorMut.R dependencyCount: 17 Package: CORREP Version: 1.52.0 Imports: e1071, stats Suggests: cluster, MASS License: GPL (>= 2) MD5sum: 4e43238d777525018a95edfc3403b0c5 NeedsCompilation: no Title: Multivariate Correlation Estimator and Statistical Inference Procedures. Description: Multivariate correlation estimation and statistical inference. See package vignette. biocViews: Microarray, Clustering, GraphAndNetwork Author: Dongxiao Zhu and Youjuan Li Maintainer: Dongxiao Zhu git_url: https://git.bioconductor.org/packages/CORREP git_branch: RELEASE_3_10 git_last_commit: 6fd7414 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CORREP_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CORREP_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CORREP_1.52.0.tgz vignettes: vignettes/CORREP/inst/doc/CORREP.pdf vignetteTitles: Multivariate Correlation Estimator hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CORREP/inst/doc/CORREP.R dependencyCount: 8 Package: coseq Version: 1.10.0 Depends: R (>= 3.4.0), SummarizedExperiment, S4Vectors Imports: edgeR, DESeq2, capushe, Rmixmod, e1071, BiocParallel, ggplot2 (>= 2.1.0), scales, HTSFilter, corrplot, HTSCluster (>= 2.0.8), grDevices, graphics, stats, methods, compositions, mvtnorm Suggests: Biobase, knitr, rmarkdown, testthat License: GPL (>=3) MD5sum: 7c6e9a0dcc2dc06a2f5114afe16b068d NeedsCompilation: no Title: Co-Expression Analysis of Sequencing Data Description: Co-expression analysis for expression profiles arising from high-throughput sequencing data. Feature (e.g., gene) profiles are clustered using adapted transformations and mixture models or a K-means algorithm, and model selection criteria (to choose an appropriate number of clusters) are provided. biocViews: GeneExpression, RNASeq, Sequencing, Software, ImmunoOncology Author: Andrea Rau, Cathy Maugis-Rabusseau, Antoine Godichon-Baggioni Maintainer: Andrea Rau VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/coseq git_branch: RELEASE_3_10 git_last_commit: 95c9cf3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/coseq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/coseq_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/coseq_1.10.0.tgz vignettes: vignettes/coseq/inst/doc/coseq.html vignetteTitles: coseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coseq/inst/doc/coseq.R dependencyCount: 139 Package: cosmiq Version: 1.20.0 Depends: R (>= 3.6), Rcpp Imports: pracma, xcms, MassSpecWavelet, faahKO Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: a6b8ed1a7d6abbfbdc74c9d3e94b281a NeedsCompilation: yes Title: cosmiq - COmbining Single Masses Into Quantities Description: cosmiq is a tool for the preprocessing of liquid- or gas - chromatography mass spectrometry (LCMS/GCMS) data with a focus on metabolomics or lipidomics applications. To improve the detection of low abundant signals, cosmiq generates master maps of the mZ/RT space from all acquired runs before a peak detection algorithm is applied. The result is a more robust identification and quantification of low-intensity MS signals compared to conventional approaches where peak picking is performed in each LCMS/GCMS file separately. The cosmiq package builds on the xcmsSet object structure and can be therefore integrated well with the package xcms as an alternative preprocessing step. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: David Fischer [aut, cre], Christian Panse [aut] (), Endre Laczko [ctb] Maintainer: David Fischer URL: http://www.bioconductor.org/packages/devel/bioc/html/cosmiq.html git_url: https://git.bioconductor.org/packages/cosmiq git_branch: RELEASE_3_10 git_last_commit: 80fa59f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cosmiq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cosmiq_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cosmiq_1.20.0.tgz vignettes: vignettes/cosmiq/inst/doc/cosmiq.pdf vignetteTitles: cosmiq primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cosmiq/inst/doc/cosmiq.R dependencyCount: 99 Package: COSNet Version: 1.20.0 Suggests: bionetdata, PerfMeas, RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 9af20268347707f7b8cb7509a1df8bc8 NeedsCompilation: yes Title: Cost Sensitive Network for node label prediction on graphs with highly unbalanced labelings Description: Package that implements the COSNet classification algorithm. The algorithm predicts node labels in partially labeled graphs where few positives are available for the class being predicted. biocViews: GraphAndNetwork, Classification,Network, NeuralNetwork Author: Marco Frasca and Giorgio Valentini -- Universita' degli Studi di Milano Maintainer: Marco Frasca URL: https://github.com/m1frasca/COSNet_GitHub git_url: https://git.bioconductor.org/packages/COSNet git_branch: RELEASE_3_10 git_last_commit: 46020bb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/COSNet_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/COSNet_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/COSNet_1.20.0.tgz vignettes: vignettes/COSNet/inst/doc/COSNet_v.pdf vignetteTitles: An R Package for Predicting Binary Labels in Partially-Labeled Graphs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COSNet/inst/doc/COSNet_v.R dependencyCount: 0 Package: CountClust Version: 1.14.0 Depends: R (>= 3.4), ggplot2 (>= 2.1.0) Imports: SQUAREM, slam, maptpx, plyr(>= 1.7.1), cowplot, gtools, flexmix, picante, limma, parallel, reshape2, stats, utils, graphics, grDevices Suggests: knitr, kableExtra, BiocStyle, Biobase, roxygen2, RColorBrewer, devtools, xtable License: GPL (>= 2) Archs: i386, x64 MD5sum: d9c7863cce50ef0f86c50c6b5e773f76 NeedsCompilation: no Title: Clustering and Visualizing RNA-Seq Expression Data using Grade of Membership Models Description: Fits grade of membership models (GoM, also known as admixture models) to cluster RNA-seq gene expression count data, identifies characteristic genes driving cluster memberships, and provides a visual summary of the cluster memberships. biocViews: ImmunoOncology, RNASeq, GeneExpression, Clustering, Sequencing, StatisticalMethod, Software, Visualization Author: Kushal Dey [aut, cre], Joyce Hsiao [aut], Matthew Stephens [aut] Maintainer: Kushal Dey URL: https://github.com/kkdey/CountClust VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CountClust git_branch: RELEASE_3_10 git_last_commit: 84275ce git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CountClust_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CountClust_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CountClust_1.14.0.tgz vignettes: vignettes/CountClust/inst/doc/count-clust.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CountClust/inst/doc/count-clust.R dependencyCount: 74 Package: countsimQC Version: 1.4.0 Depends: R (>= 3.5) Imports: rmarkdown (>= 0.9.5), edgeR, DESeq2 (>= 1.16.0), dplyr, tidyr, ggplot2, grDevices, tools, SummarizedExperiment, genefilter, DT, GenomeInfoDbData, caTools, randtests, stats, utils, methods Suggests: knitr, testthat License: GPL (>=2) MD5sum: 44b4d72e7de0160625cb908d5d917d01 NeedsCompilation: no Title: Compare Characteristic Features of Count Data Sets Description: countsimQC provides functionality to create a comprehensive report comparing a broad range of characteristics across a collection of count matrices. One important use case is the comparison of one or more synthetic count matrices to a real count matrix, possibly the one underlying the simulations. However, any collection of count matrices can be compared. biocViews: Microbiome, RNASeq, SingleCell, ExperimentalDesign, QualityControl, ReportWriting, Visualization, ImmunoOncology Author: Charlotte Soneson [aut, cre] () Maintainer: Charlotte Soneson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/countsimQC git_branch: RELEASE_3_10 git_last_commit: 83e93bc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/countsimQC_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/countsimQC_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/countsimQC_1.4.0.tgz vignettes: vignettes/countsimQC/inst/doc/countsimQC.html vignetteTitles: countsimQC User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/countsimQC/inst/doc/countsimQC.R dependencyCount: 136 Package: covEB Version: 1.12.0 Depends: R (>= 3.3), mvtnorm, igraph, gsl, Biobase, stats, LaplacesDemon, Matrix Suggests: curatedBladderData License: GPL-3 MD5sum: a0c34dde83a5d993e0e37b489724395d NeedsCompilation: no Title: Empirical Bayes estimate of block diagonal covariance matrices Description: Using bayesian methods to estimate correlation matrices assuming that they can be written and estimated as block diagonal matrices. These block diagonal matrices are determined using shrinkage parameters that values below this parameter to zero. biocViews: ImmunoOncology, Bayesian, Microarray, RNASeq, Preprocessing, Software, GeneExpression, StatisticalMethod Author: C. Pacini Maintainer: C. Pacini git_url: https://git.bioconductor.org/packages/covEB git_branch: RELEASE_3_10 git_last_commit: 2f89c3e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/covEB_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/covEB_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/covEB_1.12.0.tgz vignettes: vignettes/covEB/inst/doc/covEB.pdf vignetteTitles: covEB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/covEB/inst/doc/covEB.R dependencyCount: 17 Package: CoverageView Version: 1.24.0 Depends: R (>= 2.10), methods, Rsamtools (>= 1.19.17), rtracklayer Imports: S4Vectors (>= 0.7.21), IRanges(>= 2.3.23), GenomicRanges, GenomicAlignments, parallel, tools License: Artistic-2.0 MD5sum: 92f0d558f58732c49366191ab529a886 NeedsCompilation: no Title: Coverage visualization package for R Description: This package provides a framework for the visualization of genome coverage profiles. It can be used for ChIP-seq experiments, but it can be also used for genome-wide nucleosome positioning experiments or other experiment types where it is important to have a framework in order to inspect how the coverage distributed across the genome biocViews: ImmunoOncology, Visualization,RNASeq,ChIPSeq,Sequencing,Technology,Software Author: Ernesto Lowy Maintainer: Ernesto Lowy git_url: https://git.bioconductor.org/packages/CoverageView git_branch: RELEASE_3_10 git_last_commit: 396b4ad git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CoverageView_1.24.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CoverageView_1.24.0.tgz vignettes: vignettes/CoverageView/inst/doc/CoverageView.pdf vignetteTitles: Easy visualization of the read coverage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoverageView/inst/doc/CoverageView.R dependencyCount: 38 Package: covRNA Version: 1.12.0 Depends: ade4, Biobase Imports: parallel, genefilter, grDevices, stats, graphics Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 59c8af4ccc6f7e17fe10546018efd7e2 NeedsCompilation: no Title: Multivariate Analysis of Transcriptomic Data Description: This package provides the analysis methods fourthcorner and RLQ analysis for large-scale transcriptomic data. biocViews: GeneExpression, Transcription Author: Lara Urban Maintainer: Lara Urban VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/covRNA git_branch: RELEASE_3_10 git_last_commit: 585a9b4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/covRNA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/covRNA_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/covRNA_1.12.0.tgz vignettes: vignettes/covRNA/inst/doc/covRNA.html vignetteTitles: An Introduction to covRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/covRNA/inst/doc/covRNA.R dependencyCount: 42 Package: cpvSNP Version: 1.18.0 Depends: R (>= 2.10), GenomicFeatures, GSEABase (>= 1.24.0) Imports: methods, corpcor, BiocParallel, ggplot2, plyr Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: 2ee43da251d0b8c46e0cffaf938286f9 NeedsCompilation: no Title: Gene set analysis methods for SNP association p-values that lie in genes in given gene sets Description: Gene set analysis methods exist to combine SNP-level association p-values into gene sets, calculating a single association p-value for each gene set. This package implements two such methods that require only the calculated SNP p-values, the gene set(s) of interest, and a correlation matrix (if desired). One method (GLOSSI) requires independent SNPs and the other (VEGAS) can take into account correlation (LD) among the SNPs. Built-in plotting functions are available to help users visualize results. biocViews: Genetics, StatisticalMethod, Pathways, GeneSetEnrichment, GenomicVariation Author: Caitlin McHugh, Jessica Larson, and Jason Hackney Maintainer: Caitlin McHugh git_url: https://git.bioconductor.org/packages/cpvSNP git_branch: RELEASE_3_10 git_last_commit: abc893f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cpvSNP_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cpvSNP_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cpvSNP_1.18.0.tgz vignettes: vignettes/cpvSNP/inst/doc/cpvSNP.pdf vignetteTitles: Running gene set analyses with the "cpvSNP" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cpvSNP/inst/doc/cpvSNP.R dependencyCount: 116 Package: cqn Version: 1.32.0 Depends: R (>= 2.10.0), mclust, nor1mix, stats, preprocessCore, splines, quantreg Imports: splines Suggests: scales, edgeR License: Artistic-2.0 MD5sum: 12ff9ddc06c4cb3b46dc79b6c2995e5f NeedsCompilation: no Title: Conditional quantile normalization Description: A normalization tool for RNA-Seq data, implementing the conditional quantile normalization method. biocViews: ImmunoOncology, RNASeq, Preprocessing, DifferentialExpression Author: Jean (Zhijin) Wu, Kasper Daniel Hansen Maintainer: Kasper Daniel Hansen git_url: https://git.bioconductor.org/packages/cqn git_branch: RELEASE_3_10 git_last_commit: f0fee89 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cqn_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cqn_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cqn_1.32.0.tgz vignettes: vignettes/cqn/inst/doc/cqn.pdf vignetteTitles: CQN (Conditional Quantile Normalization) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cqn/inst/doc/cqn.R importsMe: KnowSeq, tweeDEseq dependencyCount: 15 Package: CRImage Version: 1.34.0 Depends: EBImage, DNAcopy, aCGH Imports: MASS, e1071, foreach, sgeostat License: Artistic-2.0 MD5sum: 7ccc61f08743b2e8aa81375317de0672 NeedsCompilation: no Title: CRImage a package to classify cells and calculate tumour cellularity Description: CRImage provides functionality to process and analyze images, in particular to classify cells in biological images. Furthermore, in the context of tumor images, it provides functionality to calculate tumour cellularity. biocViews: CellBiology, Classification Author: Henrik Failmezger , Yinyin Yuan , Oscar Rueda , Florian Markowetz Maintainer: Henrik Failmezger , Yinyin Yuan git_url: https://git.bioconductor.org/packages/CRImage git_branch: RELEASE_3_10 git_last_commit: a318c37 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CRImage_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CRImage_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CRImage_1.34.0.tgz vignettes: vignettes/CRImage/inst/doc/CRImage.pdf vignetteTitles: CRImage Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CRImage/inst/doc/CRImage.R dependencyCount: 41 Package: CRISPRseek Version: 1.26.0 Depends: R (>= 3.0.1), BiocGenerics, Biostrings Imports: parallel, data.table, seqinr, S4Vectors (>= 0.9.25), IRanges, BSgenome, BiocParallel, hash Suggests: RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: GPL (>= 2) MD5sum: 3f22d903e6f70475b3fa0f22f60b30c7 NeedsCompilation: no Title: Design of target-specific guide RNAs in CRISPR-Cas9, genome-editing systems Description: The package includes functions to find potential guide RNAs for input target sequences, optionally filter guide RNAs without restriction enzyme cut site, or without paired guide RNAs, genome-wide search for off-targets, score, rank, fetch flank sequence and indicate whether the target and off-targets are located in exon region or not. Potential guide RNAs are annotated with total score of the top5 and topN off-targets, detailed topN mismatch sites, restriction enzyme cut sites, and paired guide RNAs. This package leverages Biostrings and BSgenome packages. biocViews: ImmunoOncology, GeneRegulation, SequenceMatching, CRISPR Author: Lihua Julie Zhu, Benjamin R. Holmes, Hervé Pagès, Michael Lawrence, Isana Veksler-Lublinsky, Victor Ambros, Neil Aronin and Michael Brodsky Maintainer: Lihua Julie Zhu git_url: https://git.bioconductor.org/packages/CRISPRseek git_branch: RELEASE_3_10 git_last_commit: 4ebbce2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CRISPRseek_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CRISPRseek_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CRISPRseek_1.26.0.tgz vignettes: vignettes/CRISPRseek/inst/doc/CRISPRseek.pdf vignetteTitles: CRISPRseek Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CRISPRseek/inst/doc/CRISPRseek.R dependsOnMe: crisprseekplus importsMe: GUIDEseq dependencyCount: 47 Package: crisprseekplus Version: 1.12.0 Depends: R (>= 3.3.0), shiny, shinyjs, CRISPRseek Imports: DT, utils, GUIDEseq, GenomicRanges, GenomicFeatures, BiocManager, BSgenome, AnnotationDbi, hash Suggests: testthat, rmarkdown, knitr, R.rsp License: GPL-3 + file LICENSE MD5sum: cdf8149d588cb9300a961cb4589ebcdc NeedsCompilation: no Title: crisprseekplus Description: Bioinformatics platform containing interface to work with offTargetAnalysis and compare2Sequences in the CRISPRseek package, and GUIDEseqAnalysis. biocViews: GeneRegulation, SequenceMatching, Software Author: Sophie Wigmore , Alper Kucukural , Lihua Julie Zhu , Michael Brodsky , Manuel Garber Maintainer: Alper Kucukural URL: https://github.com/UMMS-Biocore/crisprseekplus VignetteBuilder: knitr, R.rsp BugReports: https://github.com/UMMS-Biocore/crisprseekplus/issues/new git_url: https://git.bioconductor.org/packages/crisprseekplus git_branch: RELEASE_3_10 git_last_commit: f49fff5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/crisprseekplus_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/crisprseekplus_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/crisprseekplus_1.12.0.tgz vignettes: vignettes/crisprseekplus/inst/doc/crisprseekplus.html vignetteTitles: DEBrowser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprseekplus/inst/doc/crisprseekplus.R dependencyCount: 123 Package: CrispRVariants Version: 1.14.0 Depends: R (>= 3.5), ggplot2 (>= 2.2.0) Imports: AnnotationDbi, BiocParallel, Biostrings, methods, GenomeInfoDb, GenomicAlignments, GenomicRanges, grDevices, grid, gridExtra, IRanges, reshape2, Rsamtools, S4Vectors (>= 0.9.38), utils Suggests: BiocStyle, gdata, GenomicFeatures, knitr, rmarkdown, rtracklayer, sangerseqR, testthat, VariantAnnotation License: GPL-2 MD5sum: b3b07ab088dea8388983b8276ca56db4 NeedsCompilation: no Title: Tools for counting and visualising mutations in a target location Description: CrispRVariants provides tools for analysing the results of a CRISPR-Cas9 mutagenesis sequencing experiment, or other sequencing experiments where variants within a given region are of interest. These tools allow users to localize variant allele combinations with respect to any genomic location (e.g. the Cas9 cut site), plot allele combinations and calculate mutation rates with flexible filtering of unrelated variants. biocViews: ImmunoOncology, CRISPR, GenomicVariation, VariantDetection, GeneticVariability, DataRepresentation, Visualization Author: Helen Lindsay [aut, cre] Maintainer: Helen Lindsay VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CrispRVariants git_branch: RELEASE_3_10 git_last_commit: 3b4e3c3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CrispRVariants_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CrispRVariants_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CrispRVariants_1.14.0.tgz vignettes: vignettes/CrispRVariants/inst/doc/user_guide.pdf vignetteTitles: CrispRVariants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CrispRVariants/inst/doc/user_guide.R dependencyCount: 94 Package: crlmm Version: 1.44.0 Depends: R (>= 2.14.0), oligoClasses (>= 1.21.12), preprocessCore (>= 1.17.7) Imports: methods, Biobase (>= 2.15.4), BiocGenerics, affyio (>= 1.23.2), illuminaio, ellipse, mvtnorm, splines, stats, SNPchip, utils, lattice, ff, foreach, RcppEigen (>= 0.3.1.2.1), matrixStats, VGAM, parallel, graphics, limma, beanplot LinkingTo: preprocessCore (>= 1.17.7) Suggests: hapmapsnp6, genomewidesnp6Crlmm (>= 1.0.7), GGdata, snpStats, RUnit License: Artistic-2.0 MD5sum: 64e0bc15df4e7de21eb68cf2ee2f28d9 NeedsCompilation: yes Title: Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays Description: Faster implementation of CRLMM specific to SNP 5.0 and 6.0 arrays, as well as a copy number tool specific to 5.0, 6.0, and Illumina platforms. biocViews: Microarray, Preprocessing, SNP, CopyNumberVariation Author: Benilton S Carvalho, Robert Scharpf, Matt Ritchie, Ingo Ruczinski, Rafael A Irizarry Maintainer: Benilton S Carvalho , Robert Scharpf , Matt Ritchie git_url: https://git.bioconductor.org/packages/crlmm git_branch: RELEASE_3_10 git_last_commit: 6f0bbea git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/crlmm_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/crlmm_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/crlmm_1.44.0.tgz vignettes: vignettes/crlmm/inst/doc/AffyGW.pdf, vignettes/crlmm/inst/doc/CopyNumberOverview.pdf, vignettes/crlmm/inst/doc/genotyping.pdf, vignettes/crlmm/inst/doc/gtypeDownstream.pdf, vignettes/crlmm/inst/doc/IlluminaPreprocessCN.pdf, vignettes/crlmm/inst/doc/Infrastructure.pdf vignetteTitles: Copy number estimation, Overview of copy number vignettes, crlmm Vignette - Genotyping, crlmm Vignette - Downstream Analysis, Preprocessing and genotyping Illumina arrays for copy number analysis, Infrastructure for copy number analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/crlmm/inst/doc/genotyping.R importsMe: VanillaICE suggestsMe: ArrayTV, oligoClasses, SNPchip dependencyCount: 68 Package: CrossICC Version: 1.0.0 Depends: R (>= 3.5), MASS Imports: data.table, methods, MergeMaid, ConsensusClusterPlus, limma, cluster, dplyr, Biobase, grDevices, stats, graphics, utils Suggests: rmarkdown, testthat, knitr, shiny, shinydashboard, shinyWidgets, shinycssloaders, DT, ggthemes, ggplot2, pheatmap, RColorBrewer, tibble, ggalluvial License: GPL-3 | file LICENSE Archs: i386, x64 MD5sum: 7ef9c46d6883f7e051e8ceb8700a1f23 NeedsCompilation: no Title: An Interactive Consensus Clustering Framework for Multi-platform Data Analysis Description: CrossICC utilizes an iterative strategy to derive the optimal gene set and cluster number from consensus similarity matrix generated by consensus clustering and it is able to deal with multiple cross platform datasets so that requires no between-dataset normalizations. This package also provides abundant functions for visualization and identifying subtypes of cancer. Specially, many cancer-related analysis methods are embedded to facilitate the clinical translation of the identified cancer subtypes. biocViews: Software, GeneExpression, DifferentialExpression, GUI, GeneSetEnrichment, Classification, Clustering, FeatureExtraction, Survival, Microarray, RNASeq, BatchEffect, Normalization, Preprocessing, Visualization Author: Yu Sun [aut, cre] (), Qi Zhao [aut] () Maintainer: Yu Sun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CrossICC git_branch: RELEASE_3_10 git_last_commit: 4eee26b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CrossICC_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CrossICC_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CrossICC_1.0.0.tgz vignettes: vignettes/CrossICC/inst/doc/CrossICC.html vignetteTitles: How to use CrossICC? hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CrossICC/inst/doc/CrossICC.R dependencyCount: 43 Package: crossmeta Version: 1.12.0 Depends: R (>= 3.5) Imports: affy (>= 1.52.0), affxparser (>= 1.46.0), AnnotationDbi (>= 1.36.2), Biobase (>= 2.34.0), BiocGenerics (>= 0.20.0), BiocManager (>= 1.30.4), ccmap, DT (>= 0.2), DBI (>= 1.0.0), data.table (>= 1.10.4), doParallel (>= 1.0.10), doRNG (>= 1.6), foreach (>= 1.4.3), fdrtool (>= 1.2.15), ggplot2 (>= 2.2.1), GEOquery (>= 2.40.0), limma (>= 3.30.13), matrixStats (>= 0.51.0), metaMA (>= 3.1.2), metap (>= 0.8), miniUI (>= 0.1.1), oligo (>= 1.38.0), plotly(>= 4.5.6), reshape (>= 0.8.6), reader(>= 1.0.6), RColorBrewer (>= 1.1.2), RCurl (>= 1.95.4.11), RSQLite (>= 2.1.1), rdrop2 (>= 0.7.0), stringr (>= 1.2.0), sva (>= 3.22.0), shiny (>= 1.0.0), stats (>= 3.3.3), XML (>= 3.98.1.17), Suggests: knitr, rmarkdown, lydata, org.Hs.eg.db, testthat, ccdata License: MIT + file LICENSE MD5sum: ca9fff0d56b9bbfe5e213d70be8532a7 NeedsCompilation: no Title: Cross Platform Meta-Analysis of Microarray Data Description: Implements cross-platform and cross-species meta-analyses of Affymentrix, Illumina, and Agilent microarray data. This package automates common tasks such as downloading, normalizing, and annotating raw GEO data. The user then selects control and treatment samples in order to perform differential expression/pathway analyses for all comparisons. After analysing each contrast seperately, the user can select tissue sources for each contrast and specify any tissue sources that should be grouped for the subsequent meta-analyses. Finally, effect size and pathway meta-analyses can proceed and the results graphically explored. biocViews: GeneExpression, Transcription, DifferentialExpression, Microarray, TissueMicroarray, OneChannel, Annotation, BatchEffect, Preprocessing, GUI Author: Alex Pickering Maintainer: Alex Pickering VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/crossmeta git_branch: RELEASE_3_10 git_last_commit: eaa2bf7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/crossmeta_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/crossmeta_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/crossmeta_1.12.0.tgz vignettes: vignettes/crossmeta/inst/doc/crossmeta-vignette.html vignetteTitles: crossmeta vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crossmeta/inst/doc/crossmeta-vignette.R suggestsMe: ccmap dependencyCount: 195 Package: CSAR Version: 1.38.0 Depends: R (>= 2.15.0), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges Imports: stats, utils Suggests: ShortRead, Biostrings License: Artistic-2.0 MD5sum: 73ed2b250c451f08f02857b196b18e70 NeedsCompilation: yes Title: Statistical tools for the analysis of ChIP-seq data Description: Statistical tools for ChIP-seq data analysis. The package includes the statistical method described in Kaufmann et al. (2009) PLoS Biology: 7(4):e1000090. Briefly, Taking the average DNA fragment size subjected to sequencing into account, the software calculates genomic single-nucleotide read-enrichment values. After normalization, sample and control are compared using a test based on the Poisson distribution. Test statistic thresholds to control the false discovery rate are obtained through random permutation. biocViews: ChIPSeq, Transcription, Genetics Author: Jose M Muino Maintainer: Jose M Muino git_url: https://git.bioconductor.org/packages/CSAR git_branch: RELEASE_3_10 git_last_commit: bca533c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CSAR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CSAR_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CSAR_1.38.0.tgz vignettes: vignettes/CSAR/inst/doc/CSAR.pdf vignetteTitles: CSAR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSAR/inst/doc/CSAR.R importsMe: NarrowPeaks suggestsMe: NarrowPeaks dependencyCount: 16 Package: csaw Version: 1.20.0 Depends: GenomicRanges, SummarizedExperiment Imports: Rcpp, BiocGenerics, Rsamtools, edgeR, limma, GenomicFeatures, AnnotationDbi, methods, S4Vectors, IRanges, GenomeInfoDb, stats, BiocParallel, utils LinkingTo: Rhtslib, zlibbioc, Rcpp Suggests: org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene, testthat, GenomicAlignments, knitr, BiocStyle, rmarkdown, BiocManager License: GPL-3 Archs: i386, x64 MD5sum: 901b971c007be79060e258712d6dab89 NeedsCompilation: yes Title: ChIP-Seq Analysis with Windows Description: Detection of differentially bound regions in ChIP-seq data with sliding windows, with methods for normalization and proper FDR control. biocViews: MultipleComparison, ChIPSeq, Normalization, Sequencing, Coverage, Genetics, Annotation, DifferentialPeakCalling Author: Aaron Lun [aut, cre], Gordon Smyth [aut] Maintainer: Aaron Lun SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/csaw git_branch: RELEASE_3_10 git_last_commit: 11728da git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/csaw_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/csaw_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/csaw_1.20.0.tgz vignettes: vignettes/csaw/inst/doc/csaw.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/csaw/inst/doc/csaw.R importsMe: diffHic, icetea, NADfinder, vulcan suggestsMe: tximport dependencyCount: 86 Package: CSSP Version: 1.24.0 Imports: methods, splines, stats, utils Suggests: testthat License: GPL-2 Archs: i386, x64 MD5sum: c68545a6ea474912316b9d362d2edca4 NeedsCompilation: yes Title: ChIP-Seq Statistical Power Description: Power computation for ChIP-Seq data based on Bayesian estimation for local poisson counting process. biocViews: ChIPSeq, Sequencing, QualityControl, Bayesian Author: Chandler Zuo, Sunduz Keles Maintainer: Chandler Zuo git_url: https://git.bioconductor.org/packages/CSSP git_branch: RELEASE_3_10 git_last_commit: e95443c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CSSP_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CSSP_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CSSP_1.24.0.tgz vignettes: vignettes/CSSP/inst/doc/cssp.pdf vignetteTitles: cssp.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSSP/inst/doc/cssp.R dependencyCount: 4 Package: ctc Version: 1.60.0 Depends: amap License: GPL-2 Archs: i386, x64 MD5sum: db25344cdb4dd5deb5c60f7c619b603b NeedsCompilation: no Title: Cluster and Tree Conversion. Description: Tools for export and import classification trees and clusters to other programs biocViews: Microarray, Clustering, Classification, DataImport, Visualization Author: Antoine Lucas , Laurent Gautier Maintainer: Antoine Lucas URL: http://antoinelucas.free.fr/ctc git_url: https://git.bioconductor.org/packages/ctc git_branch: RELEASE_3_10 git_last_commit: 85b9683 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ctc_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ctc_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ctc_1.60.0.tgz vignettes: vignettes/ctc/inst/doc/ctc.pdf vignetteTitles: Introduction to ctc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctc/inst/doc/ctc.R importsMe: miRLAB, multiClust dependencyCount: 1 Package: CTDquerier Version: 1.5.0 Depends: R (>= 3.4.0) Imports: RCurl, stringr, S4Vectors, stringdist, ggplot2, igraph, utils, grid, gridExtra, methods, stats, BiocFileCache, rappdirs Suggests: BiocStyle, knitr License: MIT + file LICENSE MD5sum: ece6030afc1f393718a58dc45f6f2b2d NeedsCompilation: no Title: Package for CTDbase data query, visualization and downstream analysis Description: Package to retrieve and visualize data from the Comparative Toxicogenomics Database (http://ctdbase.org/). The downloaded data is formated as DataFrames for further downstream analyses. biocViews: Software, BiomedicalInformatics, Infrastructure, DataImport, DataRepresentation, GeneSetEnrichment, NetworkEnrichment, Pathways, Network, GO, KEGG Author: Carles Hernandez-Ferrer [aut, cre], Jaun R. Gonzalez [aut] Maintainer: Carles Hernandez-Ferrer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CTDquerier git_branch: master git_last_commit: 1281ccd git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-04 source.ver: src/contrib/CTDquerier_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CTDquerier_1.5.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CTDquerier_1.5.0.tgz vignettes: vignettes/CTDquerier/inst/doc/batch_query.html, vignettes/CTDquerier/inst/doc/case_study.html, vignettes/CTDquerier/inst/doc/vignette.html vignetteTitles: Simple comparison between CTDquerier R package and CTDbase Batch Query web tool, Case study on Environmental Chemicals and asthma-related genes, CTDquerier: A package to retrieve CTDbase data for downstream analysis and data visualization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CTDquerier/inst/doc/batch_query.R, vignettes/CTDquerier/inst/doc/case_study.R, vignettes/CTDquerier/inst/doc/vignette.R dependencyCount: 86 Package: cTRAP Version: 1.4.0 Depends: R (>= 3.6.0) Imports: biomaRt, cowplot, data.table, dplyr, fgsea, ggplot2, ggrepel, graphics, httr, limma, methods, pbapply, R.utils, readxl, reshape2, rhdf5, stats, tools, utils Suggests: testthat, knitr, covr, rmarkdown License: MIT + file LICENSE MD5sum: 0f32c0453544c796875622288a4adccd NeedsCompilation: no Title: Identification of candidate causal perturbations from differential gene expression data Description: Compare differential gene expression results with those from known cellular perturbations (such as gene knock-down, overexpression or small molecules) derived from the Connectivity Map. Such analyses allow not only to infer the molecular causes of the observed difference in gene expression but also to identify small molecules that could drive or revert specific transcriptomic alterations. biocViews: DifferentialExpression, GeneExpression, RNASeq, Transcriptomics, Pathways, ImmunoOncology, GeneSetEnrichment Author: Bernardo P. de Almeida [aut], Nuno Saraiva-Agostinho [aut, cre], Nuno L. Barbosa-Morais [aut, led] Maintainer: Nuno Saraiva-Agostinho URL: https://github.com/nuno-agostinho/cTRAP VignetteBuilder: knitr BugReports: https://github.com/nuno-agostinho/cTRAP/issues git_url: https://git.bioconductor.org/packages/cTRAP git_branch: RELEASE_3_10 git_last_commit: 17ddee4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cTRAP_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cTRAP_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cTRAP_1.4.0.tgz vignettes: vignettes/cTRAP/inst/doc/cTRAP.html vignetteTitles: cTRAP: DGE comparison with cellular perturbations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cTRAP/inst/doc/cTRAP.R dependencyCount: 112 Package: ctsGE Version: 1.12.0 Depends: R (>= 3.2) Imports: ccaPP, ggplot2, limma, reshape2, shiny, stats, stringr, utils Suggests: BiocStyle, dplyr, DT, GEOquery, knitr, pander, rmarkdown, testthat License: GPL-2 MD5sum: c93f6f0bba573c02f80c085cc52bdb6d NeedsCompilation: no Title: Clustering of Time Series Gene Expression data Description: Methodology for supervised clustering of potentially many predictor variables, such as genes etc., in time series datasets Provides functions that help the user assigning genes to predefined set of model profiles. biocViews: ImmunoOncology, GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, Genetics, Bayesian, Clustering, TimeCourse, Sequencing, RNASeq Author: Michal Sharabi-Schwager [aut, cre], Ron Ophir [aut] Maintainer: Michal Sharabi-Schwager URL: https://github.com/michalsharabi/ctsGE VignetteBuilder: knitr BugReports: https://github.com/michalsharabi/ctsGE/issues git_url: https://git.bioconductor.org/packages/ctsGE git_branch: RELEASE_3_10 git_last_commit: ba874a2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ctsGE_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ctsGE_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ctsGE_1.12.0.tgz vignettes: vignettes/ctsGE/inst/doc/ctsGE.html vignetteTitles: ctsGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctsGE/inst/doc/ctsGE.R dependencyCount: 77 Package: cummeRbund Version: 2.28.0 Depends: R (>= 2.7.0), BiocGenerics (>= 0.3.2), RSQLite, ggplot2, reshape2, fastcluster, rtracklayer, Gviz Imports: methods, plyr, BiocGenerics, S4Vectors (>= 0.9.25), Biobase Suggests: cluster, plyr, NMFN, stringr, GenomicFeatures, GenomicRanges, rjson License: Artistic-2.0 MD5sum: 1420d34283af61b3cbc3d8b3aa982f41 NeedsCompilation: no Title: Analysis, exploration, manipulation, and visualization of Cufflinks high-throughput sequencing data. Description: Allows for persistent storage, access, exploration, and manipulation of Cufflinks high-throughput sequencing data. In addition, provides numerous plotting functions for commonly used visualizations. biocViews: HighThroughputSequencing, HighThroughputSequencingData, RNAseq, RNAseqData, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Bioinformatics, Clustering, MultipleComparisons, QualityControl Author: L. Goff, C. Trapnell, D. Kelley Maintainer: Loyal A. Goff git_url: https://git.bioconductor.org/packages/cummeRbund git_branch: RELEASE_3_10 git_last_commit: 0228672 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cummeRbund_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cummeRbund_2.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cummeRbund_2.28.0.tgz vignettes: vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.pdf, vignettes/cummeRbund/inst/doc/cummeRbund-manual.pdf vignetteTitles: Sample cummeRbund workflow, CummeRbund User Guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.R, vignettes/cummeRbund/inst/doc/cummeRbund-manual.R importsMe: meshr suggestsMe: IsoformSwitchAnalyzeR dependencyCount: 146 Package: customProDB Version: 1.26.1 Depends: R (>= 3.0.1), IRanges, AnnotationDbi, biomaRt(>= 2.17.1) Imports: S4Vectors (>= 0.9.25), DBI, GenomeInfoDb, GenomicRanges, Rsamtools (>= 1.10.2), GenomicAlignments, Biostrings (>= 2.26.3), GenomicFeatures (>= 1.32.0), stringr, RCurl, plyr, VariantAnnotation (>= 1.13.44), rtracklayer, RSQLite, AhoCorasickTrie, methods Suggests: RMariaDB, BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: 3e0ebccbd884d523a87b005e2953e1f3 NeedsCompilation: no Title: Generate customized protein database from NGS data, with a focus on RNA-Seq data, for proteomics search Description: Database search is the most widely used approach for peptide and protein identification in mass spectrometry-based proteomics studies. Our previous study showed that sample-specific protein databases derived from RNA-Seq data can better approximate the real protein pools in the samples and thus improve protein identification. More importantly, single nucleotide variations, short insertion and deletions and novel junctions identified from RNA-Seq data make protein database more complete and sample-specific. Here, we report an R package customProDB that enables the easy generation of customized databases from RNA-Seq data for proteomics search. This work bridges genomics and proteomics studies and facilitates cross-omics data integration. biocViews: ImmunoOncology, Sequencing, MassSpectrometry, Proteomics, SNP, RNASeq, Software, Transcription, AlternativeSplicing, FunctionalGenomics Author: Xiaojing Wang Maintainer: Xiaojing Wang Bo Wen git_url: https://git.bioconductor.org/packages/customProDB git_branch: RELEASE_3_10 git_last_commit: b8d27af git_last_commit_date: 2020-04-12 Date/Publication: 2020-04-12 source.ver: src/contrib/customProDB_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/customProDB_1.26.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/customProDB_1.26.1.tgz vignettes: vignettes/customProDB/inst/doc/customProDB.pdf vignetteTitles: Introduction to customProDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/customProDB/inst/doc/customProDB.R importsMe: PGA dependencyCount: 87 Package: CVE Version: 1.11.2 Depends: R (>= 3.4.0), tidyverse, plyr, ggplot2 Imports: shiny, ConsensusClusterPlus, RColorBrewer, gplots, jsonlite, ape, WGCNA, RTCGAToolbox Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: aaffd085823c868849a496ece8dfb399 NeedsCompilation: no Title: Cancer Variant Explorer Description: Shiny app for interactive variant prioritisation in precision oncology. The input file for CVE is the output file of the recently released Oncotator Variant Annotation tool summarising variant-centric information from 14 different publicly available resources relevant for cancer researches. Interactive priortisation in CVE is based on known germline and cancer variants, DNA repair genes and functional prediction scores. An optional feature of CVE is the exploration of the tumour-specific pathway context that is facilitated using co-expression modules generated from publicly available transcriptome data. Finally druggability of prioritised variants is assessed using the Drug Gene Interaction Database (DGIdb). biocViews: BiomedicalInformatics Author: Andreas Mock [aut, cre] Maintainer: Andreas Mock VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CVE git_branch: master git_last_commit: 26224fa git_last_commit_date: 2019-06-07 Date/Publication: 2019-06-07 source.ver: src/contrib/CVE_1.11.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/CVE_1.11.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CVE_1.11.2.tgz vignettes: vignettes/CVE/inst/doc/CVE_tutorial.html, vignettes/CVE/inst/doc/WGCNA_from_TCGA_RNAseq.html vignetteTitles: Cancer Variant Explorer (CVE) tutorial, Weighted gene co-expression network analysis with TCGA RNAseq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CVE/inst/doc/CVE_tutorial.R, vignettes/CVE/inst/doc/WGCNA_from_TCGA_RNAseq.R dependencyCount: 190 Package: cycle Version: 1.40.0 Depends: R (>= 2.10.0), Mfuzz Imports: Biobase, stats License: GPL-2 MD5sum: 92ed47f504ed23ef0264b2b2104039ed NeedsCompilation: no Title: Significance of periodic expression pattern in time-series data Description: Package for assessing the statistical significance of periodic expression based on Fourier analysis and comparison with data generated by different background models biocViews: Microarray, TimeCourse Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://cycle.sysbiolab.eu git_url: https://git.bioconductor.org/packages/cycle git_branch: RELEASE_3_10 git_last_commit: 03ae84e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cycle_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cycle_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cycle_1.40.0.tgz vignettes: vignettes/cycle/inst/doc/cycle.pdf vignetteTitles: Introduction to cycle hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cycle/inst/doc/cycle.R dependencyCount: 17 Package: cydar Version: 1.10.0 Depends: BiocParallel, SingleCellExperiment Imports: viridis, methods, shiny, graphics, stats, grDevices, utils, BiocGenerics, S4Vectors, flowCore, Biobase, Rcpp, BiocNeighbors, SummarizedExperiment LinkingTo: Rcpp Suggests: ncdfFlow, testthat, knitr, edgeR, limma, glmnet, BiocStyle, flowStats License: GPL-3 MD5sum: c088a4650feea2e26ae2fe5e811672ba NeedsCompilation: yes Title: Using Mass Cytometry for Differential Abundance Analyses Description: Identifies differentially abundant populations between samples and groups in mass cytometry data. Provides methods for counting cells into hyperspheres, controlling the spatial false discovery rate, and visualizing changes in abundance in the high-dimensional marker space. biocViews: ImmunoOncology, FlowCytometry, MultipleComparison, Proteomics, SingleCell Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cydar git_branch: RELEASE_3_10 git_last_commit: cca0508 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-30 source.ver: src/contrib/cydar_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cydar_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cydar_1.10.0.tgz vignettes: vignettes/cydar/inst/doc/cydar.html vignetteTitles: Detecting differentially abundant subpopulations in mass cytometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cydar/inst/doc/cydar.R dependencyCount: 95 Package: CytoDx Version: 1.6.0 Depends: R (>= 3.5) Imports: doParallel, dplyr, glmnet, rpart, rpart.plot, stats, flowCore,grDevices, graphics, utils Suggests: knitr License: GPL-2 Archs: i386, x64 MD5sum: e1e7c8c0d12dbad6edd43b4b931fa2d4 NeedsCompilation: no Title: Robust prediction of clinical outcomes using cytometry data without cell gating Description: This package provides functions that predict clinical outcomes using single cell data (such as flow cytometry data, RNA single cell sequencing data) without the requirement of cell gating or clustering. biocViews: ImmunoOncology, CellBiology, FlowCytometry, StatisticalMethod, Software, CellBasedAssays, Regression, Classification, Survival Author: Zicheng Hu Maintainer: Zicheng Hu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CytoDx git_branch: RELEASE_3_10 git_last_commit: 478dfa1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/CytoDx_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/CytoDx_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CytoDx_1.6.0.tgz vignettes: vignettes/CytoDx/inst/doc/CytoDx_Vignette.pdf vignetteTitles: Introduction to CytoDx hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoDx/inst/doc/CytoDx_Vignette.R dependencyCount: 46 Package: cytofast Version: 1.2.0 Depends: R (>= 3.6.0) Imports: flowCore, ggplot2, ggridges, RColorBrewer, reshape2, stats, grDevices, Rdpack, methods, grid, FlowSOM Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 78d824546a06236d66ee0f134d7b02e3 NeedsCompilation: no Title: cytofast - A quick visualization and analysis tool for CyTOF data Description: Multi-parametric flow and mass cytometry allows exceptional high-resolution exploration of the cellular composition of the immune system. Together with tools like FlowSOM and Cytosplore it is possible to identify novel cell types. By introducing cytofast we hope to offer a workflow for visualization and quantification of cell clusters for an efficient discovery of cell populations associated with diseases or other clinical outcomes. biocViews: FlowCytometry, Visualization, Clustering Author: K.A. Stam [aut, cre], G. Beyrend [aut] Maintainer: K.A. Stam VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cytofast git_branch: RELEASE_3_10 git_last_commit: cd8d8a2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cytofast_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cytofast_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cytofast_1.2.0.tgz vignettes: vignettes/cytofast/inst/doc/spitzer.html vignetteTitles: Spitzer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytofast/inst/doc/spitzer.R dependencyCount: 128 Package: cytolib Version: 1.8.0 Depends: R (>= 3.4) Suggests: knitr License: Artistic-2.0 MD5sum: 13205b79dd96542a3a36f9df61622a66 NeedsCompilation: no Title: C++ infrastructure for representing and interacting with the gated cytometry Description: This package provides the core data structure and API to represent and interact with the gated cytometry data. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang Maintainer: Mike Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cytolib git_branch: RELEASE_3_10 git_last_commit: 47dcb83 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/cytolib_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/cytolib_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/cytolib_1.8.0.tgz vignettes: vignettes/cytolib/inst/doc/cytolib.html vignetteTitles: Using cytolib hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytolib/inst/doc/cytolib.R linksToMe: CytoML, flowCore, flowWorkspace dependencyCount: 0 Package: CytoML Version: 1.12.1 Imports: flowCore (>= 1.43.10), flowWorkspace (>= 3.33.10), openCyto (>= 1.11.3), XML, data.table, jsonlite, RBGL, ncdfFlow, Rgraphviz, Biobase, methods, graph, graphics, utils, base64enc, plyr, dplyr, grDevices, methods, ggcyto (>= 1.11.4), yaml, lattice, stats, corpcor, RUnit LinkingTo: Rcpp, BH(>= 1.62.0-1), RProtoBufLib(>= 1.3.7), cytolib(>= 1.3.3), RcppParallel Suggests: testthat, flowWorkspaceData (>= 2.11.1), knitr, parallel License: Artistic-2.0 MD5sum: d3ec97ff9be487ecc8702ed5a8b9990d NeedsCompilation: yes Title: A GatingML Interface for Cross Platform Cytometry Data Sharing Description: Uses platform-specific implemenations of the GatingML2.0 standard to exchange gated cytometry data with other software platforms. biocViews: ImmunoOncology, FlowCytometry, DataImport, DataRepresentation Author: Mike Jiang, J. Spidlen., N. Gopalakrishnan, F. Hahne, B. Ellis, R. Gentleman, M. Dalphin, N. Le Meur, B. Purcell Maintainer: Mike Jiang URL: https://github.com/RGLab/CytoML SystemRequirements: xml2, GNU make, C++11 VignetteBuilder: knitr BugReports: https://github.com/RGLab/CytoML/issues git_url: https://git.bioconductor.org/packages/CytoML git_branch: RELEASE_3_10 git_last_commit: 2167017 git_last_commit_date: 2020-03-26 Date/Publication: 2020-03-26 source.ver: src/contrib/CytoML_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/CytoML_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/CytoML_1.12.1.tgz vignettes: vignettes/CytoML/inst/doc/cytobank2GatingSet.html, vignettes/CytoML/inst/doc/HowToExportGatingSet.html vignetteTitles: How to import Cytobank into a GatingSet, How to export a GatingSet to GatingML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoML/inst/doc/cytobank2GatingSet.R, vignettes/CytoML/inst/doc/HowToExportGatingSet.R importsMe: FlowSOM suggestsMe: flowWorkspace, openCyto dependencyCount: 117 Package: dada2 Version: 1.14.1 Depends: R (>= 3.4.0), Rcpp (>= 0.12.0), methods (>= 3.4.0) Imports: Biostrings (>= 2.42.1), ggplot2 (>= 2.1.0), reshape2 (>= 1.4.1), ShortRead (>= 1.32.0), RcppParallel (>= 4.3.0), parallel (>= 3.2.0), IRanges (>= 2.6.0), XVector (>= 0.16.0), BiocGenerics (>= 0.22.0) LinkingTo: Rcpp, RcppParallel Suggests: BiocStyle, knitr, rmarkdown License: LGPL-3 Archs: i386, x64 MD5sum: 11175d8c1d6a3e2c21f39cb398f66ab0 NeedsCompilation: yes Title: Accurate, high-resolution sample inference from amplicon sequencing data Description: The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching. biocViews: ImmunoOncology, Microbiome, Sequencing, Classification, Metagenomics Author: Benjamin Callahan , Paul McMurdie, Susan Holmes Maintainer: Benjamin Callahan URL: http://benjjneb.github.io/dada2/ SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/benjjneb/dada2/issues git_url: https://git.bioconductor.org/packages/dada2 git_branch: RELEASE_3_10 git_last_commit: 26fefaa git_last_commit_date: 2020-02-21 Date/Publication: 2020-02-22 source.ver: src/contrib/dada2_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/dada2_1.14.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/dada2_1.14.1.tgz vignettes: vignettes/dada2/inst/doc/dada2-intro.html vignetteTitles: Introduction to dada2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dada2/inst/doc/dada2-intro.R dependencyCount: 91 Package: dagLogo Version: 1.24.0 Depends: R (>= 3.0.1), methods, biomaRt, grImport2, grid, motifStack Imports: pheatmap, Biostrings, UniProt.ws, BiocGenerics, utils Suggests: XML, BiocStyle, knitr, rmarkdown, testthat License: GPL (>=2) Archs: i386, x64 MD5sum: 8899426387a13a9c533691c88a3619f8 NeedsCompilation: no Title: dagLogo: a bioconductor package for visualizeing conserved amino acid sequence pattern in groups based on probability theory Description: Visualize significant conserved amino acid sequence pattern in groups based on probability theory. biocViews: SequenceMatching, Visualization Author: Jianhong Ou, Haibo Liu, Alexey Stukalov, Niraj Nirala, Usha Acharya, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dagLogo git_branch: RELEASE_3_10 git_last_commit: 0b532ba git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/dagLogo_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/dagLogo_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/dagLogo_1.24.0.tgz vignettes: vignettes/dagLogo/inst/doc/dagLogo.html vignetteTitles: dagLogo Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dagLogo/inst/doc/dagLogo.R dependencyCount: 126 Package: daMA Version: 1.58.0 Imports: MASS, stats License: GPL (>= 2) MD5sum: e543338446d034badfc8b223bac10ef9 NeedsCompilation: no Title: Efficient design and analysis of factorial two-colour microarray data Description: This package contains functions for the efficient design of factorial two-colour microarray experiments and for the statistical analysis of factorial microarray data. Statistical details are described in Bretz et al. (2003, submitted) biocViews: Microarray, TwoChannel, DifferentialExpression Author: Jobst Landgrebe and Frank Bretz Maintainer: Jobst Landgrebe URL: http://www.microarrays.med.uni-goettingen.de git_url: https://git.bioconductor.org/packages/daMA git_branch: RELEASE_3_10 git_last_commit: c78e917 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/daMA_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/daMA_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/daMA_1.58.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: DaMiRseq Version: 1.10.0 Depends: R (>= 3.4), SummarizedExperiment, ggplot2 Imports: DESeq2, limma, EDASeq, RColorBrewer, sva, Hmisc, pheatmap, FactoMineR, corrplot, randomForest, e1071, caret, MASS, lubridate, plsVarSel, kknn, FSelector, methods, stats, utils, graphics, grDevices, reshape2, ineq, arm, pls, RSNNS, edgeR Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: 7bb7ea212d0108b35c8ffc32c4632e1d NeedsCompilation: no Title: Data Mining for RNA-seq data: normalization, feature selection and classification Description: The DaMiRseq package offers a tidy pipeline of data mining procedures to identify transcriptional biomarkers and exploit them for both binary and multi-class classification purposes. The package accepts any kind of data presented as a table of raw counts and allows including both continous and factorial variables that occur with the experimental setting. A series of functions enable the user to clean up the data by filtering genomic features and samples, to adjust data by identifying and removing the unwanted source of variation (i.e. batches and confounding factors) and to select the best predictors for modeling. Finally, a "stacking" ensemble learning technique is applied to build a robust classification model. Every step includes a checkpoint that the user may exploit to assess the effects of data management by looking at diagnostic plots, such as clustering and heatmaps, RLE boxplots, MDS or correlation plot. biocViews: Sequencing, RNASeq, Classification, ImmunoOncology Author: Mattia Chiesa , Luca Piacentini Maintainer: Mattia Chiesa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DaMiRseq git_branch: RELEASE_3_10 git_last_commit: ca3ab5d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DaMiRseq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DaMiRseq_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DaMiRseq_1.10.0.tgz vignettes: vignettes/DaMiRseq/inst/doc/DaMiRseq.pdf vignetteTitles: Data Mining for RNA-seq data: normalization,, features selection and classification - DaMiRseq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DaMiRseq/inst/doc/DaMiRseq.R importsMe: GARS dependencyCount: 239 Package: DAPAR Version: 1.18.5 Depends: R (>= 3.6.1), foreach, parallel, doParallel, igraph Imports: MSnbase, RColorBrewer,stats,preprocessCore,Cairo,png, lattice,reshape2,gplots,pcaMethods,ggplot2, limma,knitr,tmvtnorm,norm,impute, stringr, grDevices, graphics, openxlsx, utils, cp4p (>= 0.3.5), scales, Matrix, vioplot, imp4p (>= 0.8), highcharter (>= 0.7.0), DAPARdata (>= 1.16.0), siggenes, graph, lme4, readxl, clusterProfiler, dplyr, tidyr,AnnotationDbi, vsn, FactoMineR, factoextra, visNetwork Suggests: BiocGenerics, Biobase, testthat, BiocStyle License: Artistic-2.0 MD5sum: 243bb0f82462dcc1eaba6948f02ca3ab NeedsCompilation: no Title: Tools for the Differential Analysis of Proteins Abundance with R Description: This package contains a collection of functions for the visualisation and the statistical analysis of proteomic data. biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry, QualityControl, GO, DataImport Author: Samuel Wieczorek [cre,aut], Florence Combes [aut], Thomas Burger [aut], Cosmin Lazar [ctb], Alexia Dorffer [ctb] Maintainer: Samuel Wieczorek VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DAPAR git_branch: RELEASE_3_10 git_last_commit: 926c136 git_last_commit_date: 2020-01-23 Date/Publication: 2020-01-23 source.ver: src/contrib/DAPAR_1.18.5.tar.gz win.binary.ver: bin/windows/contrib/3.6/DAPAR_1.18.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DAPAR_1.18.5.tgz vignettes: vignettes/DAPAR/inst/doc/Prostar_UserManual.pdf vignetteTitles: Prostar user manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DAPAR/inst/doc/Prostar_UserManual.R importsMe: Prostar Package: DART Version: 1.34.0 Depends: R (>= 2.10.0), igraph (>= 0.6.0) Suggests: breastCancerVDX, breastCancerMAINZ, Biobase License: GPL-2 MD5sum: 93dc85365ab4176d3427fb792ce38407 NeedsCompilation: no Title: Denoising Algorithm based on Relevance network Topology Description: Denoising Algorithm based on Relevance network Topology (DART) is an algorithm designed to evaluate the consistency of prior information molecular signatures (e.g in-vitro perturbation expression signatures) in independent molecular data (e.g gene expression data sets). If consistent, a pruning network strategy is then used to infer the activation status of the molecular signature in individual samples. biocViews: GeneExpression, DifferentialExpression, GraphAndNetwork, Pathways Author: Yan Jiao, Katherine Lawler, Andrew E Teschendorff, Charles Shijie Zheng Maintainer: Charles Shijie Zheng git_url: https://git.bioconductor.org/packages/DART git_branch: RELEASE_3_10 git_last_commit: 6cbaa10 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DART_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DART_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DART_1.34.0.tgz vignettes: vignettes/DART/inst/doc/DART.pdf vignetteTitles: DART Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DART/inst/doc/DART.R dependencyCount: 11 Package: DBChIP Version: 1.30.0 Depends: R (>= 2.15.0), edgeR, DESeq Suggests: ShortRead, BiocGenerics License: GPL (>= 2) MD5sum: 75c676926c1b3582ab95b3a0895d15c7 NeedsCompilation: no Title: Differential Binding of Transcription Factor with ChIP-seq Description: DBChIP detects differentially bound sharp binding sites across multiple conditions, with or without matching control samples. biocViews: ChIPSeq, Sequencing, Transcription, Genetics Author: Kun Liang Maintainer: Kun Liang git_url: https://git.bioconductor.org/packages/DBChIP git_branch: RELEASE_3_10 git_last_commit: 8cc33d8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DBChIP_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DBChIP_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DBChIP_1.30.0.tgz vignettes: vignettes/DBChIP/inst/doc/DBChIP.pdf vignetteTitles: DBChIP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DBChIP/inst/doc/DBChIP.R importsMe: metagene, metagene2 dependencyCount: 45 Package: dcanr Version: 1.2.0 Depends: R (>= 3.6.0) Imports: igraph, foreach, plyr, stringr, reshape2, methods, Matrix, graphics, stats, RColorBrewer, circlize, doRNG Suggests: EBcoexpress, testthat, EBarrays, GeneNet, COSINE, mclust, minqa, SummarizedExperiment, Biobase, knitr, rmarkdown, BiocStyle, edgeR Enhances: parallel, doSNOW, doParallel License: GPL-3 MD5sum: 285d1997481828fef7f7cf5448c71d05 NeedsCompilation: no Title: Differential co-expression/association network analysis Description: Methods and an evaluation framework for the inference of differential co-expression/association networks. biocViews: NetworkInference, GraphAndNetwork, DifferentialExpression, Network Author: Dharmesh D. Bhuva [aut, cre] () Maintainer: Dharmesh D. Bhuva URL: https://davislaboratory.github.io/dcanr/, https://github.com/DavisLaboratory/dcanr VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/dcanr/issues git_url: https://git.bioconductor.org/packages/dcanr git_branch: RELEASE_3_10 git_last_commit: 53e0500 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/dcanr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/dcanr_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/dcanr_1.2.0.tgz vignettes: vignettes/dcanr/inst/doc/dcanr_evaluation_vignette.html, vignettes/dcanr/inst/doc/dcanr_vignette.html vignetteTitles: 2. DC method evaluation, 1. Differential co-expression analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dcanr/inst/doc/dcanr_evaluation_vignette.R, vignettes/dcanr/inst/doc/dcanr_vignette.R dependencyCount: 30 Package: dcGSA Version: 1.14.0 Depends: R (>= 3.3), Matrix Imports: BiocParallel Suggests: knitr License: GPL-2 MD5sum: c9e8c209a10fc851f8c4d6dc13069036 NeedsCompilation: no Title: Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles Description: Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles. In longitudinal studies, the gene expression profiles were collected at each visit from each subject and hence there are multiple measurements of the gene expression profiles for each subject. The dcGSA package could be used to assess the associations between gene sets and clinical outcomes of interest by fully taking advantage of the longitudinal nature of both the gene expression profiles and clinical outcomes. biocViews: ImmunoOncology, GeneSetEnrichment,Microarray, StatisticalMethod, Sequencing, RNASeq, GeneExpression Author: Jiehuan Sun [aut, cre], Jose Herazo-Maya [aut], Xiu Huang [aut], Naftali Kaminski [aut], and Hongyu Zhao [aut] Maintainer: Jiehuan sun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dcGSA git_branch: RELEASE_3_10 git_last_commit: 926160d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/dcGSA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/dcGSA_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/dcGSA_1.14.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 16 Package: DChIPRep Version: 1.16.0 Depends: R (>= 3.4), DESeq2 Imports: methods, stats, utils, ggplot2, fdrtool, reshape2, GenomicRanges, SummarizedExperiment, smoothmest, plyr, tidyr, assertthat, S4Vectors, purrr, soGGi, ChIPpeakAnno Suggests: mgcv, testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENCE MD5sum: 16c801f06bea6bc489cc6b6ad11abcf0 NeedsCompilation: no Title: DChIPRep - Analysis of chromatin modification ChIP-Seq data with replication Description: The DChIPRep package implements a methodology to assess differences between chromatin modification profiles in replicated ChIP-Seq studies as described in Chabbert et. al - http://www.dx.doi.org/10.15252/msb.20145776. A detailed description of the method is given in the software paper at https://doi.org/10.7717/peerj.1981 biocViews: Sequencing, ChIPSeq, WholeGenome Author: Bernd Klaus [aut, cre], Christophe Chabbert [aut], Sebastian Gibb [ctb] Maintainer: Bernd Klaus VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DChIPRep git_branch: RELEASE_3_10 git_last_commit: 5f187c1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DChIPRep_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DChIPRep_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DChIPRep_1.16.0.tgz vignettes: vignettes/DChIPRep/inst/doc/DChIPRepVignette.html vignetteTitles: DChIPRepVignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DChIPRep/inst/doc/DChIPRepVignette.R dependencyCount: 171 Package: ddCt Version: 1.42.0 Depends: R (>= 2.3.0), methods Imports: Biobase (>= 1.10.0), RColorBrewer (>= 0.1-3), xtable, lattice, BiocGenerics Suggests: RUnit License: LGPL-3 MD5sum: 901a0b2157bbd0b9c6bb98f340b82af0 NeedsCompilation: no Title: The ddCt Algorithm for the Analysis of Quantitative Real-Time PCR (qRT-PCR) Description: The Delta-Delta-Ct (ddCt) Algorithm is an approximation method to determine relative gene expression with quantitative real-time PCR (qRT-PCR) experiments. Compared to other approaches, it requires no standard curve for each primer-target pair, therefore reducing the working load and yet returning accurate enough results as long as the assumptions of the amplification efficiency hold. The ddCt package implements a pipeline to collect, analyse and visualize qRT-PCR results, for example those from TaqMan SDM software, mainly using the ddCt method. The pipeline can be either invoked by a script in command-line or through the API consisting of S4-Classes, methods and functions. biocViews: GeneExpression, DifferentialExpression, MicrotitrePlateAssay, qPCR Author: Jitao David Zhang, Rudolf Biczok, and Markus Ruschhaupt Maintainer: Jitao David Zhang git_url: https://git.bioconductor.org/packages/ddCt git_branch: RELEASE_3_10 git_last_commit: aefa917 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ddCt_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ddCt_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ddCt_1.42.0.tgz vignettes: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.pdf, vignettes/ddCt/inst/doc/rtPCR-usage.pdf, vignettes/ddCt/inst/doc/rtPCR.pdf vignetteTitles: How to apply the ddCt method, Analyse RT-PCR data with the end-to-end script in ddCt package, Introduction to the ddCt method for qRT-PCR data analysis: background,, algorithm and example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.R, vignettes/ddCt/inst/doc/rtPCR-usage.R, vignettes/ddCt/inst/doc/rtPCR.R dependencyCount: 12 Package: ddPCRclust Version: 1.6.0 Depends: R (>= 3.5) Imports: plotrix, clue, parallel, ggplot2, openxlsx, R.utils, flowCore, flowDensity (>= 1.13.3), SamSPECTRAL, flowPeaks Suggests: BiocStyle License: Artistic-2.0 MD5sum: bad57a519fafe6dd639ab66f14566da6 NeedsCompilation: no Title: Clustering algorithm for ddPCR data Description: The ddPCRclust algorithm can automatically quantify the CPDs of non-orthogonal ddPCR reactions with up to four targets. In order to determine the correct droplet count for each target, it is crucial to both identify all clusters and label them correctly based on their position. For more information on what data can be analyzed and how a template needs to be formatted, please check the vignette. biocViews: ddPCR, Clustering Author: Benedikt G. Brink [aut, cre], Justin Meskas [ctb], Ryan R. Brinkman [ctb] Maintainer: Benedikt G. Brink URL: https://github.com/bgbrink/ddPCRclust BugReports: https://github.com/bgbrink/ddPCRclust/issues git_url: https://git.bioconductor.org/packages/ddPCRclust git_branch: RELEASE_3_10 git_last_commit: 2a35874 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ddPCRclust_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ddPCRclust_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ddPCRclust_1.6.0.tgz vignettes: vignettes/ddPCRclust/inst/doc/ddPCRclust.pdf vignetteTitles: Bioconductor LaTeX Style hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddPCRclust/inst/doc/ddPCRclust.R dependencyCount: 143 Package: debCAM Version: 1.4.0 Depends: R (>= 3.5) Imports: methods, rJava, BiocParallel, stats, Biobase, SummarizedExperiment, corpcor, geometry, NMF, nnls, DMwR, pcaPP, apcluster, graphics Suggests: knitr, rmarkdown, BiocStyle, testthat, GEOquery, rgl License: GPL-2 MD5sum: 1021b7d8a0f8026db2e6f7d010cdfd93 NeedsCompilation: no Title: Deconvolution by Convex Analysis of Mixtures Description: An R package for fully unsupervised deconvolution of complex tissues. It provides basic functions to perform unsupervised deconvolution on mixture expression profiles by Convex Analysis of Mixtures (CAM) and some auxiliary functions to help understand the subpopulation-specific results. It also implements functions to perform supervised deconvolution based on prior knowledge of molecular markers, S matrix or A matrix. Combining molecular markers from CAM and from prior knowledge can achieve semi-supervised deconvolution of mixtures. biocViews: Software, CellBiology, GeneExpression Author: Lulu Chen Maintainer: Lulu Chen SystemRequirements: Java (>= 1.8) VignetteBuilder: knitr BugReports: https://github.com/Lululuella/debCAM/issues git_url: https://git.bioconductor.org/packages/debCAM git_branch: RELEASE_3_10 git_last_commit: 81cd07a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/debCAM_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/debCAM_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/debCAM_1.4.0.tgz vignettes: vignettes/debCAM/inst/doc/debcam.html vignetteTitles: debCAM User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/debCAM/inst/doc/debcam.R dependencyCount: 120 Package: debrowser Version: 1.14.2 Depends: R (>= 3.5.0), Imports: shiny, jsonlite, shinyjs, shinydashboard, shinyBS, gplots, DT, ggplot2, RColorBrewer, annotate, AnnotationDbi, DESeq2, DOSE, igraph, grDevices, graphics, stats, utils, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, stringi, reshape2, d3heatmap, org.Hs.eg.db, org.Mm.eg.db, limma, edgeR, clusterProfiler, methods, sva, RCurl, enrichplot, colourpicker, plotly, heatmaply, Harman, pathview Suggests: testthat, rmarkdown, knitr, R.rsp License: GPL-3 + file LICENSE MD5sum: 60e4916b4986994566a159b26d712e17 NeedsCompilation: no Title: Interactive Differential Expresion Analysis Browser Description: Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps. biocViews: Sequencing, ChIPSeq, RNASeq, DifferentialExpression, GeneExpression, Clustering, ImmunoOncology Author: Alper Kucukural , Onur Yukselen , Manuel Garber Maintainer: Alper Kucukural URL: https://github.com/UMMS-Biocore/debrowser VignetteBuilder: knitr, R.rsp BugReports: https://github.com/UMMS-Biocore/debrowser/issues/new git_url: https://git.bioconductor.org/packages/debrowser git_branch: RELEASE_3_10 git_last_commit: 0a99d66 git_last_commit_date: 2019-12-23 Date/Publication: 2019-12-23 source.ver: src/contrib/debrowser_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/debrowser_1.14.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/debrowser_1.14.2.tgz vignettes: vignettes/debrowser/inst/doc/DEBrowser.html vignetteTitles: DEBrowser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/debrowser/inst/doc/DEBrowser.R dependencyCount: 209 Package: DECIPHER Version: 2.14.0 Depends: R (>= 3.3.0), Biostrings (>= 2.35.12), RSQLite (>= 1.1), stats, parallel Imports: methods, DBI, S4Vectors, IRanges, XVector LinkingTo: Biostrings, S4Vectors, IRanges, XVector License: GPL-3 MD5sum: 647cef126b0a6cf38aeab5ae366c0851 NeedsCompilation: yes Title: Tools for curating, analyzing, and manipulating biological sequences Description: A toolset for deciphering and managing biological sequences. biocViews: Clustering, Genetics, Sequencing, DataImport, Visualization, Microarray, QualityControl, qPCR, Alignment, WholeGenome, Microbiome, ImmunoOncology Author: Erik Wright Maintainer: Erik Wright git_url: https://git.bioconductor.org/packages/DECIPHER git_branch: RELEASE_3_10 git_last_commit: 45615ee git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DECIPHER_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DECIPHER_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DECIPHER_2.14.0.tgz vignettes: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.pdf, vignettes/DECIPHER/inst/doc/ClassifySequences.pdf, vignettes/DECIPHER/inst/doc/DECIPHERing.pdf, vignettes/DECIPHER/inst/doc/DesignMicroarray.pdf, vignettes/DECIPHER/inst/doc/DesignPrimers.pdf, vignettes/DECIPHER/inst/doc/DesignProbes.pdf, vignettes/DECIPHER/inst/doc/DesignSignatures.pdf, vignettes/DECIPHER/inst/doc/FindChimeras.pdf vignetteTitles: The Art of Multiple Sequence Alignment in R, Classify Sequences, Getting Started DECIPHERing, Design Microarray Probes, Design Group-Specific Primers, Design Group-Specific FISH Probes, Design Primers That Yield Group-Specific Signatures, Finding Chimeric Sequences hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.R, vignettes/DECIPHER/inst/doc/ClassifySequences.R, vignettes/DECIPHER/inst/doc/DECIPHERing.R, vignettes/DECIPHER/inst/doc/DesignMicroarray.R, vignettes/DECIPHER/inst/doc/DesignPrimers.R, vignettes/DECIPHER/inst/doc/DesignProbes.R, vignettes/DECIPHER/inst/doc/DesignSignatures.R, vignettes/DECIPHER/inst/doc/FindChimeras.R dependsOnMe: AssessORF importsMe: metagenomeFeatures, openPrimeR dependencyCount: 27 Package: deco Version: 1.2.0 Depends: R (>= 3.5.0), AnnotationDbi, BiocParallel, SummarizedExperiment, limma Imports: stats, methods, ggplot2, foreign, graphics, BiocStyle, Biobase, cluster, gplots, RColorBrewer, locfit, made4, ade4, sfsmisc, scatterplot3d, gdata, grDevices, utils, reshape2, gridExtra Suggests: knitr, curatedTCGAData, MultiAssayExperiment, Homo.sapiens License: GPL (>=3) Archs: i386, x64 MD5sum: 901e7b70f92ac1dcaa63970ce8ad7c1c NeedsCompilation: no Title: Decomposing Heterogeneous Cohorts using Omic Data Profiling Description: This package discovers differential features in hetero- and homogeneous omic data by a two-step method including subsampling LIMMA and NSCA. DECO reveals feature associations to hidden subclasses not exclusively related to higher deregulation levels. biocViews: Software, FeatureExtraction, Clustering, MultipleComparison, DifferentialExpression, Transcriptomics, BiomedicalInformatics, Proteomics, Bayesian, GeneExpression, Transcription, Sequencing, Microarray, ExonArray, RNASeq, MicroRNAArray, mRNAMicroarray Author: Francisco Jose Campos-Laborie, Jose Manuel Sanchez-Santos and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain. Maintainer: Francisco Jose Campos Laborie URL: https://github.com/fjcamlab/deco VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deco git_branch: RELEASE_3_10 git_last_commit: 5587eaf git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/deco_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/deco_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/deco_1.2.0.tgz vignettes: vignettes/deco/inst/doc/DECO.html vignetteTitles: deco hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deco/inst/doc/DECO.R dependencyCount: 119 Package: DEComplexDisease Version: 1.6.0 Depends: R (>= 3.3.3) Imports: Rcpp (>= 0.12.7), DESeq2, edgeR, SummarizedExperiment, ComplexHeatmap, grid, parallel, BiocParallel, grDevices, graphics, stats, methods, utils LinkingTo: Rcpp Suggests: knitr License: GPL-3 MD5sum: 0b2c6295a415351351c64a3588dccad8 NeedsCompilation: yes Title: A tool for differential expression analysis and DEGs based investigation to complex diseases by bi-clustering analysis Description: It is designed to find the differential expressed genes (DEGs) for complex disease, which is characterized by the heterogeneous genomic expression profiles. Different from the established DEG analysis tools, it does not assume the patients of complex diseases to share the common DEGs. By applying a bi-clustering algorithm, DECD finds the DEGs shared by as many patients. In this way, DECD describes the DEGs of complex disease in a novel syntax, e.g. a gene list composed of 200 genes are differentially expressed in 30% percent of studied complex disease. Applying the DECD analysis results, users are possible to find the patients affected by the same mechanism based on the shared signatures. biocViews: DNASeq, WholeGenome, FunctionalGenomics, DifferentialExpression,GeneExpression, Clustering Author: Guofeng Meng Maintainer: Guofeng Meng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEComplexDisease git_branch: RELEASE_3_10 git_last_commit: e50399a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DEComplexDisease_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DEComplexDisease_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DEComplexDisease_1.6.0.tgz vignettes: vignettes/DEComplexDisease/inst/doc/vignettes.pdf, vignettes/DEComplexDisease/inst/doc/decd.html vignetteTitles: DEComplexDisease: a R package for DE analysis, DEComplexDisease: a R package for DE analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEComplexDisease/inst/doc/decd.R dependencyCount: 130 Package: decompTumor2Sig Version: 2.2.0 Depends: R(>= 3.6), ggplot2 Imports: methods, Matrix, quadprog(>= 1.5-5), vcfR, GenomicRanges, stats, GenomicFeatures, Biostrings, BiocGenerics, S4Vectors, plyr, utils, graphics, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, SummarizedExperiment, ggseqlogo, gridExtra, data.table Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: 43da910c3b77e6d61bbf5a625d7d8637 NeedsCompilation: no Title: Decomposition of individual tumors into mutational signatures by signature refitting Description: Uses quadratic programming for signature refitting, i.e., to decompose the mutation catalog from an individual tumor sample into a set of given mutational signatures (either Alexandrov-model signatures or Shiraishi-model signatures), computing weights that reflect the contributions of the signatures to the mutation load of the tumor. biocViews: Software, SNP, Sequencing, DNASeq, GenomicVariation, SomaticMutation, BiomedicalInformatics, Genetics, BiologicalQuestion, StatisticalMethod Author: Rosario M. Piro [aut, cre], Sandra Krueger [ctb] Maintainer: Rosario M. Piro URL: http://rmpiro.net/decompTumor2Sig/, https://github.com/rmpiro/decompTumor2Sig VignetteBuilder: knitr BugReports: https://github.com/rmpiro/decompTumor2Sig/issues git_url: https://git.bioconductor.org/packages/decompTumor2Sig git_branch: RELEASE_3_10 git_last_commit: b695d01 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/decompTumor2Sig_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/decompTumor2Sig_2.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/decompTumor2Sig_2.2.0.tgz vignettes: vignettes/decompTumor2Sig/inst/doc/decompTumor2Sig.html vignetteTitles: A brief introduction to decompTumor2Sig hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/decompTumor2Sig/inst/doc/decompTumor2Sig.R dependencyCount: 126 Package: DeconRNASeq Version: 1.28.0 Depends: R (>= 2.14.0), limSolve, pcaMethods, ggplot2, grid License: GPL-2 MD5sum: 9ccaab63d80443c96b92b06d0d296732 NeedsCompilation: no Title: Deconvolution of Heterogeneous Tissue Samples for mRNA-Seq data Description: DeconSeq is an R package for deconvolution of heterogeneous tissues based on mRNA-Seq data. It modeled expression levels from heterogeneous cell populations in mRNA-Seq as the weighted average of expression from different constituting cell types and predicted cell type proportions of single expression profiles. biocViews: DifferentialExpression Author: Ting Gong Joseph D. Szustakowski Maintainer: Ting Gong git_url: https://git.bioconductor.org/packages/DeconRNASeq git_branch: RELEASE_3_10 git_last_commit: a7942a3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DeconRNASeq_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DeconRNASeq_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DeconRNASeq_1.28.0.tgz vignettes: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.pdf vignetteTitles: DeconRNASeq Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.R dependencyCount: 61 Package: decontam Version: 1.6.0 Depends: R (>= 3.4.1), methods (>= 3.4.1) Imports: ggplot2 (>= 2.1.0), reshape2 (>= 1.4.1), stats Suggests: BiocStyle, knitr, rmarkdown, phyloseq License: Artistic-2.0 MD5sum: 55db2a9c9c7fdf8e889596b759ee14c0 NeedsCompilation: no Title: Identify Contaminants in Marker-gene and Metagenomics Sequencing Data Description: Simple statistical identification of contaminating sequence features in marker-gene or metagenomics data. Works on any kind of feature derived from environmental sequencing data (e.g. ASVs, OTUs, taxonomic groups, MAGs,...). Requires DNA quantitation data or sequenced negative control samples. biocViews: ImmunoOncology, Microbiome, Sequencing, Classification, Metagenomics Author: Benjamin Callahan , Nicole Marie Davis Maintainer: Benjamin Callahan URL: https://github.com/benjjneb/decontam VignetteBuilder: knitr BugReports: https://github.com/benjjneb/decontam/issues git_url: https://git.bioconductor.org/packages/decontam git_branch: RELEASE_3_10 git_last_commit: 09fe7ba git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/decontam_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/decontam_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/decontam_1.6.0.tgz vignettes: vignettes/decontam/inst/doc/decontam_intro.html vignetteTitles: Introduction to dada2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/decontam/inst/doc/decontam_intro.R dependencyCount: 58 Package: DEDS Version: 1.60.0 Depends: R (>= 1.7.0) License: LGPL MD5sum: e2aa5f2fb43a195291769db87897f489 NeedsCompilation: yes Title: Differential Expression via Distance Summary for Microarray Data Description: This library contains functions that calculate various statistics of differential expression for microarray data, including t statistics, fold change, F statistics, SAM, moderated t and F statistics and B statistics. It also implements a new methodology called DEDS (Differential Expression via Distance Summary), which selects differentially expressed genes by integrating and summarizing a set of statistics using a weighted distance approach. biocViews: Microarray, DifferentialExpression Author: Yuanyuan Xiao , Jean Yee Hwa Yang . Maintainer: Yuanyuan Xiao git_url: https://git.bioconductor.org/packages/DEDS git_branch: RELEASE_3_10 git_last_commit: 781d892 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DEDS_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DEDS_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DEDS_1.60.0.tgz vignettes: vignettes/DEDS/inst/doc/DEDS.pdf vignetteTitles: DEDS.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEDS/inst/doc/DEDS.R dependencyCount: 0 Package: DeepBlueR Version: 1.12.0 Depends: R (>= 3.3), XML, RCurl Imports: GenomicRanges, data.table, stringr, diffr, dplyr, methods, rjson, utils, R.utils, foreach, withr, rtracklayer, GenomeInfoDb, settings, filehash Suggests: knitr, rmarkdown, LOLA, Gviz, gplots, ggplot2, tidyr, RColorBrewer, matrixStats License: GPL (>=2.0) Archs: i386, x64 MD5sum: 60b19e443c4411d58b858c5381ac56ef NeedsCompilation: no Title: DeepBlueR Description: Accessing the DeepBlue Epigenetics Data Server through R. biocViews: DataImport, DataRepresentation, ThirdPartyClient, GeneRegulation, GenomeAnnotation, CpGIsland, DNAMethylation, Epigenetics, Annotation, Preprocessing, ImmunoOncology Author: Felipe Albrecht, Markus List Maintainer: Felipe Albrecht , Markus List VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeepBlueR git_branch: RELEASE_3_10 git_last_commit: a2bf161 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DeepBlueR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DeepBlueR_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DeepBlueR_1.12.0.tgz vignettes: vignettes/DeepBlueR/inst/doc/DeepBlueR.html vignetteTitles: The DeepBlue epigenomic data server - R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeepBlueR/inst/doc/DeepBlueR.R dependencyCount: 77 Package: deepSNV Version: 1.32.0 Depends: R (>= 2.13.0), methods, graphics, parallel, Rhtslib, IRanges, GenomicRanges, SummarizedExperiment, Biostrings, VGAM, VariantAnnotation (>= 1.13.44), Imports: Rhtslib LinkingTo: Rhtslib (>= 1.13.1) Suggests: RColorBrewer, knitr, rmarkdown License: GPL-3 MD5sum: ca390828353e330944e5b691d9ac2eb2 NeedsCompilation: yes Title: Detection of subclonal SNVs in deep sequencing data. Description: This package provides provides quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs. The shearwater algorithm computes a Bayes classifier based on a beta-binomial model for variant calling with multiple samples for precisely estimating model parameters - such as local error rates and dispersion - and prior knowledge, e.g. from variation data bases such as COSMIC. biocViews: GeneticVariability, SNP, Sequencing, Genetics, DataImport Author: Niko Beerenwinkel [ths], David Jones [ctb], Inigo Martincorena [ctb], Moritz Gerstung [aut, cre] Maintainer: Moritz Gerstung URL: http://github.com/gerstung-lab/deepSNV SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deepSNV git_branch: RELEASE_3_10 git_last_commit: 342a37e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/deepSNV_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/deepSNV_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/deepSNV_1.32.0.tgz vignettes: vignettes/deepSNV/inst/doc/deepSNV.pdf, vignettes/deepSNV/inst/doc/shearwater.pdf, vignettes/deepSNV/inst/doc/shearwaterML.html vignetteTitles: An R package for detecting low frequency variants in deep sequencing experiments, Subclonal variant calling with multiple samples and prior knowledge using shearwater, Shearwater ML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deepSNV/inst/doc/deepSNV.R, vignettes/deepSNV/inst/doc/shearwater.R, vignettes/deepSNV/inst/doc/shearwaterML.R suggestsMe: GenomicFiles dependencyCount: 87 Package: DEFormats Version: 1.14.0 Imports: checkmate, data.table, DESeq2, edgeR (>= 3.13.4), GenomicRanges, methods, S4Vectors, stats, SummarizedExperiment Suggests: BiocStyle (>= 1.8.0), knitr, rmarkdown, testthat License: GPL-3 Archs: i386, x64 MD5sum: ff0865faaf368f2a261fcaaade075e22 NeedsCompilation: no Title: Differential gene expression data formats converter Description: Convert between different data formats used by differential gene expression analysis tools. biocViews: ImmunoOncology, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Transcription Author: Andrzej Oleś Maintainer: Andrzej Oleś URL: https://github.com/aoles/DEFormats VignetteBuilder: knitr BugReports: https://github.com/aoles/DEFormats/issues git_url: https://git.bioconductor.org/packages/DEFormats git_branch: RELEASE_3_10 git_last_commit: 55e6cf6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DEFormats_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DEFormats_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DEFormats_1.14.0.tgz vignettes: vignettes/DEFormats/inst/doc/DEFormats.html vignetteTitles: Differential gene expression data formats converter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEFormats/inst/doc/DEFormats.R importsMe: regionReport suggestsMe: ideal dependencyCount: 123 Package: DEGraph Version: 1.38.0 Depends: R (>= 2.10.0), R.utils Imports: graph, KEGGgraph, lattice, mvtnorm, R.methodsS3, RBGL, Rgraphviz, rrcov, NCIgraph Suggests: corpcor, fields, graph, KEGGgraph, lattice, marray, RBGL, rrcov, Rgraphviz, NCIgraph License: GPL-3 MD5sum: 3275104ce2c5f2882bade0664258e4e5 NeedsCompilation: no Title: Two-sample tests on a graph Description: DEGraph implements recent hypothesis testing methods which directly assess whether a particular gene network is differentially expressed between two conditions. This is to be contrasted with the more classical two-step approaches which first test individual genes, then test gene sets for enrichment in differentially expressed genes. These recent methods take into account the topology of the network to yield more powerful detection procedures. DEGraph provides methods to easily test all KEGG pathways for differential expression on any gene expression data set and tools to visualize the results. biocViews: Microarray, DifferentialExpression, GraphAndNetwork, Network, NetworkEnrichment, DecisionTree Author: Laurent Jacob, Pierre Neuvial and Sandrine Dudoit Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/DEGraph git_branch: RELEASE_3_10 git_last_commit: e7dd55e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DEGraph_1.38.0.tar.gz vignettes: vignettes/DEGraph/inst/doc/DEGraph.pdf vignetteTitles: DEGraph: differential expression testing for gene networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGraph/inst/doc/DEGraph.R dependencyCount: 42 Package: DEGreport Version: 1.22.0 Depends: R (>= 3.5.0) Imports: utils, methods, Biobase, BiocGenerics, broom, circlize, ComplexHeatmap, cowplot, ConsensusClusterPlus, cluster, DESeq2, dplyr, edgeR, ggplot2, ggdendro, grid, ggrepel, grDevices, knitr, logging, lasso2, magrittr, Nozzle.R1, psych, RColorBrewer, reshape, rlang, scales, stats, stringr, S4Vectors, SummarizedExperiment, tidyr, tibble Suggests: BiocStyle, AnnotationDbi, limma, pheatmap, rmarkdown, statmod, testthat License: MIT + file LICENSE MD5sum: 9d356a3f768532bcd04867b0708a52c5 NeedsCompilation: no Title: Report of DEG analysis Description: Creation of a HTML report of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene. biocViews: DifferentialExpression, Visualization, RNASeq, ReportWriting, GeneExpression, ImmunoOncology Author: Lorena Pantano [aut, cre], John Hutchinson [ctb], Victor Barrera [ctb], Mary Piper [ctb], Radhika Khetani [ctb], Kenneth Daily [ctb], Thanneer Malai Perumal [ctb], Rory Kirchner [ctb], Michael Steinbaugh [ctb] Maintainer: Lorena Pantano URL: http://lpantano.github.io/DEGreport/ VignetteBuilder: knitr BugReports: https://github.com/lpantano/DEGreport/issues git_url: https://git.bioconductor.org/packages/DEGreport git_branch: RELEASE_3_10 git_last_commit: 31ec410 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DEGreport_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DEGreport_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DEGreport_1.22.0.tgz vignettes: vignettes/DEGreport/inst/doc/DEGreport.html vignetteTitles: QC and downstream analysis for differential expression RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DEGreport/inst/doc/DEGreport.R importsMe: isomiRs dependencyCount: 149 Package: DEGseq Version: 1.40.0 Depends: R (>= 2.8.0), qvalue, methods Imports: graphics, grDevices, methods, stats, utils License: LGPL (>=2) MD5sum: f61f9ad7fc7b0b65401e726e2f4648c4 NeedsCompilation: yes Title: Identify Differentially Expressed Genes from RNA-seq data Description: DEGseq is an R package to identify differentially expressed genes from RNA-Seq data. biocViews: RNASeq, Preprocessing, GeneExpression, DifferentialExpression, ImmunoOncology Author: Likun Wang and Xi Wang . Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/DEGseq git_branch: RELEASE_3_10 git_last_commit: 41edce8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DEGseq_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DEGseq_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DEGseq_1.40.0.tgz vignettes: vignettes/DEGseq/inst/doc/DEGseq.pdf vignetteTitles: DEGseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGseq/inst/doc/DEGseq.R dependencyCount: 59 Package: DelayedArray Version: 0.12.3 Depends: R (>= 3.4), methods, stats4, matrixStats, BiocGenerics (>= 0.31.5), S4Vectors (>= 0.24.4), IRanges (>= 2.17.3), BiocParallel Imports: stats, Matrix LinkingTo: S4Vectors Suggests: HDF5Array, genefilter, SummarizedExperiment, airway, pryr, DelayedMatrixStats, knitr, BiocStyle, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: e32a42555bcff7780ae635d2e1e38f88 NeedsCompilation: yes Title: A unified framework for working transparently with on-disk and in-memory array-like datasets Description: Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, and ordinary arrays and data frames. biocViews: Infrastructure, DataRepresentation, Annotation, GenomeAnnotation Author: Hervé Pagès , with contributions from Peter Hickey and Aaron Lun Maintainer: Hervé Pagès VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DelayedArray git_branch: RELEASE_3_10 git_last_commit: 8419a37 git_last_commit_date: 2020-04-06 Date/Publication: 2020-04-09 source.ver: src/contrib/DelayedArray_0.12.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/DelayedArray_0.12.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DelayedArray_0.12.3.tgz vignettes: vignettes/DelayedArray/inst/doc/01-Working_with_large_arrays.pdf, vignettes/DelayedArray/inst/doc/02-Implementing_a_backend.html vignetteTitles: Working with large arrays in R, Implementing A DelayedArray Backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedArray/inst/doc/01-Working_with_large_arrays.R dependsOnMe: DelayedDataFrame, DelayedMatrixStats, GDSArray, HDF5Array, rhdf5client, singleCellTK, SummarizedExperiment, VCFArray importsMe: batchelor, beachmat, BiocSingular, bsseq, CAGEr, celaref, ChIPpeakAnno, clusterExperiment, DEScan2, DSS, ELMER, GenoGAM, hipathia, LoomExperiment, mbkmeans, methrix, minfi, netSmooth, PCAtools, RTCGAToolbox, scater, scDblFinder, scMerge, scmeth, scran, signatureSearch, SingleR, VariantExperiment suggestsMe: BiocGenerics, gwascat, iSEE, MAST, S4Vectors, SQLDataFrame dependencyCount: 21 Package: DelayedDataFrame Version: 1.2.0 Depends: R (>= 3.6), S4Vectors (>= 0.23.19), DelayedArray (>= 0.7.5) Imports: methods, stats, BiocGenerics Suggests: testthat, knitr, rmarkdown, SeqArray, GDSArray License: GPL-3 Archs: i386, x64 MD5sum: 7c946cb48021a7733d64e807d6fd9d64 NeedsCompilation: no Title: Delayed operation on DataFrame using standard DataFrame metaphor Description: Based on the standard DataFrame metaphor, we are trying to implement the feature of delayed operation on the DelayedDataFrame, with a slot of lazyIndex, which saves the mapping indexes for each column of DelayedDataFrame. Methods like show, validity check, [/[[ subsetting, rbind/cbind are implemented for DelayedDataFrame to be operated around lazyIndex. The listData slot stays untouched until a realization call e.g., DataFrame constructor OR as.list() is invoked. biocViews: Infrastructure, DataRepresentation Author: Qian Liu [aut, cre], Hervé Pagès [aut], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/DelayedDataFrame VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/DelayedDataFrame/issues git_url: https://git.bioconductor.org/packages/DelayedDataFrame git_branch: RELEASE_3_10 git_last_commit: 530eefd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DelayedDataFrame_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DelayedDataFrame_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DelayedDataFrame_1.2.0.tgz vignettes: vignettes/DelayedDataFrame/inst/doc/DelayedDataFrame.html vignetteTitles: DelayedDataFrame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedDataFrame/inst/doc/DelayedDataFrame.R dependsOnMe: VariantExperiment dependencyCount: 22 Package: DelayedMatrixStats Version: 1.8.0 Depends: DelayedArray (>= 0.11.1) Imports: methods, matrixStats (>= 0.55.0), Matrix, S4Vectors (>= 0.17.5), IRanges, HDF5Array (>= 1.7.10), BiocParallel Suggests: testthat, knitr, rmarkdown, covr, BiocStyle, microbenchmark, profmem License: MIT + file LICENSE MD5sum: 7c98091fcd565d40c3a3d83422cac508 NeedsCompilation: no Title: Functions that Apply to Rows and Columns of 'DelayedMatrix' Objects Description: A port of the 'matrixStats' API for use with DelayedMatrix objects from the 'DelayedArray' package. High-performing functions operating on rows and columns of DelayedMatrix objects, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. biocViews: Infrastructure, DataRepresentation, Software Author: Peter Hickey Maintainer: Peter Hickey URL: https://github.com/PeteHaitch/DelayedMatrixStats VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/DelayedMatrixStats/issues git_url: https://git.bioconductor.org/packages/DelayedMatrixStats git_branch: RELEASE_3_10 git_last_commit: a366e5e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DelayedMatrixStats_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DelayedMatrixStats_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DelayedMatrixStats_1.8.0.tgz vignettes: vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.html vignetteTitles: Overview of DelayedMatrixStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.R importsMe: batchelor, biscuiteer, bsseq, dmrseq, methrix, minfi, PCAtools, scater, scMerge, scran, SingleR suggestsMe: DelayedArray, mbkmeans dependencyCount: 26 Package: deltaCaptureC Version: 1.0.0 Depends: R (>= 3.6) Imports: IRanges, GenomicRanges, SummarizedExperiment, ggplot2, DESeq2 Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: 3702e039b28c52c0bc54d5008ad92e48 NeedsCompilation: no Title: This Package Discovers Meso-scale Chromatin Remodeling from 3C Data Description: This package discovers meso-scale chromatin remodelling from 3C data. 3C data is local in nature. It givens interaction counts between restriction enzyme digestion fragments and a preferred 'viewpoint' region. By binning this data and using permutation testing, this package can test whether there are statistically significant changes in the interaction counts between the data from two cell types or two treatments. biocViews: BiologicalQuestion, StatisticalMethod Author: Michael Shapiro [aut, cre] () Maintainer: Michael Shapiro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deltaCaptureC git_branch: RELEASE_3_10 git_last_commit: d4ea8b0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/deltaCaptureC_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/deltaCaptureC_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/deltaCaptureC_1.0.0.tgz vignettes: vignettes/deltaCaptureC/inst/doc/deltaCaptureC.html vignetteTitles: Delta Capture-C hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/deltaCaptureC/inst/doc/deltaCaptureC.R dependencyCount: 121 Package: deltaGseg Version: 1.26.0 Depends: R (>= 2.15.1), methods, ggplot2, changepoint, wavethresh, tseries, pvclust, fBasics, grid, reshape, scales Suggests: knitr License: GPL-2 MD5sum: 817f980676f1c001d50d24d1ef9b9c22 NeedsCompilation: no Title: deltaGseg Description: Identifying distinct subpopulations through multiscale time series analysis biocViews: Proteomics, TimeCourse, Visualization, Clustering Author: Diana Low, Efthymios Motakis Maintainer: Diana Low VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deltaGseg git_branch: RELEASE_3_10 git_last_commit: d99232a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/deltaGseg_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/deltaGseg_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/deltaGseg_1.26.0.tgz vignettes: vignettes/deltaGseg/inst/doc/deltaGseg.pdf vignetteTitles: deltaGseg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deltaGseg/inst/doc/deltaGseg.R dependencyCount: 72 Package: DeMAND Version: 1.16.0 Depends: R (>= 2.14.0), KernSmooth, methods License: file LICENSE MD5sum: 70f3b0722a2ffd2886ff9ca51247c788 NeedsCompilation: no Title: DeMAND Description: DEMAND predicts Drug MoA by interrogating a cell context specific regulatory network with a small number (N >= 6) of compound-induced gene expression signatures, to elucidate specific proteins whose interactions in the network is dysregulated by the compound. biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, StatisticalMethod, Network Author: Jung Hoon Woo , Yishai Shimoni Maintainer: Jung Hoon Woo , Mariano Alvarez git_url: https://git.bioconductor.org/packages/DeMAND git_branch: RELEASE_3_10 git_last_commit: 7d14f7e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DeMAND_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DeMAND_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DeMAND_1.16.0.tgz vignettes: vignettes/DeMAND/inst/doc/DeMAND.pdf vignetteTitles: Using DeMAND hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DeMAND/inst/doc/DeMAND.R dependencyCount: 3 Package: DeMixT Version: 1.2.5 Depends: R (>= 3.6.0), parallel, Rcpp (>= 1.0.0), SummarizedExperiment, knitr, KernSmooth, matrixcalc Imports: matrixStats, stats, truncdist, base64enc, ggplot2 LinkingTo: Rcpp License: GPL-3 MD5sum: 38d1d48ba3397cd5564cf63184f0deef NeedsCompilation: yes Title: Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms Description: DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components. biocViews: Software, StatisticalMethod, Classification, GeneExpression, Sequencing, Microarray, TissueMicroarray, Coverage Author: Zeya Wang , Shaolong Cao, Wenyi Wang Maintainer: Shaolong Cao, Peng Yang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeMixT git_branch: RELEASE_3_10 git_last_commit: ada2dc5 git_last_commit_date: 2020-02-15 Date/Publication: 2020-02-16 source.ver: src/contrib/DeMixT_1.2.5.tar.gz win.binary.ver: bin/windows/contrib/3.6/DeMixT_1.2.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DeMixT_1.2.5.tgz vignettes: vignettes/DeMixT/inst/doc/demixt.html vignetteTitles: DeMixT.Rmd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeMixT/inst/doc/demixt.R dependencyCount: 90 Package: DEP Version: 1.8.0 Depends: R (>= 3.5) Imports: ggplot2, dplyr, purrr, readr, tibble, tidyr, SummarizedExperiment (>= 1.11.5), MSnbase, limma, vsn, fdrtool, ggrepel, ComplexHeatmap, RColorBrewer, circlize, shiny, shinydashboard, DT, rmarkdown, assertthat, gridExtra, grid, stats, imputeLCMD, cluster Suggests: testthat, enrichR, knitr, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 71d780187f9ce1eadefd772b730dab66 NeedsCompilation: no Title: Differential Enrichment analysis of Proteomics data Description: This package provides an integrated analysis workflow for robust and reproducible analysis of mass spectrometry proteomics data for differential protein expression or differential enrichment. It requires tabular input (e.g. txt files) as generated by quantitative analysis softwares of raw mass spectrometry data, such as MaxQuant or IsobarQuant. Functions are provided for data preparation, filtering, variance normalization and imputation of missing values, as well as statistical testing of differentially enriched / expressed proteins. It also includes tools to check intermediate steps in the workflow, such as normalization and missing values imputation. Finally, visualization tools are provided to explore the results, including heatmap, volcano plot and barplot representations. For scientists with limited experience in R, the package also contains wrapper functions that entail the complete analysis workflow and generate a report. Even easier to use are the interactive Shiny apps that are provided by the package. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, DifferentialExpression, DataRepresentation Author: Arne Smits [cre, aut], Wolfgang Huber [aut] Maintainer: Arne Smits VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEP git_branch: RELEASE_3_10 git_last_commit: 086f5d1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DEP_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DEP_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DEP_1.8.0.tgz vignettes: vignettes/DEP/inst/doc/DEP.html, vignettes/DEP/inst/doc/MissingValues.html vignetteTitles: DEP: Introduction, DEP: Missing value handling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEP/inst/doc/DEP.R, vignettes/DEP/inst/doc/MissingValues.R suggestsMe: proDA dependencyCount: 150 Package: DepecheR Version: 1.2.2 Depends: R (>= 3.6) Imports: ggplot2 (>= 3.1.0), MASS (>= 7.3.51), Rcpp (>= 1.0.0), dplyr (>= 0.7.8), gplots (>= 3.0.1), viridis (>= 0.5.1), foreach (>= 1.4.4), doSNOW (>= 1.0.16), matrixStats (>= 0.54.0), mixOmics (>= 6.6.1), moments (>= 0.14), grDevices (>= 3.5.2), graphics (>= 3.5.2), stats (>= 3.5.2), utils (>= 3.5), methods (>= 3.5), parallel (>= 3.5.2), reshape2 (>= 1.4.3), beanplot (>= 1.2), FNN (>= 1.1.3), robustbase (>= 0.93.5) LinkingTo: Rcpp, RcppEigen Suggests: Rtsne, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 7aa8013442695256d9e04a892a705f60 NeedsCompilation: yes Title: Determination of essential phenotypic elements of clusters in high-dimensional entities Description: The purpose of this package is to identify traits in a dataset that can separate groups. This is done on two levels. First, clustering is performed, using an implementation of sparse K-means. Secondly, the generated clusters are used to predict outcomes of groups of individuals based on their distribution of observations in the different clusters. As certain clusters with separating information will be identified, and these clusters are defined by a sparse number of variables, this method can reduce the complexity of data, to only emphasize the data that actually matters. biocViews: Software,CellBasedAssays,Transcription,DifferentialExpression, DataRepresentation,ImmunoOncology,Transcriptomics,Classification,Clustering, DimensionReduction,FeatureExtraction,FlowCytometry,RNASeq,SingleCell, Visualization Author: Jakob Theorell [aut, cre], Axel Theorell [aut] Maintainer: Jakob Theorell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DepecheR git_branch: RELEASE_3_10 git_last_commit: 42a15a4 git_last_commit_date: 2020-03-02 Date/Publication: 2020-03-02 source.ver: src/contrib/DepecheR_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/DepecheR_1.2.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DepecheR_1.2.2.tgz vignettes: vignettes/DepecheR/inst/doc/DepecheR_test.html vignetteTitles: Example of a cytometry data analysis with DepecheR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DepecheR/inst/doc/DepecheR_test.R suggestsMe: flowSpecs dependencyCount: 91 Package: DEqMS Version: 1.4.0 Depends: R(>= 3.5),graphics,stats,ggplot2,limma(>= 3.34) Suggests: BiocStyle,knitr,rmarkdown,plyr,matrixStats,reshape2,farms,utils,ggrepel,ExperimentHub,LSD License: LGPL Archs: i386, x64 MD5sum: 72788c88bb67d717e97efcba2e651cf1 NeedsCompilation: no Title: a tool to perform statistical analysis of differential protein expression for quantitative proteomics data. Description: DEqMS is developped on top of Limma. However, Limma assumes same prior variance for all genes. In proteomics, the accuracy of protein abundance estimates varies by the number of peptides/PSMs quantified in both label-free and labelled data. Proteins quantification by multiple peptides or PSMs are more accurate. DEqMS package is able to estimate different prior variances for proteins quantified by different number of PSMs/peptides, therefore acchieving better accuracy. The package can be applied to analyze both label-free and labelled proteomics data. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Preprocessing, DifferentialExpression, MultipleComparison,Normalization,Bayesian Author: Yafeng Zhu Maintainer: Yafeng Zhu VignetteBuilder: knitr BugReports: https://github.com/yafeng/DEqMS/issues git_url: https://git.bioconductor.org/packages/DEqMS git_branch: RELEASE_3_10 git_last_commit: f0adbf1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DEqMS_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DEqMS_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DEqMS_1.4.0.tgz vignettes: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.html vignetteTitles: DEqMS R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.R dependencyCount: 55 Package: derfinder Version: 1.20.0 Depends: R (>= 3.5.0) Imports: BiocGenerics (>= 0.25.1), AnnotationDbi (>= 1.27.9), BiocParallel (>= 1.15.15), bumphunter (>= 1.9.2), derfinderHelper (>= 1.1.0), GenomeInfoDb (>= 1.3.3), GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges (>= 1.17.40), Hmisc, IRanges (>= 2.3.23), methods, qvalue (>= 1.99.0), Rsamtools (>= 1.25.0), rtracklayer, S4Vectors (>= 0.23.19), stats Suggests: BiocStyle (>= 2.5.19), biovizBase, sessioninfo, derfinderData (>= 0.99.0), derfinderPlot, DESeq2, ggplot2, knitcitations (>= 1.0.1), knitr (>= 1.6), limma, RefManageR, rmarkdown (>= 0.3.3), testthat (>= 2.1.0), TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 8bb4ba2b490c1f029a26d4746ce1e35a NeedsCompilation: no Title: Annotation-agnostic differential expression analysis of RNA-seq data at base-pair resolution via the DER Finder approach Description: This package provides functions for annotation-agnostic differential expression analysis of RNA-seq data. Two implementations of the DER Finder approach are included in this package: (1) single base-level F-statistics and (2) DER identification at the expressed regions-level. The DER Finder approach can also be used to identify differentially bounded ChIP-seq peaks. biocViews: DifferentialExpression, Sequencing, RNASeq, ChIPSeq, DifferentialPeakCalling, Software, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (), Alyssa C. Frazee [ctb], Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/derfinder VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinder/ git_url: https://git.bioconductor.org/packages/derfinder git_branch: RELEASE_3_10 git_last_commit: 8f18959 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/derfinder_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/derfinder_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/derfinder_1.20.0.tgz vignettes: vignettes/derfinder/inst/doc/derfinder-quickstart.html, vignettes/derfinder/inst/doc/derfinder-users-guide.html vignetteTitles: derfinder quick start guide, derfinder users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinder/inst/doc/derfinder-quickstart.R, vignettes/derfinder/inst/doc/derfinder-users-guide.R importsMe: brainflowprobes, derfinderPlot, recount, regionReport dependencyCount: 149 Package: derfinderHelper Version: 1.20.0 Depends: R(>= 3.2.2) Imports: IRanges (>= 1.99.27), Matrix, methods, S4Vectors (>= 0.2.2) Suggests: sessioninfo, knitcitations (>= 1.0.1), knitr (>= 1.6), BiocStyle (>= 2.5.19), rmarkdown (>= 0.3.3), testthat License: Artistic-2.0 MD5sum: 112219290a9ade5f0ef0b82b781e1ab2 NeedsCompilation: no Title: derfinder helper package Description: Helper package for speeding up the derfinder package when using multiple cores. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (), Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/derfinderHelper VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinderHelper git_url: https://git.bioconductor.org/packages/derfinderHelper git_branch: RELEASE_3_10 git_last_commit: 2d2e7c3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/derfinderHelper_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/derfinderHelper_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/derfinderHelper_1.20.0.tgz vignettes: vignettes/derfinderHelper/inst/doc/derfinderHelper.html vignetteTitles: Introduction to derfinderHelper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderHelper/inst/doc/derfinderHelper.R importsMe: derfinder dependencyCount: 13 Package: derfinderPlot Version: 1.20.0 Depends: R(>= 3.2) Imports: derfinder (>= 1.1.0), GenomeInfoDb (>= 1.3.3), GenomicFeatures, GenomicRanges (>= 1.17.40), ggbio (>= 1.13.13), ggplot2, graphics, grDevices, IRanges (>= 1.99.28), limma, methods, plyr, RColorBrewer, reshape2, S4Vectors (>= 0.9.38), scales, utils Suggests: biovizBase (>= 1.27.2), bumphunter (>= 1.7.6), derfinderData (>= 0.99.0), sessioninfo, knitcitations (>= 1.0.1), knitr (>= 1.6), BiocStyle (>= 2.5.19), org.Hs.eg.db, RefManageR, rmarkdown (>= 0.3.3), testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 97b07710130a47acf0262633377d5b85 NeedsCompilation: no Title: Plotting functions for derfinder Description: This package provides plotting functions for results from the derfinder package. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (), Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/derfinderPlot VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinderPlot git_url: https://git.bioconductor.org/packages/derfinderPlot git_branch: RELEASE_3_10 git_last_commit: 9a9b52b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/derfinderPlot_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/derfinderPlot_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/derfinderPlot_1.20.0.tgz vignettes: vignettes/derfinderPlot/inst/doc/derfinderPlot.html vignetteTitles: Introduction to derfinderPlot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderPlot/inst/doc/derfinderPlot.R importsMe: brainflowprobes suggestsMe: derfinder, regionReport dependencyCount: 163 Package: DEScan2 Version: 1.6.0 Depends: R (>= 3.5), GenomicRanges Imports: BiocParallel, BiocGenerics, ChIPpeakAnno, data.table, DelayedArray, GenomeInfoDb, GenomicAlignments, glue, IRanges, plyr, Rcpp (>= 0.12.13), rtracklayer, S4Vectors (>= 0.23.19), SummarizedExperiment, tools, utils LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, edgeR, limma, EDASeq, RUVSeq, RColorBrewer, statmod License: Artistic-2.0 MD5sum: c808381ccdc5f2571c5fd4f64834b79d NeedsCompilation: yes Title: Differential Enrichment Scan 2 Description: Integrated peak and differential caller, specifically designed for broad epigenomic signals. biocViews: ImmunoOncology, PeakDetection, Epigenetics, Software, Sequencing, Coverage Author: Dario Righelli [aut, cre], John Koberstein [aut], Bruce Gomes [aut], Nancy Zhang [aut], Claudia Angelini [aut], Lucia Peixoto [aut], Davide Risso [aut] Maintainer: Dario Righelli VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEScan2 git_branch: RELEASE_3_10 git_last_commit: 018d658 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DEScan2_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DEScan2_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DEScan2_1.6.0.tgz vignettes: vignettes/DEScan2/inst/doc/DEScan2.html vignetteTitles: DEScan2 Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEScan2/inst/doc/DEScan2.R dependencyCount: 109 Package: DESeq Version: 1.38.0 Depends: BiocGenerics (>= 0.7.5), Biobase (>= 2.21.7), locfit, lattice Imports: genefilter, geneplotter, methods, MASS, RColorBrewer Suggests: pasilla (>= 0.2.10), vsn, gplots License: GPL (>= 3) Archs: i386, x64 MD5sum: 4f10b5dc49240d374cfb855aa61f0a89 NeedsCompilation: yes Title: Differential gene expression analysis based on the negative binomial distribution Description: Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution biocViews: ImmunoOncology, Sequencing, ChIPSeq, RNASeq, SAGE, DifferentialExpression Author: Simon Anders, EMBL Heidelberg Maintainer: Simon Anders URL: http://www-huber.embl.de/users/anders/DESeq git_url: https://git.bioconductor.org/packages/DESeq git_branch: RELEASE_3_10 git_last_commit: 756f041 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DESeq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DESeq_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DESeq_1.38.0.tgz vignettes: vignettes/DESeq/inst/doc/DESeq.pdf vignetteTitles: Analysing RNA-Seq data with the "DESeq" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESeq/inst/doc/DESeq.R dependsOnMe: DBChIP, metaseqR, Polyfit, SeqGSEA, TCC, tRanslatome importsMe: APAlyzer, ArrayExpressHTS, DEsubs, easyRNASeq, EDASeq, EDDA, gCMAP, HTSFilter, rnaSeqMap, scGPS, vulcan suggestsMe: BitSeq, compcodeR, dexus, DiffBind, ELBOW, gage, genefilter, regionReport, SSPA, ToPASeq, XBSeq dependencyCount: 42 Package: DESeq2 Version: 1.26.0 Depends: S4Vectors (>= 0.23.18), IRanges, GenomicRanges, SummarizedExperiment (>= 1.1.6) Imports: BiocGenerics (>= 0.7.5), Biobase, BiocParallel, genefilter, methods, stats4, locfit, geneplotter, ggplot2, Hmisc, Rcpp (>= 0.11.0) LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, vsn, pheatmap, RColorBrewer, IHW, apeglm, ashr, tximport, tximeta, tximportData, readr, pbapply, airway, pasilla (>= 0.2.10) License: LGPL (>= 3) Archs: i386, x64 MD5sum: 0b9fa4be191aba1c74d010e5865e2d38 NeedsCompilation: yes Title: Differential gene expression analysis based on the negative binomial distribution Description: Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. biocViews: Sequencing, RNASeq, ChIPSeq, GeneExpression, Transcription, Normalization, DifferentialExpression, Bayesian, Regression, PrincipalComponent, Clustering, ImmunoOncology Author: Michael Love [aut, cre], Constantin Ahlmann-Eltze [ctb], Simon Anders [aut, ctb], Wolfgang Huber [aut, ctb] Maintainer: Michael Love URL: https://github.com/mikelove/DESeq2 VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/DESeq2 git_branch: RELEASE_3_10 git_last_commit: 8221895 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DESeq2_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DESeq2_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DESeq2_1.26.0.tgz vignettes: vignettes/DESeq2/inst/doc/DESeq2.html vignetteTitles: Analyzing RNA-seq data with DESeq2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESeq2/inst/doc/DESeq2.R dependsOnMe: DChIPRep, DEWSeq, DEXSeq, FourCSeq, rgsepd, TCC, XBSeq importsMe: anamiR, Anaquin, animalcules, circRNAprofiler, consensusDE, coseq, countsimQC, DaMiRseq, debrowser, DEComplexDisease, DEFormats, DEGreport, deltaCaptureC, DEsubs, DiffBind, eegc, ERSSA, FourCSeq, GDCRNATools, GenoGAM, HTSFilter, icetea, ideal, ImpulseDE2, INSPEcT, IntEREst, isomiRs, JunctionSeq, kissDE, MLSeq, muscat, NBAMSeq, ORFik, OUTRIDER, PathoStat, pcaExplorer, PowerExplorer, regionReport, ReportingTools, Rmmquant, RNASeqR, scBFA, singleCellTK, SNPhood, srnadiff, systemPipeR, TimeSeriesExperiment, vidger suggestsMe: apeglm, biobroom, BiocGenerics, BioCor, BiocSet, CAGEr, compcodeR, derfinder, diffloop, EnhancedVolcano, fishpond, gage, GenomicAlignments, GenomicRanges, Glimma, IHW, miRmine, OPWeight, phyloseq, progeny, recount, RUVSeq, scran, subSeq, SummarizedBenchmark, TFEA.ChIP, ToPASeq, topconfects, tximeta, tximport, variancePartition, Wrench, zinbwave dependencyCount: 120 Package: DEsingle Version: 1.6.0 Depends: R (>= 3.4.0) Imports: stats, Matrix (>= 1.2-14), MASS (>= 7.3-45), VGAM (>= 1.0-2), bbmle (>= 1.0.18), gamlss (>= 4.4-0), maxLik (>= 1.3-4), pscl (>= 1.4.9), BiocParallel (>= 1.12.0), Suggests: knitr, rmarkdown, SingleCellExperiment License: GPL-2 Archs: i386, x64 MD5sum: a5011373dddddd2c3593e525b4889ce0 NeedsCompilation: no Title: DEsingle for detecting three types of differential expression in single-cell RNA-seq data Description: DEsingle is an R package for differential expression (DE) analysis of single-cell RNA-seq (scRNA-seq) data. It defines and detects 3 types of differentially expressed genes between two groups of single cells, with regard to different expression status (DEs), differential expression abundance (DEa), and general differential expression (DEg). DEsingle employs Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect the 3 types of DE genes. Results showed that DEsingle outperforms existing methods for scRNA-seq DE analysis, and can reveal different types of DE genes that are enriched in different biological functions. biocViews: DifferentialExpression, GeneExpression, SingleCell, ImmunoOncology, RNASeq, Transcriptomics, Sequencing, Preprocessing, Software Author: Zhun Miao Maintainer: Zhun Miao URL: https://miaozhun.github.io/DEsingle/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEsingle git_branch: RELEASE_3_10 git_last_commit: 31bc32f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DEsingle_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DEsingle_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DEsingle_1.6.0.tgz vignettes: vignettes/DEsingle/inst/doc/DEsingle.html vignetteTitles: DEsingle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEsingle/inst/doc/DEsingle.R dependencyCount: 36 Package: destiny Version: 3.0.1 Depends: R (>= 3.4.0) Imports: methods, graphics, grDevices, utils, stats, Matrix, Rcpp (>= 0.10.3), RcppEigen, RSpectra (>= 0.14-0), irlba, pcaMethods, Biobase, BiocGenerics, SummarizedExperiment, SingleCellExperiment, ggplot2, ggplot.multistats, tidyr, tidyselect, ggthemes, VIM, knn.covertree, proxy, RcppHNSW, smoother, scales, scatterplot3d LinkingTo: Rcpp, RcppEigen, grDevices Suggests: nbconvertR (>= 1.3.2), igraph, testthat, FNN, tidyr Enhances: rgl, SingleCellExperiment License: GPL MD5sum: 31794121d92e8429415b3b22ece8c6b2 NeedsCompilation: yes Title: Creates diffusion maps Description: Create and plot diffusion maps. biocViews: CellBiology, CellBasedAssays, Clustering, Software, Visualization Author: Philipp Angerer [cre, aut] (), Laleh Haghverdi [ctb], Maren Büttner [ctb] (), Fabian Theis [ctb] (), Carsten Marr [ctb] (), Florian Büttner [ctb] () Maintainer: Philipp Angerer URL: https://theislab.github.io/destiny/, https://github.com/theislab/destiny/, https://www.helmholtz-muenchen.de/icb/destiny, https://bioconductor.org/packages/destiny, https://doi.org/10.1093/bioinformatics/btv715 SystemRequirements: C++11, jupyter nbconvert (see nbconvertR’s INSTALL file) VignetteBuilder: nbconvertR BugReports: https://github.com/theislab/destiny/issues git_url: https://git.bioconductor.org/packages/destiny git_branch: RELEASE_3_10 git_last_commit: 1348b8f git_last_commit_date: 2020-01-16 Date/Publication: 2020-01-16 source.ver: src/contrib/destiny_3.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/destiny_3.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/destiny_3.0.1.tgz vignettes: vignettes/destiny/inst/doc/Diffusion-Map-recap.pdf, vignettes/destiny/inst/doc/Diffusion-Maps.pdf, vignettes/destiny/inst/doc/DPT.pdf, vignettes/destiny/inst/doc/Gene-Relevance.pdf, vignettes/destiny/inst/doc/Global-Sigma.pdf, vignettes/destiny/inst/doc/tidyverse.pdf vignetteTitles: Diffusion-Map-recap.pdf, Diffusion-Maps.pdf, DPT.pdf, Gene-Relevance.pdf, Global-Sigma.pdf, tidyverse.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: flowSpy, phemd suggestsMe: CellTrails, monocle, scater, slingshot dependencyCount: 139 Package: DEsubs Version: 1.12.0 Depends: R (>= 3.3), locfit Imports: graph, igraph, RBGL, circlize, limma, edgeR, EBSeq, NBPSeq, DESeq, stats, grDevices, graphics, pheatmap, utils, ggplot2, Matrix, jsonlite, tools, DESeq2, methods Suggests: RUnit, BiocGenerics, knitr License: GPL-3 Archs: i386, x64 MD5sum: 368277cf1986411d190a65cfc6658325 NeedsCompilation: no Title: DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq expression experiments Description: DEsubs is a network-based systems biology package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customizable framework covering a broad range of operation modes at all stages of the subpathway analysis, enabling a case-specific approach. The operation modes refer to the pathway network construction and processing, the subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render it a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level biomarkers for complex diseases. biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG, GeneExpression, NetworkEnrichment, Network, RNASeq, DifferentialExpression, Normalization, ImmunoOncology Author: Aristidis G. Vrahatis and Panos Balomenos Maintainer: Aristidis G. Vrahatis , Panos Balomenos VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEsubs git_branch: RELEASE_3_10 git_last_commit: ce25db2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DEsubs_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DEsubs_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DEsubs_1.12.0.tgz vignettes: vignettes/DEsubs/inst/doc/DEsubs.pdf vignetteTitles: DEsubs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEsubs/inst/doc/DEsubs.R dependencyCount: 142 Package: DEWSeq Version: 1.0.6 Depends: R(>= 3.6.0), R.utils, DESeq2, BiocParallel Imports: BiocGenerics, data.table(>= 1.11.8), GenomeInfoDb, GenomicRanges, methods, S4Vectors, SummarizedExperiment, stats, utils Suggests: knitr, rmarkdown, testthat, BiocStyle, IHW License: LGPL (>= 3) MD5sum: b2cb0b04ce0a1b0cfa7ab878fa47d411 NeedsCompilation: no Title: Differential Expressed Windows Based on Negative Binomial Distribution Description: Differential expression analysis of windows for next-generation sequencing data like eCLIP or iCLIP data. biocViews: Sequencing, GeneRegulation, FunctionalGenomics, DifferentialExpression Author: Sudeep Sahadevan , Thomas Schwarzl Maintainer: Hentze bioinformatics team URL: https://github.com/EMBL-Hentze-group/DEWSeq/ VignetteBuilder: knitr BugReports: https://github.com/EMBL-Hentze-group/DEWSeq/issues git_url: https://git.bioconductor.org/packages/DEWSeq git_branch: RELEASE_3_10 git_last_commit: 6866854 git_last_commit_date: 2020-04-08 Date/Publication: 2020-04-08 source.ver: src/contrib/DEWSeq_1.0.6.tar.gz win.binary.ver: bin/windows/contrib/3.6/DEWSeq_1.0.6.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DEWSeq_1.0.6.tgz vignettes: vignettes/DEWSeq/inst/doc/DEWSeq.html vignetteTitles: Analyzing eCLIP/iCLIP data with DEWSeq hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEWSeq/inst/doc/DEWSeq.R dependencyCount: 124 Package: DEXSeq Version: 1.32.0 Depends: BiocParallel, Biobase, SummarizedExperiment, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), DESeq2 (>= 1.9.11), AnnotationDbi, RColorBrewer, S4Vectors (>= 0.23.18) Imports: BiocGenerics, biomaRt, hwriter, methods, stringr, Rsamtools, statmod, geneplotter, genefilter Suggests: GenomicFeatures (>= 1.13.29), pasilla (>= 0.2.22), parathyroidSE, BiocStyle, knitr, rmarkdown License: GPL (>= 3) MD5sum: cb2920bf243d705f538d334fd1eec33f NeedsCompilation: no Title: Inference of differential exon usage in RNA-Seq Description: The package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, AlternativeSplicing, DifferentialSplicing, GeneExpression, Visualization Author: Simon Anders and Alejandro Reyes Maintainer: Alejandro Reyes VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEXSeq git_branch: RELEASE_3_10 git_last_commit: ecc9bca git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DEXSeq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DEXSeq_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DEXSeq_1.32.0.tgz vignettes: vignettes/DEXSeq/inst/doc/DEXSeq.html vignetteTitles: Inferring differential exon usage in RNA-Seq data with the DEXSeq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEXSeq/inst/doc/DEXSeq.R dependsOnMe: IsoformSwitchAnalyzeR importsMe: IntEREst suggestsMe: GenomicRanges, stageR, subSeq dependencyCount: 140 Package: dexus Version: 1.26.0 Depends: R (>= 2.15), methods, BiocGenerics Imports: stats Suggests: parallel, statmod, DESeq, RColorBrewer License: LGPL (>= 2.0) MD5sum: 5ec9ba9b036e48d73370a07c29cd92e2 NeedsCompilation: yes Title: DEXUS - Identifying Differential Expression in RNA-Seq Studies with Unknown Conditions or without Replicates Description: DEXUS identifies differentially expressed genes in RNA-Seq data under all possible study designs such as studies without replicates, without sample groups, and with unknown conditions. DEXUS works also for known conditions, for example for RNA-Seq data with two or multiple conditions. RNA-Seq read count data can be provided both by the S4 class Count Data Set and by read count matrices. Differentially expressed transcripts can be visualized by heatmaps, in which unknown conditions, replicates, and samples groups are also indicated. This software is fast since the core algorithm is written in C. For very large data sets, a parallel version of DEXUS is provided in this package. DEXUS is a statistical model that is selected in a Bayesian framework by an EM algorithm. DEXUS does not need replicates to detect differentially expressed transcripts, since the replicates (or conditions) are estimated by the EM method for each transcript. The method provides an informative/non-informative value to extract differentially expressed transcripts at a desired significance level or power. biocViews: ImmunoOncology, Sequencing, RNASeq, GeneExpression, DifferentialExpression, CellBiology, Classification, QualityControl Author: Guenter Klambauer Maintainer: Guenter Klambauer git_url: https://git.bioconductor.org/packages/dexus git_branch: RELEASE_3_10 git_last_commit: 858bfc6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/dexus_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/dexus_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/dexus_1.26.0.tgz vignettes: vignettes/dexus/inst/doc/dexus.pdf vignetteTitles: dexus: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dexus/inst/doc/dexus.R dependencyCount: 6 Package: DFP Version: 1.44.0 Depends: methods, Biobase (>= 2.5.5) License: GPL-2 Archs: i386, x64 MD5sum: d98fb38d0c7280a2e0665844af508ec2 NeedsCompilation: no Title: Gene Selection Description: This package provides a supervised technique able to identify differentially expressed genes, based on the construction of \emph{Fuzzy Patterns} (FPs). The Fuzzy Patterns are built by means of applying 3 Membership Functions to discretized gene expression values. biocViews: Microarray, DifferentialExpression Author: R. Alvarez-Gonzalez, D. Glez-Pena, F. Diaz, F. Fdez-Riverola Maintainer: Rodrigo Alvarez-Glez git_url: https://git.bioconductor.org/packages/DFP git_branch: RELEASE_3_10 git_last_commit: 78b4468 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DFP_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DFP_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DFP_1.44.0.tgz vignettes: vignettes/DFP/inst/doc/DFP.pdf vignetteTitles: Howto: Discriminat Fuzzy Pattern hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DFP/inst/doc/DFP.R dependencyCount: 7 Package: DiffBind Version: 2.14.0 Depends: R (>= 3.5), GenomicRanges, SummarizedExperiment Imports: RColorBrewer, amap, edgeR, gplots, grDevices, limma, GenomicAlignments, locfit, stats, utils, IRanges, lattice, systemPipeR, tools, Rcpp, dplyr, ggplot2, BiocParallel, parallel, S4Vectors, Rsamtools (>= 1.99.1), DESeq2, methods, graphics, ggrepel LinkingTo: Rhtslib (>= 1.15.3), Rcpp Suggests: DESeq, BiocStyle, testthat Enhances: rgl, XLConnect License: Artistic-2.0 MD5sum: a50de5e6c1854b3fd8499493758386f2 NeedsCompilation: yes Title: Differential Binding Analysis of ChIP-Seq Peak Data Description: Compute differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions. biocViews: Sequencing, ChIPSeq, DifferentialPeakCalling Author: Rory Stark, Gord Brown Maintainer: Rory Stark SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/DiffBind git_branch: RELEASE_3_10 git_last_commit: aa93ca8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DiffBind_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DiffBind_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DiffBind_2.14.0.tgz vignettes: vignettes/DiffBind/inst/doc/DiffBind.pdf vignetteTitles: DiffBind: Differential binding analysis of ChIP-Seq peak data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiffBind/inst/doc/DiffBind.R dependsOnMe: ChIPQC, vulcan dependencyCount: 169 Package: diffcoexp Version: 1.6.0 Depends: R (>= 3.5), WGCNA, SummarizedExperiment Imports: stats, DiffCorr, psych, igraph, BiocGenerics Suggests: GEOquery License: GPL (>2) Archs: i386, x64 MD5sum: 8bcce7fa16341da028840e514d823719 NeedsCompilation: no Title: Differential Co-expression Analysis Description: A tool for the identification of differentially coexpressed links (DCLs) and differentially coexpressed genes (DCGs). DCLs are gene pairs with significantly different correlation coefficients under two conditions. DCGs are genes with significantly more DCLs than by chance. biocViews: GeneExpression, DifferentialExpression, Transcription, Microarray, OneChannel, TwoChannel, RNASeq, Sequencing, Coverage, ImmunoOncology Author: Wenbin Wei, Sandeep Amberkar, Winston Hide Maintainer: Wenbin Wei URL: https://github.com/hidelab/diffcoexp git_url: https://git.bioconductor.org/packages/diffcoexp git_branch: RELEASE_3_10 git_last_commit: 06e6333 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/diffcoexp_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/diffcoexp_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/diffcoexp_1.6.0.tgz vignettes: vignettes/diffcoexp/inst/doc/diffcoexp.pdf vignetteTitles: About diffcoexp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffcoexp/inst/doc/diffcoexp.R dependencyCount: 130 Package: diffcyt Version: 1.6.6 Depends: R (>= 3.4.0) Imports: flowCore, FlowSOM, SummarizedExperiment, S4Vectors, limma, edgeR, lme4, multcomp, dplyr, tidyr, reshape2, magrittr, stats, methods, utils, grDevices, graphics, ComplexHeatmap, circlize, grid Suggests: BiocStyle, knitr, rmarkdown, testthat, HDCytoData, CATALYST License: MIT + file LICENSE MD5sum: e083c334c830a09e72398ce9e8d4b8da NeedsCompilation: no Title: Differential discovery in high-dimensional cytometry via high-resolution clustering Description: Statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics. biocViews: ImmunoOncology, FlowCytometry, Proteomics, SingleCell, CellBasedAssays, CellBiology, Clustering, FeatureExtraction, Software Author: Lukas M. Weber [aut, cre] () Maintainer: Lukas M. Weber URL: https://github.com/lmweber/diffcyt VignetteBuilder: knitr BugReports: https://github.com/lmweber/diffcyt/issues git_url: https://git.bioconductor.org/packages/diffcyt git_branch: RELEASE_3_10 git_last_commit: 24bb88c git_last_commit_date: 2020-04-06 Date/Publication: 2020-04-07 source.ver: src/contrib/diffcyt_1.6.6.tar.gz win.binary.ver: bin/windows/contrib/3.6/diffcyt_1.6.6.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/diffcyt_1.6.6.tgz vignettes: vignettes/diffcyt/inst/doc/diffcyt_workflow.html vignetteTitles: diffcyt workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diffcyt/inst/doc/diffcyt_workflow.R suggestsMe: CATALYST dependencyCount: 162 Package: diffGeneAnalysis Version: 1.68.0 Imports: graphics, grDevices, minpack.lm (>= 1.0-4), stats, utils License: GPL MD5sum: c08d27999affc2a2b4c352a6b0f6e5a4 NeedsCompilation: no Title: Performs differential gene expression Analysis Description: Analyze microarray data biocViews: Microarray, DifferentialExpression Author: Choudary Jagarlamudi Maintainer: Choudary Jagarlamudi git_url: https://git.bioconductor.org/packages/diffGeneAnalysis git_branch: RELEASE_3_10 git_last_commit: b1a3382 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/diffGeneAnalysis_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/diffGeneAnalysis_1.68.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/diffGeneAnalysis_1.68.0.tgz vignettes: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.pdf vignetteTitles: Documentation on diffGeneAnalysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.R dependencyCount: 5 Package: diffHic Version: 1.18.0 Depends: R (>= 3.5), GenomicRanges, InteractionSet, SummarizedExperiment Imports: Rsamtools, Rhtslib, Biostrings, BSgenome, rhdf5, edgeR, limma, csaw, locfit, methods, IRanges, S4Vectors, GenomeInfoDb, BiocGenerics, grDevices, graphics, stats, utils, Rcpp, rtracklayer LinkingTo: Rhtslib (>= 1.13.1), zlibbioc, Rcpp Suggests: BSgenome.Ecoli.NCBI.20080805, Matrix, testthat License: GPL-3 MD5sum: fc6ee0c05d03cde951a38fb5e85a6d03 NeedsCompilation: yes Title: Differential Analyis of Hi-C Data Description: Detects differential interactions across biological conditions in a Hi-C experiment. Methods are provided for read alignment and data pre-processing into interaction counts. Statistical analysis is based on edgeR and supports normalization and filtering. Several visualization options are also available. biocViews: MultipleComparison, Preprocessing, Sequencing, Coverage, Alignment, Normalization, Clustering, HiC Author: Aaron Lun [aut, cre], Gordon Smyth [aut] Maintainer: Aaron Lun SystemRequirements: C++11, GNU make git_url: https://git.bioconductor.org/packages/diffHic git_branch: RELEASE_3_10 git_last_commit: 425ec0a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/diffHic_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/diffHic_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/diffHic_1.18.0.tgz vignettes: vignettes/diffHic/inst/doc/diffHic.pdf, vignettes/diffHic/inst/doc/diffHicUsersGuide.pdf vignetteTitles: diffHic Vignette, diffHicUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 91 Package: DiffLogo Version: 2.10.0 Depends: R (>= 3.4), stats, cba Imports: grDevices, graphics, utils, tools Suggests: knitr, testthat, seqLogo, MotifDb License: GPL (>= 2) Archs: i386, x64 MD5sum: 01ed1adc8409333b7072fe3965670cf7 NeedsCompilation: no Title: DiffLogo: A comparative visualisation of biooligomer motifs Description: DiffLogo is an easy-to-use tool to visualize motif differences. biocViews: Software, SequenceMatching, MultipleComparison, MotifAnnotation, Visualization, Alignment Author: c( person("Martin", "Nettling", role = c("aut", "cre"), email = "martin.nettling@informatik.uni-halle.de"), person("Hendrik", "Treutler", role = c("aut", "cre"), email = "hendrik.treutler@ipb-halle.de"), person("Jan", "Grau", role = c("aut", "ctb"), email = "grau@informatik.uni-halle.de"), person("Andrey", "Lando", role = c("aut", "ctb"), email = "dronte@autosome.ru"), person("Jens", "Keilwagen", role = c("aut", "ctb"), email = "jens.keilwagen@julius-kuehn.de"), person("Stefan", "Posch", role = "aut", email = "posch@informatik.uni-halle.de"), person("Ivo", "Grosse", role = "aut", email = "grosse@informatik.uni-halle.de")) Maintainer: Hendrik Treutler URL: https://github.com/mgledi/DiffLogo/ VignetteBuilder: knitr BugReports: https://github.com/mgledi/DiffLogo/issues git_url: https://git.bioconductor.org/packages/DiffLogo git_branch: RELEASE_3_10 git_last_commit: ddeec2a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DiffLogo_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DiffLogo_2.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DiffLogo_2.10.0.tgz vignettes: vignettes/DiffLogo/inst/doc/DiffLogoBasics.pdf vignetteTitles: Basics of the DiffLogo package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiffLogo/inst/doc/DiffLogoBasics.R dependencyCount: 9 Package: diffloop Version: 1.14.0 Imports: methods, GenomicRanges, foreach, plyr, dplyr, reshape2, ggplot2, matrixStats, Sushi, edgeR, locfit, statmod, biomaRt, GenomeInfoDb, S4Vectors, IRanges, grDevices, graphics, stats, utils, Biobase, readr, data.table, rtracklayer, pbapply, limma Suggests: DESeq2, diffloopdata, ggrepel, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: d7678e7a896d12620ee4f44bdf9d8c7a NeedsCompilation: no Title: Identifying differential DNA loops from chromatin topology data Description: A suite of tools for subsetting, visualizing, annotating, and statistically analyzing the results of one or more ChIA-PET experiments or other assays that infer chromatin loops. biocViews: Preprocessing, QualityControl, Visualization, DataImport, DataRepresentation, GO Author: Caleb Lareau [aut, cre], Martin Aryee [aut] Maintainer: Caleb Lareau URL: https://github.com/aryeelab/diffloop VignetteBuilder: knitr BugReports: https://github.com/aryeelab/diffloop/issues git_url: https://git.bioconductor.org/packages/diffloop git_branch: RELEASE_3_10 git_last_commit: 947e3bd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/diffloop_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/diffloop_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/diffloop_1.14.0.tgz vignettes: vignettes/diffloop/inst/doc/diffloop.html vignetteTitles: diffloop: Identifying differential DNA loops from chromatin topology data. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diffloop/inst/doc/diffloop.R dependencyCount: 124 Package: diffuStats Version: 1.6.0 Depends: R (>= 3.4) Imports: grDevices, stats, methods, Matrix, MASS, expm, igraph, Rcpp, RcppArmadillo, RcppParallel, plyr, precrec LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: testthat, knitr, rmarkdown, ggplot2, ggsci, igraphdata, BiocStyle, reshape2 License: GPL-3 MD5sum: 7b3912bc189e175283cc484f7f4edc65 NeedsCompilation: yes Title: Diffusion scores on biological networks Description: Label propagation approaches are a widely used procedure in computational biology for giving context to molecular entities using network data. Node labels, which can derive from gene expression, genome-wide association studies, protein domains or metabolomics profiling, are propagated to their neighbours in the network, effectively smoothing the scores through prior annotated knowledge and prioritising novel candidates. The R package diffuStats contains a collection of diffusion kernels and scoring approaches that facilitates their computation and benchmarking. biocViews: Network, GeneExpression, GraphAndNetwork Author: Sergio Picart-Armada and Alexandre Perera-Lluna Maintainer: Sergio Picart-Armada SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/diffuStats git_branch: RELEASE_3_10 git_last_commit: 5a93ba6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/diffuStats_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/diffuStats_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/diffuStats_1.6.0.tgz vignettes: vignettes/diffuStats/inst/doc/diffuStats.pdf, vignettes/diffuStats/inst/doc/intro.html vignetteTitles: Case study: predicting protein function, Quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffuStats/inst/doc/diffuStats.R, vignettes/diffuStats/inst/doc/intro.R dependencyCount: 62 Package: diggit Version: 1.18.0 Depends: R (>= 3.0.2), Biobase, methods Imports: ks, viper(>= 1.3.1), parallel Suggests: diggitdata License: file LICENSE Archs: i386, x64 MD5sum: e6dc6cd5246ba6c92c1f67fca585d5b3 NeedsCompilation: no Title: Inference of Genetic Variants Driving Cellular Phenotypes Description: Inference of Genetic Variants Driving Cellullar Phenotypes by the DIGGIT algorithm biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, FunctionalPrediction, GeneRegulation Author: Mariano J Alvarez Maintainer: Mariano J Alvarez git_url: https://git.bioconductor.org/packages/diggit git_branch: RELEASE_3_10 git_last_commit: 357fea4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/diggit_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/diggit_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/diggit_1.18.0.tgz vignettes: vignettes/diggit/inst/doc/diggit.pdf vignetteTitles: Using DIGGIT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diggit/inst/doc/diggit.R dependencyCount: 29 Package: Director Version: 1.12.0 Depends: R (>= 3.3) Imports: htmltools, utils, grDevices License: GPL-3 + file LICENSE MD5sum: c58d862a34d7616d404fe75145b98805 NeedsCompilation: no Title: A dynamic visualization tool of multi-level data Description: Director is an R package designed to streamline the visualization of molecular effects in regulatory cascades. It utilizes the R package htmltools and a modified Sankey plugin of the JavaScript library D3 to provide a fast and easy, browser-enabled solution to discovering potentially interesting downstream effects of regulatory and/or co-expressed molecules. The diagrams are robust, interactive, and packaged as highly-portable HTML files that eliminate the need for third-party software to view. This enables a straightforward approach for scientists to interpret the data produced, and bioinformatics developers an alternative means to present relevant data. biocViews: Visualization Author: Katherine Icay [aut, cre] Maintainer: Katherine Icay URL: https://github.com/kzouchka/Director BugReports: https://github.com/kzouchka/Director/issues git_url: https://git.bioconductor.org/packages/Director git_branch: RELEASE_3_10 git_last_commit: 1f530de git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Director_1.12.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Director_1.12.0.tgz vignettes: vignettes/Director/inst/doc/vignette.pdf vignetteTitles: Using Director hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Director/inst/doc/vignette.R dependencyCount: 7 Package: DirichletMultinomial Version: 1.28.0 Depends: S4Vectors, IRanges Imports: stats4, methods, BiocGenerics Suggests: lattice, parallel, MASS, RColorBrewer, xtable License: LGPL-3 MD5sum: 308a1512d5be6930c260b14ccc9736b6 NeedsCompilation: yes Title: Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome Data Description: Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed further in the man page for this package, ?DirichletMultinomial. biocViews: ImmunoOncology, Microbiome, Sequencing, Clustering, Classification, Metagenomics Author: Martin Morgan Maintainer: Martin Morgan SystemRequirements: gsl git_url: https://git.bioconductor.org/packages/DirichletMultinomial git_branch: RELEASE_3_10 git_last_commit: dd069fc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DirichletMultinomial_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DirichletMultinomial_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DirichletMultinomial_1.28.0.tgz vignettes: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.pdf vignetteTitles: An introduction to DirichletMultinomial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.R importsMe: TFBSTools dependencyCount: 9 Package: discordant Version: 1.10.0 Depends: R (>= 3.4) Imports: Biobase, stats, biwt, gtools, MASS, tools Suggests: BiocStyle, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: 523ef1e67e21343088159f30c3a07bdb NeedsCompilation: yes Title: The Discordant Method: A Novel Approach for Differential Correlation Description: Discordant is a method to determine differential correlation of molecular feature pairs from -omics data using mixture models. Algorithm is explained further in Siska et al. biocViews: ImmunoOncology, BiologicalQuestion, StatisticalMethod, mRNAMicroarray, Microarray, Genetics, RNASeq Author: Charlotte Siska [cre,aut], Katerina Kechris [aut] Maintainer: Charlotte Siska URL: https://github.com/siskac/discordant VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/discordant git_branch: RELEASE_3_10 git_last_commit: 11e6f2c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/discordant_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/discordant_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/discordant_1.10.0.tgz vignettes: vignettes/discordant/inst/doc/Discordant_vignette.pdf vignetteTitles: Discordant hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/discordant/inst/doc/Discordant_vignette.R dependencyCount: 20 Package: DiscoRhythm Version: 1.2.1 Depends: R (>= 3.6.0) Imports: matrixTests, matrixStats, MetaCycle (>= 1.2.0), data.table, ggplot2, ggExtra, dplyr, broom, shiny, shinyBS, shinycssloaders, shinydashboard, shinyjs, BiocStyle, rmarkdown, knitr, kableExtra, magick, VennDiagram, UpSetR, heatmaply, viridis, plotly, DT, gridExtra, methods, stats, SummarizedExperiment, BiocGenerics, S4Vectors, zip, reshape2 Suggests: testthat License: GPL-3 Archs: i386, x64 MD5sum: e0b805945e718ae531f5cc2007e1fd9d NeedsCompilation: no Title: Interactive Workflow for Discovering Rhythmicity in Biological Data Description: Set of functions for estimation of cyclical characteristics, such as period, phase, amplitude, and statistical significance in large temporal datasets. Supporting functions are available for quality control, dimensionality reduction, spectral analysis, and analysis of experimental replicates. Contains a R Shiny web interface to execute all workflow steps. biocViews: Software, TimeCourse, QualityControl, Visualization, GUI, PrincipalComponent Author: Matthew Carlucci [aut, cre], Algimantas Kriščiūnas [aut], Haohan Li [aut], Povilas Gibas [aut], Karolis Koncevičius [aut], Art Petronis [aut], Gabriel Oh [aut] Maintainer: Matthew Carlucci URL: https://github.com/matthewcarlucci/DiscoRhythm SystemRequirements: To generate html reports pandoc (http://pandoc.org/installing.html) is required. VignetteBuilder: knitr BugReports: https://github.com/matthewcarlucci/DiscoRhythm/issues git_url: https://git.bioconductor.org/packages/DiscoRhythm git_branch: RELEASE_3_10 git_last_commit: 2660c2f git_last_commit_date: 2019-11-06 Date/Publication: 2019-11-29 source.ver: src/contrib/DiscoRhythm_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/DiscoRhythm_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DiscoRhythm_1.2.1.tgz vignettes: vignettes/DiscoRhythm/inst/doc/disco_workflow_vignette.html vignetteTitles: Introduction to DiscoRhythm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiscoRhythm/inst/doc/disco_workflow_vignette.R dependencyCount: 166 Package: divergence Version: 1.2.0 Depends: R (>= 3.6), SummarizedExperiment Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 9ab1af34514c27a9f11e71f6b8e78bbe NeedsCompilation: no Title: Divergence: Functionality for assessing omics data by divergence with respect to a baseline Description: This package provides functionality for performing divergence analysis as presented in Dinalankara et al, "Digitizing omics profiles by divergence from a baseline", PANS 2018. This allows the user to simplify high dimensional omics data into a binary or ternary format which encapsulates how the data is divergent from a specified baseline group with the same univariate or multivariate features. biocViews: Software, StatisticalMethod Author: Wikum Dinalankara , Luigi Marchionni , Qian Ke Maintainer: Wikum Dinalankara , Luigi Marchionni VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/divergence git_branch: RELEASE_3_10 git_last_commit: f0357ed git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/divergence_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/divergence_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/divergence_1.2.0.tgz vignettes: vignettes/divergence/inst/doc/divergence.html vignetteTitles: Performing Divergence Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/divergence/inst/doc/divergence.R dependencyCount: 32 Package: dks Version: 1.32.0 Depends: R (>= 2.8) Imports: cubature License: GPL MD5sum: 598ee70b0cac2ffd2a92ed64c83108a3 NeedsCompilation: no Title: The double Kolmogorov-Smirnov package for evaluating multiple testing procedures. Description: The dks package consists of a set of diagnostic functions for multiple testing methods. The functions can be used to determine if the p-values produced by a multiple testing procedure are correct. These functions are designed to be applied to simulated data. The functions require the entire set of p-values from multiple simulated studies, so that the joint distribution can be evaluated. biocViews: MultipleComparison, QualityControl Author: Jeffrey T. Leek Maintainer: Jeffrey T. Leek git_url: https://git.bioconductor.org/packages/dks git_branch: RELEASE_3_10 git_last_commit: 7daee68 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/dks_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/dks_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/dks_1.32.0.tgz vignettes: vignettes/dks/inst/doc/dks.pdf vignetteTitles: dksTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dks/inst/doc/dks.R dependencyCount: 4 Package: DMCFB Version: 1.0.0 Depends: R (>= 3.6.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges Imports: utils, stats, speedglm, MASS, data.table, splines, arm, rtracklayer, benchmarkme, tibble, matrixStats, fastDummies, graphics Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: 0fbc32fae7840ff4e894ab2fd751078d NeedsCompilation: no Title: Differentially Methylated Cytosines via a Bayesian Functional Approach Description: DMCFB is a pipeline for identifying differentially methylated cytosines using a Bayesian functional regression model in bisulfite sequencing data. By using a functional regression data model, it tries to capture position-specific, group-specific and other covariates-specific methylation patterns as well as spatial correlation patterns and unknown underlying models of methylation data. It is robust and flexible with respect to the true underlying models and inclusion of any covariates, and the missing values are imputed using spatial correlation between positions and samples. A Bayesian approach is adopted for estimation and inference in the proposed method. biocViews: DifferentialMethylation, Sequencing, Coverage, Bayesian, Regression Author: Farhad Shokoohi Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/DMCFB/issues git_url: https://git.bioconductor.org/packages/DMCFB git_branch: RELEASE_3_10 git_last_commit: 834d392 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DMCFB_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DMCFB_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DMCFB_1.0.0.tgz vignettes: vignettes/DMCFB/inst/doc/DMCFB.html vignetteTitles: Identifying DMCs using Bayesian functional regressions in BS-Seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMCFB/inst/doc/DMCFB.R dependencyCount: 89 Package: DMCHMM Version: 1.8.0 Depends: R (>= 3.5.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges, fdrtool Imports: utils, stats, grDevices, rtracklayer, multcomp, calibrate, graphics Suggests: testthat, knitr License: GPL-3 MD5sum: da242d31dc91ea1411433cc6249961cc NeedsCompilation: no Title: Differentially Methylated CpG using Hidden Markov Model Description: A pipeline for identifying differentially methylated CpG sites using Hidden Markov Model in bisulfite sequencing data. biocViews: DifferentialMethylation, Sequencing, HiddenMarkovModel, Coverage Author: Farhad Shokoohi Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/DMCHMM/issues git_url: https://git.bioconductor.org/packages/DMCHMM git_branch: RELEASE_3_10 git_last_commit: 1ec095e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DMCHMM_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DMCHMM_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DMCHMM_1.8.0.tgz vignettes: vignettes/DMCHMM/inst/doc/DMCHMM.html vignetteTitles: DMCHMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMCHMM/inst/doc/DMCHMM.R dependencyCount: 49 Package: DMRcaller Version: 1.18.0 Depends: R (>= 3.5), GenomicRanges, IRanges, S4Vectors (>= 0.23.10) Imports: parallel, Rcpp, RcppRoll, betareg, grDevices, graphics, methods, stats, utils Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 9522d59918d86de7ecc4d4ce149899ca NeedsCompilation: no Title: Differentially Methylated Regions caller Description: Uses Bisulfite sequencing data in two conditions and identifies differentially methylated regions between the conditions in CG and non-CG context. The input is the CX report files produced by Bismark and the output is a list of DMRs stored as GRanges objects. biocViews: DifferentialMethylation, DNAMethylation, Software, Sequencing, Coverage Author: Nicolae Radu Zabet , Jonathan Michael Foonlan Tsang , Alessandro Pio Greco and Ryan Merritt Maintainer: Nicolae Radu Zabet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DMRcaller git_branch: RELEASE_3_10 git_last_commit: 8edb463 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DMRcaller_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DMRcaller_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DMRcaller_1.18.0.tgz vignettes: vignettes/DMRcaller/inst/doc/DMRcaller.pdf vignetteTitles: DMRcaller hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRcaller/inst/doc/DMRcaller.R dependencyCount: 29 Package: DMRcate Version: 2.0.7 Depends: R (>= 3.6.0), minfi, SummarizedExperiment Imports: ExperimentHub, bsseq, GenomeInfoDb, limma, edgeR, DSS, missMethyl, GenomicRanges, methods, graphics, plyr, Gviz, IRanges, stats, utils, S4Vectors Suggests: knitr, RUnit, BiocGenerics, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 License: file LICENSE MD5sum: 6b1ca5cae6c1f7efdb9c10d1d333c2c3 NeedsCompilation: no Title: Methylation array and sequencing spatial analysis methods Description: De novo identification and extraction of differentially methylated regions (DMRs) from the human genome using Whole Genome Bisulfite Sequencing (WGBS) and Illumina Infinium Array (450K and EPIC) data. Provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. Includes GRanges generation and plotting functions. biocViews: DifferentialMethylation, GeneExpression, Microarray, MethylationArray, Genetics, DifferentialExpression, GenomeAnnotation, DNAMethylation, OneChannel, TwoChannel, MultipleComparison, QualityControl, TimeCourse, Sequencing, WholeGenome, Epigenetics, Coverage, Preprocessing, DataImport Author: Tim Peters Maintainer: Tim Peters VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DMRcate git_branch: RELEASE_3_10 git_last_commit: 705da73 git_last_commit_date: 2020-01-09 Date/Publication: 2020-01-10 source.ver: src/contrib/DMRcate_2.0.7.tar.gz win.binary.ver: bin/windows/contrib/3.6/DMRcate_1.21.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DMRcate_2.0.7.tgz vignettes: vignettes/DMRcate/inst/doc/DMRcate.pdf vignetteTitles: The DMRcate package user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DMRcate/inst/doc/DMRcate.R importsMe: MEAL dependencyCount: 210 Package: DMRforPairs Version: 1.22.0 Depends: R (>= 2.15.2), Gviz (>= 1.2.1), R2HTML (>= 2.2.1), GenomicRanges (>= 1.10.7), parallel License: GPL (>= 2) MD5sum: 78b1ecfd901f98f975e5cbbf7116bc0e NeedsCompilation: no Title: DMRforPairs: identifying Differentially Methylated Regions between unique samples using array based methylation profiles Description: DMRforPairs (formerly DMR2+) allows researchers to compare n>=2 unique samples with regard to their methylation profile. The (pairwise) comparison of n unique single samples distinguishes DMRforPairs from other existing pipelines as these often compare groups of samples in either single CpG locus or region based analysis. DMRforPairs defines regions of interest as genomic ranges with sufficient probes located in close proximity to each other. Probes in one region are optionally annotated to the same functional class(es). Differential methylation is evaluated by comparing the methylation values within each region between individual samples and (if the difference is sufficiently large), testing this difference formally for statistical significance. biocViews: Microarray, DNAMethylation, DifferentialMethylation, ReportWriting, Visualization, Annotation Author: Martin Rijlaarsdam [aut, cre], Yvonne vd Zwan [aut], Lambert Dorssers [aut], Leendert Looijenga [aut] Maintainer: Martin Rijlaarsdam URL: http://www.martinrijlaarsdam.nl, http://www.erasmusmc.nl/pathologie/research/lepo/3898639/ git_url: https://git.bioconductor.org/packages/DMRforPairs git_branch: RELEASE_3_10 git_last_commit: 2f53ad1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DMRforPairs_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DMRforPairs_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DMRforPairs_1.22.0.tgz vignettes: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.pdf vignetteTitles: DMRforPairs_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.R dependencyCount: 144 Package: DMRScan Version: 1.11.0 Depends: R (>= 3.4.0) Imports: Matrix, MASS, RcppRoll,GenomicRanges, IRanges, GenomeInfoDb, methods, mvtnorm, stats, parallel Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: 567f486fd881a5f833e235789ad755c9 NeedsCompilation: no Title: Detection of Differentially Methylated Regions Description: This package detects significant differentially methylated regions (for both qualitative and quantitative traits), using a scan statistic with underlying Poisson heuristics. The scan statistic will depend on a sequence of window sizes (# of CpGs within each window) and on a threshold for each window size. This threshold can be calculated by three different means: i) analytically using Siegmund et.al (2012) solution (preferred), ii) an important sampling as suggested by Zhang (2008), and a iii) full MCMC modeling of the data, choosing between a number of different options for modeling the dependency between each CpG. biocViews: Software, Technology, Sequencing, WholeGenome Author: Christian M Page [aut, cre], Linda Vos [aut], Trine B Rounge [ctb, dtc], Hanne F Harbo [ths], Bettina K Andreassen [aut] Maintainer: Christian M Page URL: https://github.com/christpa/DMRScan VignetteBuilder: knitr BugReports: https://github.com/christpa/DMRScan/issues PackageStatus: Active git_url: https://git.bioconductor.org/packages/DMRScan git_branch: master git_last_commit: 3364619 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-02 source.ver: src/contrib/DMRScan_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DMRScan_1.11.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DMRScan_1.11.0.tgz vignettes: vignettes/DMRScan/inst/doc/DMRScan_vignette.pdf vignetteTitles: DMRScan.Rmd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRScan/inst/doc/DMRScan_vignette.R dependencyCount: 24 Package: dmrseq Version: 1.6.0 Depends: R (>= 3.5), bsseq Imports: GenomicRanges, nlme, ggplot2, S4Vectors, RColorBrewer, bumphunter, DelayedMatrixStats (>= 1.1.13), matrixStats, BiocParallel, outliers, methods, locfit, IRanges, grDevices, graphics, stats, utils, annotatr, AnnotationHub, rtracklayer, GenomeInfoDb, splines Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: a862e1334c72d84272f6f0872871abe6 NeedsCompilation: no Title: Detection and inference of differentially methylated regions from Whole Genome Bisulfite Sequencing Description: This package implements an approach for scanning the genome to detect and perform accurate inference on differentially methylated regions from Whole Genome Bisulfite Sequencing data. The method is based on comparing detected regions to a pooled null distribution, that can be implemented even when as few as two samples per population are available. Region-level statistics are obtained by fitting a generalized least squares (GLS) regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions. biocViews: ImmunoOncology, DNAMethylation, Epigenetics, MultipleComparison, Software, Sequencing, DifferentialMethylation, WholeGenome, Regression, FunctionalGenomics Author: Keegan Korthauer [cre, aut] (), Rafael Irizarry [aut] (), Yuval Benjamini [aut], Sutirtha Chakraborty [aut] Maintainer: Keegan Korthauer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dmrseq git_branch: RELEASE_3_10 git_last_commit: ec3335a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/dmrseq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/dmrseq_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/dmrseq_1.6.0.tgz vignettes: vignettes/dmrseq/inst/doc/dmrseq.html vignetteTitles: Analyzing Bisulfite-seq data with dmrseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dmrseq/inst/doc/dmrseq.R importsMe: biscuiteer dependencyCount: 151 Package: DNABarcodeCompatibility Version: 1.2.0 Depends: R (>= 3.6.0) Imports: dplyr, tidyr, numbers, purrr, stringr, DNABarcodes, stats, utils, methods Suggests: knitr, rmarkdown, BiocStyle, testthat License: file LICENSE MD5sum: ab945d183e6b65d65223137c6f5926cd NeedsCompilation: no Title: A Tool for Optimizing Combinations of DNA Barcodes Used in Multiplexed Experiments on Next Generation Sequencing Platforms Description: The package allows one to obtain optimised combinations of DNA barcodes to be used for multiplex sequencing. In each barcode combination, barcodes are pooled with respect to Illumina chemistry constraints. Combinations can be filtered to keep those that are robust against substitution and insertion/deletion errors thereby facilitating the demultiplexing step. In addition, the package provides an optimiser function to further favor the selection of barcode combinations with least heterogeneity in barcode usage. biocViews: Preprocessing, Sequencing Author: Céline Trébeau [cre] (), Jacques Boutet de Monvel [aut] (), Fabienne Wong Jun Tai [ctb], Raphaël Etournay [aut] () Maintainer: Céline Trébeau VignetteBuilder: knitr BugReports: https://github.com/comoto-pasteur-fr/DNABarcodeCompatibility/issues git_url: https://git.bioconductor.org/packages/DNABarcodeCompatibility git_branch: RELEASE_3_10 git_last_commit: 82c2254 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DNABarcodeCompatibility_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DNABarcodeCompatibility_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DNABarcodeCompatibility_1.2.0.tgz vignettes: vignettes/DNABarcodeCompatibility/inst/doc/introduction.html vignetteTitles: Introduction to DNABarcodeCompatibility hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DNABarcodeCompatibility/inst/doc/introduction.R dependencyCount: 37 Package: DNABarcodes Version: 1.16.0 Depends: Matrix, parallel Imports: Rcpp (>= 0.11.2), BH LinkingTo: Rcpp, BH Suggests: knitr, BiocStyle, rmarkdown License: GPL-2 MD5sum: be0faa8de0a29ff6687223243381c237 NeedsCompilation: yes Title: A tool for creating and analysing DNA barcodes used in Next Generation Sequencing multiplexing experiments Description: The package offers a function to create DNA barcode sets capable of correcting insertion, deletion, and substitution errors. Existing barcodes can be analysed regarding their minimal, maximal and average distances between barcodes. Finally, reads that start with a (possibly mutated) barcode can be demultiplexed, i.e., assigned to their original reference barcode. biocViews: Preprocessing, Sequencing Author: Tilo Buschmann Maintainer: Tilo Buschmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DNABarcodes git_branch: RELEASE_3_10 git_last_commit: 24c7d1e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DNABarcodes_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DNABarcodes_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DNABarcodes_1.16.0.tgz vignettes: vignettes/DNABarcodes/inst/doc/DNABarcodes.html vignetteTitles: DNABarcodes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNABarcodes/inst/doc/DNABarcodes.R importsMe: DNABarcodeCompatibility dependencyCount: 11 Package: DNAcopy Version: 1.60.0 License: GPL (>= 2) Archs: i386, x64 MD5sum: f49feb8fd40cf2609296db4b79a32a87 NeedsCompilation: yes Title: DNA copy number data analysis Description: Implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number. biocViews: Microarray, CopyNumberVariation Author: Venkatraman E. Seshan, Adam Olshen Maintainer: Venkatraman E. Seshan git_url: https://git.bioconductor.org/packages/DNAcopy git_branch: RELEASE_3_10 git_last_commit: 8781fcb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DNAcopy_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DNAcopy_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DNAcopy_1.60.0.tgz vignettes: vignettes/DNAcopy/inst/doc/DNAcopy.pdf vignetteTitles: DNAcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAcopy/inst/doc/DNAcopy.R dependsOnMe: CGHcall, cghMCR, Clonality, CRImage, PureCN importsMe: ADaCGH2, AneuFinder, ArrayTV, ChAMP, cn.farms, CNAnorm, CNVrd2, contiBAIT, conumee, CopywriteR, GWASTools, MDTS, MEDIPS, MethCP, MinimumDistance, QDNAseq, Repitools, sesame, snapCGH suggestsMe: beadarraySNP, cn.mops, CopyNumberPlots, fastseg, genoset dependencyCount: 0 Package: DNAshapeR Version: 1.14.0 Depends: R (>= 3.4), GenomicRanges Imports: Rcpp (>= 0.12.1), Biostrings, fields LinkingTo: Rcpp Suggests: AnnotationHub, knitr, rmarkdown, testthat, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Hsapiens.UCSC.hg19, caret License: GPL-2 Archs: i386, x64 MD5sum: df81eb689950f569b808bd439ea6a9ba NeedsCompilation: yes Title: High-throughput prediction of DNA shape features Description: DNAhapeR is an R/BioConductor package for ultra-fast, high-throughput predictions of DNA shape features. The package allows to predict, visualize and encode DNA shape features for statistical learning. biocViews: StructuralPrediction, DNA3DStructure, Software Author: Tsu-Pei Chiu and Federico Comoglio Maintainer: Tsu-Pei Chiu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DNAshapeR git_branch: RELEASE_3_10 git_last_commit: 6fa04de git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DNAshapeR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DNAshapeR_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DNAshapeR_1.14.0.tgz vignettes: vignettes/DNAshapeR/inst/doc/DNAshapeR.html vignetteTitles: DNAshapeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAshapeR/inst/doc/DNAshapeR.R dependencyCount: 23 Package: DominoEffect Version: 1.6.0 Depends: R(>= 3.5) Imports: biomaRt, data.table, utils, stats, Biostrings, SummarizedExperiment, VariantAnnotation, AnnotationDbi, GenomeInfoDb, IRanges, GenomicRanges, methods Suggests: knitr, testthat License: GPL (>= 3) Archs: i386, x64 MD5sum: f5a7ed25cdaa4a83b8e166bbab48169a NeedsCompilation: no Title: Identification and Annotation of Protein Hotspot Residues Description: The functions support identification and annotation of hotspot residues in proteins. These are individual amino acids that accumulate mutations at a much higher rate than their surrounding regions. biocViews: Software, SomaticMutation, Proteomics, SequenceMatching, Alignment Author: Marija Buljan and Peter Blattmann Maintainer: Marija Buljan , Peter Blattmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DominoEffect git_branch: RELEASE_3_10 git_last_commit: 486794f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DominoEffect_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DominoEffect_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DominoEffect_1.6.0.tgz vignettes: vignettes/DominoEffect/inst/doc/Vignette.html vignetteTitles: Vignette for DominoEffect package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DominoEffect/inst/doc/Vignette.R dependencyCount: 86 Package: doppelgangR Version: 1.14.0 Depends: R (>= 3.5.0), Biobase, BiocParallel Imports: sva, impute, digest, mnormt, methods, grDevices, graphics, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, rmarkdown, curatedOvarianData, ROCR, pROC (>= 1.15.0), RUnit, simulatorZ, proxy License: GPL (>=2.0) MD5sum: 6f52829a986d5e5342afccf79ce91d23 NeedsCompilation: no Title: Identify likely duplicate samples from genomic or meta-data Description: The main function is doppelgangR(), which takes as minimal input a list of ExpressionSet object, and searches all list pairs for duplicated samples. The search is based on the genomic data (exprs(eset)), phenotype/clinical data (pData(eset)), and "smoking guns" - supposedly unique identifiers found in pData(eset). biocViews: ImmunoOncology, RNASeq, Microarray, GeneExpression, QualityControl Author: Levi Waldron [aut, cre], Markus Reister [aut, ctb], Marcel Ramos [ctb] Maintainer: Levi Waldron URL: https://github.com/lwaldron/doppelgangR VignetteBuilder: knitr BugReports: https://github.com/lwaldron/doppelgangR/issues git_url: https://git.bioconductor.org/packages/doppelgangR git_branch: RELEASE_3_10 git_last_commit: ee9a763 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/doppelgangR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/doppelgangR_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/doppelgangR_1.14.0.tgz vignettes: vignettes/doppelgangR/inst/doc/doppelgangR.html vignetteTitles: doppelgangR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doppelgangR/inst/doc/doppelgangR.R dependencyCount: 59 Package: Doscheda Version: 1.8.1 Depends: R (>= 3.6) Imports: methods, drc, stats, httr, jsonlite, reshape2 , vsn, affy, limma, stringr, ggplot2, graphics, grDevices, calibrate, corrgram, gridExtra, DT, shiny, shinydashboard, readxl, d3heatmap, prodlim, matrixStats Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: c2396f4f612a61a5323c45ba490b7145 NeedsCompilation: no Title: A DownStream Chemo-Proteomics Analysis Pipeline Description: Doscheda focuses on quantitative chemoproteomics used to determine protein interaction profiles of small molecules from whole cell or tissue lysates using Mass Spectrometry data. The package provides a shiny application to run the pipeline, several visualisations and a downloadable report of an experiment. biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry, QualityControl, DataImport, Regression Author: Bruno Contrino, Piero Ricchiuto Maintainer: Bruno Contrino VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Doscheda git_branch: RELEASE_3_10 git_last_commit: bdd766f git_last_commit_date: 2020-02-05 Date/Publication: 2020-02-05 source.ver: src/contrib/Doscheda_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/Doscheda_1.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Doscheda_1.8.1.tgz vignettes: vignettes/Doscheda/inst/doc/Doscheda.html vignetteTitles: Doscheda hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Doscheda/inst/doc/Doscheda.R dependencyCount: 159 Package: DOSE Version: 3.12.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, BiocParallel, DO.db, fgsea, ggplot2, GOSemSim (>= 2.0.0), methods, qvalue, reshape2, S4Vectors, stats, utils Suggests: prettydoc, clusterProfiler, knitr, org.Hs.eg.db, testthat License: Artistic-2.0 MD5sum: 693643aa99469b3e91694464e75ef3c4 NeedsCompilation: no Title: Disease Ontology Semantic and Enrichment analysis Description: This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data. biocViews: Annotation, Visualization, MultipleComparison, GeneSetEnrichment, Pathways, Software Author: Guangchuang Yu [aut, cre], Li-Gen Wang [ctb], Vladislav Petyuk [ctb], Giovanni Dall'Olio [ctb] Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/software/DOSE VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/DOSE/issues git_url: https://git.bioconductor.org/packages/DOSE git_branch: RELEASE_3_10 git_last_commit: 94422dc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DOSE_3.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DOSE_3.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DOSE_3.12.0.tgz vignettes: vignettes/DOSE/inst/doc/DOSE.html, vignettes/DOSE/inst/doc/semanticAnalysis.html vignetteTitles: DOSE, 01 DOSE semantic similarity analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DOSE/inst/doc/DOSE.R, vignettes/DOSE/inst/doc/semanticAnalysis.R importsMe: bioCancer, clusterProfiler, debrowser, eegc, enrichplot, GDCRNATools, LINC, MAGeCKFlute, meshes, miRspongeR, MoonlightR, PathwaySplice, ReactomePA, RNASeqR, scTensor, signatureSearch suggestsMe: cola, GOSemSim, scGPS dependencyCount: 87 Package: doseR Version: 1.2.0 Depends: R (>= 3.6) Imports: edgeR, methods, stats, graphics, matrixStats, mclust, lme4, RUnit, SummarizedExperiment, digest, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: GPL MD5sum: c90d46b5a6ce2e703291dfdefcce73d7 NeedsCompilation: no Title: doseR Description: doseR package is a next generation sequencing package for sex chromosome dosage compensation which can be applied broadly to detect shifts in gene expression among an arbitrary number of pre-defined groups of loci. doseR is a differential gene expression package for count data, that detects directional shifts in expression for multiple, specific subsets of genes, broad utility in systems biology research. doseR has been prepared to manage the nature of the data and the desired set of inferences. doseR uses S4 classes to store count data from sequencing experiment. It contains functions to normalize and filter count data, as well as to plot and calculate statistics of count data. It contains a framework for linear modeling of count data. The package has been tested using real and simulated data. biocViews: Infrastructure, Software, DataRepresentation, Sequencing, GeneExpression, SystemsBiology, DifferentialExpression Author: AJ Vaestermark, JR Walters. Maintainer: ake.vastermark VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/doseR git_branch: RELEASE_3_10 git_last_commit: b8d94b4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/doseR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/doseR_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/doseR_1.2.0.tgz vignettes: vignettes/doseR/inst/doc/doseR.html vignetteTitles: "doseR" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doseR/inst/doc/doseR.R dependencyCount: 48 Package: drawProteins Version: 1.6.0 Depends: R (>= 3.4) Imports: ggplot2, httr, dplyr, readr, stringr, tidyr Suggests: covr, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 84f41020aef698fa680d1d66d4de981a NeedsCompilation: no Title: Package to Draw Protein Schematics from Uniprot API output Description: This package draws protein schematics from Uniprot API output. From the JSON returned by the GET command, it creates a dataframe from the Uniprot Features API. This dataframe can then be used by geoms based on ggplot2 and base R to draw protein schematics. biocViews: Visualization, FunctionalPrediction, Proteomics Author: Paul Brennan [aut, cre] Maintainer: Paul Brennan URL: https://github.com/brennanpincardiff/drawProteins VignetteBuilder: knitr BugReports: https://github.com/brennanpincardiff/drawProteins/issues/new git_url: https://git.bioconductor.org/packages/drawProteins git_branch: RELEASE_3_10 git_last_commit: 041f4a8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/drawProteins_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/drawProteins_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/drawProteins_1.6.0.tgz vignettes: vignettes/drawProteins/inst/doc/drawProteins_BiocStyle.html, vignettes/drawProteins/inst/doc/drawProteins_extract_transcripts_BiocStyle.html vignetteTitles: Using drawProteins, Using extract_transcripts in drawProteins hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/drawProteins/inst/doc/drawProteins_BiocStyle.R, vignettes/drawProteins/inst/doc/drawProteins_extract_transcripts_BiocStyle.R dependencyCount: 72 Package: DRIMSeq Version: 1.14.0 Depends: R (>= 3.4.0) Imports: utils, stats, MASS, GenomicRanges, IRanges, S4Vectors, BiocGenerics, methods, BiocParallel, limma, edgeR, ggplot2, reshape2 Suggests: PasillaTranscriptExpr, GeuvadisTranscriptExpr, grid, BiocStyle, knitr, testthat License: GPL (>= 3) MD5sum: ff5c9e43c1f60846d4b0e914e7ee524f NeedsCompilation: no Title: Differential transcript usage and tuQTL analyses with Dirichlet-multinomial model in RNA-seq Description: The package provides two frameworks. One for the differential transcript usage analysis between different conditions and one for the tuQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts) with the Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results. biocViews: ImmunoOncology, SNP, AlternativeSplicing, DifferentialSplicing, Genetics, RNASeq, Sequencing, WorkflowStep, MultipleComparison, GeneExpression, DifferentialExpression Author: Malgorzata Nowicka [aut, cre] Maintainer: Malgorzata Nowicka VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DRIMSeq git_branch: RELEASE_3_10 git_last_commit: da84af4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DRIMSeq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DRIMSeq_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DRIMSeq_1.14.0.tgz vignettes: vignettes/DRIMSeq/inst/doc/DRIMSeq.pdf vignetteTitles: Differential transcript usage and transcript usage QTL analyses in RNA-seq with the DRIMSeq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DRIMSeq/inst/doc/DRIMSeq.R importsMe: BANDITS, IsoformSwitchAnalyzeR dependencyCount: 80 Package: DriverNet Version: 1.26.0 Depends: R (>= 2.10), methods License: GPL-3 MD5sum: 516d28be6c9a0aa40625b12a05265d55 NeedsCompilation: no Title: Drivernet: uncovering somatic driver mutations modulating transcriptional networks in cancer Description: DriverNet is a package to predict functional important driver genes in cancer by integrating genome data (mutation and copy number variation data) and transcriptome data (gene expression data). The different kinds of data are combined by an influence graph, which is a gene-gene interaction network deduced from pathway data. A greedy algorithm is used to find the possible driver genes, which may mutated in a larger number of patients and these mutations will push the gene expression values of the connected genes to some extreme values. biocViews: Network Author: Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth Liu, Jamie Rosner and Sohrab Shah Maintainer: Jiarui Ding git_url: https://git.bioconductor.org/packages/DriverNet git_branch: RELEASE_3_10 git_last_commit: 320d4fa git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DriverNet_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DriverNet_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DriverNet_1.26.0.tgz vignettes: vignettes/DriverNet/inst/doc/DriverNet-Overview.pdf vignetteTitles: An introduction to DriverNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DriverNet/inst/doc/DriverNet-Overview.R dependencyCount: 1 Package: DropletUtils Version: 1.6.1 Depends: SingleCellExperiment Imports: S4Vectors, BiocParallel, Rcpp, Matrix, methods, utils, stats, edgeR, rhdf5, HDF5Array, R.utils, dqrng LinkingTo: Rcpp, beachmat, Rhdf5lib, BH, dqrng Suggests: testthat, beachmat, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: 91216fa34115bacf4dbb4e35b92772dc NeedsCompilation: yes Title: Utilities for Handling Single-Cell Droplet Data Description: Provides a number of utility functions for handling single-cell (RNA-seq) data from droplet technologies such as 10X Genomics. This includes data loading from count matrices or molecule information files, identification of cells from empty droplets, removal of barcode-swapped pseudo-cells, and downsampling of the count matrix. biocViews: ImmunoOncology, SingleCell, Sequencing, RNASeq, GeneExpression, Transcriptomics, DataImport, Coverage Author: Aaron Lun [aut, cre], Jonathan Griffiths [ctb], Davis McCarthy [ctb] Maintainer: Aaron Lun SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DropletUtils git_branch: RELEASE_3_10 git_last_commit: fff4292 git_last_commit_date: 2019-10-30 Date/Publication: 2019-10-30 source.ver: src/contrib/DropletUtils_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/DropletUtils_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DropletUtils_1.6.1.tgz vignettes: vignettes/DropletUtils/inst/doc/DropletUtils.html vignetteTitles: Utilities for handling droplet-based single-cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DropletUtils/inst/doc/DropletUtils.R suggestsMe: BUSpaRse, scater, splatter dependencyCount: 46 Package: DrugVsDisease Version: 2.28.0 Depends: R (>= 2.10), affy, limma, biomaRt, ArrayExpress, GEOquery, DrugVsDiseasedata, cMap2data, qvalue Imports: annotate, hgu133a.db, hgu133a2.db, hgu133plus2.db, RUnit, BiocGenerics, xtable License: GPL-3 Archs: i386, x64 MD5sum: ec32ed6cedcdef9c07818fab027c1d36 NeedsCompilation: no Title: Comparison of disease and drug profiles using Gene set Enrichment Analysis Description: This package generates ranked lists of differential gene expression for either disease or drug profiles. Input data can be downloaded from Array Express or GEO, or from local CEL files. Ranked lists of differential expression and associated p-values are calculated using Limma. Enrichment scores (Subramanian et al. PNAS 2005) are calculated to a reference set of default drug or disease profiles, or a set of custom data supplied by the user. Network visualisation of significant scores are output in Cytoscape format. biocViews: Microarray, GeneExpression, Clustering Author: C. Pacini Maintainer: j. Saez-Rodriguez git_url: https://git.bioconductor.org/packages/DrugVsDisease git_branch: RELEASE_3_10 git_last_commit: f786032 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DrugVsDisease_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DrugVsDisease_2.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DrugVsDisease_2.28.0.tgz vignettes: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.pdf vignetteTitles: DrugVsDisease hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.R dependencyCount: 135 Package: DSS Version: 2.34.0 Depends: R (>= 3.3), Biobase, bsseq, splines, methods Imports: stats, graphics, DelayedArray Suggests: BiocStyle, knitr, rmarkdown License: GPL MD5sum: 7209914dc67ddcae45f63961649505ca NeedsCompilation: yes Title: Dispersion shrinkage for sequencing data Description: DSS is an R library performing differntial analysis for count-based sequencing data. It detectes differentially expressed genes (DEGs) from RNA-seq, and differentially methylated loci or regions (DML/DMRs) from bisulfite sequencing (BS-seq). The core of DSS is a new dispersion shrinkage method for estimating the dispersion parameter from Gamma-Poisson or Beta-Binomial distributions. biocViews: Sequencing, RNASeq, DNAMethylation,GeneExpression, DifferentialExpression,DifferentialMethylation Author: Hao Wu Maintainer: Hao Wu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DSS git_branch: RELEASE_3_10 git_last_commit: f9819c7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DSS_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DSS_2.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DSS_2.34.0.tgz vignettes: vignettes/DSS/inst/doc/DSS.html vignetteTitles: The DSS User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DSS/inst/doc/DSS.R importsMe: DMRcate, kissDE, MethCP suggestsMe: biscuiteer, methrix dependencyCount: 66 Package: DTA Version: 2.32.0 Depends: R (>= 2.10), LSD Imports: scatterplot3d License: Artistic-2.0 Archs: i386, x64 MD5sum: 42ba3cd8892916c11517abe0654b6a1f NeedsCompilation: no Title: Dynamic Transcriptome Analysis Description: Dynamic Transcriptome Analysis (DTA) can monitor the cellular response to perturbations with higher sensitivity and temporal resolution than standard transcriptomics. The package implements the underlying kinetic modeling approach capable of the precise determination of synthesis- and decay rates from individual microarray or RNAseq measurements. biocViews: Microarray, DifferentialExpression, GeneExpression, Transcription Author: Bjoern Schwalb, Benedikt Zacher, Sebastian Duemcke, Achim Tresch Maintainer: Bjoern Schwalb git_url: https://git.bioconductor.org/packages/DTA git_branch: RELEASE_3_10 git_last_commit: cec5da0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DTA_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DTA_2.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DTA_2.32.0.tgz vignettes: vignettes/DTA/inst/doc/DTA.pdf vignetteTitles: A guide to Dynamic Transcriptome Analysis (DTA) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DTA/inst/doc/DTA.R dependencyCount: 5 Package: dualKS Version: 1.46.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.0), affy, methods Imports: graphics License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: fb377090bced70e3ba042cf8deede2d7 NeedsCompilation: no Title: Dual KS Discriminant Analysis and Classification Description: This package implements a Kolmogorov Smirnov rank-sum based algorithm for training (i.e. discriminant analysis--identification of genes that discriminate between classes) and classification of gene expression data sets. One of the chief strengths of this approach is that it is amenable to the "multiclass" problem. That is, it can discriminate between more than 2 classes. biocViews: Microarray, Classification Author: Eric J. Kort, Yarong Yang Maintainer: Eric J. Kort , Yarong Yang git_url: https://git.bioconductor.org/packages/dualKS git_branch: RELEASE_3_10 git_last_commit: 6cf94bd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/dualKS_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/dualKS_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/dualKS_1.46.0.tgz vignettes: vignettes/dualKS/inst/doc/dualKS.pdf vignetteTitles: dualKS.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dualKS/inst/doc/dualKS.R dependencyCount: 13 Package: DupChecker Version: 1.24.0 Imports: tools, R.utils, RCurl Suggests: knitr License: GPL (>= 2) MD5sum: 0b0f8290ac14cea96b849a082f047ff5 NeedsCompilation: no Title: a package for checking high-throughput genomic data redundancy in meta-analysis Description: Meta-analysis has become a popular approach for high-throughput genomic data analysis because it often can significantly increase power to detect biological signals or patterns in datasets. However, when using public-available databases for meta-analysis, duplication of samples is an often encountered problem, especially for gene expression data. Not removing duplicates would make study results questionable. We developed a Bioconductor package DupChecker that efficiently identifies duplicated samples by generating MD5 fingerprints for raw data. biocViews: Preprocessing Author: Quanhu Sheng, Yu Shyr, Xi Chen Maintainer: "Quanhu SHENG" VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DupChecker git_branch: RELEASE_3_10 git_last_commit: 9679fbb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DupChecker_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DupChecker_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DupChecker_1.24.0.tgz vignettes: vignettes/DupChecker/inst/doc/DupChecker.pdf vignetteTitles: Validate genomic data with "DupChecker" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DupChecker/inst/doc/DupChecker.R dependencyCount: 8 Package: dupRadar Version: 1.16.0 Depends: R (>= 3.2.0) Imports: Rsubread (>= 1.14.1) Suggests: BiocStyle, knitr, rmarkdown, AnnotationHub License: GPL-3 MD5sum: 7b6e3e71278a91f1d4e3e2981fd7acf1 NeedsCompilation: no Title: Assessment of duplication rates in RNA-Seq datasets Description: Duplication rate quality control for RNA-Seq datasets. biocViews: Technology, Sequencing, RNASeq, QualityControl, ImmunoOncology Author: Sergi Sayols , Holger Klein Maintainer: Sergi Sayols , Holger Klein VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dupRadar git_branch: RELEASE_3_10 git_last_commit: 1da0986 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/dupRadar_1.16.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/dupRadar_1.16.0.tgz vignettes: vignettes/dupRadar/inst/doc/dupRadar.html vignetteTitles: Using dupRadar hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dupRadar/inst/doc/dupRadar.R dependencyCount: 4 Package: dyebias Version: 1.46.0 Depends: R (>= 1.4.1), marray, Biobase Suggests: limma, convert, GEOquery, dyebiasexamples, methods License: GPL-3 MD5sum: e71917f07545eed17a66454af4025d46 NeedsCompilation: no Title: The GASSCO method for correcting for slide-dependent gene-specific dye bias Description: Many two-colour hybridizations suffer from a dye bias that is both gene-specific and slide-specific. The former depends on the content of the nucleotide used for labeling; the latter depends on the labeling percentage. The slide-dependency was hitherto not recognized, and made addressing the artefact impossible. Given a reasonable number of dye-swapped pairs of hybridizations, or of same vs. same hybridizations, both the gene- and slide-biases can be estimated and corrected using the GASSCO method (Margaritis et al., Mol. Sys. Biol. 5:266 (2009), doi:10.1038/msb.2009.21) biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Philip Lijnzaad and Thanasis Margaritis Maintainer: Philip Lijnzaad URL: http://www.holstegelab.nl/publications/margaritis_lijnzaad git_url: https://git.bioconductor.org/packages/dyebias git_branch: RELEASE_3_10 git_last_commit: 7963542 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/dyebias_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/dyebias_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/dyebias_1.46.0.tgz vignettes: vignettes/dyebias/inst/doc/dyebias-vignette.pdf vignetteTitles: dye bias correction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dyebias/inst/doc/dyebias-vignette.R dependencyCount: 10 Package: DynDoc Version: 1.64.0 Depends: methods, utils Imports: methods License: Artistic-2.0 MD5sum: 86124cbf609486759269b3e794b22379 NeedsCompilation: no Title: Dynamic document tools Description: A set of functions to create and interact with dynamic documents and vignettes. biocViews: ReportWriting, Infrastructure Author: R. Gentleman, Jeff Gentry Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/DynDoc git_branch: RELEASE_3_10 git_last_commit: 355a0b2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/DynDoc_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/DynDoc_1.64.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/DynDoc_1.64.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tkWidgets dependencyCount: 2 Package: EasyqpcR Version: 1.28.0 Imports: plyr, matrixStats, plotrix, gWidgetsRGtk2 Suggests: SLqPCR, qpcrNorm, qpcR, knitr License: GPL (>=2) MD5sum: b1d90b65d58707ab769b52872cdc1ff4 NeedsCompilation: no Title: EasyqpcR for low-throughput real-time quantitative PCR data analysis Description: This package is based on the qBase algorithms published by Hellemans et al. in 2007. The EasyqpcR package allows you to import easily qPCR data files as described in the vignette. Thereafter, you can calculate amplification efficiencies, relative quantities and their standard errors, normalization factors based on the best reference genes choosen (using the SLqPCR package), and then the normalized relative quantities, the NRQs scaled to your control and their standard errors. This package has been created for low-throughput qPCR data analysis. biocViews: qPCR, GeneExpression Author: Le Pape Sylvain Maintainer: Le Pape Sylvain git_url: https://git.bioconductor.org/packages/EasyqpcR git_branch: RELEASE_3_10 git_last_commit: 25c4bdb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/EasyqpcR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/EasyqpcR_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/EasyqpcR_1.28.0.tgz vignettes: vignettes/EasyqpcR/inst/doc/vignette_EasyqpcR.pdf vignetteTitles: EasyqpcR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EasyqpcR/inst/doc/vignette_EasyqpcR.R dependencyCount: 13 Package: easyRNASeq Version: 2.22.2 Imports: Biobase (>= 2.44.0), BiocFileCache (>= 1.7.10), BiocGenerics (>= 0.30.0), BiocParallel (>= 1.18.1), biomaRt (>= 2.40.5), Biostrings (>= 2.52.0), DESeq (>= 1.36.0), edgeR (>= 3.26.8), GenomeInfoDb (>= 1.20.0), genomeIntervals (>= 1.40.0), GenomicAlignments (>= 1.20.1), GenomicRanges (>= 1.36.1), SummarizedExperiment (>= 1.14.1), graphics, IRanges (>= 2.18.3), LSD (>= 4.0), locfit, methods, parallel, rappdirs (>= 0.3.1), Rsamtools (>= 2.0.3), S4Vectors (>= 0.22.1), ShortRead (>= 1.42.0), utils Suggests: BiocStyle (>= 2.12.0), BSgenome (>= 1.52.0), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.4.0), curl, knitr, rmarkdown, RUnit (>= 0.4.32) License: Artistic-2.0 MD5sum: 3662acfa845cf9e13570b31c85ddfb6e NeedsCompilation: no Title: Count summarization and normalization for RNA-Seq data Description: Calculates the coverage of high-throughput short-reads against a genome of reference and summarizes it per feature of interest (e.g. exon, gene, transcript). The data can be normalized as 'RPKM' or by the 'DESeq' or 'edgeR' package. biocViews: GeneExpression, RNASeq, Genetics, Preprocessing, ImmunoOncology Author: Nicolas Delhomme, Ismael Padioleau, Bastian Schiffthaler, Niklas Maehler Maintainer: Nicolas Delhomme VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/easyRNASeq git_branch: RELEASE_3_10 git_last_commit: 66b2c35 git_last_commit_date: 2019-11-22 Date/Publication: 2019-11-23 source.ver: src/contrib/easyRNASeq_2.22.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/easyRNASeq_2.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/easyRNASeq_2.22.2.tgz vignettes: vignettes/easyRNASeq/inst/doc/easyRNASeq.pdf, vignettes/easyRNASeq/inst/doc/simpleRNASeq.html vignetteTitles: R / Bioconductor for High Throughput Sequence Analysis, geneNetworkR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/easyRNASeq/inst/doc/easyRNASeq.R, vignettes/easyRNASeq/inst/doc/simpleRNASeq.R importsMe: msgbsR suggestsMe: SeqGSEA dependencyCount: 101 Package: EBarrays Version: 2.50.0 Depends: R (>= 1.8.0), Biobase, lattice, methods Imports: Biobase, cluster, graphics, grDevices, lattice, methods, stats License: GPL (>= 2) MD5sum: ddd452d524f893d06e86f27fb721c288 NeedsCompilation: yes Title: Unified Approach for Simultaneous Gene Clustering and Differential Expression Identification Description: EBarrays provides tools for the analysis of replicated/unreplicated microarray data. biocViews: Clustering, DifferentialExpression Author: Ming Yuan, Michael Newton, Deepayan Sarkar and Christina Kendziorski Maintainer: Ming Yuan git_url: https://git.bioconductor.org/packages/EBarrays git_branch: RELEASE_3_10 git_last_commit: a5415a9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/EBarrays_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/EBarrays_2.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/EBarrays_2.50.0.tgz vignettes: vignettes/EBarrays/inst/doc/vignette.pdf vignetteTitles: Introduction to EBarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBarrays/inst/doc/vignette.R dependsOnMe: EBcoexpress, gaga, geNetClassifier importsMe: casper suggestsMe: Category, dcanr dependencyCount: 11 Package: EBcoexpress Version: 1.30.0 Depends: EBarrays, mclust, minqa Suggests: graph, igraph, colorspace License: GPL (>= 2) MD5sum: ee4a1230d4b4ae71c2ac9f4276606de3 NeedsCompilation: yes Title: EBcoexpress for Differential Co-Expression Analysis Description: An Empirical Bayesian Approach to Differential Co-Expression Analysis at the Gene-Pair Level biocViews: Bayesian Author: John A. Dawson Maintainer: John A. Dawson git_url: https://git.bioconductor.org/packages/EBcoexpress git_branch: RELEASE_3_10 git_last_commit: 658c76c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/EBcoexpress_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/EBcoexpress_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/EBcoexpress_1.30.0.tgz vignettes: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.pdf vignetteTitles: EBcoexpress Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.R dependsOnMe: SRGnet suggestsMe: dcanr dependencyCount: 15 Package: EBImage Version: 4.28.1 Depends: methods Imports: BiocGenerics (>= 0.7.1), graphics, grDevices, stats, abind, tiff, jpeg, png, locfit, fftwtools (>= 0.9-7), utils, htmltools, htmlwidgets, RCurl Suggests: BiocStyle, digest, knitr, rmarkdown, shiny License: LGPL Archs: i386, x64 MD5sum: 8ba17e1446d825eeb6045f55f68a44bc NeedsCompilation: yes Title: Image processing and analysis toolbox for R Description: EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and facilitates the use of other tools in the R environment for signal processing, statistical modeling, machine learning and visualization with image data. biocViews: Visualization Author: Andrzej Oleś, Gregoire Pau, Mike Smith, Oleg Sklyar, Wolfgang Huber, with contributions from Joseph Barry and Philip A. Marais Maintainer: Andrzej Oleś URL: https://github.com/aoles/EBImage VignetteBuilder: knitr BugReports: https://github.com/aoles/EBImage/issues git_url: https://git.bioconductor.org/packages/EBImage git_branch: RELEASE_3_10 git_last_commit: 7b7ed31 git_last_commit_date: 2019-12-02 Date/Publication: 2019-12-06 source.ver: src/contrib/EBImage_4.28.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/EBImage_4.28.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/EBImage_4.28.1.tgz vignettes: vignettes/EBImage/inst/doc/EBImage-introduction.html vignetteTitles: Introduction to EBImage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBImage/inst/doc/EBImage-introduction.R dependsOnMe: Cardinal, CRImage, flowcatchR, imageHTS importsMe: bnbc, flowCHIC, heatmaps, MaxContrastProjection, yamss suggestsMe: HilbertVis, tofsims dependencyCount: 24 Package: EBSEA Version: 1.14.0 Imports: edgeR, limma, graphics, stats, plyr License: GPL-2 Archs: i386, x64 MD5sum: 8c36f9da7a17010bafc9abe9bdbb309a NeedsCompilation: no Title: Exon Based Strategy for Expression Analysis of genes Description: Calculates differential expression of genes based on exon counts of genes obtained from RNA-seq sequencing data. biocViews: Software, DifferentialExpression, GeneExpression, Sequencing Author: Arfa Mehmood, Asta Laiho, Laura L. Elo Maintainer: Arfa Mehmood git_url: https://git.bioconductor.org/packages/EBSEA git_branch: RELEASE_3_10 git_last_commit: 17ad078 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/EBSEA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/EBSEA_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/EBSEA_1.14.0.tgz vignettes: vignettes/EBSEA/inst/doc/EBSEA.pdf vignetteTitles: EBSEA: Exon Based Strategy for Expression Analysis of genes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSEA/inst/doc/EBSEA.R dependencyCount: 12 Package: EBSeq Version: 1.26.0 Depends: blockmodeling, gplots, testthat, R (>= 3.0.0) License: Artistic-2.0 Archs: i386, x64 MD5sum: 5f8255d8df63e4e9592cfac24f8da538 NeedsCompilation: no Title: An R package for gene and isoform differential expression analysis of RNA-seq data Description: Differential Expression analysis at both gene and isoform level using RNA-seq data biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng git_url: https://git.bioconductor.org/packages/EBSeq git_branch: RELEASE_3_10 git_last_commit: 4902bd0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/EBSeq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/EBSeq_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/EBSeq_1.26.0.tgz vignettes: vignettes/EBSeq/inst/doc/EBSeq_Vignette.pdf vignetteTitles: EBSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSeq/inst/doc/EBSeq_Vignette.R dependsOnMe: EBSeqHMM, Oscope importsMe: DEsubs, scDD suggestsMe: compcodeR dependencyCount: 41 Package: EBSeqHMM Version: 1.20.0 Depends: EBSeq License: Artistic-2.0 MD5sum: 1ffd9e9b620e5bcc5d33f4110a637f46 NeedsCompilation: no Title: Bayesian analysis for identifying gene or isoform expression changes in ordered RNA-seq experiments Description: The EBSeqHMM package implements an auto-regressive hidden Markov model for statistical analysis in ordered RNA-seq experiments (e.g. time course or spatial course data). The EBSeqHMM package provides functions to identify genes and isoforms that have non-constant expression profile over the time points/positions, and cluster them into expression paths. biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing, GeneExpression, Bayesian, HiddenMarkovModel, TimeCourse Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng git_url: https://git.bioconductor.org/packages/EBSeqHMM git_branch: RELEASE_3_10 git_last_commit: d25aec0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/EBSeqHMM_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/EBSeqHMM_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/EBSeqHMM_1.20.0.tgz vignettes: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.pdf vignetteTitles: HMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.R dependencyCount: 42 Package: ecolitk Version: 1.58.0 Depends: R (>= 2.10) Imports: Biobase, graphics, methods Suggests: ecoliLeucine, ecolicdf, graph, multtest, affy License: GPL (>= 2) MD5sum: 928283534ea5d72aebbd522baf9a637a NeedsCompilation: no Title: Meta-data and tools for E. coli Description: Meta-data and tools to work with E. coli. The tools are mostly plotting functions to work with circular genomes. They can used with other genomes/plasmids. biocViews: Annotation, Visualization Author: Laurent Gautier Maintainer: Laurent Gautier git_url: https://git.bioconductor.org/packages/ecolitk git_branch: RELEASE_3_10 git_last_commit: 0bdda76 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ecolitk_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ecolitk_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ecolitk_1.58.0.tgz vignettes: vignettes/ecolitk/inst/doc/ecolitk.pdf vignetteTitles: ecolitk hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ecolitk/inst/doc/ecolitk.R dependencyCount: 7 Package: EDASeq Version: 2.20.0 Depends: Biobase (>= 2.15.1), ShortRead (>= 1.11.42) Imports: methods, graphics, BiocGenerics, IRanges (>= 1.13.9), DESeq, aroma.light, Rsamtools (>= 1.5.75), biomaRt, Biostrings, AnnotationDbi, GenomicFeatures, GenomicRanges, BiocManager Suggests: BiocStyle, knitr, yeastRNASeq, leeBamViews, edgeR, KernSmooth, testthat License: Artistic-2.0 MD5sum: 0d46c4169f40daef38f3f2a32de78868 NeedsCompilation: no Title: Exploratory Data Analysis and Normalization for RNA-Seq Description: Numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010). biocViews: ImmunoOncology, Sequencing, RNASeq, Preprocessing, QualityControl, DifferentialExpression Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Ludwig Geistlinger [ctb] Maintainer: Davide Risso URL: https://github.com/drisso/EDASeq VignetteBuilder: knitr BugReports: https://github.com/drisso/EDASeq/issues git_url: https://git.bioconductor.org/packages/EDASeq git_branch: RELEASE_3_10 git_last_commit: c81a85c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/EDASeq_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/EDASeq_2.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/EDASeq_2.20.0.tgz vignettes: vignettes/EDASeq/inst/doc/EDASeq.html vignetteTitles: EDASeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EDASeq/inst/doc/EDASeq.R dependsOnMe: metaseqR, RUVSeq importsMe: consensusDE, DaMiRseq, TCGAbiolinks suggestsMe: bigPint, DEScan2, HTSFilter dependencyCount: 103 Package: EDDA Version: 1.24.0 Depends: Rcpp (>= 0.10.4),parallel,methods,ROCR,DESeq,baySeq,snow,edgeR Imports: graphics, stats, utils, parallel, methods, ROCR, DESeq, baySeq, snow, edgeR LinkingTo: Rcpp License: GPL (>= 2) MD5sum: e69070728ffa8ca110cb66e0b36ae773 NeedsCompilation: yes Title: Experimental Design in Differential Abundance analysis Description: EDDA can aid in the design of a range of common experiments such as RNA-seq, Nanostring assays, RIP-seq and Metagenomic sequencing, and enables researchers to comprehensively investigate the impact of experimental decisions on the ability to detect differential abundance. This work was published on 3 December 2014 at Genome Biology under the title "The importance of study design for detecting differentially abundant features in high-throughput experiments" (http://genomebiology.com/2014/15/12/527). biocViews: ImmunoOncology, Sequencing, ExperimentalDesign, Normalization, RNASeq, ChIPSeq Author: Li Juntao, Luo Huaien, Chia Kuan Hui Burton, Niranjan Nagarajan Maintainer: Chia Kuan Hui Burton , Niranjan Nagarajan URL: http://edda.gis.a-star.edu.sg/, http://genomebiology.com/2014/15/12/527 git_url: https://git.bioconductor.org/packages/EDDA git_branch: RELEASE_3_10 git_last_commit: 8c84f80 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/EDDA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/EDDA_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/EDDA_1.24.0.tgz vignettes: vignettes/EDDA/inst/doc/EDDA.pdf vignetteTitles: EDDA Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 59 Package: edge Version: 2.18.0 Depends: R(>= 3.1.0), Biobase Imports: methods, splines, sva, snm, jackstraw, qvalue(>= 1.99.0), MASS Suggests: testthat, knitr, ggplot2, reshape2 License: MIT + file LICENSE MD5sum: f75465c6cf5982fb3d409bef5911f091 NeedsCompilation: yes Title: Extraction of Differential Gene Expression Description: The edge package implements methods for carrying out differential expression analyses of genome-wide gene expression studies. Significance testing using the optimal discovery procedure and generalized likelihood ratio tests (equivalent to F-tests and t-tests) are implemented for general study designs. Special functions are available to facilitate the analysis of common study designs, including time course experiments. Other packages such as snm, sva, and qvalue are integrated in edge to provide a wide range of tools for gene expression analysis. biocViews: MultipleComparison, DifferentialExpression, TimeCourse, Regression, GeneExpression, DataImport Author: John D. Storey, Jeffrey T. Leek and Andrew J. Bass Maintainer: John D. Storey , Andrew J. Bass URL: https://github.com/jdstorey/edge VignetteBuilder: knitr BugReports: https://github.com/jdstorey/edge/issues git_url: https://git.bioconductor.org/packages/edge git_branch: RELEASE_3_10 git_last_commit: 5424022 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/edge_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/edge_2.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/edge_2.18.0.tgz vignettes: vignettes/edge/inst/doc/edge.pdf vignetteTitles: edge Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/edge/inst/doc/edge.R dependencyCount: 107 Package: edgeR Version: 3.28.1 Depends: R (>= 3.6.0), limma (>= 3.41.5) Imports: graphics, stats, utils, methods, locfit, Rcpp LinkingTo: Rcpp Suggests: AnnotationDbi, jsonlite, org.Hs.eg.db, readr, rhdf5, splines License: GPL (>=2) Archs: i386, x64 MD5sum: aaf52446df523eb67ac2952ccefb3f6b NeedsCompilation: yes Title: Empirical Analysis of Digital Gene Expression Data in R Description: Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of other types of genomic data that produce counts, including ChIP-seq, Bisulfite-seq, SAGE and CAGE. biocViews: GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, DifferentialMethylation, GeneSetEnrichment, Pathways, Genetics, DNAMethylation, Bayesian, Clustering, ChIPSeq, Regression, TimeCourse, Sequencing, RNASeq, BatchEffect, SAGE, Normalization, QualityControl, MultipleComparison, BiomedicalInformatics, CellBiology, FunctionalGenomics, Epigenetics, Genetics, ImmunoOncology, SystemsBiology, Transcriptomics Author: Yunshun Chen, Aaron TL Lun, Davis J McCarthy, Matthew E Ritchie, Belinda Phipson, Yifang Hu, Xiaobei Zhou, Mark D Robinson, Gordon K Smyth Maintainer: Yunshun Chen , Aaron Lun , Mark Robinson , Gordon Smyth URL: http://bioinf.wehi.edu.au/edgeR SystemRequirements: C++11 git_url: https://git.bioconductor.org/packages/edgeR git_branch: RELEASE_3_10 git_last_commit: 3e0d7a7 git_last_commit_date: 2020-02-26 Date/Publication: 2020-02-26 source.ver: src/contrib/edgeR_3.28.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/edgeR_3.28.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/edgeR_3.28.1.tgz vignettes: vignettes/edgeR/inst/doc/edgeR.pdf, vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf vignetteTitles: edgeR Vignette, edgeRUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ASpli, DBChIP, IntEREst, manta, methylMnM, RNASeqR, RUVSeq, TCC, tRanslatome importsMe: affycoretools, ArrayExpressHTS, ATACseqQC, AWFisher, baySeq, BioQC, circRNAprofiler, clusterExperiment, CNVRanger, compcodeR, consensusDE, coseq, countsimQC, csaw, DaMiRseq, debrowser, DEComplexDisease, DEFormats, DEGreport, DEsubs, DiffBind, diffcyt, diffHic, diffloop, DMRcate, doseR, DRIMSeq, DropletUtils, easyRNASeq, EBSEA, EDDA, eegc, EGSEA, EnrichmentBrowser, erccdashboard, ERSSA, GDCRNATools, Glimma, GSEABenchmarkeR, HTSFilter, icetea, infercnv, IsoformSwitchAnalyzeR, KnowSeq, Maaslin2, MEDIPS, metaseqR, MIGSA, MLSeq, msgbsR, msmsTests, multiHiCcompare, muscat, NBSplice, PathoStat, PROPER, psichomics, RCM, regsplice, Repitools, rnaSeqMap, scde, scone, scran, SIMD, singscore, splatter, STATegRa, SVAPLSseq, systemPipeR, TCGAbiolinks, TCseq, TimeSeriesExperiment, tradeSeq, tweeDEseq, vidger, yarn, zinbwave suggestsMe: ABSSeq, bigPint, biobroom, BitSeq, ClassifyR, clonotypeR, cqn, cydar, dcanr, DEScan2, EDASeq, gage, gCrisprTools, GenomicAlignments, GenomicRanges, goseq, groHMM, GSAR, GSVA, ideal, missMethyl, multiMiR, regionReport, SSPA, stageR, subSeq, SummarizedBenchmark, ToPASeq, topconfects, tximeta, tximport, variancePartition, Wrench, zFPKM dependencyCount: 10 Package: eegc Version: 1.12.0 Depends: R (>= 3.4.0) Imports: R.utils, gplots, sna, wordcloud, igraph, pheatmap, edgeR, DESeq2, clusterProfiler, S4Vectors, ggplot2, org.Hs.eg.db, org.Mm.eg.db, limma, DOSE, AnnotationDbi Suggests: knitr License: GPL-2 Archs: i386, x64 MD5sum: ccca8221a9ba831889f82927e49a856b NeedsCompilation: no Title: Engineering Evaluation by Gene Categorization (eegc) Description: This package has been developed to evaluate cellular engineering processes for direct differentiation of stem cells or conversion (transdifferentiation) of somatic cells to primary cells based on high throughput gene expression data screened either by DNA microarray or RNA sequencing. The package takes gene expression profiles as inputs from three types of samples: (i) somatic or stem cells to be (trans)differentiated (input of the engineering process), (ii) induced cells to be evaluated (output of the engineering process) and (iii) target primary cells (reference for the output). The package performs differential gene expression analysis for each pair-wise sample comparison to identify and evaluate the transcriptional differences among the 3 types of samples (input, output, reference). The ideal goal is to have induced and primary reference cell showing overlapping profiles, both very different from the original cells. biocViews: ImmunoOncology, Microarray, Sequencing, RNASeq, DifferentialExpression, GeneRegulation, GeneSetEnrichment, GeneExpression, GeneTarget Author: Xiaoyuan Zhou, Guofeng Meng, Christine Nardini, Hongkang Mei Maintainer: Xiaoyuan Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/eegc git_branch: RELEASE_3_10 git_last_commit: 2fce0f8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/eegc_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/eegc_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/eegc_1.12.0.tgz vignettes: vignettes/eegc/inst/doc/eegc.pdf vignetteTitles: Engineering Evaluation by Gene Categorization (eegc) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eegc/inst/doc/eegc.R dependencyCount: 180 Package: EGAD Version: 1.14.0 Depends: R(>= 3.3) Imports: gplots, Biobase, GEOquery, limma, arrayQualityMetrics, impute, RColorBrewer, zoo, igraph, plyr, Matrix, MASS, RCurl, affy Suggests: knitr, rmarkdown, testthat License: GPL-2 Archs: i386, x64 MD5sum: 356b2bcbe7549b1eaf1d30d77216d804 NeedsCompilation: no Title: Extending guilt by association by degree Description: The package implements a series of highly efficient tools to calculate functional properties of networks based on guilt by association methods. biocViews: Software, FunctionalGenomics, SystemsBiology, GenePrediction, FunctionalPrediction, NetworkEnrichment, GraphAndNetwork, Network Author: Sara Ballouz [aut, cre], Melanie Weber [aut, ctb], Paul Pavlidis [aut], Jesse Gillis [aut, ctb] Maintainer: Sara Ballouz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EGAD git_branch: RELEASE_3_10 git_last_commit: 75ccf2c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/EGAD_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/EGAD_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/EGAD_1.14.0.tgz vignettes: vignettes/EGAD/inst/doc/EGAD.pdf vignetteTitles: "EGAD user guide" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EGAD/inst/doc/EGAD.R dependencyCount: 153 Package: EGSEA Version: 1.14.0 Depends: R (>= 3.5), Biobase, gage (>= 2.14.4), AnnotationDbi, topGO (>= 2.16.0), pathview (>= 1.4.2) Imports: PADOG (>= 1.6.0), GSVA (>= 1.12.0), globaltest (>= 5.18.0), limma (>= 3.20.9), edgeR (>= 3.6.8), HTMLUtils (>= 0.1.5), hwriter (>= 1.2.2), gplots (>= 2.14.2), ggplot2 (>= 1.0.0), safe (>= 3.4.0), stringi (>= 0.5.0), parallel, stats, metap, grDevices, graphics, utils, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, RColorBrewer, methods, EGSEAdata (>= 1.3.1), Glimma (>= 1.4.0), htmlwidgets, plotly, DT Suggests: BiocStyle, knitr, testthat License: GPL-3 MD5sum: 97899d33a76d3f43d7ae97aa189ac40e NeedsCompilation: no Title: Ensemble of Gene Set Enrichment Analyses Description: This package implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing. biocViews: ImmunoOncology, DifferentialExpression, GO, GeneExpression, GeneSetEnrichment, Genetics, Microarray, MultipleComparison, OneChannel, Pathways, RNASeq, Sequencing, Software, SystemsBiology, TwoChannel,Metabolomics, Proteomics, KEGG, GraphAndNetwork, GeneSignaling, GeneTarget, NetworkEnrichment, Network, Classification Author: Monther Alhamdoosh, Luyi Tian, Milica Ng and Matthew Ritchie Maintainer: Monther Alhamdoosh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EGSEA git_branch: RELEASE_3_10 git_last_commit: 6cc31e9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/EGSEA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/EGSEA_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/EGSEA_1.14.0.tgz vignettes: vignettes/EGSEA/inst/doc/EGSEA.pdf vignetteTitles: EGSEA vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EGSEA/inst/doc/EGSEA.R dependencyCount: 169 Package: eiR Version: 1.26.0 Depends: R (>= 2.10.0), ChemmineR (>= 2.15.15), methods, DBI Imports: snow, tools, snowfall, RUnit, methods, ChemmineR, RCurl, digest, BiocGenerics, gespeR,RcppAnnoy (>= 0.0.9) Suggests: BiocStyle, knitcitations, knitr, knitrBootstrap License: Artistic-2.0 MD5sum: dc9f4cda67aa2c498073168c4cce4646 NeedsCompilation: yes Title: Accelerated similarity searching of small molecules Description: The eiR package provides utilities for accelerated structure similarity searching of very large small molecule data sets using an embedding and indexing approach. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Kevin Horan, Yiqun Cao and Tyler Backman Maintainer: Thomas Girke URL: https://github.com/girke-lab/eiR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/eiR git_branch: RELEASE_3_10 git_last_commit: 104b70f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/eiR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/eiR_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/eiR_1.26.0.tgz vignettes: vignettes/eiR/inst/doc/eiR.html vignetteTitles: eiR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/eiR/inst/doc/eiR.R dependencyCount: 142 Package: eisa Version: 1.38.0 Depends: isa2, Biobase (>= 2.17.8), AnnotationDbi, methods Imports: BiocGenerics, Category, genefilter, DBI Suggests: igraph (>= 0.6), Matrix, GOstats, GO.db, KEGG.db, biclust, MASS, xtable, ALL, hgu95av2.db, targetscan.Hs.eg.db, org.Hs.eg.db License: GPL (>= 2) MD5sum: 73996fef91886403da9fdca29a76481f NeedsCompilation: no Title: Expression data analysis via the Iterative Signature Algorithm Description: The Iterative Signature Algorithm (ISA) is a biclustering method; it finds correlated blocks (transcription modules) in gene expression (or other tabular) data. The ISA is capable of finding overlapping modules and it is resilient to noise. This package provides a convenient interface to the ISA, using standard BioConductor data structures; and also contains various visualization tools that can be used with other biclustering algorithms. biocViews: Classification, Visualization, Microarray, GeneExpression Author: Gabor Csardi Maintainer: Gabor Csardi git_url: https://git.bioconductor.org/packages/eisa git_branch: RELEASE_3_10 git_last_commit: 37f9e47 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/eisa_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/eisa_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/eisa_1.38.0.tgz vignettes: vignettes/eisa/inst/doc/EISA_tutorial.pdf vignetteTitles: The Iterative Signature Algorithm for Gene Expression Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eisa/inst/doc/EISA_tutorial.R dependsOnMe: ExpressionView importsMe: ExpressionView dependencyCount: 43 Package: ELBOW Version: 1.22.0 Depends: R (>= 2.15.0) Imports: graphics, stats, utils Suggests: DESeq, GEOquery, limma, simpleaffy, affyPLM, RColorBrewer, hgu133plus2cdf, hgu133plus2probe License: file LICENSE License_is_FOSS: yes License_restricts_use: no Archs: x64 MD5sum: c3bd8abd5ba51b5a76014af11f9e39af NeedsCompilation: no Title: ELBOW - Evaluating foLd change By the lOgit Way Description: Elbow an improved fold change test that uses cluster analysis and pattern recognition to set cut off limits that are derived directly from intrareplicate variance without assuming a normal distribution for as few as 2 biological replicates. Elbow also provides the same consistency as fold testing in cross platform analysis. Elbow has lower false positive and false negative rates than standard fold testing when both are evaluated using T testing and Statistical Analysis of Microarray using 12 replicates (six replicates each for initial and final conditions). Elbow provides a null value based on initial condition replicates and gives error bounds for results to allow better evaluation of significance. biocViews: ImmunoOncology, Technology, Microarray, RNASeq, Sequencing, Sequencing, Software, MultiChannel, OneChannel, TwoChannel, GeneExpression Author: Xiangli Zhang, Natalie Bjorklund, Graham Alvare, Tom Ryzdak, Richard Sparling, Brian Fristensky Maintainer: Graham Alvare , Xiangli Zhang git_url: https://git.bioconductor.org/packages/ELBOW git_branch: RELEASE_3_10 git_last_commit: a13963c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ELBOW_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ELBOW_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ELBOW_1.22.0.tgz vignettes: vignettes/ELBOW/inst/doc/Elbow_tutorial_vignette.pdf vignetteTitles: Using ELBOW --- the definitive ELBOW tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ELBOW/inst/doc/Elbow_tutorial_vignette.R dependencyCount: 3 Package: ELMER Version: 2.10.0 Depends: R (>= 3.4.0), ELMER.data (>= 2.9.3) Imports: GenomicRanges, ggplot2, reshape, grid, grDevices, graphics, methods, parallel, stats, utils, IRanges, GenomeInfoDb, S4Vectors, GenomicFeatures, TCGAbiolinks (>= 2.9.2), plyr, Matrix, dplyr, Gviz, ComplexHeatmap, circlize, MultiAssayExperiment, SummarizedExperiment, biomaRt, doParallel, downloader, ggrepel, lattice, magrittr, readr, scales, rvest, xml2, plotly, gridExtra, rmarkdown, stringr, tibble, tidyr, progress, purrr, ggpubr, rtracklayer, DelayedArray Suggests: BiocStyle, knitr, testthat, data.table, DT, GenomicInteractions, webshot, R.utils, covr License: GPL-3 MD5sum: bf20d8551c9a7c4fc6d442e7c4a1868c NeedsCompilation: no Title: Inferring Regulatory Element Landscapes and Transcription Factor Networks Using Cancer Methylomes Description: ELMER is designed to use DNA methylation and gene expression from a large number of samples to infere regulatory element landscape and transcription factor network in primary tissue. biocViews: DNAMethylation, GeneExpression, MotifAnnotation, Software, GeneRegulation, Transcription, Network Author: Tiago Chedraoui Silva [aut, cre], Lijing Yao [aut], Simon Coetzee [aut], Nicole Gull [ctb], Hui Shen [ctb], Peter Laird [ctb], Peggy Farnham [aut], Dechen Li [ctb], Benjamin Berman [aut] Maintainer: Tiago Chedraoui Silva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ELMER git_branch: RELEASE_3_10 git_last_commit: e736a05 git_last_commit_date: 2019-11-06 Date/Publication: 2019-11-06 source.ver: src/contrib/ELMER_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ELMER_2.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ELMER_2.10.0.tgz vignettes: vignettes/ELMER/inst/doc/analysis_data_input.html, vignettes/ELMER/inst/doc/analysis_diff_meth.html, vignettes/ELMER/inst/doc/analysis_get_pair.html, vignettes/ELMER/inst/doc/analysis_gui.html, vignettes/ELMER/inst/doc/analysis_motif_enrichment.html, vignettes/ELMER/inst/doc/analysis_regulatory_tf.html, vignettes/ELMER/inst/doc/index.html, vignettes/ELMER/inst/doc/input.html, vignettes/ELMER/inst/doc/pipe.html, vignettes/ELMER/inst/doc/plots_heatmap.html, vignettes/ELMER/inst/doc/plots_motif_enrichment.html, vignettes/ELMER/inst/doc/plots_scatter.html, vignettes/ELMER/inst/doc/plots_schematic.html, vignettes/ELMER/inst/doc/plots_TF.html, vignettes/ELMER/inst/doc/usecase.html vignetteTitles: "3.1 - Data input - Creating MAE object", "3.2 - Identifying differentially methylated probes", "3.3 - Identifying putative probe-gene pairs", 5 - Integrative analysis workshop with TCGAbiolinks and ELMER - Analysis GUI, "3.4 - Motif enrichment analysis on the selected probes", "3.5 - Identifying regulatory TFs", "1 - ELMER v.2: An R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles", "2 - Introduction: Input data", "3.6 - TCGA.pipe: Running ELMER for TCGA data in a compact way", "4.5 - Heatmap plots", "4.3 - Motif enrichment plots", "4.1 - Scatter plots", "4.2 - Schematic plots", "4.4 - Regulatory TF plots", "11 - ELMER: Use case" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ELMER/inst/doc/analysis_data_input.R, vignettes/ELMER/inst/doc/analysis_diff_meth.R, vignettes/ELMER/inst/doc/analysis_get_pair.R, vignettes/ELMER/inst/doc/analysis_gui.R, vignettes/ELMER/inst/doc/analysis_motif_enrichment.R, vignettes/ELMER/inst/doc/analysis_regulatory_tf.R, vignettes/ELMER/inst/doc/index.R, vignettes/ELMER/inst/doc/input.R, vignettes/ELMER/inst/doc/pipe.R, vignettes/ELMER/inst/doc/plots_heatmap.R, vignettes/ELMER/inst/doc/plots_motif_enrichment.R, vignettes/ELMER/inst/doc/plots_scatter.R, vignettes/ELMER/inst/doc/plots_schematic.R, vignettes/ELMER/inst/doc/plots_TF.R, vignettes/ELMER/inst/doc/usecase.R importsMe: TCGAbiolinksGUI dependencyCount: 213 Package: EMDomics Version: 2.16.0 Depends: R (>= 3.2.1) Imports: emdist, BiocParallel, matrixStats, ggplot2, CDFt, preprocessCore Suggests: knitr License: MIT + file LICENSE MD5sum: 32a3fe696a7b77ebe6df200e8ce14dff NeedsCompilation: no Title: Earth Mover's Distance for Differential Analysis of Genomics Data Description: The EMDomics algorithm is used to perform a supervised multi-class analysis to measure the magnitude and statistical significance of observed continuous genomics data between groups. Usually the data will be gene expression values from array-based or sequence-based experiments, but data from other types of experiments can also be analyzed (e.g. copy number variation). Traditional methods like Significance Analysis of Microarrays (SAM) and Linear Models for Microarray Data (LIMMA) use significance tests based on summary statistics (mean and standard deviation) of the distributions. This approach lacks power to identify expression differences between groups that show high levels of intra-group heterogeneity. The Earth Mover's Distance (EMD) algorithm instead computes the "work" needed to transform one distribution into another, thus providing a metric of the overall difference in shape between two distributions. Permutation of sample labels is used to generate q-values for the observed EMD scores. This package also incorporates the Komolgorov-Smirnov (K-S) test and the Cramer von Mises test (CVM), which are both common distribution comparison tests. biocViews: Software, DifferentialExpression, GeneExpression, Microarray Author: Sadhika Malladi [aut, cre], Daniel Schmolze [aut, cre], Andrew Beck [aut], Sheida Nabavi [aut] Maintainer: Sadhika Malladi and Daniel Schmolze VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EMDomics git_branch: RELEASE_3_10 git_last_commit: 24ffc14 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/EMDomics_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/EMDomics_2.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/EMDomics_2.16.0.tgz vignettes: vignettes/EMDomics/inst/doc/EMDomics.html vignetteTitles: EMDomics Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EMDomics/inst/doc/EMDomics.R dependencyCount: 66 Package: EmpiricalBrownsMethod Version: 1.14.0 Depends: R (>= 3.2.0) Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 660abe9dfa8a69ba3fb72a4001af2e60 NeedsCompilation: no Title: Uses Brown's method to combine p-values from dependent tests Description: Combining P-values from multiple statistical tests is common in bioinformatics. However, this procedure is non-trivial for dependent P-values. This package implements an empirical adaptation of Brown’s Method (an extension of Fisher’s Method) for combining dependent P-values which is appropriate for highly correlated data sets found in high-throughput biological experiments. biocViews: StatisticalMethod, GeneExpression, Pathways Author: William Poole Maintainer: David Gibbs URL: https://github.com/IlyaLab/CombiningDependentPvaluesUsingEBM.git VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EmpiricalBrownsMethod git_branch: RELEASE_3_10 git_last_commit: 4a99329 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/EmpiricalBrownsMethod_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/EmpiricalBrownsMethod_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/EmpiricalBrownsMethod_1.14.0.tgz vignettes: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.html vignetteTitles: Empirical Browns Method hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.R dependencyCount: 0 Package: ENCODExplorer Version: 2.12.1 Depends: R (>= 3.6) Imports: methods, tools, jsonlite, RCurl, tidyr, data.table, dplyr, stringr, stringi, utils, AnnotationHub, GenomicRanges, rtracklayer, S4Vectors, GenomeInfoDb Suggests: RUnit,BiocGenerics,knitr, curl, httr, shiny, shinythemes, DT License: Artistic-2.0 MD5sum: ad6c989fab7794483c4dc245f69e16f4 NeedsCompilation: no Title: A compilation of ENCODE metadata Description: This package allows user to quickly access ENCODE project files metadata and give access to helper functions to query the ENCODE rest api, download ENCODE datasets and save the database in SQLite format. biocViews: Infrastructure, DataImport Author: Charles Joly Beauparlant [aut, cre], Audrey Lemacon [aut], Eric Fournier [aut], Louis Gendron [ctb], Astrid-Louise Deschenes [ctb], Arnaud Droit [aut] Maintainer: Charles Joly Beauparlant VignetteBuilder: knitr BugReports: https://github.com/CharlesJB/ENCODExplorer/issues git_url: https://git.bioconductor.org/packages/ENCODExplorer git_branch: RELEASE_3_10 git_last_commit: 303e786 git_last_commit_date: 2019-12-12 Date/Publication: 2019-12-16 source.ver: src/contrib/ENCODExplorer_2.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/ENCODExplorer_2.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ENCODExplorer_2.12.1.tgz vignettes: vignettes/ENCODExplorer/inst/doc/ENCODExplorer.html vignetteTitles: ENCODExplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENCODExplorer/inst/doc/ENCODExplorer.R suggestsMe: TSRchitect dependencyCount: 93 Package: EnhancedVolcano Version: 1.4.0 Depends: ggplot2, ggrepel Suggests: RUnit, BiocGenerics, knitr, DESeq2, pasilla, airway, gridExtra, magrittr License: GPL-3 MD5sum: 68841dbc8c3f32c82e20937fbdd1f99c NeedsCompilation: no Title: Publication-ready volcano plots with enhanced colouring and labeling Description: Volcano plots represent a useful way to visualise the results of differential expression analyses. Here, we present a highly-configurable function that produces publication-ready volcano plots. EnhancedVolcano will attempt to fit as many point labels in the plot window as possible, thus avoiding 'clogging' up the plot with labels that could not otherwise have been read. Other functionality allows the user to identify up to 4 different types of attributes in the same plot space via colour, shape, size, and shade parameter configurations. biocViews: RNASeq, GeneExpression, Transcription, DifferentialExpression, ImmunoOncology Author: Kevin Blighe, Sharmila Rana, Myles Lewis Maintainer: Kevin Blighe URL: https://github.com/kevinblighe/EnhancedVolcano VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnhancedVolcano git_branch: RELEASE_3_10 git_last_commit: 6086485 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/EnhancedVolcano_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/EnhancedVolcano_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/EnhancedVolcano_1.4.0.tgz vignettes: vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.html vignetteTitles: Publication-ready volcano plots with enhanced colouring and labeling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.R dependencyCount: 55 Package: ENmix Version: 1.22.6 Depends: parallel,doParallel,foreach,SummarizedExperiment,stats Imports: grDevices,graphics,preprocessCore,matrixStats,methods,utils,irr, geneplotter,impute,minfi,RPMM,illuminaio,dynamicTreeCut,IRanges, Biobase,ExperimentHub,AnnotationHub,genefilter,gplots,quadprog,S4Vectors Suggests: minfiData, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 50af06ca143a01257883a0981f5b02cb NeedsCompilation: no Title: Data preprocessing and quality control for Illumina HumanMethylation450 and MethylationEPIC BeadChip Description: The ENmix package provides a set of quality control and data pre-processing tools for Illumina HumanMethylation450 and MethylationEPIC Beadchips. It includes ENmix background correction, RELIC dye bias correction, RCP probe-type bias adjustment, along with a number of additional tools. These functions can be used to remove unwanted experimental noise and thus to improve accuracy and reproducibility of methylation measures. ENmix functions are flexible and transparent. Users have option to choose a single pipeline command to finish all data pre-processing steps (including background correction, dye-bias adjustment, inter-array normalization and probe-type bias correction) or to use individual functions sequentially to perform data pre-processing in a more customized manner. In addition the ENmix package has selectable complementary functions for efficient data visualization (such as data distribution plots); quality control (identifing and filtering low quality data points, samples, probes, and outliers, along with imputation of missing values); identification of probes with multimodal distributions due to SNPs or other factors; exploration of data variance structure using principal component regression analysis plot; preparation of experimental factors related surrogate control variables to be adjusted in downstream statistical analysis; an efficient algorithm oxBS-MLE to estimate 5-methylcytosine and 5-hydroxymethylcytosine level; estimation of celltype proporitons; methlation age calculation and differentially methylated region (DMR) analysis. biocViews: DNAMethylation, Preprocessing, QualityControl, TwoChannel, Microarray, OneChannel, MethylationArray, BatchEffect, Normalization, DataImport, Regression, PrincipalComponent,Epigenetics, MultiChannel, DifferentialMethylation, ImmunoOncology Author: Zongli Xu [cre, aut], Liang Niu [aut], Leping Li [ctb], Jack Taylor [ctb] Maintainer: Zongli Xu git_url: https://git.bioconductor.org/packages/ENmix git_branch: RELEASE_3_10 git_last_commit: 5e46587 git_last_commit_date: 2020-04-09 Date/Publication: 2020-04-09 source.ver: src/contrib/ENmix_1.22.6.tar.gz win.binary.ver: bin/windows/contrib/3.6/ENmix_1.22.6.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ENmix_1.22.6.tgz vignettes: vignettes/ENmix/inst/doc/ENmix.pdf vignetteTitles: ENmix User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENmix/inst/doc/ENmix.R dependencyCount: 148 Package: EnrichedHeatmap Version: 1.16.0 Depends: R (>= 3.1.2), methods, grid, ComplexHeatmap (>= 1.99.0), GenomicRanges Imports: matrixStats, stats, GetoptLong, Rcpp, utils, locfit, circlize (>= 0.4.5), IRanges LinkingTo: Rcpp Suggests: testthat (>= 0.3), knitr, markdown, genefilter, RColorBrewer License: MIT + file LICENSE MD5sum: cab858080fbc9e427057208dc53831df NeedsCompilation: yes Title: Making Enriched Heatmaps Description: Enriched heatmap is a special type of heatmap which visualizes the enrichment of genomic signals on specific target regions. Here we implement enriched heatmap by ComplexHeatmap package. Since this type of heatmap is just a normal heatmap but with some special settings, with the functionality of ComplexHeatmap, it would be much easier to customize the heatmap as well as concatenating to a list of heatmaps to show correspondance between different data sources. biocViews: Software, Visualization, Sequencing, GenomeAnnotation, Coverage Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/EnrichedHeatmap VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnrichedHeatmap git_branch: RELEASE_3_10 git_last_commit: da6a435 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/EnrichedHeatmap_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/EnrichedHeatmap_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/EnrichedHeatmap_1.16.0.tgz vignettes: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.html, vignettes/EnrichedHeatmap/inst/doc/roadmap.html, vignettes/EnrichedHeatmap/inst/doc/row_odering.html, vignettes/EnrichedHeatmap/inst/doc/visualize_categorical_signals_wrapper.html vignetteTitles: 1. Make Enriched Heatmaps, 4. Visualize Comprehensive Associations in Roadmap dataset, 3. Compare row ordering methods, 2. Visualize Categorical Signals hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.R, vignettes/EnrichedHeatmap/inst/doc/roadmap.R, vignettes/EnrichedHeatmap/inst/doc/row_odering.R, vignettes/EnrichedHeatmap/inst/doc/visualize_categorical_signals_wrapper.R importsMe: profileplyr dependencyCount: 33 Package: EnrichmentBrowser Version: 2.16.1 Depends: SummarizedExperiment, graph Imports: AnnotationDbi, BiocFileCache, BiocManager, ComplexHeatmap, GSEABase, GO.db, KEGGREST, KEGGgraph, Rgraphviz, S4Vectors, SPIA, biocGraph, edgeR, geneplotter, graphite, hwriter, limma, methods, pathview, rappdirs, safe, topGO Suggests: ALL, BiocStyle, ReportingTools, airway, hgu95av2.db, knitr License: Artistic-2.0 MD5sum: 80c9e76ef70a62a51b17c6e623aa09b1 NeedsCompilation: no Title: Seamless navigation through combined results of set-based and network-based enrichment analysis Description: The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways. biocViews: ImmunoOncology, Microarray, RNASeq, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara Santarelli [ctb], Marcel Ramos [ctb], Levi Waldron [ctb], Ralf Zimmer [aut] Maintainer: Ludwig Geistlinger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnrichmentBrowser git_branch: RELEASE_3_10 git_last_commit: 31bdf3f git_last_commit_date: 2019-12-04 Date/Publication: 2019-12-04 source.ver: src/contrib/EnrichmentBrowser_2.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/EnrichmentBrowser_2.16.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/EnrichmentBrowser_2.16.1.tgz vignettes: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.pdf vignetteTitles: EnrichmentBrowser Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.R importsMe: GSEABenchmarkeR, PathwaySplice suggestsMe: ToPASeq dependencyCount: 107 Package: enrichplot Version: 1.6.1 Depends: R (>= 3.4.0) Imports: AnnotationDbi, cowplot, DOSE (>= 3.5.1), europepmc, ggplot2, ggplotify, ggraph, ggridges, GOSemSim, graphics, grDevices, grid, gridExtra, igraph, methods, purrr, RColorBrewer, reshape2, stats, utils Suggests: clusterProfiler, dplyr, ggupset, knitr, org.Hs.eg.db, prettydoc, tibble License: Artistic-2.0 Archs: i386, x64 MD5sum: 4c285b1764d7aaf56196e4310188050a NeedsCompilation: no Title: Visualization of Functional Enrichment Result Description: The 'enrichplot' package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analysis. All the visualization methods are developed based on 'ggplot2' graphics. biocViews: Annotation, GeneSetEnrichment, GO, KEGG, Pathways, Software, Visualization Author: Guangchuang Yu [aut, cre] () Maintainer: Guangchuang Yu URL: https://github.com/GuangchuangYu/enrichplot VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/enrichplot/issues git_url: https://git.bioconductor.org/packages/enrichplot git_branch: RELEASE_3_10 git_last_commit: 67291ef git_last_commit_date: 2019-12-13 Date/Publication: 2019-12-16 source.ver: src/contrib/enrichplot_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/enrichplot_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/enrichplot_1.6.1.tgz vignettes: vignettes/enrichplot/inst/doc/enrichplot.html vignetteTitles: enrichplot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: ChIPseeker, clusterProfiler, debrowser, MAGeCKFlute, meshes, ReactomePA suggestsMe: methylGSA dependencyCount: 122 Package: enrichTF Version: 1.2.3 Depends: pipeFrame Imports: BSgenome, rtracklayer, motifmatchr, TFBSTools, R.utils, methods, JASPAR2018, GenomeInfoDb, GenomicRanges, IRanges, BiocGenerics, S4Vectors, utils, parallel, stats, ggpubr, heatmap3, ggplot2, clusterProfiler, rmarkdown, grDevices, magrittr Suggests: knitr, testthat, webshot License: GPL-3 MD5sum: 00fb69482f2a5ad8717b959ada10151a NeedsCompilation: no Title: Transcription Factors Enrichment Analysis Description: As transcription factors (TFs) play a crucial role in regulating the transcription process through binding on the genome alone or in a combinatorial manner, TF enrichment analysis is an efficient and important procedure to locate the candidate functional TFs from a set of experimentally defined regulatory regions. While it is commonly accepted that structurally related TFs may have similar binding preference to sequences (i.e. motifs) and one TF may have multiple motifs, TF enrichment analysis is much more challenging than motif enrichment analysis. Here we present a R package for TF enrichment analysis which combine motif enrichment with the PECA model. biocViews: Software, GeneTarget, MotifAnnotation, GraphAndNetwork, Transcription Author: Zheng Wei, Zhana Duren, Shining Ma Maintainer: Zheng Wei URL: https://github.com/wzthu/enrichTF VignetteBuilder: knitr BugReports: https://github.com/wzthu/enrichTF/issues git_url: https://git.bioconductor.org/packages/enrichTF git_branch: RELEASE_3_10 git_last_commit: f78ac0a git_last_commit_date: 2019-11-18 Date/Publication: 2019-11-18 source.ver: src/contrib/enrichTF_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/enrichTF_1.2.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/enrichTF_1.2.3.tgz vignettes: vignettes/enrichTF/inst/doc/enrichTF.html vignetteTitles: An Introduction to enrichTF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/enrichTF/inst/doc/enrichTF.R dependencyCount: 179 Package: ensembldb Version: 2.10.2 Depends: BiocGenerics (>= 0.15.10), GenomicRanges (>= 1.31.18), GenomicFeatures (>= 1.29.10), AnnotationFilter (>= 1.5.2) Imports: methods, RSQLite (>= 1.1), DBI, Biobase, GenomeInfoDb, AnnotationDbi (>= 1.31.19), rtracklayer, S4Vectors (>= 0.23.10), Rsamtools, IRanges (>= 2.13.24), ProtGenerics, Biostrings (>= 2.47.9), curl Suggests: BiocStyle, knitr, EnsDb.Hsapiens.v86 (>= 0.99.8), testthat, BSgenome.Hsapiens.NCBI.GRCh38, ggbio (>= 1.24.0), Gviz (>= 1.20.0), magrittr, AnnotationHub Enhances: RMariaDB, shiny License: LGPL MD5sum: 8bef548947326fb8f1b64fa84ab877f3 NeedsCompilation: no Title: Utilities to create and use Ensembl-based annotation databases Description: The package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, ensembldb provides a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. EnsDb databases built with ensembldb contain also protein annotations and mappings between proteins and their encoding transcripts. Finally, ensembldb provides functions to map between genomic, transcript and protein coordinates. biocViews: Genetics, AnnotationData, Sequencing, Coverage Author: Johannes Rainer with contributions from Tim Triche, Sebastian Gibb, Laurent Gatto and Christian Weichenberger. Maintainer: Johannes Rainer URL: https://github.com/jorainer/ensembldb VignetteBuilder: knitr BugReports: https://github.com/jorainer/ensembldb/issues git_url: https://git.bioconductor.org/packages/ensembldb git_branch: RELEASE_3_10 git_last_commit: 8a14fa5 git_last_commit_date: 2019-11-20 Date/Publication: 2019-11-20 source.ver: src/contrib/ensembldb_2.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/ensembldb_2.10.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ensembldb_2.10.2.tgz vignettes: vignettes/ensembldb/inst/doc/coordinate-mapping-use-cases.html, vignettes/ensembldb/inst/doc/coordinate-mapping.html, vignettes/ensembldb/inst/doc/ensembldb.html, vignettes/ensembldb/inst/doc/MySQL-backend.html, vignettes/ensembldb/inst/doc/proteins.html vignetteTitles: Use cases for coordinate mapping with ensembldb, Mapping between genome,, transcript and protein coordinates, Generating an using Ensembl based annotation packages, Using a MariaDB/MySQL server backend, Querying protein features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensembldb/inst/doc/coordinate-mapping-use-cases.R, vignettes/ensembldb/inst/doc/coordinate-mapping.R, vignettes/ensembldb/inst/doc/ensembldb.R, vignettes/ensembldb/inst/doc/MySQL-backend.R, vignettes/ensembldb/inst/doc/proteins.R dependsOnMe: chimeraviz importsMe: biovizBase, BUSpaRse, ChIPpeakAnno, consensusDE, epivizrData, ggbio, ldblock, metagene, PathwaySplice, Pbase, TVTB, tximeta suggestsMe: alpine, CNVRanger, GenomicFeatures, TxRegInfra, wiggleplotr dependencyCount: 86 Package: ensemblVEP Version: 1.28.0 Depends: methods, BiocGenerics, GenomicRanges, VariantAnnotation Imports: S4Vectors (>= 0.9.25), Biostrings, SummarizedExperiment, GenomeInfoDb, stats Suggests: RUnit License: Artistic-2.0 MD5sum: 15c9b97567e872c2f1509517c2f665a1 NeedsCompilation: no Title: R Interface to Ensembl Variant Effect Predictor Description: Query the Ensembl Variant Effect Predictor via the perl API. biocViews: Annotation, VariantAnnotation, SNP Author: Valerie Obenchain and Lori Shepherd Maintainer: Bioconductor Package Maintainer SystemRequirements: Ensembl VEP (API version 98) and the Perl modules DBI and DBD::mysql must be installed. See the package README and Ensembl installation instructions: http://www.ensembl.org/info/docs/tools/vep/script/vep_download.html#installer git_url: https://git.bioconductor.org/packages/ensemblVEP git_branch: RELEASE_3_10 git_last_commit: 0cb3d0e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ensemblVEP_1.28.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ensemblVEP_1.28.0.tgz vignettes: vignettes/ensemblVEP/inst/doc/ensemblVEP.pdf, vignettes/ensemblVEP/inst/doc/PreV90EnsemblVEP.pdf vignetteTitles: ensemblVEP, PreV90EnsemblVEP hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensemblVEP/inst/doc/ensemblVEP.R, vignettes/ensemblVEP/inst/doc/PreV90EnsemblVEP.R importsMe: MMAPPR2, TVTB dependencyCount: 85 Package: ENVISIONQuery Version: 1.34.0 Depends: rJava, XML, utils License: GPL-2 MD5sum: a6e78abebf4c16f75cb48ad4d040db89 NeedsCompilation: no Title: Retrieval from the ENVISION bioinformatics data portal into R Description: Tools to retrieve data from ENVISION, the Database for Annotation, Visualization and Integrated Discovery portal biocViews: Annotation Author: Alex Lisovich, Roger Day Maintainer: Alex Lisovich , Roger Day git_url: https://git.bioconductor.org/packages/ENVISIONQuery git_branch: RELEASE_3_10 git_last_commit: cd60ab0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ENVISIONQuery_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ENVISIONQuery_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ENVISIONQuery_1.34.0.tgz vignettes: vignettes/ENVISIONQuery/inst/doc/ENVISIONQuery.pdf vignetteTitles: An R Package for retrieving data from EnVision into R objects. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENVISIONQuery/inst/doc/ENVISIONQuery.R importsMe: IdMappingRetrieval dependencyCount: 4 Package: EpiDISH Version: 2.2.2 Depends: R (>= 3.5) Imports: MASS, e1071, quadprog, parallel, stats, matrixStats, stringr, locfdr Suggests: roxygen2, GEOquery, BiocStyle, knitr, rmarkdown, Biobase, testthat License: GPL-2 MD5sum: ef9412aec9b2f5d537e089d3d8504313 NeedsCompilation: no Title: Epigenetic Dissection of Intra-Sample-Heterogeneity Description: EpiDISH is a R package to infer the proportions of a priori known cell-types present in a sample representing a mixture of such cell-types. Right now, the package can be used on DNAm data of whole blood, generic epithelial tissue and breast tissue. Besides, the package provides a function that allows the identification of differentially methylated cell-types and their directionality of change in Epigenome-Wide Association Studies. biocViews: DNAMethylation, MethylationArray, Epigenetics, DifferentialMethylation, ImmunoOncology Author: Andrew E. Teschendorff , Shijie C. Zheng Maintainer: Shijie Charles Zheng URL: https://github.com/sjczheng/EpiDISH VignetteBuilder: knitr BugReports: https://github.com/sjczheng/EpiDISH/issues git_url: https://git.bioconductor.org/packages/EpiDISH git_branch: RELEASE_3_10 git_last_commit: dab2454 git_last_commit_date: 2020-03-01 Date/Publication: 2020-03-02 source.ver: src/contrib/EpiDISH_2.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/EpiDISH_2.2.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/EpiDISH_2.2.2.tgz vignettes: vignettes/EpiDISH/inst/doc/EpiDISH.html vignetteTitles: Epigenetic Dissection of Intra-Sample-Heterogeneity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiDISH/inst/doc/EpiDISH.R dependsOnMe: TOAST dependencyCount: 18 Package: epigenomix Version: 1.26.0 Depends: R (>= 3.2.0), methods, Biobase, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment Imports: BiocGenerics, MCMCpack, Rsamtools, parallel, GenomeInfoDb, beadarray License: LGPL-3 MD5sum: 9698a3a3b00b60f64bb03324a2c85f23 NeedsCompilation: no Title: Epigenetic and gene transcription data normalization and integration with mixture models Description: A package for the integrative analysis of RNA-seq or microarray based gene transcription and histone modification data obtained by ChIP-seq. The package provides methods for data preprocessing and matching as well as methods for fitting bayesian mixture models in order to detect genes with differences in both data types. biocViews: ChIPSeq, GeneExpression, DifferentialExpression, Classification Author: Hans-Ulrich Klein, Martin Schaefer Maintainer: Hans-Ulrich Klein git_url: https://git.bioconductor.org/packages/epigenomix git_branch: RELEASE_3_10 git_last_commit: ef69184 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/epigenomix_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/epigenomix_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/epigenomix_1.26.0.tgz vignettes: vignettes/epigenomix/inst/doc/epigenomix.pdf vignetteTitles: epigenomix package vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epigenomix/inst/doc/epigenomix.R dependencyCount: 107 Package: epihet Version: 1.2.0 Depends: R(>= 3.6), GenomicRanges, IRanges, S4Vectors, ggplot2, foreach, Rtsne, igraph Imports: data.table, doParallel, EntropyExplorer, graphics, stats, grDevices, pheatmap, utils, qvalue, WGCNA, ReactomePA Suggests: knitr, clusterProfiler, ggfortify, org.Hs.eg.db, rmarkdown License: Artistic-2.0 MD5sum: d68c80466c769caf60b503bbdda35e85 NeedsCompilation: no Title: Determining Epigenetic Heterogeneity from Bisulfite Sequencing Data Description: epihet is an R-package that calculates the epigenetic heterogeneity between cancer cells and/or normal cells. The functions establish a pipeline that take in bisulfite sequencing data from multiple samples and use the data to track similarities and differences in epipolymorphism,proportion of discordantly methylated sequencing reads (PDR),and Shannon entropy values at epialleles that are shared between the samples.epihet can be used to perform analysis on the data by creating pheatmaps, box plots, PCA plots, and t-SNE plots. MA plots can also be created by calculating the differential heterogeneity of the samples. And we construct co-epihet network and perform network analysis. biocViews: DNAMethylation, Epigenetics, MethylSeq, Sequencing, Software Author: Xiaowen Chen [aut, cre], Haitham Ashoor [aut], Ryan Musich [aut], Mingsheng Zhang [aut], Jiahui Wang [aut], Sheng Li [aut] Maintainer: Xiaowen Chen URL: https://github.com/TheJacksonLaboratory/epihet VignetteBuilder: knitr BugReports: https://github.com/TheJacksonLaboratory/epihet/issues git_url: https://git.bioconductor.org/packages/epihet git_branch: RELEASE_3_10 git_last_commit: b51b4f4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/epihet_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/epihet_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/epihet_1.2.0.tgz vignettes: vignettes/epihet/inst/doc/epihet.pdf vignetteTitles: epihet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epihet/inst/doc/epihet.R dependencyCount: 169 Package: epiNEM Version: 1.10.0 Depends: R (>= 3.4) Imports: BoolNet, e1071, gtools, stats, igraph, nem, utils, lattice, latticeExtra, RColorBrewer, pcalg, minet, grDevices, graph Suggests: knitr, RUnit, BiocGenerics, STRINGdb, devtools, rmarkdown, GOSemSim, AnnotationHub, org.Sc.sgd.db License: GPL-3 MD5sum: 32ea3aa3a7af034ee1668bab6256a68f NeedsCompilation: no Title: epiNEM Description: epiNEM is an extension of the original Nested Effects Models (NEM). EpiNEM is able to take into account double knockouts and infer more complex network signalling pathways. biocViews: Pathways, SystemsBiology, NetworkInference, Network Author: Madeline Diekmann & Martin Pirkl Maintainer: Martin Pirkl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epiNEM git_branch: RELEASE_3_10 git_last_commit: 8c5c406 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/epiNEM_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/epiNEM_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/epiNEM_1.10.0.tgz vignettes: vignettes/epiNEM/inst/doc/epiNEM.pdf vignetteTitles: epiNEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epiNEM/inst/doc/epiNEM.R importsMe: mnem dependencyCount: 52 Package: epivizr Version: 2.16.0 Depends: R (>= 3.0), methods, Imports: epivizrServer (>= 1.1.1), epivizrData (>= 1.3.4), GenomicRanges, S4Vectors, IRanges Suggests: testthat, roxygen2, knitr, Biobase, SummarizedExperiment, antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle License: Artistic-2.0 MD5sum: 4e15543ee39fb8542e41cd892aba7352 NeedsCompilation: no Title: R Interface to epiviz web app Description: This package provides connections to the epiviz web app (http://epiviz.cbcb.umd.edu) for interactive visualization of genomic data. Objects in R/bioc interactive sessions can be displayed in genome browser tracks or plots to be explored by navigation through genomic regions. Fundamental Bioconductor data structures are supported (e.g., GenomicRanges and RangedSummarizedExperiment objects), while providing an easy mechanism to support other data structures (through package epivizrData). Visualizations (using d3.js) can be easily added to the web app as well. biocViews: Visualization, Infrastructure, GUI Author: Hector Corrada Bravo, Florin Chelaru, Llewellyn Smith, Naomi Goldstein, Jayaram Kancherla, Morgan Walter, Brian Gottfried Maintainer: Hector Corrada Bravo VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=099c4wUxozA git_url: https://git.bioconductor.org/packages/epivizr git_branch: RELEASE_3_10 git_last_commit: 25b07cb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/epivizr_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/epivizr_2.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/epivizr_2.16.0.tgz vignettes: vignettes/epivizr/inst/doc/IntroToEpivizr.html vignetteTitles: Introduction to epivizr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizr/inst/doc/IntroToEpivizr.R dependsOnMe: epivizrStandalone importsMe: metavizr dependencyCount: 97 Package: epivizrChart Version: 1.8.0 Depends: R (>= 3.4.0) Imports: epivizrData (>= 1.5.1), epivizrServer, htmltools, rjson, methods, BiocGenerics Suggests: testthat, roxygen2, knitr, Biobase, GenomicRanges, S4Vectors, IRanges, SummarizedExperiment, antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle, Homo.sapiens, shiny, minfi, Rsamtools, rtracklayer, RColorBrewer, magrittr, AnnotationHub License: Artistic-2.0 MD5sum: 16ccf6faebb3ca90711bd8b58ef8637a NeedsCompilation: no Title: R interface to epiviz web components Description: This package provides an API for interactive visualization of genomic data using epiviz web components. Objects in R/BioConductor can be used to generate interactive R markdown/notebook documents or can be visualized in the R Studio's default viewer. biocViews: Visualization, GUI Author: Brian Gottfried [aut], Jayaram Kancherla [aut], Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epivizrChart git_branch: RELEASE_3_10 git_last_commit: 2c8ddca git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/epivizrChart_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/epivizrChart_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/epivizrChart_1.8.0.tgz vignettes: vignettes/epivizrChart/inst/doc/IntegrationWithIGVjs.html, vignettes/epivizrChart/inst/doc/IntegrationWithShiny.html, vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.html, vignettes/epivizrChart/inst/doc/VisualizeSumExp.html vignetteTitles: Visualizing Files with epivizrChart, Visualizing genomic data in Shiny Apps using epivizrChart, Introduction to epivizrChart, Visualizing `RangeSummarizedExperiment` objects Shiny Apps using epivizrChart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizrChart/inst/doc/IntegrationWithIGVjs.R, vignettes/epivizrChart/inst/doc/IntegrationWithShiny.R, vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.R, vignettes/epivizrChart/inst/doc/VisualizeSumExp.R dependencyCount: 98 Package: epivizrData Version: 1.14.0 Depends: R (>= 3.4), methods, epivizrServer (>= 1.1.1), Biobase Imports: S4Vectors, GenomicRanges, SummarizedExperiment (>= 0.2.0), OrganismDbi, GenomicFeatures, GenomeInfoDb, IRanges, ensembldb Suggests: testthat, roxygen2, bumphunter, hgu133plus2.db, Mus.musculus, TxDb.Mmusculus.UCSC.mm10.knownGene, rjson, knitr, rmarkdown, BiocStyle, EnsDb.Mmusculus.v79, AnnotationHub, rtracklayer, utils, RMySQL, DBI License: MIT + file LICENSE MD5sum: dd47930fe44368e28be4c2f2cafb4796 NeedsCompilation: no Title: Data Management API for epiviz interactive visualization app Description: Serve data from Bioconductor Objects through a WebSocket connection. biocViews: Infrastructure, Visualization Author: Hector Corrada Bravo [aut, cre], Florin Chelaru [aut] Maintainer: Hector Corrada Bravo URL: http://epiviz.github.io VignetteBuilder: knitr BugReports: https://github.com/epiviz/epivizrData/issues git_url: https://git.bioconductor.org/packages/epivizrData git_branch: RELEASE_3_10 git_last_commit: e1ef5b6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/epivizrData_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/epivizrData_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/epivizrData_1.14.0.tgz vignettes: vignettes/epivizrData/inst/doc/epivizrData.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epivizrData/inst/doc/epivizrData.R importsMe: epivizr, epivizrChart, metavizr dependencyCount: 96 Package: epivizrServer Version: 1.14.0 Depends: R (>= 3.2.3), methods Imports: httpuv (>= 1.3.0), R6 (>= 2.0.0), rjson, mime (>= 0.2) Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 50bfe8f5cd3749d26446dee40493e2ee NeedsCompilation: no Title: WebSocket server infrastructure for epivizr apps and packages Description: This package provides objects to manage WebSocket connections to epiviz apps. Other epivizr package use this infrastructure. biocViews: Infrastructure, Visualization Author: Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo URL: https://epiviz.github.io VignetteBuilder: knitr BugReports: https://github.com/epiviz/epivizrServer git_url: https://git.bioconductor.org/packages/epivizrServer git_branch: RELEASE_3_10 git_last_commit: 60da989 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/epivizrServer_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/epivizrServer_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/epivizrServer_1.14.0.tgz vignettes: vignettes/epivizrServer/inst/doc/epivizrServer.html vignetteTitles: epivizrServer Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: epivizrData importsMe: epivizr, epivizrChart, epivizrStandalone, metavizr dependencyCount: 14 Package: epivizrStandalone Version: 1.14.0 Depends: R (>= 3.2.3), epivizr (>= 2.3.6), methods Imports: git2r, epivizrServer, GenomeInfoDb, BiocGenerics, GenomicFeatures, S4Vectors Suggests: testthat, knitr, rmarkdown, OrganismDbi (>= 1.13.9), Mus.musculus, Biobase, BiocStyle License: MIT + file LICENSE MD5sum: ef4ddf75911efa5558f315a866018b4b NeedsCompilation: no Title: Run Epiviz Interactive Genomic Data Visualization App within R Description: This package imports the epiviz visualization JavaScript app for genomic data interactive visualization. The 'epivizrServer' package is used to provide a web server running completely within R. This standalone version allows to browse arbitrary genomes through genome annotations provided by Bioconductor packages. biocViews: Visualization, Infrastructure, GUI Author: Hector Corrada Bravo, Jayaram Kancherla Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epivizrStandalone git_branch: RELEASE_3_10 git_last_commit: 6ca99c7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/epivizrStandalone_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/epivizrStandalone_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/epivizrStandalone_1.14.0.tgz vignettes: vignettes/epivizrStandalone/inst/doc/EpivizrStandalone.html vignetteTitles: Introduction to epivizrStandalone hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: metavizr dependencyCount: 99 Package: erccdashboard Version: 1.20.0 Depends: R (>= 3.2), ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0) Imports: edgeR, gplots, grid, gtools, limma, locfit, MASS, plyr, qvalue, reshape2, ROCR, scales, stringr License: GPL (>=2) MD5sum: c53b277a16b0dc25af9d980c117e04ce NeedsCompilation: no Title: Assess Differential Gene Expression Experiments with ERCC Controls Description: Technical performance metrics for differential gene expression experiments using External RNA Controls Consortium (ERCC) spike-in ratio mixtures. biocViews: ImmunoOncology, GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Genetics, Microarray, mRNAMicroarray, RNASeq, BatchEffect, MultipleComparison, QualityControl Author: Sarah Munro, Steve Lund Maintainer: Sarah Munro URL: https://github.com/munrosa/erccdashboard, http://tinyurl.com/erccsrm BugReports: https://github.com/munrosa/erccdashboard/issues git_url: https://git.bioconductor.org/packages/erccdashboard git_branch: RELEASE_3_10 git_last_commit: e2942b1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/erccdashboard_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/erccdashboard_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/erccdashboard_1.20.0.tgz vignettes: vignettes/erccdashboard/inst/doc/erccdashboard.pdf vignetteTitles: erccdashboard examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erccdashboard/inst/doc/erccdashboard.R dependencyCount: 70 Package: erma Version: 1.2.0 Depends: R (>= 3.1), methods, Homo.sapiens, GenomicFiles (>= 1.5.2) Imports: rtracklayer (>= 1.38.1), S4Vectors (>= 0.23.18), BiocGenerics, GenomicRanges, SummarizedExperiment, ggplot2, GenomeInfoDb, Biobase, shiny, BiocParallel, IRanges, AnnotationDbi Suggests: rmarkdown, BiocStyle, knitr, GO.db, png, DT, doParallel License: Artistic-2.0 MD5sum: 2df74b74766dc63aa2b7a6c1e67bcc08 NeedsCompilation: no Title: epigenomic road map adventures Description: Software and data to support epigenomic road map adventures. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/erma git_branch: RELEASE_3_10 git_last_commit: eadfd0d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/erma_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/erma_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/erma_1.2.0.tgz vignettes: vignettes/erma/inst/doc/erma.html vignetteTitles: ermaInteractive hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erma/inst/doc/erma.R importsMe: gQTLstats suggestsMe: gQTLBase dependencyCount: 129 Package: ERSSA Version: 1.4.0 Depends: R (>= 3.5.0) Imports: edgeR (>= 3.23.3), DESeq2 (>= 1.21.16), ggplot2 (>= 3.0.0), RColorBrewer (>= 1.1-2), plyr (>= 1.8.4), BiocParallel (>= 1.15.8), grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 | file LICENSE MD5sum: bce35410a7908e5a2643c05e8b0ff04a NeedsCompilation: no Title: Empirical RNA-seq Sample Size Analysis Description: The ERSSA package takes user supplied RNA-seq differential expression dataset and calculates the number of differentially expressed genes at varying biological replicate levels. This allows the user to determine, without relying on any a priori assumptions, whether sufficient differential detection has been acheived with their RNA-seq dataset. biocViews: ImmunoOncology, GeneExpression, Transcription, DifferentialExpression, RNASeq, MultipleComparison, QualityControl Author: Zixuan Shao [aut, cre] Maintainer: Zixuan Shao URL: https://github.com/zshao1/ERSSA VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ERSSA git_branch: RELEASE_3_10 git_last_commit: 24b1e52 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ERSSA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ERSSA_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ERSSA_1.4.0.tgz vignettes: vignettes/ERSSA/inst/doc/ERSSA.html vignetteTitles: ERSSA Package Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ERSSA/inst/doc/ERSSA.R dependencyCount: 124 Package: esATAC Version: 1.8.0 Depends: R (>= 3.5), Rsamtools, GenomicRanges, ShortRead Imports: Rcpp (>= 0.12.11), methods, knitr, Rbowtie2, rtracklayer, ggplot2, Biostrings, ChIPseeker, clusterProfiler, igraph, rJava, DiagrammeR, magrittr, digest, BSgenome, AnnotationDbi, GenomicFeatures, R.utils, GenomeInfoDb, BiocGenerics, S4Vectors, IRanges, rmarkdown, tools, VennDiagram, grid, JASPAR2016, TFBSTools, grDevices, graphics, stats, utils, parallel, corrplot, BiocManager, motifmatchr LinkingTo: Rcpp Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, DiagrammeRsvg, testthat, webshot License: GPL-3 | file LICENSE MD5sum: 2370e8ab74be9b75fccfa0324d268d9f NeedsCompilation: yes Title: An Easy-to-use Systematic pipeline for ATACseq data analysis Description: This package provides a framework and complete preset pipeline for quantification and analysis of ATAC-seq Reads. It covers raw sequencing reads preprocessing (FASTQ files), reads alignment (Rbowtie2), aligned reads file operations (SAM, BAM, and BED files), peak calling (F-seq), genome annotations (Motif, GO, SNP analysis) and quality control report. The package is managed by dataflow graph. It is easy for user to pass variables seamlessly between processes and understand the workflow. Users can process FASTQ files through end-to-end preset pipeline which produces a pretty HTML report for quality control and preliminary statistical results, or customize workflow starting from any intermediate stages with esATAC functions easily and flexibly. biocViews: ImmunoOncology, Sequencing, DNASeq, QualityControl, Alignment, Preprocessing, Coverage, ATACSeq, DNaseSeq Author: Zheng Wei, Wei Zhang Maintainer: Zheng Wei URL: https://github.com/wzthu/esATAC SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/wzthu/esATAC/issues git_url: https://git.bioconductor.org/packages/esATAC git_branch: RELEASE_3_10 git_last_commit: dca253f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/esATAC_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/esATAC_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/esATAC_1.8.0.tgz vignettes: vignettes/esATAC/inst/doc/esATAC-Introduction.html vignetteTitles: An Introduction to esATAC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/esATAC/inst/doc/esATAC-Introduction.R dependencyCount: 195 Package: esetVis Version: 1.12.0 Imports: mpm, hexbin, Rtsne, MLP, grid, Biobase, MASS, stats, utils, grDevices, methods Suggests: ggplot2, ggvis, rbokeh, ggrepel, knitr, rmarkdown, ALL, hgu95av2.db, AnnotationDbi, pander, SummarizedExperiment License: GPL-3 MD5sum: bf5a714390128a7e696bbdab1843afa5 NeedsCompilation: no Title: Visualizations of expressionSet Bioconductor object Description: Utility functions for visualization of expressionSet (or SummarizedExperiment) Bioconductor object, including spectral map, tsne and linear discriminant analysis. Static plot via the ggplot2 package or interactive via the ggvis or rbokeh packages are available. biocViews: Visualization, DataRepresentation, DimensionReduction, PrincipalComponent, Pathways Author: Laure Cougnaud Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/esetVis git_branch: RELEASE_3_10 git_last_commit: 400462d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/esetVis_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/esetVis_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/esetVis_1.12.0.tgz vignettes: vignettes/esetVis/inst/doc/esetVis-vignette.html vignetteTitles: esetVis package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/esetVis/inst/doc/esetVis-vignette.R dependencyCount: 47 Package: eudysbiome Version: 1.16.0 Depends: R (>= 3.1.0) Imports: plyr, Rsamtools, R.utils, Biostrings License: GPL-2 Archs: x64 MD5sum: 772067ecdd236787f0d643c7c8058cfb NeedsCompilation: no Title: Cartesian plot and contingency test on 16S Microbial data Description: eudysbiome a package that permits to annotate the differential genera as harmful/harmless based on their ability to contribute to host diseases (as indicated in literature) or unknown based on their ambiguous genus classification. Further, the package statistically measures the eubiotic (harmless genera increase or harmful genera decrease) or dysbiotic(harmless genera decrease or harmful genera increase) impact of a given treatment or environmental change on the (gut-intestinal, GI) microbiome in comparison to the microbiome of the reference condition. Author: Xiaoyuan Zhou, Christine Nardini Maintainer: Xiaoyuan Zhou git_url: https://git.bioconductor.org/packages/eudysbiome git_branch: RELEASE_3_10 git_last_commit: 0926ba6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/eudysbiome_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/eudysbiome_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/eudysbiome_1.16.0.tgz vignettes: vignettes/eudysbiome/inst/doc/eudysbiome.pdf vignetteTitles: eudysbiome User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eudysbiome/inst/doc/eudysbiome.R dependencyCount: 32 Package: evaluomeR Version: 1.2.41 Depends: R (>= 3.6), SummarizedExperiment, MultiAssayExperiment, cluster (>= 2.0.7-1), fpc (>= 2.2-3) Imports: corrplot (>= 0.84), grDevices, graphics, reshape2, ggplot2, ggdendro, plotrix, stats, matrixStats, Rdpack Suggests: BiocStyle, knitr, rmarkdown, kableExtra, magrittr License: GPL-3 MD5sum: 11ab8b08c19a88ee20a25fc01b08490b NeedsCompilation: no Title: Evaluation of Bioinformatics Metrics Description: Evaluating the reliability of your own metrics and the measurements done on your own datasets by analysing the stability and goodness of the classifications of such metrics. biocViews: Clustering, Classification, FeatureExtraction Author: José Antonio Bernabé-Díaz [aut, cre], Manuel Franco [aut], Juana-María Vivo [aut], Manuel Quesada-Martínez [aut], Astrid Duque-Ramos [aut], Jesualdo Tomás Fernández-Breis [aut] Maintainer: José Antonio Bernabé-Díaz URL: https://github.com/neobernad/evaluomeR VignetteBuilder: knitr BugReports: https://github.com/neobernad/evaluomeR/issues git_url: https://git.bioconductor.org/packages/evaluomeR git_branch: RELEASE_3_10 git_last_commit: fa43266 git_last_commit_date: 2020-01-09 Date/Publication: 2020-01-09 source.ver: src/contrib/evaluomeR_1.2.41.tar.gz win.binary.ver: bin/windows/contrib/3.6/evaluomeR_1.2.41.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/evaluomeR_1.2.41.tgz vignettes: vignettes/evaluomeR/inst/doc/manual.html vignetteTitles: Evaluation of Bioinformatics Metrics with evaluomeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/evaluomeR/inst/doc/manual.R dependencyCount: 105 Package: EventPointer Version: 2.4.0 Depends: R (>= 3.4), SGSeq, Matrix, SummarizedExperiment Imports: GenomicFeatures, stringr, GenomeInfoDb, igraph, MASS, nnls, limma, matrixStats, RBGL, prodlim, graph, methods, utils, stats, doParallel, foreach, affxparser, GenomicRanges, S4Vectors, IRanges, qvalue, cobs, rhdf5, BSgenome, BSgenome.Hsapiens.UCSC.hg38, Biostrings Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, dplyr, kableExtra License: Artistic-2.0 MD5sum: eeb10c72f62d4749371860dc0eb662fe NeedsCompilation: no Title: An effective identification of alternative splicing events using junction arrays and RNA-Seq data Description: EventPointer is an R package to identify alternative splicing events that involve either simple (case-control experiment) or complex experimental designs such as time course experiments and studies including paired-samples. The algorithm can be used to analyze data from either junction arrays (Affymetrix Arrays) or sequencing data (RNA-Seq). The software returns a data.frame with the detected alternative splicing events: gene name, type of event (cassette, alternative 3',...,etc), genomic position, statistical significance and increment of the percent spliced in (Delta PSI) for all the events. The algorithm can generate a series of files to visualize the detected alternative splicing events in IGV. This eases the interpretation of results and the design of primers for standard PCR validation. biocViews: AlternativeSplicing, DifferentialSplicing, mRNAMicroarray, RNASeq, Transcription, Sequencing, TimeCourse, ImmunoOncology Author: Juan Pablo Romero, Juan A. Ferrer-Bonsoms, Pablo Sacristan, Ander Muniategui, Fernando Carazo, Ander Aramburu, Angel Rubio Maintainer: Juan Pablo Romero VignetteBuilder: knitr BugReports: https://github.com/jpromeror/EventPointer/issues git_url: https://git.bioconductor.org/packages/EventPointer git_branch: RELEASE_3_10 git_last_commit: cb83cf7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/EventPointer_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/EventPointer_2.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/EventPointer_2.4.0.tgz vignettes: vignettes/EventPointer/inst/doc/EventPointer.html vignetteTitles: EventPointer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EventPointer/inst/doc/EventPointer.R dependencyCount: 139 Package: ExCluster Version: 1.4.0 Depends: Rsubread, GenomicRanges, rtracklayer, matrixStats, IRanges Imports: stats, methods, grDevices, graphics, utils License: GPL-3 MD5sum: b3d266c7246155131483d3a6467ed8ff NeedsCompilation: no Title: ExCluster robustly detects differentially expressed exons between two conditions of RNA-seq data, requiring at least two independent biological replicates per condition Description: ExCluster flattens Ensembl and GENCODE GTF files into GFF files, which are used to count reads per non-overlapping exon bin from BAM files. This read counting is done using the function featureCounts from the package Rsubread. Library sizes are normalized across all biological replicates, and ExCluster then compares two different conditions to detect signifcantly differentially spliced genes. This process requires at least two independent biological repliates per condition, and ExCluster accepts only exactly two conditions at a time. ExCluster ultimately produces false discovery rates (FDRs) per gene, which are used to detect significance. Exon log2 fold change (log2FC) means and variances may be plotted for each significantly differentially spliced gene, which helps scientists develop hypothesis and target differential splicing events for RT-qPCR validation in the wet lab. biocViews: ImmunoOncology, DifferentialSplicing, RNASeq, Software Author: R. Matthew Tanner, William L. Stanford, and Theodore J. Perkins Maintainer: R. Matthew Tanner git_url: https://git.bioconductor.org/packages/ExCluster git_branch: RELEASE_3_10 git_last_commit: 40d4eac git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ExCluster_1.4.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ExCluster_1.4.0.tgz vignettes: vignettes/ExCluster/inst/doc/ExCluster.pdf vignetteTitles: ExCluster Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExCluster/inst/doc/ExCluster.R dependencyCount: 39 Package: ExiMiR Version: 2.28.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), affy (>= 1.26.1), limma Imports: affyio(>= 1.13.3), Biobase(>= 2.5.5), preprocessCore(>= 1.10.0) Suggests: mirna10cdf License: GPL-2 MD5sum: 964b5dcab57d2910f98c7182ea3e506f NeedsCompilation: no Title: R functions for the normalization of Exiqon miRNA array data Description: This package contains functions for reading raw data in ImaGene TXT format obtained from Exiqon miRCURY LNA arrays, annotating them with appropriate GAL files, and normalizing them using a spike-in probe-based method. Other platforms and data formats are also supported. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, GeneExpression, Transcription Author: Sylvain Gubian , Alain Sewer , PMP SA Maintainer: Sylvain Gubian git_url: https://git.bioconductor.org/packages/ExiMiR git_branch: RELEASE_3_10 git_last_commit: ac7b3fd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ExiMiR_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ExiMiR_2.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ExiMiR_2.28.0.tgz vignettes: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.pdf vignetteTitles: Description of ExiMiR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.R dependencyCount: 14 Package: exomeCopy Version: 1.32.0 Depends: IRanges (>= 2.5.27), GenomicRanges (>= 1.23.16), Rsamtools Imports: stats4, methods, GenomeInfoDb Suggests: Biostrings License: GPL (>= 2) MD5sum: d4ad5b86d3b95753e968154f4bb454a8 NeedsCompilation: yes Title: Copy number variant detection from exome sequencing read depth Description: Detection of copy number variants (CNV) from exome sequencing samples, including unpaired samples. The package implements a hidden Markov model which uses positional covariates, such as background read depth and GC-content, to simultaneously normalize and segment the samples into regions of constant copy count. biocViews: CopyNumberVariation, Sequencing, Genetics Author: Michael Love Maintainer: Michael Love git_url: https://git.bioconductor.org/packages/exomeCopy git_branch: RELEASE_3_10 git_last_commit: c9a8844 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/exomeCopy_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/exomeCopy_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/exomeCopy_1.32.0.tgz vignettes: vignettes/exomeCopy/inst/doc/exomeCopy.pdf vignetteTitles: Copy number variant detection in exome sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/exomeCopy/inst/doc/exomeCopy.R importsMe: cn.mops, CNVPanelizer, contiBAIT, Rariant dependencyCount: 26 Package: ExperimentHub Version: 1.12.0 Depends: methods, BiocGenerics (>= 0.15.10), AnnotationHub (>= 2.17.9), BiocFileCache (>= 1.5.1) Imports: utils, S4Vectors, BiocManager, curl, rappdirs Suggests: knitr, BiocStyle Enhances: ExperimentHubData License: Artistic-2.0 MD5sum: e5bc5c34a78445e4662cdfa6409e5259 NeedsCompilation: no Title: Client to access ExperimentHub resources Description: This package provides a client for the Bioconductor ExperimentHub web resource. ExperimentHub provides a central location where curated data from experiments, publications or training courses can be accessed. Each resource has associated metadata, tags and date of modification. The client creates and manages a local cache of files retrieved enabling quick and reproducible access. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Package Maintainer Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExperimentHub git_branch: RELEASE_3_10 git_last_commit: f0406a8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ExperimentHub_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ExperimentHub_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ExperimentHub_1.12.0.tgz vignettes: vignettes/ExperimentHub/inst/doc/CreateAnExperimentHubPackage.html, vignettes/ExperimentHub/inst/doc/ExperimentHub.html vignetteTitles: Creating An ExperimentHub Package, ExperimentHub: Access the ExperimentHub Web Service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExperimentHub/inst/doc/CreateAnExperimentHubPackage.R, vignettes/ExperimentHub/inst/doc/ExperimentHub.R dependsOnMe: adductomicsR, SeqSQC importsMe: DMRcate, ExperimentHubData, GSEABenchmarkeR, PhyloProfile, restfulSE, signatureSearch, SingleR suggestsMe: ANF, AnnotationHub, celaref, CellMapper, HDF5Array, muscat dependencyCount: 64 Package: ExperimentHubData Version: 1.12.0 Depends: utils, BiocGenerics (>= 0.15.10), S4Vectors, AnnotationHubData (>= 1.13.11) Imports: methods, ExperimentHub, BiocManager, DBI, BiocCheck, httr, curl, biocViews, graph Suggests: GenomeInfoDb, RUnit, knitr, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: a35a2b605ebd88336829dbb037df21ba NeedsCompilation: no Title: Add resources to ExperimentHub Description: Functions to add metadata to ExperimentHub db and resource files to AWS S3 buckets. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExperimentHubData git_branch: RELEASE_3_10 git_last_commit: 00a9a46 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ExperimentHubData_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ExperimentHubData_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ExperimentHubData_1.12.0.tgz vignettes: vignettes/ExperimentHubData/inst/doc/CreateAnExperimentHubPackage.html, vignettes/ExperimentHubData/inst/doc/ExperimentHubData.html vignetteTitles: Creating An ExperimentHub Package, Introduction to ExperimentHubData hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExperimentHubData/inst/doc/CreateAnExperimentHubPackage.R dependencyCount: 116 Package: explorase Version: 1.50.0 Depends: R (>= 2.6.2) Imports: limma, rggobi, RGtk2 Suggests: cairoDevice License: GPL-2 MD5sum: 1c7ee4685038c6f99537acd05ebe95fd NeedsCompilation: no Title: GUI for exploratory data analysis of systems biology data Description: explore and analyze *omics data with R and GGobi biocViews: Visualization,Microarray,GUI Author: Michael Lawrence, Eun-kyung Lee, Dianne Cook, Jihong Kim, Hogeun An, and Dongshin Kim Maintainer: Michael Lawrence URL: http://www.metnetdb.org/MetNet_exploRase.htm git_url: https://git.bioconductor.org/packages/explorase git_branch: RELEASE_3_10 git_last_commit: 5e074c5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/explorase_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/explorase_1.50.0.zip vignettes: vignettes/explorase/inst/doc/explorase.pdf vignetteTitles: Introduction to exploRase hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: ExpressionAtlas Version: 1.14.0 Depends: R (>= 3.2.0), methods, Biobase, SummarizedExperiment, limma, S4Vectors, xml2 Imports: utils, XML, httr Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) MD5sum: 20155b9b96eca9826824caf67efa1a4b NeedsCompilation: no Title: Download datasets from EMBL-EBI Expression Atlas Description: This package is for searching for datasets in EMBL-EBI Expression Atlas, and downloading them into R for further analysis. Each Expression Atlas dataset is represented as a SimpleList object with one element per platform. Sequencing data is contained in a SummarizedExperiment object, while microarray data is contained in an ExpressionSet or MAList object. biocViews: ExpressionData, ExperimentData, SequencingData, MicroarrayData, ArrayExpress Author: Maria Keays Maintainer: Suhaib Mohammed VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExpressionAtlas git_branch: RELEASE_3_10 git_last_commit: 2983787 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ExpressionAtlas_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ExpressionAtlas_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ExpressionAtlas_1.14.0.tgz vignettes: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.html vignetteTitles: ExpressionAtlas hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.R suggestsMe: Pi dependencyCount: 43 Package: ExpressionView Version: 1.38.0 Depends: caTools, bitops, methods, isa2, eisa, GO.db, KEGG.db, AnnotationDbi Imports: methods, isa2, eisa, GO.db, KEGG.db, AnnotationDbi Suggests: ALL, hgu95av2.db, biclust, affy License: GPL (>= 2) MD5sum: 956dd71461458562572ca31e286952ce NeedsCompilation: yes Title: Visualize biclusters identified in gene expression data Description: ExpressionView visualizes possibly overlapping biclusters in a gene expression matrix. It can use the result of the ISA method (eisa package) or the algorithms in the biclust package or others. The viewer itself was developed using Adobe Flex and runs in a flash-enabled web browser. biocViews: Classification, Visualization, Microarray, GeneExpression, GO, KEGG Author: Andreas Luscher Maintainer: Gabor Csardi git_url: https://git.bioconductor.org/packages/ExpressionView git_branch: RELEASE_3_10 git_last_commit: f10cb14 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ExpressionView_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ExpressionView_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ExpressionView_1.38.0.tgz vignettes: vignettes/ExpressionView/inst/doc/ExpressionView.format.pdf, vignettes/ExpressionView/inst/doc/ExpressionView.ordering.pdf, vignettes/ExpressionView/inst/doc/ExpressionView.tutorial.pdf vignetteTitles: ExpressionView file format, How the ordering algorithm works, ExpressionView hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExpressionView/inst/doc/ExpressionView.ordering.R, vignettes/ExpressionView/inst/doc/ExpressionView.tutorial.R dependencyCount: 47 Package: fabia Version: 2.32.0 Depends: R (>= 3.6.0), Biobase Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) MD5sum: a35f65d352a1436bd866897b88278006 NeedsCompilation: yes Title: FABIA: Factor Analysis for Bicluster Acquisition Description: Biclustering by "Factor Analysis for Bicluster Acquisition" (FABIA). FABIA is a model-based technique for biclustering, that is clustering rows and columns simultaneously. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. It captures realistic non-Gaussian data distributions with heavy tails as observed in gene expression measurements. FABIA utilizes well understood model selection techniques like the EM algorithm and variational approaches and is embedded into a Bayesian framework. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters. The code is written in C. biocViews: StatisticalMethod, Microarray, DifferentialExpression, MultipleComparison, Clustering, Visualization Author: Sepp Hochreiter Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/fabia/fabia.html git_url: https://git.bioconductor.org/packages/fabia git_branch: RELEASE_3_10 git_last_commit: ef6240a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/fabia_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/fabia_2.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/fabia_2.32.0.tgz vignettes: vignettes/fabia/inst/doc/fabia.pdf vignetteTitles: FABIA: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fabia/inst/doc/fabia.R dependsOnMe: hapFabia importsMe: miRSM dependencyCount: 8 Package: factDesign Version: 1.62.0 Depends: Biobase (>= 2.5.5) Imports: stats Suggests: affy, genefilter, multtest License: LGPL Archs: i386, x64 MD5sum: 0708b95624a447f17913ae50dcd00b7a NeedsCompilation: no Title: Factorial designed microarray experiment analysis Description: This package provides a set of tools for analyzing data from a factorial designed microarray experiment, or any microarray experiment for which a linear model is appropriate. The functions can be used to evaluate tests of contrast of biological interest and perform single outlier detection. biocViews: Microarray, DifferentialExpression Author: Denise Scholtens Maintainer: Denise Scholtens git_url: https://git.bioconductor.org/packages/factDesign git_branch: RELEASE_3_10 git_last_commit: 4b70198 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/factDesign_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/factDesign_1.62.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/factDesign_1.62.0.tgz vignettes: vignettes/factDesign/inst/doc/factDesign.pdf vignetteTitles: factDesign hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/factDesign/inst/doc/factDesign.R dependencyCount: 7 Package: FamAgg Version: 1.14.0 Depends: methods, kinship2, igraph Imports: gap (>= 1.1-17), Matrix, BiocGenerics, utils, survey Suggests: BiocStyle, knitr, RUnit, rmarkdown License: MIT + file LICENSE Archs: i386, x64 MD5sum: ef09d2337da95c7a7e6716a28ca061b6 NeedsCompilation: no Title: Pedigree Analysis and Familial Aggregation Description: Framework providing basic pedigree analysis and plotting utilities as well as a variety of methods to evaluate familial aggregation of traits in large pedigrees. biocViews: Genetics Author: J. Rainer, D. Taliun, C.X. Weichenberger Maintainer: Johannes Rainer URL: https://github.com/EuracBiomedicalResearch/FamAgg VignetteBuilder: knitr BugReports: https://github.com/EuracBiomedicalResearch/FamAgg/issues git_url: https://git.bioconductor.org/packages/FamAgg git_branch: RELEASE_3_10 git_last_commit: 5854391 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/FamAgg_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/FamAgg_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/FamAgg_1.14.0.tgz vignettes: vignettes/FamAgg/inst/doc/FamAgg.html vignetteTitles: Pedigree Analysis and Familial Aggregation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FamAgg/inst/doc/FamAgg.R dependencyCount: 24 Package: farms Version: 1.38.0 Depends: R (>= 2.8), affy (>= 1.20.0), MASS, methods Imports: affy, MASS, Biobase (>= 1.13.41), methods, graphics Suggests: affydata, Biobase, utils License: LGPL (>= 2.1) MD5sum: 5eb0f4de07ae695a26064e5e0a26f93a NeedsCompilation: no Title: FARMS - Factor Analysis for Robust Microarray Summarization Description: The package provides the summarization algorithm called Factor Analysis for Robust Microarray Summarization (FARMS) and a novel unsupervised feature selection criterion called "I/NI-calls" biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Djork-Arne Clevert Maintainer: Djork-Arne Clevert URL: http://www.bioinf.jku.at/software/farms/farms.html git_url: https://git.bioconductor.org/packages/farms git_branch: RELEASE_3_10 git_last_commit: 3ba4a2a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/farms_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/farms_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/farms_1.38.0.tgz vignettes: vignettes/farms/inst/doc/farms.pdf vignetteTitles: Using farms hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/farms/inst/doc/farms.R dependencyCount: 14 Package: fastLiquidAssociation Version: 1.22.0 Depends: methods, LiquidAssociation, parallel, doParallel, stats, Hmisc, utils Imports: WGCNA, impute, preprocessCore Suggests: GOstats, yeastCC, org.Sc.sgd.db License: GPL-2 MD5sum: 1ef4fce9512e66430b8be149c73b6737 NeedsCompilation: no Title: functions for genome-wide application of Liquid Association Description: This package extends the function of the LiquidAssociation package for genome-wide application. It integrates a screening method into the LA analysis to reduce the number of triplets to be examined for a high LA value and provides code for use in subsequent significance analyses. biocViews: Software, GeneExpression, Genetics, Pathways, CellBiology Author: Tina Gunderson Maintainer: Tina Gunderson git_url: https://git.bioconductor.org/packages/fastLiquidAssociation git_branch: RELEASE_3_10 git_last_commit: e167cf0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/fastLiquidAssociation_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/fastLiquidAssociation_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/fastLiquidAssociation_1.22.0.tgz vignettes: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.pdf vignetteTitles: fastLiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.R dependencyCount: 120 Package: FastqCleaner Version: 1.4.0 Imports: methods, shiny, stats, IRanges, Biostrings, ShortRead, DT, methods, S4Vectors, graphics, htmltools, shinyBS, Rcpp (>= 0.12.12) LinkingTo: Rcpp Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL (>= 2) MD5sum: 0573011b4e290be0d8e073784c236d5f NeedsCompilation: yes Title: A Shiny Application for Quality Control, Filtering and Trimming of FASTQ Files Description: An interactive web application for quality control, filtering and trimming of FASTQ files. This user-friendly tool combines a pipeline for data processing based on Biostrings and ShortRead infrastructure, with a cutting-edge visual environment. Single-Read and Paired-End files can be locally processed. Diagnostic interactive plots (CG content, per-base sequence quality, etc.) are provided for both the input and output files. biocViews: QualityControl,Sequencing,Software,SangerSeq,SequenceMatching Author: Leandro Roser [aut, cre], Fernán Agüero [aut], Daniel Sánchez [aut] Maintainer: Leandro Roser VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FastqCleaner git_branch: RELEASE_3_10 git_last_commit: cf0fcc4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/FastqCleaner_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/FastqCleaner_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/FastqCleaner_1.4.0.tgz vignettes: vignettes/FastqCleaner/inst/doc/Overview.pdf vignetteTitles: An Introduction to FastqCleaner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FastqCleaner/inst/doc/Overview.R dependencyCount: 64 Package: fastseg Version: 1.32.0 Depends: R (>= 2.13), GenomicRanges, Biobase Imports: methods, graphics, stats, BiocGenerics, S4Vectors, IRanges Suggests: DNAcopy, oligo License: LGPL (>= 2.0) MD5sum: 2d23d5826723c11912f5ba95f5d3e88a NeedsCompilation: yes Title: fastseg - a fast segmentation algorithm Description: fastseg implements a very fast and efficient segmentation algorithm. It has similar functionality as DNACopy (Olshen and Venkatraman 2004), but is considerably faster and more flexible. fastseg can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data. The segmentation criterion of fastseg is based on a statistical test in a Bayesian framework, namely the cyber t-test (Baldi 2001). The speed-up arises from the facts, that sampling is not necessary in for fastseg and that a dynamic programming approach is used for calculation of the segments' first and higher order moments. biocViews: Classification, CopyNumberVariation Author: Guenter Klambauer Maintainer: Guenter Klambauer URL: http://www.bioinf.jku.at/software/fastseg/fastseg.html git_url: https://git.bioconductor.org/packages/fastseg git_branch: RELEASE_3_10 git_last_commit: 89961ba git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/fastseg_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/fastseg_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/fastseg_1.32.0.tgz vignettes: vignettes/fastseg/inst/doc/fastseg.pdf vignetteTitles: fastseg: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastseg/inst/doc/fastseg.R importsMe: methylKit dependencyCount: 17 Package: FCBF Version: 1.4.0 Depends: R (>= 3.6) Imports: ggplot2, gridExtra, pbapply, parallel, SummarizedExperiment, stats, mclust Suggests: caret, mlbench, SingleCellExperiment, knitr, rmarkdown, testthat, BiocManager License: MIT + file LICENSE Archs: i386, x64 MD5sum: 650011de8a6ca5cd5288c5658e9ea084 NeedsCompilation: no Title: Fast Correlation Based Filter for Feature Selection Description: This package provides a simple R implementation for the Fast Correlation Based Filter described in Yu, L. and Liu, H.; Feature Selection for High-Dimensional Data: A Fast Correlation Based Filter Solution,Proc. 20th Intl. Conf. Mach. Learn. (ICML-2003), Washington DC, 2003 The current package is an intent to make easier for bioinformaticians to use FCBF for feature selection, especially regarding transcriptomic data.This implies discretizing expression (function discretize_exprs) before calculating the features that explain the class, but are not predictable by other features. The functions are implemented based on the algorithm of Yu and Liu, 2003 and Rajarshi Guha's implementation from 13/05/2005 available (as of 26/08/2018) at http://www.rguha.net/code/R/fcbf.R . biocViews: GeneTarget, FeatureExtraction, Classification, GeneExpression, SingleCell, ImmunoOncology Author: Tiago Lubiana [aut, cre], Helder Nakaya [aut, ths] Maintainer: Tiago Lubiana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FCBF git_branch: RELEASE_3_10 git_last_commit: ad6355e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/FCBF_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/FCBF_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/FCBF_1.4.0.tgz vignettes: vignettes/FCBF/inst/doc/FCBF-Vignette.html vignetteTitles: FCBF : Fast Correlation Based Filter for Feature Selection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FCBF/inst/doc/FCBF-Vignette.R importsMe: fcoex dependencyCount: 80 Package: fCCAC Version: 1.12.0 Depends: R (>= 3.3.0), S4Vectors, IRanges, GenomicRanges, grid Imports: fda, RColorBrewer, genomation, ggplot2, ComplexHeatmap, grDevices, stats, utils Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: f3246920bdf6211aa10d0795117f68e7 NeedsCompilation: no Title: functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets Description: An application of functional canonical correlation analysis to assess covariance of nucleic acid sequencing datasets such as chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). The package can be used as well with other types of sequencing data such as neMeRIP-seq (PMID: 29489750) or with single cell RNA-seq or epigenome data provided in bigWig format. biocViews: Transcription, Genetics, Sequencing, Coverage Author: Pedro Madrigal Maintainer: Pedro Madrigal git_url: https://git.bioconductor.org/packages/fCCAC git_branch: RELEASE_3_10 git_last_commit: c535272 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/fCCAC_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/fCCAC_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/fCCAC_1.12.0.tgz vignettes: vignettes/fCCAC/inst/doc/fCCAC.pdf vignetteTitles: fCCAC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fCCAC/inst/doc/fCCAC.R dependencyCount: 109 Package: fCI Version: 1.16.0 Depends: R (>= 3.1),FNN, psych, gtools, zoo, rgl, grid, VennDiagram Suggests: knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: 19a8b8c20e2f69e4859f75d22fffc0f6 NeedsCompilation: no Title: f-divergence Cutoff Index for Differential Expression Analysis in Transcriptomics and Proteomics Description: (f-divergence Cutoff Index), is to find DEGs in the transcriptomic & proteomic data, and identify DEGs by computing the difference between the distribution of fold-changes for the control-control and remaining (non-differential) case-control gene expression ratio data. fCI provides several advantages compared to existing methods. biocViews: Proteomics Author: Shaojun Tang Maintainer: Shaojun Tang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fCI git_branch: RELEASE_3_10 git_last_commit: 9787078 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/fCI_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/fCI_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/fCI_1.16.0.tgz vignettes: vignettes/fCI/inst/doc/fCI.html vignetteTitles: fCI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fCI/inst/doc/fCI.R dependencyCount: 58 Package: fcoex Version: 1.0.0 Depends: R (>= 3.6) Imports: FCBF, parallel, progress, dplyr, ggplot2, ggrepel, igraph, grid, intergraph, stringr, clusterProfiler, data.table, grDevices, methods, network, scales, sna, utils, stats, SingleCellExperiment, pathwayPCA Suggests: testthat (>= 2.1.0), devtools, BiocManager, TENxPBMCData, scater, gridExtra, scran, Seurat, knitr License: GPL-3 MD5sum: 55b5d6886e08af170f55a29d4133e857 NeedsCompilation: no Title: FCBF-based Co-Expression Networks for Single Cells Description: The fcoex package implements an easy-to use interface to co-expression analysisbased on the FCBF (Fast Correlation-Based Filter) algorithm. it was implemented especifically to deal with single-cell data. The modules found can be used to redefine cell populations, unrevel novel gene associations and predict gene function by guilt-by-association. The package structure is based on the CEMiTool package. biocViews: GeneExpression, Transcriptomics, GraphAndNetwork, mRNAMicroarray, RNASeq, Network, NetworkEnrichment, Pathways, ImmunoOncology, SingleCell Author: Tiago Lubiana [aut, cre], Helder Nakaya [aut, ths] Maintainer: Tiago Lubiana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fcoex git_branch: RELEASE_3_10 git_last_commit: 8798edb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/fcoex_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/fcoex_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/fcoex_1.0.0.tgz vignettes: vignettes/fcoex/inst/doc/fcoex.html vignetteTitles: fcoex: co-expression for single-cell data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fcoex/inst/doc/fcoex.R dependencyCount: 146 Package: fcScan Version: 1.0.0 Imports: stats, plyr, VariantAnnotation, SummarizedExperiment, rtracklayer, GenomicRanges, methods, IRanges Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 0a74a946b4cc62494df8b4f9da75964b NeedsCompilation: no Title: fcScan for detecting clusters of coordinates with user defined options Description: This package is used to detect combination of genomic coordinates falling within a user defined window size along with user defined overlap between identified neighboring clusters. It can be used for genomic data where the clusters are built on a specific chromosome or specific strand. Clustering can be performed with a "greedy" option allowing thus the presence of additional sites within the allowed window size. biocViews: GenomeAnnotation, Clustering Author: Abdallah El-Kurdi Ghiwa khalil Georges Khazen Pierre Khoueiry Maintainer: Pierre Khoueiry Abdallah El-Kurdi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fcScan git_branch: RELEASE_3_10 git_last_commit: 0fbc28e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/fcScan_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/fcScan_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/fcScan_1.0.0.tgz vignettes: vignettes/fcScan/inst/doc/fcScan_vignette.html vignetteTitles: fcScan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fcScan/inst/doc/fcScan_vignette.R dependencyCount: 86 Package: fdrame Version: 1.58.0 Imports: tcltk, graphics, grDevices, stats, utils License: GPL (>= 2) MD5sum: 4c221370aa60c082bdaf89c63a9460af NeedsCompilation: yes Title: FDR adjustments of Microarray Experiments (FDR-AME) Description: This package contains two main functions. The first is fdr.ma which takes normalized expression data array, experimental design and computes adjusted p-values It returns the fdr adjusted p-values and plots, according to the methods described in (Reiner, Yekutieli and Benjamini 2002). The second, is fdr.gui() which creates a simple graphic user interface to access fdr.ma biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Yoav Benjamini, Effi Kenigsberg, Anat Reiner, Daniel Yekutieli Maintainer: Effi Kenigsberg git_url: https://git.bioconductor.org/packages/fdrame git_branch: RELEASE_3_10 git_last_commit: 4df2ae0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/fdrame_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/fdrame_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/fdrame_1.58.0.tgz vignettes: vignettes/fdrame/inst/doc/fdrame.pdf vignetteTitles: Annotation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 5 Package: FELLA Version: 1.6.0 Depends: R (>= 3.5.0) Imports: methods, igraph, Matrix, KEGGREST, plyr, stats, graphics, utils Suggests: shiny, DT, magrittr, visNetwork, knitr, BiocStyle, rmarkdown, testthat, biomaRt, org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi, GOSemSim License: GPL-3 MD5sum: 31ef6f127fa0ebac9a38dde9e086b7b0 NeedsCompilation: no Title: Interpretation and enrichment for metabolomics data Description: Enrichment of metabolomics data using KEGG entries. Given a set of affected compounds, FELLA suggests affected reactions, enzymes, modules and pathways using label propagation in a knowledge model network. The resulting subnetwork can be visualised and exported. biocViews: Software, Metabolomics, GraphAndNetwork, KEGG, GO, Pathways, Network, NetworkEnrichment Author: Sergio Picart-Armada [aut, cre], Francesc Fernandez-Albert [aut], Alexandre Perera-Lluna [aut] Maintainer: Sergio Picart-Armada VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FELLA git_branch: RELEASE_3_10 git_last_commit: 2e6d2ce git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/FELLA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/FELLA_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/FELLA_1.6.0.tgz vignettes: vignettes/FELLA/inst/doc/FELLA.pdf, vignettes/FELLA/inst/doc/musmusculus.pdf, vignettes/FELLA/inst/doc/zebrafish.pdf, vignettes/FELLA/inst/doc/quickstart.html vignetteTitles: FELLA, Example: a fatty liver study on Mus musculus, Example: oxybenzone exposition in gilt-head bream, Quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FELLA/inst/doc/FELLA.R, vignettes/FELLA/inst/doc/musmusculus.R, vignettes/FELLA/inst/doc/quickstart.R, vignettes/FELLA/inst/doc/zebrafish.R dependencyCount: 32 Package: FEM Version: 3.14.0 Depends: AnnotationDbi,Matrix,marray,corrplot,igraph,impute,limma,org.Hs.eg.db,graph,BiocGenerics Imports: graph License: GPL (>=2) Archs: i386, x64 MD5sum: 9440cb9d78d4dc872b5410814848d77f NeedsCompilation: no Title: Identification of Functional Epigenetic Modules Description: The FEM package performs a systems-level integrative analysis of DNA methylation and gene expression data. It seeks modules of functionally related genes which exhibit differential promoter DNA methylation and differential expression, where an inverse association between promoter DNA methylation and gene expression is assumed. For full details, see Jiao et al Bioinformatics 2014. biocViews: SystemsBiology,NetworkEnrichment,DifferentialMethylation,DifferentialExpression Author: Andrew E. Teschendorff and Zhen Yang Maintainer: Zhen Yang git_url: https://git.bioconductor.org/packages/FEM git_branch: RELEASE_3_10 git_last_commit: 7b080b1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/FEM_3.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/FEM_3.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/FEM_3.14.0.tgz vignettes: vignettes/FEM/inst/doc/IntroDoFEM.pdf vignetteTitles: The FEM package performs a systems-level integrative analysis of DNA methylationa and gene expression. It seeks modules of functionally related genes which exhibit differential promoter DNA methylation and differential expression,, where an inverse association between promoter DNA methylation and gene expression is assumed. For full details,, see Jiao et al Bioinformatics 2014. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FEM/inst/doc/IntroDoFEM.R dependsOnMe: ChAMP dependencyCount: 38 Package: ffpe Version: 1.30.0 Depends: R (>= 2.10.0), TTR, methods Imports: Biobase, BiocGenerics, affy, lumi, methylumi, sfsmisc Suggests: genefilter, ffpeExampleData License: GPL (>2) MD5sum: 4097ba9058e8facadea85cab3a6a9b79 NeedsCompilation: no Title: Quality assessment and control for FFPE microarray expression data Description: Identify low-quality data using metrics developed for expression data derived from Formalin-Fixed, Paraffin-Embedded (FFPE) data. Also a function for making Concordance at the Top plots (CAT-plots). biocViews: Microarray, GeneExpression, QualityControl Author: Levi Waldron Maintainer: Levi Waldron git_url: https://git.bioconductor.org/packages/ffpe git_branch: RELEASE_3_10 git_last_commit: d607ace git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ffpe_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ffpe_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ffpe_1.30.0.tgz vignettes: vignettes/ffpe/inst/doc/ffpe.pdf vignetteTitles: ffpe package user guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ffpe/inst/doc/ffpe.R dependencyCount: 160 Package: FGNet Version: 3.20.0 Depends: R (>= 2.15) Imports: igraph (>= 0.6), hwriter, R.utils, XML, plotrix, reshape2, RColorBrewer, png Suggests: RCurl, RDAVIDWebService, gage, topGO, GO.db, KEGG.db, reactome.db, RUnit, BiocGenerics, org.Sc.sgd.db, knitr, rmarkdown, AnnotationDbi, RGtk2, BiocManager License: GPL (>= 2) MD5sum: a7fc6164ec52758327226271c61a29da NeedsCompilation: no Title: Functional Gene Networks derived from biological enrichment analyses Description: Build and visualize functional gene and term networks from clustering of enrichment analyses in multiple annotation spaces. The package includes a graphical user interface (GUI) and functions to perform the functional enrichment analysis through DAVID, GeneTerm Linker, gage (GSEA) and topGO. biocViews: Annotation, GO, Pathways, GeneSetEnrichment, Network, Visualization, FunctionalGenomics, NetworkEnrichment, Clustering Author: Sara Aibar, Celia Fontanillo, Conrad Droste and Javier De Las Rivas. Maintainer: Sara Aibar URL: http://www.cicancer.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FGNet git_branch: RELEASE_3_10 git_last_commit: 96dbc29 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/FGNet_3.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/FGNet_3.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/FGNet_3.20.0.tgz vignettes: vignettes/FGNet/inst/doc/FGNet.html vignetteTitles: FGNet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FGNet/inst/doc/FGNet.R importsMe: IntramiRExploreR dependencyCount: 26 Package: fgsea Version: 1.12.0 Depends: R (>= 3.3), Rcpp Imports: data.table, BiocParallel, stats, ggplot2 (>= 2.2.0), gridExtra, grid, fastmatch, Matrix, utils LinkingTo: Rcpp, BH Suggests: testthat, knitr, rmarkdown, reactome.db, AnnotationDbi, parallel, org.Mm.eg.db, limma, GEOquery License: MIT + file LICENCE MD5sum: c0e376202483856e91e1a32ea90f65d5 NeedsCompilation: yes Title: Fast Gene Set Enrichment Analysis Description: The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, Pathways Author: Gennady Korotkevich [aut], Vladimir Sukhov [aut], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://github.com/ctlab/fgsea/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/ctlab/fgsea/issues git_url: https://git.bioconductor.org/packages/fgsea git_branch: RELEASE_3_10 git_last_commit: 22c00b6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/fgsea_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/fgsea_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/fgsea_1.12.0.tgz vignettes: vignettes/fgsea/inst/doc/fgsea-tutorial.html, vignettes/fgsea/inst/doc/fgseaMultilevel-tutorial.html vignetteTitles: Using fgsea package, Using fgseaMultilevel function hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fgsea/inst/doc/fgsea-tutorial.R, vignettes/fgsea/inst/doc/fgseaMultilevel-tutorial.R dependsOnMe: gsean, PPInfer importsMe: ASpediaFI, CEMiTool, cTRAP, DOSE, lipidr, mCSEA, phantasus, piano, signatureSearch suggestsMe: mdp, Pi dependencyCount: 65 Package: FindMyFriends Version: 1.16.0 Imports: methods, BiocGenerics, Biobase, tools, dplyr, IRanges, Biostrings, S4Vectors, kebabs, igraph, Matrix, digest, filehash, Rcpp, ggplot2, gtable, grid, reshape2, ggdendro, BiocParallel, utils, stats LinkingTo: Rcpp Suggests: BiocStyle, testthat, knitr, rmarkdown, reutils License: GPL (>=2) MD5sum: bd8af010ee9868237a744a7edb7415c3 NeedsCompilation: yes Title: Microbial Comparative Genomics in R Description: A framework for doing microbial comparative genomics in R. The main purpose of the package is assisting in the creation of pangenome matrices where genes from related organisms are grouped by similarity, as well as the analysis of these data. FindMyFriends provides many novel approaches to doing pangenome analysis and supports a gene grouping algorithm that scales linearly, thus making the creation of huge pangenomes feasible. biocViews: ComparativeGenomics, Clustering, DataRepresentation, GenomicVariation, SequenceMatching, GraphAndNetwork Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen URL: https://github.com/thomasp85/FindMyFriends VignetteBuilder: knitr BugReports: https://github.com/thomasp85/FindMyFriends/issues git_url: https://git.bioconductor.org/packages/FindMyFriends git_branch: RELEASE_3_10 git_last_commit: 42b8e02 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/FindMyFriends_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/FindMyFriends_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/FindMyFriends_1.16.0.tgz vignettes: vignettes/FindMyFriends/inst/doc/FindMyFriends_intro.html vignetteTitles: Creating pangenomes using FindMyFriends hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FindMyFriends/inst/doc/FindMyFriends_intro.R importsMe: PanVizGenerator dependencyCount: 87 Package: FISHalyseR Version: 1.20.0 Depends: EBImage,abind Suggests: knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: ef257f240f02e634deb5f8b95fce500b NeedsCompilation: no Title: FISHalyseR a package for automated FISH quantification Description: FISHalyseR provides functionality to process and analyse digital cell culture images, in particular to quantify FISH probes within nuclei. Furthermore, it extract the spatial location of each nucleus as well as each probe enabling spatial co-localisation analysis. biocViews: CellBiology Author: Karesh Arunakirinathan , Andreas Heindl Maintainer: Karesh Arunakirinathan , Andreas Heindl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FISHalyseR git_branch: RELEASE_3_10 git_last_commit: 87325e7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/FISHalyseR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/FISHalyseR_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/FISHalyseR_1.20.0.tgz vignettes: vignettes/FISHalyseR/inst/doc/FISHalyseR.pdf vignetteTitles: FISHAlyseR Automated fluorescence in situ hybridisation quantification in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FISHalyseR/inst/doc/FISHalyseR.R dependencyCount: 25 Package: fishpond Version: 1.2.0 Imports: graphics, stats, utils, methods, abind, gtools, qvalue, S4Vectors, SummarizedExperiment, matrixStats, svMisc, Rcpp LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, macrophage, tximeta, org.Hs.eg.db, samr, DESeq2, apeglm License: GPL-2 Archs: i386, x64 MD5sum: 485aa7fcff2c7fe3ee1f92865eded930 NeedsCompilation: yes Title: Fishpond: differential transcript and gene expression with inferential replicates Description: Fishpond contains methods for differential transcript and gene expression analysis of RNA-seq data using inferential replicates for uncertainty of abundance quantification, as generated by Gibbs sampling or bootstrap sampling. Also the package contains utilities for working with Salmon and Alevin quantification files. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, Normalization, Regression, MultipleComparison, BatchEffect, Visualization, DifferentialExpression, DifferentialSplicing, AlternativeSplicing, SingleCell Author: Anqi Zhu, Avi Srivastava, Joseph Ibrahim, Rob Patro, Michael Love Maintainer: Michael Love URL: https://github.com/mikelove/fishpond SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fishpond git_branch: RELEASE_3_10 git_last_commit: fd4c1b0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/fishpond_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/fishpond_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/fishpond_1.2.0.tgz vignettes: vignettes/fishpond/inst/doc/swish.html vignetteTitles: DTE and DGE with inferential replicates hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fishpond/inst/doc/swish.R suggestsMe: tximport dependencyCount: 85 Package: FitHiC Version: 1.12.0 Imports: data.table, fdrtool, grDevices, graphics, Rcpp, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: GPL (>= 2) MD5sum: 69a9aac24c868590489ee12e05d88004 NeedsCompilation: yes Title: Confidence estimation for intra-chromosomal contact maps Description: Fit-Hi-C is a tool for assigning statistical confidence estimates to intra-chromosomal contact maps produced by genome-wide genome architecture assays such as Hi-C. biocViews: DNA3DStructure, Software Author: Ferhat Ay [aut] (Python original, https://noble.gs.washington.edu/proj/fit-hi-c/), Timothy L. Bailey [aut], William S. Noble [aut], Ruyu Tan [aut, cre, trl] (R port) Maintainer: Ruyu Tan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FitHiC git_branch: RELEASE_3_10 git_last_commit: dd1609d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/FitHiC_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/FitHiC_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/FitHiC_1.12.0.tgz vignettes: vignettes/FitHiC/inst/doc/fithic.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FitHiC/inst/doc/fithic.R dependencyCount: 8 Package: flagme Version: 1.42.0 Depends: gcspikelite, xcms, CAMERA Imports: gplots, graphics, MASS, methods, SparseM, stats, utils License: LGPL (>= 2) Archs: i386, x64 MD5sum: 22cd7ddfc09cfb4a585c29f457c66e74 NeedsCompilation: yes Title: Analysis of Metabolomics GC/MS Data Description: Fragment-level analysis of gas chromatography - mass spectrometry metabolomics data biocViews: ImmunoOncology, DifferentialExpression, MassSpectrometry Author: Mark Robinson , Riccardo Romoli Maintainer: Mark Robinson , Riccardo Romoli git_url: https://git.bioconductor.org/packages/flagme git_branch: RELEASE_3_10 git_last_commit: 946e020 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flagme_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flagme_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flagme_1.42.0.tgz vignettes: vignettes/flagme/inst/doc/flagme.pdf vignetteTitles: Using flagme -- Fragment-level analysis of GC-MS-based metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flagme/inst/doc/flagme.R dependencyCount: 136 Package: flowAI Version: 1.16.0 Depends: R (>= 3.4) Imports: ggplot2, flowCore, plyr, changepoint, knitr, reshape2, RColorBrewer, scales, methods, graphics, stats, utils, rmarkdown Suggests: testthat, shiny License: GPL (>= 2) Archs: i386, x64 MD5sum: 92fc2ee98a86bfa53a1acedd53523bd3 NeedsCompilation: no Title: Automatic and interactive quality control for flow cytometry data Description: The package is able to perform an automatic or interactive quality control on FCS data acquired using flow cytometry instruments. By evaluating three different properties: 1) flow rate, 2) signal acquisition, 3) dynamic range, the quality control enables the detection and removal of anomalies. biocViews: FlowCytometry, QualityControl, BiomedicalInformatics, ImmunoOncology Author: Gianni Monaco, Hao Chen Maintainer: Gianni Monaco VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowAI git_branch: RELEASE_3_10 git_last_commit: ca782e5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowAI_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowAI_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowAI_1.16.0.tgz vignettes: vignettes/flowAI/inst/doc/flowAI.html vignetteTitles: Automatic and GUI methods to do quality control on Flow cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowAI/inst/doc/flowAI.R dependencyCount: 79 Package: flowBeads Version: 1.24.0 Depends: R (>= 2.15.0), methods, Biobase, rrcov, flowCore Imports: flowCore, rrcov, knitr, xtable Suggests: flowViz License: Artistic-2.0 Archs: i386, x64 MD5sum: 02c2433e13eb37cee9e163e897515cf8 NeedsCompilation: no Title: flowBeads: Analysis of flow bead data Description: This package extends flowCore to provide functionality specific to bead data. One of the goals of this package is to automate analysis of bead data for the purpose of normalisation. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays Author: Nikolas Pontikos Maintainer: Nikolas Pontikos git_url: https://git.bioconductor.org/packages/flowBeads git_branch: RELEASE_3_10 git_last_commit: 96aa177 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowBeads_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowBeads_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowBeads_1.24.0.tgz vignettes: vignettes/flowBeads/inst/doc/HowTo-flowBeads.pdf vignetteTitles: Analysis of Flow Cytometry Bead Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowBeads/inst/doc/HowTo-flowBeads.R dependencyCount: 34 Package: flowBin Version: 1.22.0 Depends: methods, flowCore, flowFP, R (>= 2.10) Imports: class, limma, snow, BiocGenerics Suggests: parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: 8957fe729a84295a9a685d0bee65474e NeedsCompilation: no Title: Combining multitube flow cytometry data by binning Description: Software to combine flow cytometry data that has been multiplexed into multiple tubes with common markers between them, by establishing common bins across tubes in terms of the common markers, then determining expression within each tube for each bin in terms of the tube-specific markers. biocViews: ImmunoOncology, CellBasedAssays, FlowCytometry Author: Kieran O'Neill Maintainer: Kieran O'Neill git_url: https://git.bioconductor.org/packages/flowBin git_branch: RELEASE_3_10 git_last_commit: 7cf4785 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowBin_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowBin_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowBin_1.22.0.tgz vignettes: vignettes/flowBin/inst/doc/flowBin.pdf vignetteTitles: flowBin hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowBin/inst/doc/flowBin.R dependencyCount: 29 Package: flowcatchR Version: 1.20.0 Depends: R (>= 2.10), methods, EBImage Imports: colorRamps, abind, BiocParallel, graphics, stats, utils, plotly, shiny Suggests: BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 5d0df82e9ffda0a9f528e5c17cd44321 NeedsCompilation: no Title: Tools to analyze in vivo microscopy imaging data focused on tracking flowing blood cells Description: flowcatchR is a set of tools to analyze in vivo microscopy imaging data, focused on tracking flowing blood cells. It guides the steps from segmentation to calculation of features, filtering out particles not of interest, providing also a set of utilities to help checking the quality of the performed operations (e.g. how good the segmentation was). It allows investigating the issue of tracking flowing cells such as in blood vessels, to categorize the particles in flowing, rolling and adherent. This classification is applied in the study of phenomena such as hemostasis and study of thrombosis development. Moreover, flowcatchR presents an integrated workflow solution, based on the integration with a Shiny App and Jupyter notebooks, which is delivered alongside the package, and can enable fully reproducible bioimage analysis in the R environment. biocViews: Software, Visualization, CellBiology, Classification, Infrastructure, GUI Author: Federico Marini [aut, cre] () Maintainer: Federico Marini URL: https://github.com/federicomarini/flowcatchR, https://federicomarini.github.io/flowcatchR/ SystemRequirements: ImageMagick VignetteBuilder: knitr BugReports: https://github.com/federicomarini/flowcatchR/issues git_url: https://git.bioconductor.org/packages/flowcatchR git_branch: RELEASE_3_10 git_last_commit: 6771dc2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowcatchR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowcatchR_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowcatchR_1.20.0.tgz vignettes: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.html vignetteTitles: flowcatchR: tracking and analyzing cells in time lapse microscopy images hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.R dependencyCount: 102 Package: flowCHIC Version: 1.20.0 Depends: R (>= 3.1.0) Imports: methods, flowCore, EBImage, vegan, hexbin, ggplot2, grid License: GPL-2 MD5sum: 6835fe73716075bc1119d6167f925019 NeedsCompilation: no Title: Analyze flow cytometric data using histogram information Description: A package to analyze flow cytometric data of complex microbial communities based on histogram images biocViews: ImmunoOncology, CellBasedAssays, Clustering, FlowCytometry, Software, Visualization Author: Joachim Schumann , Christin Koch , Ingo Fetzer , Susann Müller Maintainer: Author: Joachim Schumann URL: http://www.ufz.de/index.php?en=16773 git_url: https://git.bioconductor.org/packages/flowCHIC git_branch: RELEASE_3_10 git_last_commit: 972ae5e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowCHIC_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowCHIC_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowCHIC_1.20.0.tgz vignettes: vignettes/flowCHIC/inst/doc/flowCHICmanual.pdf vignetteTitles: Analyze flow cytometric data using histogram information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCHIC/inst/doc/flowCHICmanual.R dependencyCount: 79 Package: flowCL Version: 1.24.0 Depends: R (>= 3.4), Rgraphviz, SPARQL Imports: methods, grDevices, utils, graph Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 21eb32c74b3fb7fb84c94fa86cd09818 NeedsCompilation: no Title: Semantic labelling of flow cytometric cell populations Description: Semantic labelling of flow cytometric cell populations. biocViews: FlowCytometry, ImmunoOncology Author: Justin Meskas, Radina Droumeva Maintainer: Justin Meskas git_url: https://git.bioconductor.org/packages/flowCL git_branch: RELEASE_3_10 git_last_commit: 459e964 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowCL_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowCL_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowCL_1.24.0.tgz vignettes: vignettes/flowCL/inst/doc/flowCL.pdf vignetteTitles: flowCL package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCL/inst/doc/flowCL.R dependencyCount: 15 Package: flowClean Version: 1.24.0 Depends: R (>= 2.15.0), flowCore Imports: bit, changepoint, sfsmisc Suggests: flowViz, grid, gridExtra License: Artistic-2.0 MD5sum: 790544c121fcad6b64e79fed69b3fd75 NeedsCompilation: no Title: flowClean Description: A quality control tool for flow cytometry data based on compositional data analysis. biocViews: FlowCytometry, QualityControl, ImmunoOncology Author: Kipper Fletez-Brant Maintainer: Kipper Fletez-Brant git_url: https://git.bioconductor.org/packages/flowClean git_branch: RELEASE_3_10 git_last_commit: 73495cf git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowClean_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowClean_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowClean_1.24.0.tgz vignettes: vignettes/flowClean/inst/doc/flowClean.pdf vignetteTitles: flowClean hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClean/inst/doc/flowClean.R dependencyCount: 20 Package: flowClust Version: 3.24.0 Depends: R(>= 2.5.0) Imports: BiocGenerics, methods, Biobase, graph, ellipse, flowViz, flowCore, clue, corpcor, mnormt, parallel Suggests: testthat, flowWorkspace, flowWorkspaceData, knitr, rmarkdown, openCyto License: Artistic-2.0 MD5sum: aaa5f2665f3f1b85563db692c0e076fd NeedsCompilation: yes Title: Clustering for Flow Cytometry Description: Robust model-based clustering using a t-mixture model with Box-Cox transformation. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. biocViews: ImmunoOncology, Clustering, Visualization, FlowCytometry Author: Raphael Gottardo , Kenneth Lo , Greg Finak Maintainer: Greg Finak , Mike Jiang SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowClust git_branch: RELEASE_3_10 git_last_commit: 954d34d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowClust_3.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowClust_3.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowClust_3.24.0.tgz vignettes: vignettes/flowClust/inst/doc/flowClust.html vignetteTitles: Robust Model-based Clustering of Flow Cytometry Data\\ The flowClust package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClust/inst/doc/flowClust.R importsMe: flowTrans, flowType suggestsMe: BiocGenerics dependencyCount: 31 Package: flowCore Version: 1.52.1 Depends: R (>= 3.5.0) Imports: Biobase, BiocGenerics (>= 0.29.2), grDevices, graphics, methods, stats, utils, stats4, Rcpp, matrixStats LinkingTo: Rcpp, BH(>= 1.65.0.1), cytolib(>= 1.7.2) Suggests: Rgraphviz, flowViz, flowStats (>= 3.43.4), testthat, flowWorkspace, flowWorkspaceData, openCyto, knitr, ggcyto, gridExtra License: Artistic-2.0 MD5sum: d378193a9b17288832526fc3d8fffc1b NeedsCompilation: yes Title: flowCore: Basic structures for flow cytometry data Description: Provides S4 data structures and basic functions to deal with flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays Author: B Ellis [aut], Perry Haaland [aut], Florian Hahne [aut], Nathan Le Meur [aut], Nishant Gopalakrishnan [aut], Josef Spidlen [aut], Mike Jiang [aut, cre], Greg Finak [aut], Samuel Granjeaud [ctb] Maintainer: Mike Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowCore git_branch: RELEASE_3_10 git_last_commit: 9394373 git_last_commit_date: 2019-12-03 Date/Publication: 2019-12-04 source.ver: src/contrib/flowCore_1.52.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowCore_1.52.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowCore_1.52.1.tgz vignettes: vignettes/flowCore/inst/doc/HowTo-flowCore.pdf, vignettes/flowCore/inst/doc/fcs3.html, vignettes/flowCore/inst/doc/hyperlog.notice.html vignetteTitles: Basic Functions for Flow Cytometry Data, fcs3.html, hyperlog.notice.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCore/inst/doc/HowTo-flowCore.R dependsOnMe: flowBeads, flowBin, flowClean, flowFP, flowMatch, flowTime, flowTrans, flowViz, flowVS, ggcyto, immunoClust, ncdfFlow importsMe: CATALYST, cydar, cytofast, CytoML, ddPCRclust, diffcyt, flowAI, flowBeads, flowCHIC, flowClust, flowDensity, flowFit, flowMeans, flowPloidy, FlowSOM, flowSpecs, flowSpy, flowStats, flowTrans, flowType, flowUtils, flowViz, flowWorkspace, GateFinder, ImmuneSpaceR, MetaCyto, oneSENSE, Sconify suggestsMe: COMPASS, FlowRepositoryR, RchyOptimyx dependencyCount: 13 Package: flowCyBar Version: 1.22.0 Depends: R (>= 3.0.0) Imports: gplots, vegan, methods License: GPL-2 MD5sum: aba5a851ee2465347d087d0b7095465a NeedsCompilation: no Title: Analyze flow cytometric data using gate information Description: A package to analyze flow cytometric data using gate information to follow population/community dynamics biocViews: ImmunoOncology, CellBasedAssays, Clustering, FlowCytometry, Software, Visualization Author: Joachim Schumann , Christin Koch , Susanne Günther , Ingo Fetzer , Susann Müller Maintainer: Joachim Schumann URL: http://www.ufz.de/index.php?de=16773 git_url: https://git.bioconductor.org/packages/flowCyBar git_branch: RELEASE_3_10 git_last_commit: 3fb9b91 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowCyBar_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowCyBar_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowCyBar_1.22.0.tgz vignettes: vignettes/flowCyBar/inst/doc/flowCyBar-manual.pdf vignetteTitles: Analyze flow cytometric data using gate information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCyBar/inst/doc/flowCyBar-manual.R dependencyCount: 21 Package: flowDensity Version: 1.20.0 Imports: flowCore, graphics, flowViz (>= 1.46.1), car, sp, rgeos, gplots, RFOC, flowWorkspace (>= 3.33.1), methods, stats, grDevices Suggests: knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: 46f77650ff8dc0d9321185e8a2687125 NeedsCompilation: no Title: Sequential Flow Cytometry Data Gating Description: This package provides tools for automated sequential gating analogous to the manual gating strategy based on the density of the data. biocViews: Bioinformatics, FlowCytometry, CellBiology, Clustering, Cancer, FlowCytData, DataRepresentation, StemCell, DensityGating Author: Mehrnoush Malek,M. Jafar Taghiyar Maintainer: Mehrnoush Malek SystemRequirements: xml2, GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowDensity git_branch: RELEASE_3_10 git_last_commit: 4adae8e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowDensity_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowDensity_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowDensity_1.20.0.tgz vignettes: vignettes/flowDensity/inst/doc/flowDensity.html vignetteTitles: Introduction to automated gating hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowDensity/inst/doc/flowDensity.R importsMe: ddPCRclust dependencyCount: 119 Package: flowFit Version: 1.24.0 Depends: R (>= 2.12.2) Imports: flowCore, flowViz, graphics, kza, methods, minpack.lm, gplots Suggests: flowFitExampleData License: Artistic-2.0 Archs: i386, x64 MD5sum: b43a8ffdeeafa3fb3dcd5bb740fa328d NeedsCompilation: no Title: Estimate proliferation in cell-tracking dye studies Description: This package estimate the proliferation of a cell population in cell-tracking dye studies. The package uses an R implementation of the Levenberg-Marquardt algorithm (minpack.lm) to fit a set of peaks (corresponding to different generations of cells) over the proliferation-tracking dye distribution in a FACS experiment. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays Author: Davide Rambaldi Maintainer: Davide Rambaldi BugReports: Davide Rambaldi git_url: https://git.bioconductor.org/packages/flowFit git_branch: RELEASE_3_10 git_last_commit: 41c2b0f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowFit_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowFit_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowFit_1.24.0.tgz vignettes: vignettes/flowFit/inst/doc/HowTo-flowFit.pdf vignetteTitles: Fitting Functions for Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowFit/inst/doc/HowTo-flowFit.R dependencyCount: 32 Package: flowFP Version: 1.44.0 Depends: R (>= 2.10), flowCore, flowViz Imports: Biobase, BiocGenerics (>= 0.1.6), graphics, grDevices, methods, stats, stats4 Suggests: RUnit License: Artistic-2.0 MD5sum: 21b90fa847764262cce3660dab47234f NeedsCompilation: yes Title: Fingerprinting for Flow Cytometry Description: Fingerprint generation of flow cytometry data, used to facilitate the application of machine learning and datamining tools for flow cytometry. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Clustering, Visualization Author: Herb Holyst , Wade Rogers Maintainer: Herb Holyst git_url: https://git.bioconductor.org/packages/flowFP git_branch: RELEASE_3_10 git_last_commit: 7e05c05 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowFP_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowFP_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowFP_1.44.0.tgz vignettes: vignettes/flowFP/inst/doc/flowFP_HowTo.pdf vignetteTitles: Fingerprinting for Flow Cytometry hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowFP/inst/doc/flowFP_HowTo.R dependsOnMe: flowBin importsMe: GateFinder dependencyCount: 25 Package: flowMap Version: 1.24.0 Depends: R (>= 3.0.1), ade4(>= 1.5-2), doParallel(>= 1.0.3), abind(>= 1.4.0), reshape2(>= 1.2.2), scales(>= 0.2.3), Matrix(>= 1.1-4), methods (>= 2.14) Suggests: BiocStyle, knitr License: GPL (>=2) MD5sum: ff53e5fa17e09d561886db022d092617 NeedsCompilation: no Title: Mapping cell populations in flow cytometry data for cross-sample comparisons using the Friedman-Rafsky Test Description: flowMap quantifies the similarity of cell populations across multiple flow cytometry samples using a nonparametric multivariate statistical test. The method is able to map cell populations of different size, shape, and proportion across multiple flow cytometry samples. The algorithm can be incorporate in any flow cytometry work flow that requires accurat quantification of similarity between cell populations. biocViews: ImmunoOncology, MultipleComparison, FlowCytometry Author: Chiaowen Joyce Hsiao, Yu Qian, and Richard H. Scheuermann Maintainer: Chiaowen Joyce Hsiao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowMap git_branch: RELEASE_3_10 git_last_commit: fc715af git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowMap_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowMap_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowMap_1.24.0.tgz vignettes: vignettes/flowMap/inst/doc/flowMap.pdf vignetteTitles: Mapping cell populations in flow cytometry data flowMap-FR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMap/inst/doc/flowMap.R dependencyCount: 36 Package: flowMatch Version: 1.22.0 Depends: R (>= 3.0.0), Rcpp (>= 0.11.0), methods, flowCore Imports: Biobase LinkingTo: Rcpp Suggests: healthyFlowData License: Artistic-2.0 MD5sum: 2d636b43105e06cc9a16bea86168804a NeedsCompilation: yes Title: Matching and meta-clustering in flow cytometry Description: Matching cell populations and building meta-clusters and templates from a collection of FC samples. biocViews: ImmunoOncology, Clustering, FlowCytometry Author: Ariful Azad Maintainer: Ariful Azad git_url: https://git.bioconductor.org/packages/flowMatch git_branch: RELEASE_3_10 git_last_commit: 4a22be7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowMatch_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowMatch_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowMatch_1.22.0.tgz vignettes: vignettes/flowMatch/inst/doc/flowMatch.pdf vignetteTitles: flowMatch: Cell population matching and meta-clustering in Flow Cytometry hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMatch/inst/doc/flowMatch.R dependencyCount: 14 Package: flowMeans Version: 1.46.0 Depends: R (>= 2.10.0) Imports: Biobase, graphics, grDevices, methods, rrcov, stats, feature, flowCore License: Artistic-2.0 Archs: i386, x64 MD5sum: d50d3386cb9515000f9b63d1e6cfbb78 NeedsCompilation: no Title: Non-parametric Flow Cytometry Data Gating Description: Identifies cell populations in Flow Cytometry data using non-parametric clustering and segmented-regression-based change point detection. Note: R 2.11.0 or newer is required. biocViews: ImmunoOncology, FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour Maintainer: Nima Aghaeepour git_url: https://git.bioconductor.org/packages/flowMeans git_branch: RELEASE_3_10 git_last_commit: e0c47ad git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowMeans_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowMeans_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowMeans_1.46.0.tgz vignettes: vignettes/flowMeans/inst/doc/flowMeans.pdf vignetteTitles: flowMeans: Non-parametric Flow Cytometry Data Gating hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMeans/inst/doc/flowMeans.R importsMe: flowType dependencyCount: 71 Package: flowMerge Version: 2.34.0 Depends: graph,feature,flowClust,Rgraphviz,foreach,snow Imports: rrcov,flowCore, graphics, methods, stats, utils Suggests: knitr, rmarkdown Enhances: doMC, multicore License: Artistic-2.0 MD5sum: f2aa0a09497f6d6c3607566940fe068b NeedsCompilation: no Title: Cluster Merging for Flow Cytometry Data Description: Merging of mixture components for model-based automated gating of flow cytometry data using the flowClust framework. Note: users should have a working copy of flowClust 2.0 installed. biocViews: ImmunoOncology, Clustering, FlowCytometry Author: Greg Finak , Raphael Gottardo Maintainer: Greg Finak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowMerge git_branch: RELEASE_3_10 git_last_commit: 01ab2c5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowMerge_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowMerge_2.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowMerge_2.34.0.tgz vignettes: vignettes/flowMerge/inst/doc/flowmerge.html vignetteTitles: Merging Mixture Components for Cell Population Identification in Flow Cytometry Data The flowMerge Package. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMerge/inst/doc/flowmerge.R importsMe: flowType dependencyCount: 90 Package: flowPeaks Version: 1.32.0 Depends: R (>= 2.12.0) Enhances: flowCore License: Artistic-1.0 Archs: i386, x64 MD5sum: 32496af3aaf0ed862b24128f1f359138 NeedsCompilation: yes Title: An R package for flow data clustering Description: A fast and automatic clustering to classify the cells into subpopulations based on finding the peaks from the overall density function generated by K-means. biocViews: ImmunoOncology, FlowCytometry, Clustering, Gating Author: Yongchao Ge Maintainer: Yongchao Ge SystemRequirements: gsl git_url: https://git.bioconductor.org/packages/flowPeaks git_branch: RELEASE_3_10 git_last_commit: f6beb93 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowPeaks_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowPeaks_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowPeaks_1.32.0.tgz vignettes: vignettes/flowPeaks/inst/doc/flowPeaks-guide.pdf vignetteTitles: Tutorial of flowPeaks package hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPeaks/inst/doc/flowPeaks-guide.R importsMe: ddPCRclust dependencyCount: 0 Package: flowPloidy Version: 1.12.0 Imports: flowCore, car, caTools, knitr, rmarkdown, minpack.lm, shiny, methods, graphics, stats, utils Suggests: flowPloidyData, testthat License: GPL-3 MD5sum: f4f4be95caa11d2ef96b9868c1fbf52b NeedsCompilation: no Title: Analyze flow cytometer data to determine sample ploidy Description: Determine sample ploidy via flow cytometry histogram analysis. Reads Flow Cytometry Standard (FCS) files via the flowCore bioconductor package, and provides functions for determining the DNA ploidy of samples based on internal standards. biocViews: FlowCytometry, GUI, Regression, Visualization Author: Tyler Smith Maintainer: Tyler Smith URL: https://github.com/plantarum/flowPloidy VignetteBuilder: knitr BugReports: https://github.com/plantarum/flowPloidy/issues git_url: https://git.bioconductor.org/packages/flowPloidy git_branch: RELEASE_3_10 git_last_commit: fbc5d23 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowPloidy_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowPloidy_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowPloidy_1.12.0.tgz vignettes: vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.pdf, vignettes/flowPloidy/inst/doc/histogram-tour.pdf vignetteTitles: flowPloidy: Getting Started, flowPloidy: FCM Histograms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.R, vignettes/flowPloidy/inst/doc/histogram-tour.R dependencyCount: 96 Package: flowPlots Version: 1.34.0 Depends: R (>= 2.13.0), methods Suggests: vcd License: Artistic-2.0 MD5sum: 81ec9c8ff640d894ae9cd62c51043ed5 NeedsCompilation: no Title: flowPlots: analysis plots and data class for gated flow cytometry data Description: Graphical displays with embedded statistical tests for gated ICS flow cytometry data, and a data class which stores "stacked" data and has methods for computing summary measures on stacked data, such as marginal and polyfunctional degree data. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Visualization, DataRepresentation Author: N. Hawkins, S. Self Maintainer: N. Hawkins git_url: https://git.bioconductor.org/packages/flowPlots git_branch: RELEASE_3_10 git_last_commit: 8a3b074 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowPlots_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowPlots_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowPlots_1.34.0.tgz vignettes: vignettes/flowPlots/inst/doc/flowPlots.pdf vignetteTitles: Plots with Embedded Tests for Gated Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPlots/inst/doc/flowPlots.R dependencyCount: 1 Package: FlowRepositoryR Version: 1.18.0 Depends: R (>= 3.2) Imports: XML, RCurl, tools, utils, jsonlite Suggests: RUnit, BiocGenerics, flowCore, methods License: Artistic-2.0 Archs: i386, x64 MD5sum: 40bd48eacb3d479ebee84826bc85dbe4 NeedsCompilation: no Title: FlowRepository R Interface Description: This package provides an interface to search and download data and annotations from FlowRepository (flowrepository.org). It uses the FlowRepository programming interface to communicate with a FlowRepository server. biocViews: ImmunoOncology, Infrastructure, FlowCytometry Author: Josef Spidlen [aut, cre] Maintainer: Josef Spidlen git_url: https://git.bioconductor.org/packages/FlowRepositoryR git_branch: RELEASE_3_10 git_last_commit: 964c04e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/FlowRepositoryR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/FlowRepositoryR_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/FlowRepositoryR_1.18.0.tgz vignettes: vignettes/FlowRepositoryR/inst/doc/HowTo-FlowRepositoryR.pdf vignetteTitles: FlowRepository R Interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FlowRepositoryR/inst/doc/HowTo-FlowRepositoryR.R dependencyCount: 7 Package: FlowSOM Version: 1.18.0 Depends: R (>= 3.6), igraph Imports: stats, utils, flowCore, ConsensusClusterPlus, BiocGenerics, tsne, flowWorkspace, CytoML, XML, RColorBrewer Suggests: BiocStyle License: GPL (>= 2) MD5sum: c17c8f99f70842ef78bb50fca491aab1 NeedsCompilation: yes Title: Using self-organizing maps for visualization and interpretation of cytometry data Description: FlowSOM offers visualization options for cytometry data, by using Self-Organizing Map clustering and Minimal Spanning Trees. biocViews: CellBiology, FlowCytometry, Clustering, Visualization, Software, CellBasedAssays Author: Sofie Van Gassen [aut, cre], Britt Callebaut [aut], Yvan Saeys [aut] Maintainer: Sofie Van Gassen URL: http://www.r-project.org, http://dambi.ugent.be git_url: https://git.bioconductor.org/packages/FlowSOM git_branch: RELEASE_3_10 git_last_commit: 850e5b7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/FlowSOM_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/FlowSOM_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/FlowSOM_1.18.0.tgz vignettes: vignettes/FlowSOM/inst/doc/FlowSOM.pdf vignetteTitles: FlowSOM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FlowSOM/inst/doc/FlowSOM.R importsMe: CATALYST, cytofast, diffcyt, flowSpy dependencyCount: 122 Package: flowSpecs Version: 1.0.2 Depends: R (>= 3.6) Imports: ggplot2 (>= 3.1.0), BiocGenerics (>= 0.30.0), BiocParallel (>= 1.18.1), reshape2 (>= 1.4.3), flowCore (>= 1.50.0), zoo (>= 1.8.6), stats (>= 3.6.0), methods (>= 3.6.0) Suggests: testthat, knitr, rmarkdown, BiocStyle, flowVS, vulcan, DepecheR License: MIT + file LICENSE MD5sum: 9619cacef562a04cbad17149fdea1268 NeedsCompilation: no Title: Tools for processing of high-dimensional cytometry data Description: This package is intended to fill the role of conventional cytometry pre-processing software, for spectral decomposition, transformation, visualization and cleanup, and to aid further downstream analyses, such as with DepecheR, by enabling transformation of flowFrames and flowSets to dataframes. Functions for flowCore-compliant automatic 1D-gating/filtering are in the pipe line. The package name has been chosen both as it will deal with spectral cytometry and as it will hopefully give the user a nice pair of spectacles through which to view their data. biocViews: Software,CellBasedAssays,DataRepresentation,ImmunoOncology, FlowCytometry,SingleCell,Visualization Author: Jakob Theorell [aut, cre] Maintainer: Jakob Theorell VignetteBuilder: knitr BugReports: https://github.com/jtheorell/flowSpecs/issues git_url: https://git.bioconductor.org/packages/flowSpecs git_branch: RELEASE_3_10 git_last_commit: 07c283e git_last_commit_date: 2020-01-27 Date/Publication: 2020-01-27 source.ver: src/contrib/flowSpecs_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowSpecs_1.0.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowSpecs_1.0.2.tgz vignettes: vignettes/flowSpecs/inst/doc/flowSpecs_vinjette.html vignetteTitles: Example workflow for processing of raw spectral cytometry files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowSpecs/inst/doc/flowSpecs_vinjette.R dependencyCount: 73 Package: flowSpy Version: 1.0.4 Depends: R (>= 3.6), igraph Imports: FlowSOM, Rtsne, ggplot2, destiny, gmodels, flowUtils, Biobase, Matrix, flowCore, sva, matrixStats, methods, mclust, prettydoc, RANN(>= 2.5), Rcpp (>= 0.12.0), BiocNeighbors, cluster, pheatmap, scatterpie, umap, scatterplot3d, limma, stringr, grDevices, grid, stats LinkingTo: Rcpp Suggests: BiocGenerics, knitr, RColorBrewer, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 03f017e2bb8d967098f4a1643046652e NeedsCompilation: yes Title: A Toolkit for Flow And Mass Cytometry Data Description: A trajectory inference and visualization toolkit for flow and mass cytometry data. flowSpy offers complete analyzing workflow for flow and mass cytometry data. flowSpy can be a valuable tool for application ranging from clustering and dimensionality reduction to trajectory reconstruction and pseudotime estimation for flow and mass cytometry data. biocViews: CellBiology, Clustering, Visualization, Software, CellBasedAssays, FlowCytometry, NetworkInference, Network Author: Yuting Dai [aut, cre] Maintainer: Yuting Dai URL: http://www.r-project.org, https://github.com/JhuangLab/flowSpy VignetteBuilder: knitr BugReports: https://github.com/JhuangLab/flowSpy/issues git_url: https://git.bioconductor.org/packages/flowSpy git_branch: RELEASE_3_10 git_last_commit: 24cb50a git_last_commit_date: 2020-03-19 Date/Publication: 2020-03-19 source.ver: src/contrib/flowSpy_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowSpy_1.0.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowSpy_1.0.4.tgz vignettes: vignettes/flowSpy/inst/doc/Quick_start_of_flowSpy.pdf, vignettes/flowSpy/inst/doc/Quick_start.html vignetteTitles: Quick_start_of_flowSpy.pdf, Quick_start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowSpy/inst/doc/Quick_start.R dependencyCount: 234 Package: flowStats Version: 3.44.0 Imports: BiocGenerics, MASS, flowCore (>= 1.51.6), flowWorkspace, ncdfFlow(>= 2.19.5), flowViz, fda (>= 2.2.6), Biobase, methods, grDevices, graphics, stats, cluster, utils, KernSmooth, lattice, ks, RColorBrewer, rrcov Suggests: xtable, testthat, openCyto Enhances: RBGL,graph License: Artistic-2.0 Archs: i386, x64 MD5sum: 7b9171cbbeb4b56dd0cf1fcf709b2bd5 NeedsCompilation: no Title: Statistical methods for the analysis of flow cytometry data Description: Methods and functionality to analyse flow data that is beyond the basic infrastructure provided by the flowCore package. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays Author: Florian Hahne, Nishant Gopalakrishnan, Alireza Hadj Khodabakhshi, Chao-Jen Wong, Kyongryun Lee Maintainer: Greg Finak and Mike Jiang URL: http://www.github.com/RGLab/flowStats BugReports: http://www.github.com/RGLab/flowStats/issues git_url: https://git.bioconductor.org/packages/flowStats git_branch: RELEASE_3_10 git_last_commit: 1260965 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowStats_3.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowStats_3.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowStats_3.44.0.tgz vignettes: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.pdf vignetteTitles: flowStats Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.R dependsOnMe: flowVS suggestsMe: cydar, flowCore, flowViz, ggcyto dependencyCount: 83 Package: flowTime Version: 1.10.0 Depends: R (>= 3.4), flowCore, plyr Imports: utils Suggests: knitr, rmarkdown, flowViz, ggplot2, BiocGenerics, moments, stats License: Artistic-2.0 MD5sum: 4b851c9b796dbbc97e398c2df035d43f NeedsCompilation: no Title: Annotation and analysis of biological dynamical systems using flow cytometry Description: This package was developed for analysis of both dynamic and steady state experiments examining the function of gene regulatory networks in yeast (strain W303) expressing fluorescent reporter proteins using a BD Accuri C6 and SORP cytometers. However, the functions are for the most part general and may be adapted for analysis of other organisms using other flow cytometers. Functions in this package facilitate the annotation of flow cytometry data with experimental metadata, as is requisite for dissemination and general ease-of-use. Functions for creating, saving and loading gate sets are also included. In the past, we have typically generated summary statistics for each flowset for each timepoint and then annotated and analyzed these summary statistics. This method loses a great deal of the power that comes from the large amounts of individual cell data generated in flow cytometry, by essentially collapsing this data into a bulk measurement after subsetting. In addition to these summary functions, this package also contains functions to facilitate annotation and analysis of steady-state or time-lapse data utilizing all of the data collected from the thousands of individual cells in each sample. biocViews: FlowCytometry, TimeCourse, Visualization, DataImport, CellBasedAssays, ImmunoOncology Author: R. Clay Wright [aut, cre], Nick Bolten [aut], Edith Pierre-Jerome [aut] Maintainer: R. Clay Wright VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowTime git_branch: RELEASE_3_10 git_last_commit: 0f1426c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowTime_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowTime_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowTime_1.10.0.tgz vignettes: vignettes/flowTime/inst/doc/steady-state-vignette.html, vignettes/flowTime/inst/doc/time-course-vignette.html vignetteTitles: Steady-state analysis of flow cytometry data, Time course analysis of flow cytometry data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowTime/inst/doc/steady-state-vignette.R, vignettes/flowTime/inst/doc/time-course-vignette.R dependencyCount: 15 Package: flowTrans Version: 1.38.0 Depends: R (>= 2.11.0), flowCore, flowViz,flowClust Imports: flowCore, methods, flowViz, stats, flowClust License: Artistic-2.0 Archs: i386, x64 MD5sum: cea19910394b33222c59fc4628935086 NeedsCompilation: no Title: Parameter Optimization for Flow Cytometry Data Transformation Description: Profile maximum likelihood estimation of parameters for flow cytometry data transformations. biocViews: ImmunoOncology, FlowCytometry Author: Greg Finak , Juan Manuel-Perez , Raphael Gottardo Maintainer: Greg Finak git_url: https://git.bioconductor.org/packages/flowTrans git_branch: RELEASE_3_10 git_last_commit: 8f0ee61 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowTrans_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowTrans_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowTrans_1.38.0.tgz vignettes: vignettes/flowTrans/inst/doc/flowTrans.pdf vignetteTitles: flowTrans package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowTrans/inst/doc/flowTrans.R dependencyCount: 32 Package: flowType Version: 2.24.0 Depends: R (>= 2.10), Rcpp (>= 0.10.4), BH (>= 1.51.0-3) Imports: Biobase, graphics, grDevices, methods, flowCore, flowMeans, sfsmisc, rrcov, flowClust, flowMerge, stats LinkingTo: Rcpp, BH Suggests: xtable License: Artistic-2.0 MD5sum: a7e710d5d9826931d2a4f56aa7cad816 NeedsCompilation: yes Title: Phenotyping Flow Cytometry Assays Description: Phenotyping Flow Cytometry Assays using multidimentional expansion of single dimentional partitions. biocViews: ImmunoOncology, FlowCytometry Author: Nima Aghaeepour, Kieran O'Neill, Adrin Jalali Maintainer: Nima Aghaeepour git_url: https://git.bioconductor.org/packages/flowType git_branch: RELEASE_3_10 git_last_commit: 457df53 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowType_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowType_2.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowType_2.24.0.tgz vignettes: vignettes/flowType/inst/doc/flowType.pdf vignetteTitles: flowType package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowType/inst/doc/flowType.R importsMe: RchyOptimyx dependencyCount: 93 Package: flowUtils Version: 1.50.0 Depends: R (>= 2.2.0) Imports: Biobase, graph, methods, stats, utils, corpcor, RUnit, XML, flowCore (>= 1.32.0) Suggests: gatingMLData License: Artistic-2.0 MD5sum: a5ab2502d5a429f024be663e3f387a2e NeedsCompilation: no Title: Utilities for flow cytometry Description: Provides utilities for flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays, DecisionTree Author: J. Spidlen., N. Gopalakrishnan, F. Hahne, B. Ellis, R. Gentleman, M. Dalphin, N. Le Meur, B. Purcell, W. Jiang Maintainer: Josef Spidlen URL: https://github.com/jspidlen/flowUtils BugReports: https://github.com/jspidlen/flowUtils/issues git_url: https://git.bioconductor.org/packages/flowUtils git_branch: RELEASE_3_10 git_last_commit: ca5bbfb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowUtils_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowUtils_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowUtils_1.50.0.tgz vignettes: vignettes/flowUtils/inst/doc/HowTo-flowUtils.pdf vignetteTitles: Gating-ML support in R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowUtils/inst/doc/HowTo-flowUtils.R importsMe: flowSpy dependencyCount: 18 Package: flowViz Version: 1.50.0 Depends: R (>= 2.7.0), flowCore(>= 1.41.9), lattice Imports: stats4, Biobase, flowCore, graphics, grDevices, grid, KernSmooth, lattice, latticeExtra, MASS, methods, RColorBrewer, stats, utils, hexbin,IDPmisc Suggests: colorspace, flowStats, knitr, testthat License: Artistic-2.0 MD5sum: ff8f498a72e1d6823ecf2a72e3cbc5f2 NeedsCompilation: no Title: Visualization for flow cytometry Description: Provides visualization tools for flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays, Visualization Author: B. Ellis, R. Gentleman, F. Hahne, N. Le Meur, D. Sarkar, M. Jiang Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowViz git_branch: RELEASE_3_10 git_last_commit: 078098c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowViz_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowViz_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowViz_1.50.0.tgz vignettes: vignettes/flowViz/inst/doc/filters.html vignetteTitles: Visualizing Gates with Flow Cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowViz/inst/doc/filters.R dependsOnMe: flowFP, flowVS importsMe: flowClust, flowDensity, flowFit, flowStats, flowTrans, flowWorkspace suggestsMe: flowBeads, flowClean, flowCore, flowTime, ggcyto dependencyCount: 24 Package: flowVS Version: 1.18.0 Depends: R (>= 3.2), methods, flowCore, flowViz, flowStats Suggests: knitr, vsn, License: Artistic-2.0 Archs: i386, x64 MD5sum: 46113f901be6c494b08dd82e9285df06 NeedsCompilation: no Title: Variance stabilization in flow cytometry (and microarrays) Description: Per-channel variance stabilization from a collection of flow cytometry samples by Bertlett test for homogeneity of variances. The approach is applicable to microarrays data as well. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Microarray Author: Ariful Azad Maintainer: Ariful Azad VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowVS git_branch: RELEASE_3_10 git_last_commit: da413d4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/flowVS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowVS_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowVS_1.18.0.tgz vignettes: vignettes/flowVS/inst/doc/flowVS.pdf vignetteTitles: flowVS: Cell population matching and meta-clustering in Flow Cytometry hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowVS/inst/doc/flowVS.R suggestsMe: flowSpecs dependencyCount: 84 Package: flowWorkspace Version: 3.34.1 Imports: Biobase, BiocGenerics, graph, graphics, grDevices, lattice, methods, stats, stats4, utils, RBGL, tools, gridExtra, Rgraphviz, data.table, dplyr, latticeExtra, Rcpp, RColorBrewer, stringr, scales, flowViz, matrixStats, digest, RcppParallel, flowCore(>= 1.51.4), ncdfFlow(>= 2.31.1) LinkingTo: Rcpp, BH(>= 1.62.0-1), RProtoBufLib(>= 1.7.1), cytolib(>= 1.7.4), RcppParallel Suggests: testthat, flowWorkspaceData, knitr, ggcyto, parallel, CytoML, openCyto License: Artistic-2.0 MD5sum: b8247d1703049c39f334684ad672ce7a NeedsCompilation: yes Title: Infrastructure for representing and interacting with gated and ungated cytometry data sets. Description: This package is designed to facilitate comparison of automated gating methods against manual gating done in flowJo. This package allows you to import basic flowJo workspaces into BioConductor and replicate the gating from flowJo using the flowCore functionality. Gating hierarchies, groups of samples, compensation, and transformation are performed so that the output matches the flowJo analysis. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Greg Finak, Mike Jiang Maintainer: Greg Finak ,Mike Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowWorkspace git_branch: RELEASE_3_10 git_last_commit: a7fb3ad git_last_commit_date: 2020-01-02 Date/Publication: 2020-01-02 source.ver: src/contrib/flowWorkspace_3.34.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/flowWorkspace_3.34.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/flowWorkspace_3.34.1.tgz vignettes: vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.html, vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.html, vignettes/flowWorkspace/inst/doc/plotGate.html vignetteTitles: flowWorkspace Introduction: A Package to store and maninpulate gated flow data, How to merge GatingSets, How to plot gated data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.R, vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.R, vignettes/flowWorkspace/inst/doc/plotGate.R dependsOnMe: ggcyto importsMe: CytoML, flowDensity, FlowSOM, flowStats, ImmuneSpaceR suggestsMe: COMPASS, flowClust, flowCore dependencyCount: 66 Package: fmcsR Version: 1.28.0 Depends: R (>= 2.10.0), ChemmineR, methods Imports: RUnit, methods, ChemmineR, BiocGenerics, parallel Suggests: BiocStyle, knitr, knitcitations, knitrBootstrap License: Artistic-2.0 MD5sum: ea6e8e9c1504eeac2bfbf1092a237d83 NeedsCompilation: yes Title: Mismatch Tolerant Maximum Common Substructure Searching Description: The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Yan Wang, Tyler Backman, Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/fmcsR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fmcsR git_branch: RELEASE_3_10 git_last_commit: 6176f38 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/fmcsR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/fmcsR_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/fmcsR_1.28.0.tgz vignettes: vignettes/fmcsR/inst/doc/fmcsR.html vignetteTitles: fmcsR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fmcsR/inst/doc/fmcsR.R importsMe: Rcpi suggestsMe: ChemmineR dependencyCount: 76 Package: focalCall Version: 1.20.0 Depends: R(>= 2.10.0), CGHcall Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: faf4416577a8c95f47d103a01e852665 NeedsCompilation: no Title: Detection of focal aberrations in DNA copy number data Description: Detection of genomic focal aberrations in high-resolution DNA copy number data biocViews: Microarray,Preprocessing,Visualization,Sequencing Author: Oscar Krijgsman Maintainer: Oscar Krijgsman URL: https://github.com/OscarKrijgsman/focalCall git_url: https://git.bioconductor.org/packages/focalCall git_branch: RELEASE_3_10 git_last_commit: c614d21 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/focalCall_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/focalCall_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/focalCall_1.20.0.tgz vignettes: vignettes/focalCall/inst/doc/focalCall.pdf vignetteTitles: focalCall hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/focalCall/inst/doc/focalCall.R dependencyCount: 16 Package: FoldGO Version: 1.4.0 Depends: R (>= 3.5) Imports: topGO (>= 2.30.1), ggplot2 (>= 2.2.1), tidyr (>= 0.8.0), stats, methods Suggests: knitr, rmarkdown, devtools, kableExtra License: GPL-3 Archs: i386, x64 MD5sum: cd47367dac62b2d047c5cb986e73be96 NeedsCompilation: no Title: Package for Fold-specific GO Terms Recognition Description: FoldGO is a package designed to annotate gene sets derived from expression experiments and identify fold-change-specific GO terms. biocViews: DifferentialExpression, GeneExpression, GO, Software Author: Daniil Wiebe [aut, cre] Maintainer: Daniil Wiebe VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FoldGO git_branch: RELEASE_3_10 git_last_commit: 9daa94e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/FoldGO_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/FoldGO_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/FoldGO_1.4.0.tgz vignettes: vignettes/FoldGO/inst/doc/vignette.html vignetteTitles: FoldGO: a tool for fold-change-specific functional enrichment analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FoldGO/inst/doc/vignette.R dependencyCount: 79 Package: FourCSeq Version: 1.20.0 Depends: R (>= 3.0), GenomicRanges, ggplot2, DESeq2 (>= 1.9.11), splines, methods, LSD Imports: DESeq2, Biobase, Biostrings, GenomicRanges, SummarizedExperiment, Rsamtools, ggbio, reshape2, rtracklayer, fda, GenomicAlignments, gtools, Matrix Suggests: BiocStyle, knitr, TxDb.Dmelanogaster.UCSC.dm3.ensGene License: GPL (>= 3) Archs: i386, x64 MD5sum: 7df8f716ae68d253c8424ad468ffe540 NeedsCompilation: no Title: Package analyse 4C sequencing data Description: FourCSeq is an R package dedicated to the analysis of (multiplexed) 4C sequencing data. The package provides a pipeline to detect specific interactions between DNA elements and identify differential interactions between conditions. The statistical analysis in R starts with individual bam files for each sample as inputs. To obtain these files, the package contains a python script (extdata/python/demultiplex.py) to demultiplex libraries and trim off primer sequences. With a standard alignment software the required bam files can be then be generated. biocViews: Software, Preprocessing, Sequencing Author: Felix A. Klein, EMBL Heidelberg Maintainer: Felix A. Klein VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FourCSeq git_branch: RELEASE_3_10 git_last_commit: 767cc28 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/FourCSeq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/FourCSeq_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/FourCSeq_1.20.0.tgz vignettes: vignettes/FourCSeq/inst/doc/FourCSeq.pdf vignetteTitles: FourCSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FourCSeq/inst/doc/FourCSeq.R dependencyCount: 161 Package: FRGEpistasis Version: 1.22.0 Depends: R (>= 2.15), MASS, fda, methods, stats Imports: utils License: GPL-2 MD5sum: f7f3df112470b7f1daf02600c438bb43 NeedsCompilation: no Title: Epistasis Analysis for Quantitative Traits by Functional Regression Model Description: A Tool for Epistasis Analysis Based on Functional Regression Model biocViews: Genetics, NetworkInference, GeneticVariability, Software Author: Futao Zhang Maintainer: Futao Zhang git_url: https://git.bioconductor.org/packages/FRGEpistasis git_branch: RELEASE_3_10 git_last_commit: b9dd1d0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/FRGEpistasis_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/FRGEpistasis_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/FRGEpistasis_1.22.0.tgz vignettes: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.pdf vignetteTitles: FRGEpistasis: A Tool for Epistasis Analysis Based on Functional Regression Model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.R dependencyCount: 10 Package: frma Version: 1.38.0 Depends: R (>= 2.10.0), Biobase (>= 2.6.0) Imports: Biobase, MASS, DBI, affy, methods, oligo, oligoClasses, preprocessCore, utils, BiocGenerics Suggests: hgu133afrmavecs, frmaExampleData License: GPL (>= 2) MD5sum: c8a769a86f5e804451962edc5d5dea1b NeedsCompilation: no Title: Frozen RMA and Barcode Description: Preprocessing and analysis for single microarrays and microarray batches. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry , with contributions from Terry Therneau Maintainer: Matthew N. McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/frma git_branch: RELEASE_3_10 git_last_commit: efeb489 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/frma_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/frma_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/frma_1.38.0.tgz vignettes: vignettes/frma/inst/doc/frma.pdf vignetteTitles: frma: Preprocessing for single arrays and array batches hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frma/inst/doc/frma.R importsMe: ChIPXpress suggestsMe: frmaTools dependencyCount: 60 Package: frmaTools Version: 1.38.0 Depends: R (>= 2.10.0), affy Imports: Biobase, DBI, methods, preprocessCore, stats, utils Suggests: oligo, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, frma, affyPLM, hgu133aprobe, hgu133atagprobe, hgu133plus2probe, hgu133acdf, hgu133atagcdf, hgu133plus2cdf, hgu133afrmavecs, frmaExampleData License: GPL (>= 2) MD5sum: 1fcc298352734490125e2f55ead27ad5 NeedsCompilation: no Title: Frozen RMA Tools Description: Tools for advanced use of the frma package. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry Maintainer: Matthew N. McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/frmaTools git_branch: RELEASE_3_10 git_last_commit: 32f9d59 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/frmaTools_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/frmaTools_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/frmaTools_1.38.0.tgz vignettes: vignettes/frmaTools/inst/doc/frmaTools.pdf vignetteTitles: frmaTools: Create packages containing the vectors used by frma. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frmaTools/inst/doc/frmaTools.R dependencyCount: 14 Package: FunChIP Version: 1.12.0 Depends: R (>= 3.2), GenomicRanges Imports: shiny, fda, doParallel, GenomicAlignments, Rcpp, methods, foreach, parallel, GenomeInfoDb, Rsamtools, grDevices, graphics, stats, RColorBrewer LinkingTo: Rcpp License: Artistic-2.0 MD5sum: 3b1472707675d917ac5df030706ea0c9 NeedsCompilation: yes Title: Clustering and Alignment of ChIP-Seq peaks based on their shapes Description: Preprocessing and smoothing of ChIP-Seq peaks and efficient implementation of the k-mean alignment algorithm to classify them. biocViews: StatisticalMethod, Clustering, ChIPSeq Author: Alice Parodi [aut, cre], Marco Morelli [aut, cre], Laura M. Sangalli [aut], Piercesare Secchi [aut], Simone Vantini [aut] Maintainer: Alice Parodi git_url: https://git.bioconductor.org/packages/FunChIP git_branch: RELEASE_3_10 git_last_commit: 1c945e7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/FunChIP_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/FunChIP_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/FunChIP_1.12.0.tgz vignettes: vignettes/FunChIP/inst/doc/FunChIP.pdf vignetteTitles: An introduction to FunChIP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FunChIP/inst/doc/FunChIP.R dependencyCount: 58 Package: FunciSNP Version: 1.30.0 Depends: R (>= 2.14.0), ggplot2, TxDb.Hsapiens.UCSC.hg19.knownGene, FunciSNP.data Imports: methods, BiocGenerics, Biobase, S4Vectors, IRanges, GenomicRanges, Rsamtools (>= 1.6.1), rtracklayer (>= 1.14.1), ChIPpeakAnno (>= 2.2.0), VariantAnnotation, plyr, snpStats, ggplot2 (>= 0.9.0), reshape (>= 0.8.4), scales Suggests: org.Hs.eg.db Enhances: parallel License: GPL-3 MD5sum: 630eebac20ca5bb99931083cd3c61734 NeedsCompilation: no Title: Integrating Functional Non-coding Datasets with Genetic Association Studies to Identify Candidate Regulatory SNPs Description: FunciSNP integrates information from GWAS, 1000genomes and chromatin feature to identify functional SNP in coding or non-coding regions. biocViews: Infrastructure, DataRepresentation, DataImport, SequenceMatching, Annotation Author: Simon G. Coetzee and Houtan Noushmehr, PhD Maintainer: Simon G. Coetzee URL: http://coetzeeseq.usc.edu/publication/Coetzee_SG_et_al_2012/ git_url: https://git.bioconductor.org/packages/FunciSNP git_branch: RELEASE_3_10 git_last_commit: dc1c924 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/FunciSNP_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/FunciSNP_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/FunciSNP_1.30.0.tgz vignettes: vignettes/FunciSNP/inst/doc/FunciSNP_vignette.pdf vignetteTitles: FunciSNP Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FunciSNP/inst/doc/FunciSNP_vignette.R dependencyCount: 137 Package: funtooNorm Version: 1.10.0 Depends: R(>= 3.4) Imports: pls, matrixStats, minfi, methods, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, GenomeInfoDb, grDevices, graphics, stats Suggests: prettydoc, minfiData, knitr, rmarkdown License: GPL-3 MD5sum: 3197c1dd33d4736f5fa37b955fbd42c9 NeedsCompilation: no Title: Normalization Procedure for Infinium HumanMethylation450 BeadChip Kit Description: Provides a function to normalize Illumina Infinium Human Methylation 450 BeadChip (Illumina 450K), correcting for tissue and/or cell type. biocViews: DNAMethylation, Preprocessing, Normalization Author: Celia Greenwood ,Stepan Grinek , Maxime Turgeon , Kathleen Klein Maintainer: Kathleen Klein VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/funtooNorm git_branch: RELEASE_3_10 git_last_commit: ad851b9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/funtooNorm_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/funtooNorm_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/funtooNorm_1.10.0.tgz vignettes: vignettes/funtooNorm/inst/doc/funtooNorm.pdf vignetteTitles: Normalizing Illumina Infinium Human Methylation 450k for multiple cell types with funtooNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/funtooNorm/inst/doc/funtooNorm.R dependencyCount: 125 Package: GA4GHclient Version: 1.10.0 Depends: S4Vectors Imports: BiocGenerics, Biostrings, dplyr, GenomeInfoDb, GenomicRanges, httr, IRanges, jsonlite, methods, VariantAnnotation Suggests: AnnotationDbi, BiocStyle, DT, knitr, org.Hs.eg.db, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) Archs: i386, x64 MD5sum: 33a15f83d7b3c7728cc7cec7c0e19805 NeedsCompilation: no Title: A Bioconductor package for accessing GA4GH API data servers Description: GA4GHclient provides an easy way to access public data servers through Global Alliance for Genomics and Health (GA4GH) genomics API. It provides low-level access to GA4GH API and translates response data into Bioconductor-based class objects. biocViews: DataRepresentation, ThirdPartyClient Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane Rocha [ctb] Maintainer: Welliton Souza URL: https://github.com/labbcb/GA4GHclient VignetteBuilder: knitr BugReports: https://github.com/labbcb/GA4GHclient/issues git_url: https://git.bioconductor.org/packages/GA4GHclient git_branch: RELEASE_3_10 git_last_commit: e328ce6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GA4GHclient_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GA4GHclient_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GA4GHclient_1.10.0.tgz vignettes: vignettes/GA4GHclient/inst/doc/GA4GHclient.html vignetteTitles: GA4GHclient hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GA4GHclient/inst/doc/GA4GHclient.R dependsOnMe: GA4GHshiny dependencyCount: 85 Package: GA4GHshiny Version: 1.8.0 Depends: GA4GHclient Imports: AnnotationDbi, BiocGenerics, dplyr, DT, GenomeInfoDb, openxlsx, GenomicFeatures, methods, purrr, S4Vectors, shiny, shinyjs, tidyr, shinythemes Suggests: BiocStyle, org.Hs.eg.db, knitr, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: 07797b0eb6302428914df94887ed62f3 NeedsCompilation: no Title: Shiny application for interacting with GA4GH-based data servers Description: GA4GHshiny package provides an easy way to interact with data servers based on Global Alliance for Genomics and Health (GA4GH) genomics API through a Shiny application. It also integrates with Beacon Network. biocViews: GUI Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane Rocha [ctb], Elizabeth Borgognoni [ctb] Maintainer: Welliton Souza URL: https://github.com/labbcb/GA4GHshiny VignetteBuilder: knitr BugReports: https://github.com/labbcb/GA4GHshiny/issues git_url: https://git.bioconductor.org/packages/GA4GHshiny git_branch: RELEASE_3_10 git_last_commit: d1e36e4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GA4GHshiny_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GA4GHshiny_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GA4GHshiny_1.8.0.tgz vignettes: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.html vignetteTitles: GA4GHshiny hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.R dependencyCount: 104 Package: gaga Version: 2.32.0 Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv Enhances: parallel License: GPL (>= 2) MD5sum: 7fe14f50aca1d741dc7a7aced43403e9 NeedsCompilation: yes Title: GaGa hierarchical model for high-throughput data analysis Description: Implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package). biocViews: ImmunoOncology, OneChannel, MassSpectrometry, MultipleComparison, DifferentialExpression, Classification Author: David Rossell . Maintainer: David Rossell git_url: https://git.bioconductor.org/packages/gaga git_branch: RELEASE_3_10 git_last_commit: 96e2401 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gaga_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gaga_2.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gaga_2.32.0.tgz vignettes: vignettes/gaga/inst/doc/gagamanual.pdf vignetteTitles: Manual for the gaga library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaga/inst/doc/gagamanual.R importsMe: casper dependencyCount: 17 Package: gage Version: 2.36.0 Depends: R (>= 2.10) Imports: graph, KEGGREST, AnnotationDbi Suggests: pathview, gageData, GO.db, org.Hs.eg.db, hgu133a.db, GSEABase, Rsamtools, GenomicAlignments, TxDb.Hsapiens.UCSC.hg19.knownGene, DESeq, DESeq2, edgeR, limma License: GPL (>=2.0) MD5sum: 5e81b50bc80e002eb0070c620d5299d6 NeedsCompilation: no Title: Generally Applicable Gene-set Enrichment for Pathway Analysis Description: GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods. In gage package, we provide functions for basic GAGE analysis, result processing and presentation. We have also built pipeline routines for of multiple GAGE analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies. In addition, we provide demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These funtions and data are also useful for gene set analysis using other methods. biocViews: ImmunoOncology, Pathways, GO, DifferentialExpression, Microarray, OneChannel, TwoChannel, RNASeq, Genetics, MultipleComparison, GeneSetEnrichment, GeneExpression, SystemsBiology, Sequencing Author: Weijun Luo Maintainer: Weijun Luo URL: http://www.biomedcentral.com/1471-2105/10/161 git_url: https://git.bioconductor.org/packages/gage git_branch: RELEASE_3_10 git_last_commit: 495c43b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gage_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gage_2.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gage_2.36.0.tgz vignettes: vignettes/gage/inst/doc/dataPrep.pdf, vignettes/gage/inst/doc/gage.pdf, vignettes/gage/inst/doc/RNA-seqWorkflow.pdf vignetteTitles: Gene set and data preparation, Generally Applicable Gene-set/Pathway Analysis, RNA-Seq Data Pathway and Gene-set Analysis Workflows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gage/inst/doc/dataPrep.R, vignettes/gage/inst/doc/gage.R, vignettes/gage/inst/doc/RNA-seqWorkflow.R dependsOnMe: EGSEA importsMe: anamiR suggestsMe: FGNet, pathview, SBGNview dependencyCount: 41 Package: gaggle Version: 1.54.0 Depends: R (>= 2.3.0), rJava (>= 0.4), graph (>= 1.10.2), RUnit (>= 0.4.17) License: GPL version 2 or newer MD5sum: 56418fb53779bbfdd1685f2540446181 NeedsCompilation: no Title: Broadcast data between R and Gaggle Description: This package contains functions enabling data exchange between R and Gaggle enabled bioinformatics software, including Cytoscape, Firegoose and Gaggle Genome Browser. biocViews: ThirdPartyClient, Visualization, Annotation, GraphAndNetwork, DataImport Author: Paul Shannon Maintainer: Christopher Bare URL: http://gaggle.systemsbiology.net/docs/geese/r/ git_url: https://git.bioconductor.org/packages/gaggle git_branch: RELEASE_3_10 git_last_commit: 38e8c18 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gaggle_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gaggle_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gaggle_1.54.0.tgz vignettes: vignettes/gaggle/inst/doc/gaggle.pdf vignetteTitles: Gaggle Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaggle/inst/doc/gaggle.R dependencyCount: 10 Package: gaia Version: 2.30.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: 0a4a037fe542ff91ca4b884ceca707ca NeedsCompilation: no Title: GAIA: An R package for genomic analysis of significant chromosomal aberrations. Description: This package allows to assess the statistical significance of chromosomal aberrations. biocViews: aCGH, CopyNumberVariation Author: Sandro Morganella et al. Maintainer: S. Morganella git_url: https://git.bioconductor.org/packages/gaia git_branch: RELEASE_3_10 git_last_commit: 42a27ea git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gaia_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gaia_2.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gaia_2.30.0.tgz vignettes: vignettes/gaia/inst/doc/gaia.pdf vignetteTitles: gaia hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaia/inst/doc/gaia.R dependencyCount: 0 Package: GAPGOM Version: 1.2.0 Depends: R (>= 3.6.0) Imports: stats, utils, methods, Matrix, fastmatch, plyr, dplyr, magrittr, data.table, igraph, graph, RBGL, GO.db, org.Hs.eg.db, org.Mm.eg.db, GOSemSim, GEOquery, AnnotationDbi, Biobase, BiocFileCache, matrixStats Suggests: org.Dm.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Dr.eg.db, org.Ce.eg.db, org.At.tair.db, org.EcK12.eg.db, org.Bt.eg.db, org.Cf.eg.db, org.Ag.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Pt.eg.db, org.Pf.plasmo.db, org.Mmu.eg.db, org.Ss.eg.db, org.Xl.eg.db, testthat, pryr, knitr, rmarkdown, prettydoc, ggplot2, kableExtra, profvis, reshape2 License: MIT + file LICENSE MD5sum: 80c85b252f189d38fc87dfd90b5a28f6 NeedsCompilation: no Title: GAPGOM (novel Gene Annotation Prediction and other GO Metrics) Description: Collection of various measures and tools for lncRNA annotation prediction put inside a redistributable R package. The package contains two main algorithms; lncRNA2GOA and TopoICSim. lncRNA2GOA tries to annotate novel genes (in this specific case lncRNAs) by using various correlation/geometric scoring methods on correlated expression data. After correlating/scoring, the results are annotated and enriched. TopoICSim is a topologically based method, that compares gene similarity based on the topology of the GO DAG by information content (IC) between GO terms. biocViews: GO, GeneExpression, GenePrediction Author: Casper Peters [aut, cre], Finn Drabløs [aut], Rezvan Ehsani [aut] Maintainer: Casper Peters URL: https://github.com/Berghopper/GAPGOM/ VignetteBuilder: knitr BugReports: https://github.com/Berghopper/GAPGOM/issues/ git_url: https://git.bioconductor.org/packages/GAPGOM git_branch: RELEASE_3_10 git_last_commit: 62b9936 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GAPGOM_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GAPGOM_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GAPGOM_1.2.0.tgz vignettes: vignettes/GAPGOM/inst/doc/benchmarks.html, vignettes/GAPGOM/inst/doc/GAPGOM.html vignetteTitles: Benchmarks and other GO similarity methods, An Introduction to GAPGOM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GAPGOM/inst/doc/benchmarks.R, vignettes/GAPGOM/inst/doc/GAPGOM.R dependencyCount: 73 Package: GAprediction Version: 1.12.0 Depends: R (>= 3.3) Imports: glmnet, stats, utils, Matrix Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: e787e47d7e66b27eaf0120ce279b28ac NeedsCompilation: no Title: Prediction of gestational age with Illumina HumanMethylation450 data Description: [GAprediction] predicts gestational age using Illumina HumanMethylation450 CpG data. biocViews: ImmunoOncology, DNAMethylation, Epigenetics, Regression, BiomedicalInformatics Author: Jon Bohlin Maintainer: Jon Bohlin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GAprediction git_branch: RELEASE_3_10 git_last_commit: fe8d8cd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GAprediction_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GAprediction_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GAprediction_1.12.0.tgz vignettes: vignettes/GAprediction/inst/doc/GAprediction.html vignetteTitles: GAprediction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GAprediction/inst/doc/GAprediction.R dependencyCount: 13 Package: garfield Version: 1.14.0 Suggests: knitr License: GPL-3 MD5sum: 5bea2098a03e413b9814cfbabe75e360 NeedsCompilation: yes Title: GWAS Analysis of Regulatory or Functional Information Enrichment with LD correction Description: GARFIELD is a non-parametric functional enrichment analysis approach described in the paper GARFIELD: GWAS analysis of regulatory or functional information enrichment with LD correction. Briefly, it is a method that leverages GWAS findings with regulatory or functional annotations (primarily from ENCODE and Roadmap epigenomics data) to find features relevant to a phenotype of interest. It performs greedy pruning of GWAS SNPs (LD r2 > 0.1) and then annotates them based on functional information overlap. Next, it quantifies Fold Enrichment (FE) at various GWAS significance cutoffs and assesses them by permutation testing, while matching for minor allele frequency, distance to nearest transcription start site and number of LD proxies (r2 > 0.8). biocViews: Software, StatisticalMethod, Annotation, FunctionalPrediction, GenomeAnnotation Author: Sandro Morganella Maintainer: Valentina Iotchkova VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/garfield git_branch: RELEASE_3_10 git_last_commit: c342f6f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/garfield_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/garfield_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/garfield_1.14.0.tgz vignettes: vignettes/garfield/inst/doc/vignette.pdf vignetteTitles: garfield Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 0 Package: GARS Version: 1.6.0 Depends: R (>= 3.5), ggplot2, cluster Imports: DaMiRseq, MLSeq, stats, methods, SummarizedExperiment Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: 3ab0ba2bb49e2503a266da0cd7b89d03 NeedsCompilation: no Title: GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets Description: Feature selection aims to identify and remove redundant, irrelevant and noisy variables from high-dimensional datasets. Selecting informative features affects the subsequent classification and regression analyses by improving their overall performances. Several methods have been proposed to perform feature selection: most of them relies on univariate statistics, correlation, entropy measurements or the usage of backward/forward regressions. Herein, we propose an efficient, robust and fast method that adopts stochastic optimization approaches for high-dimensional. GARS is an innovative implementation of a genetic algorithm that selects robust features in high-dimensional and challenging datasets. biocViews: Classification, FeatureExtraction, Clustering Author: Mattia Chiesa , Luca Piacentini Maintainer: Mattia Chiesa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GARS git_branch: RELEASE_3_10 git_last_commit: 585a195 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GARS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GARS_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GARS_1.6.0.tgz vignettes: vignettes/GARS/inst/doc/GARS.pdf vignetteTitles: Titolo hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GARS/inst/doc/GARS.R dependencyCount: 243 Package: GateFinder Version: 1.6.0 Imports: splancs, mvoutlier, methods, stats, diptest, flowCore, flowFP Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: ba0c5250f2b86b940509038029017fdd NeedsCompilation: no Title: Projection-based Gating Strategy Optimization for Flow and Mass Cytometry Description: Given a vector of cluster memberships for a cell population, identifies a sequence of gates (polygon filters on 2D scatter plots) for isolation of that cell type. biocViews: ImmunoOncology, FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour , Erin F. Simonds Maintainer: Nima Aghaeepour git_url: https://git.bioconductor.org/packages/GateFinder git_branch: RELEASE_3_10 git_last_commit: bc575f3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GateFinder_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GateFinder_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GateFinder_1.6.0.tgz vignettes: vignettes/GateFinder/inst/doc/GateFinder.pdf vignetteTitles: GateFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GateFinder/inst/doc/GateFinder.R dependencyCount: 144 Package: gcapc Version: 1.10.0 Depends: R (>= 3.4) Imports: BiocGenerics, GenomeInfoDb, S4Vectors, IRanges, Biostrings, BSgenome, GenomicRanges, Rsamtools, GenomicAlignments, matrixStats, MASS, splines, grDevices, graphics, stats, methods Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10 License: GPL-3 Archs: i386, x64 MD5sum: 0db687d4176302902d1e0a63a77a8dab NeedsCompilation: no Title: GC Aware Peak Caller Description: Peak calling for ChIP-seq data with consideration of potential GC bias in sequencing reads. GC bias is first estimated with generalized linear mixture models using effective GC strategy, then applied into peak significance estimation. biocViews: Sequencing, ChIPSeq, BatchEffect, PeakDetection Author: Mingxiang Teng and Rafael A. Irizarry Maintainer: Mingxiang Teng URL: https://github.com/tengmx/gcapc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gcapc git_branch: RELEASE_3_10 git_last_commit: 1a15321 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gcapc_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gcapc_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gcapc_1.10.0.tgz vignettes: vignettes/gcapc/inst/doc/gcapc.html vignetteTitles: The gcapc user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcapc/inst/doc/gcapc.R dependencyCount: 41 Package: gcatest Version: 1.16.0 Depends: R (>= 3.2) Imports: lfa Suggests: knitr, ggplot2 License: GPL-3 MD5sum: 3a185d45ed564ef8b63e7a060311ebfa NeedsCompilation: yes Title: Genotype Conditional Association TEST Description: GCAT is an association test for genome wide association studies that controls for population structure under a general class of trait. models. biocViews: SNP, DimensionReduction, PrincipalComponent, GenomeWideAssociation Author: Wei Hao, Minsun Song, John D. Storey Maintainer: Wei Hao , John D. Storey URL: https://github.com/StoreyLab/gcatest VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/gcatest/issues git_url: https://git.bioconductor.org/packages/gcatest git_branch: RELEASE_3_10 git_last_commit: 936e3ca git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gcatest_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gcatest_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gcatest_1.16.0.tgz vignettes: vignettes/gcatest/inst/doc/gcatest.pdf vignetteTitles: gcat Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcatest/inst/doc/gcatest.R dependencyCount: 3 Package: gCMAP Version: 1.30.0 Depends: GSEABase, limma (>= 3.20.0) Imports: Biobase, methods, GSEAlm, Category, Matrix (>= 1.0.9), parallel, annotate, genefilter, AnnotationDbi, DESeq, grDevices, graphics, stats, utils, bigmemory, bigmemoryExtras (>= 1.1.2) Suggests: BiocGenerics, KEGG.db, reactome.db, RUnit, GO.db, mgsa License: Artistic-2.0 OS_type: unix MD5sum: 3611402da38904edbb197c8f6e4ea08c NeedsCompilation: no Title: Tools for Connectivity Map-like analyses Description: The gCMAP package provides a toolkit for comparing differential gene expression profiles through gene set enrichment analysis. Starting from normalized microarray or RNA-seq gene expression values (stored in lists of ExpressionSet and CountDataSet objects) the package performs differential expression analysis using the limma or DESeq packages. Supplying a simple list of gene identifiers, global differential expression profiles or data from complete experiments as input, users can use a unified set of several well-known gene set enrichment analysis methods to retrieve experiments with similar changes in gene expression. To take into account the directionality of gene expression changes, gCMAPQuery introduces the SignedGeneSet class, directly extending GeneSet from the GSEABase package. To increase performance of large queries, multiple gene sets are stored as sparse incidence matrices within CMAPCollection eSets. gCMAP offers implementations of 1. Fisher's exact test (Fisher, J R Stat Soc, 1922) 2. The "connectivity map" method (Lamb et al, Science, 2006) 3. Parametric and non-parametric t-statistic summaries (Jiang & Gentleman, Bioinformatics, 2007) and 4. Wilcoxon / Mann-Whitney rank sum statistics (Wilcoxon, Biometrics Bulletin, 1945) as well as wrappers for the 5. camera (Wu & Smyth, Nucleic Acid Res, 2012) 6. mroast and romer (Wu et al, Bioinformatics, 2010) functions from the limma package and 7. wraps the gsea method from the mgsa package (Bauer et al, NAR, 2010). All methods return CMAPResult objects, an S4 class inheriting from AnnotatedDataFrame, containing enrichment statistics as well as annotation data and providing simple high-level summary plots. biocViews: Microarray, Software, Pathways, Annotation Author: Thomas Sandmann , Richard Bourgon and Sarah Kummerfeld Maintainer: Thomas Sandmann git_url: https://git.bioconductor.org/packages/gCMAP git_branch: RELEASE_3_10 git_last_commit: d00b2f0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gCMAP_1.30.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gCMAP_1.30.0.tgz vignettes: vignettes/gCMAP/inst/doc/diffExprAnalysis.pdf, vignettes/gCMAP/inst/doc/gCMAP.pdf vignetteTitles: Creating reference datasets, gCMAP classes and methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCMAP/inst/doc/diffExprAnalysis.R, vignettes/gCMAP/inst/doc/gCMAP.R dependsOnMe: gCMAPWeb dependencyCount: 52 Package: gCMAPWeb Version: 1.26.0 Depends: Biobase, gCMAP (>= 1.3.0), methods, R (>= 3.4), Rook Imports: brew, BiocGenerics, annotate, AnnotationDbi, graphics, grDevices, GSEABase, hwriter, parallel, stats, utils, yaml Suggests: affy, ArrayExpress, hgfocus.db, hgu133a.db, mgug4104a.db, org.Hs.eg.db, org.Mm.eg.db, RUnit Enhances: bigmemory, bigmemoryExtras License: Artistic-2.0 OS_type: unix MD5sum: 5595d509d6189389a5667e7b340b0965 NeedsCompilation: no Title: A web interface for gene-set enrichment analyses Description: The gCMAPWeb R package provides a graphical user interface for the gCMAP package. gCMAPWeb uses the Rook package and can be used either on a local machine, leveraging R's internal web server, or run on a dedicated rApache web server installation. gCMAPWeb allows users to search their own data sources and instructions to generate reference datasets from public repositories are included with the package. The package supports three common types of analyses, specifically queries with 1. one or two sets of query gene identifiers, whose members are expected to show changes in gene expression in a consistent direction. For example, an up-regulated gene set might contain genes activated by a transcription factor, a down-regulated geneset targets repressed by the same factor. 2. a single set of query gene identifiers, whose members are expected to show divergent differential expression (non-directional query). For example, members of a particular signaling pathway, some of which may be up- some down-regulated in response to a stimulus. 3. a query with the complete results of a differential expression profiling experiment. For example, gene identifiers and z-scores from a previous perturbation experiment. gCMAPWeb accepts three types of identifiers: EntreIds, gene Symbols and microarray probe ids and can be configured to work with any species supported by Bioconductor. For each query submission, significantly similar reference datasets will be identified and reported in graphical and tabular form. biocViews: GUI, GeneSetEnrichment, Visualization, GeneExpression, Transcription, Microarray, DifferentialExpression Author: Thomas Sandmann Maintainer: Thomas Sandmann git_url: https://git.bioconductor.org/packages/gCMAPWeb git_branch: RELEASE_3_10 git_last_commit: f0bf9ab git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gCMAPWeb_1.26.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gCMAPWeb_1.26.0.tgz vignettes: vignettes/gCMAPWeb/inst/doc/gCMAPWeb.pdf, vignettes/gCMAPWeb/inst/doc/referenceDatasets.pdf vignetteTitles: gCMAPWeb configuration, Recreating the Broad Connectivity Map v1 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCMAPWeb/inst/doc/gCMAPWeb.R, vignettes/gCMAPWeb/inst/doc/referenceDatasets.R dependencyCount: 58 Package: gCrisprTools Version: 1.14.0 Depends: R (>= 3.3) Imports: Biobase, limma, RobustRankAggreg, ggplot2, PANTHER.db, rmarkdown, grDevices, graphics, stats, utils, parallel Suggests: edgeR, knitr, grid, AnnotationDbi, org.Mm.eg.db, org.Hs.eg.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 9975ef9ec0c42cf8cebc5f9b4eba532f NeedsCompilation: no Title: Suite of Functions for Pooled Crispr Screen QC and Analysis Description: Set of tools for evaluating pooled high-throughput screening experiments, typically employing CRISPR/Cas9 or shRNA expression cassettes. Contains methods for interrogating library and cassette behavior within an experiment, identifying differentially abundant cassettes, aggregating signals to identify candidate targets for empirical validation, hypothesis testing, and comprehensive reporting. biocViews: ImmunoOncology, CRISPR, PooledScreens, ExperimentalDesign, BiomedicalInformatics, CellBiology, FunctionalGenomics, Pharmacogenomics, Pharmacogenetics, SystemsBiology, DifferentialExpression, GeneSetEnrichment, Genetics, MultipleComparison, Normalization, Preprocessing, QualityControl, RNASeq, Regression, Software, Visualization Author: Russell Bainer, Dariusz Ratman, Steve Lianoglou, Peter Haverty Maintainer: Peter Haverty VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gCrisprTools git_branch: RELEASE_3_10 git_last_commit: ad12ed1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gCrisprTools_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gCrisprTools_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gCrisprTools_1.14.0.tgz vignettes: vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.html, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.html vignetteTitles: Example_Workflow_gCrisprTools, gCrisprTools_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.R, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.R dependencyCount: 107 Package: gcrma Version: 2.58.0 Depends: R (>= 2.6.0), affy (>= 1.23.2), graphics, methods, stats, utils Imports: Biobase, affy (>= 1.23.2), affyio (>= 1.13.3), XVector, Biostrings (>= 2.11.32), splines, BiocManager Suggests: affydata, tools, splines, hgu95av2cdf, hgu95av2probe License: LGPL MD5sum: eebdd0da64c4b0d92b22af22168f03fd NeedsCompilation: yes Title: Background Adjustment Using Sequence Information Description: Background adjustment using sequence information biocViews: Microarray, OneChannel, Preprocessing Author: Jean(ZHIJIN) Wu, Rafael Irizarry with contributions from James MacDonald Jeff Gentry Maintainer: Z. Wu git_url: https://git.bioconductor.org/packages/gcrma git_branch: RELEASE_3_10 git_last_commit: 38ebb44 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gcrma_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gcrma_2.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gcrma_2.58.0.tgz vignettes: vignettes/gcrma/inst/doc/gcrma2.0.pdf vignetteTitles: gcrma1.2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyILM, affyPLM, bgx, maskBAD, simpleaffy, webbioc importsMe: affycoretools, affylmGUI, simpleaffy suggestsMe: AffyExpress, ArrayTools, BiocCaseStudies, panp dependencyCount: 19 Package: GCSscore Version: 1.0.0 Depends: R (>= 3.6) Imports: BiocManager, Biobase, utils, methods, RSQLite, AnnotationForge, devtools, dplR, stringr, graphics, stats, affxparser, data.table License: GPL (>=3) Archs: i386, x64 MD5sum: 18c6008719935e67feff81853d4125c9 NeedsCompilation: no Title: GCSscore: an R package for microarray analysis for Affymetrix/Thermo Fisher arrays Description: For differential expression analysis of 3'IVT and WT-style microarrays from Affymetrix/Thermo-Fisher. Based on S-score algorithm originally described by Zhang et al 2002. biocViews: DifferentialExpression, Microarray, OneChannel, ProprietaryPlatforms, DataImport Author: Guy M. Harris & Shahroze Abbas & Michael F. Miles Maintainer: Guy M. Harris git_url: https://git.bioconductor.org/packages/GCSscore git_branch: RELEASE_3_10 git_last_commit: 4875a8a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GCSscore_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GCSscore_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GCSscore_1.0.0.tgz vignettes: vignettes/GCSscore/inst/doc/GCSscore.pdf vignetteTitles: SScore primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GCSscore/inst/doc/GCSscore.R dependencyCount: 113 Package: GDCRNATools Version: 1.6.0 Depends: R (>= 3.5.0) Imports: shiny, jsonlite, rjson, XML, limma, edgeR, DESeq2, clusterProfiler, DOSE, org.Hs.eg.db, biomaRt, survival, survminer, pathview, ggplot2, gplots, DT, GenomicDataCommons, BiocParallel Suggests: knitr, testthat License: Artistic-2.0 MD5sum: bf32fbeb6874845e8397f932f41e9aff NeedsCompilation: no Title: GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC Description: This is an easy-to-use package for downloading, organizing, and integrative analyzing RNA expression data in GDC with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer. Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. Besides some routine visualization functions such as volcano plot, bar plot, and KM plot, a few simply shiny apps are developed to facilitate visualization of results on a local webpage. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, GeneRegulation, GeneTarget, NetworkInference, Survival, Visualization, GeneSetEnrichment, NetworkEnrichment, Network, RNASeq, GO, KEGG Author: Ruidong Li, Han Qu, Shibo Wang, Julong Wei, Le Zhang, Renyuan Ma, Jianming Lu, Jianguo Zhu, Wei-De Zhong, Zhenyu Jia Maintainer: Ruidong Li , Han Qu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GDCRNATools git_branch: RELEASE_3_10 git_last_commit: 0e1363a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GDCRNATools_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GDCRNATools_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GDCRNATools_1.6.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GDCRNATools/inst/doc/GDCRNATools.R dependencyCount: 207 Package: GDSArray Version: 1.6.0 Depends: R (>= 3.5), gdsfmt, methods, BiocGenerics, DelayedArray (>= 0.5.32) Imports: tools, S4Vectors (>= 0.17.34), SNPRelate, SeqArray Suggests: testthat, knitr, BiocStyle, BiocManager License: GPL-3 Archs: i386, x64 MD5sum: f3a5ebb545320529c94adc320cd79afd NeedsCompilation: no Title: Representing GDS files as array-like objects Description: GDS files are widely used to represent genotyping or sequence data. The GDSArray package implements the `GDSArray` class to represent nodes in GDS files in a matrix-like representation that allows easy manipulation (e.g., subsetting, mathematical transformation) in _R_. The data remains on disk until needed, so that very large files can be processed. biocViews: Infrastructure, DataRepresentation, Sequencing, GenotypingArray Author: Qian Liu [aut, cre], Martin Morgan [aut], Hervé Pagès [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/GDSArray VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GDSArray/issues git_url: https://git.bioconductor.org/packages/GDSArray git_branch: RELEASE_3_10 git_last_commit: 971612a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GDSArray_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GDSArray_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GDSArray_1.6.0.tgz vignettes: vignettes/GDSArray/inst/doc/GDSArray.html vignetteTitles: GDSArray: Representing GDS files as array-like objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GDSArray/inst/doc/GDSArray.R dependsOnMe: VariantExperiment importsMe: CNVRanger suggestsMe: DelayedDataFrame dependencyCount: 34 Package: gdsfmt Version: 1.22.0 Depends: R (>= 2.15.0), methods Suggests: parallel, digest, crayon, RUnit, knitr, BiocGenerics License: LGPL-3 MD5sum: e57f542e1fd0dc7b6be4cf3d610e324d NeedsCompilation: yes Title: R Interface to CoreArray Genomic Data Structure (GDS) Files Description: This package provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files, which are portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. It is also allowed to read a GDS file in parallel with multiple R processes supported by the package parallel. biocViews: Infrastructure, DataImport Author: Xiuwen Zheng [aut, cre] (), Stephanie Gogarten [ctb], Jean-loup Gailly and Mark Adler [ctb] (for the included zlib sources), Yann Collet [ctb] (for the included LZ4 sources), xz contributors [ctb] (for the included liblzma sources) Maintainer: Xiuwen Zheng URL: http://corearray.sourceforge.net/, http://github.com/zhengxwen/gdsfmt VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/gdsfmt/issues git_url: https://git.bioconductor.org/packages/gdsfmt git_branch: RELEASE_3_10 git_last_commit: d1a0ec9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gdsfmt_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gdsfmt_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gdsfmt_1.22.0.tgz vignettes: vignettes/gdsfmt/inst/doc/gdsfmt.html vignetteTitles: Introduction to GDS Format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gdsfmt/inst/doc/gdsfmt.R dependsOnMe: bigmelon, GDSArray, SAIGEgds, SeqArray, SNPRelate importsMe: CNVRanger, GENESIS, GWASTools, SeqSQC, SeqVarTools, VariantExperiment suggestsMe: AnnotationHub, HIBAG linksToMe: SeqArray, SNPRelate dependencyCount: 1 Package: geecc Version: 1.20.0 Depends: R (>= 3.3.0), methods Imports: MASS, hypergea (>= 1.3.0), gplots, Rcpp (>= 0.11.3), graphics, stats, utils LinkingTo: Rcpp Suggests: hgu133plus2.db, GO.db, AnnotationDbi License: GPL (>= 2) MD5sum: c53dd77ed87ff9458ef6a9b75558df5b NeedsCompilation: yes Title: Gene Set Enrichment Analysis Extended to Contingency Cubes Description: Use log-linear models to perform hypergeometric and chi-squared tests for gene set enrichments for two (based on contingency tables) or three categories (contingency cubes). Categories can be differentially expressed genes, GO terms, sequence length, GC content, chromosomal position, phylostrata, divergence-strata, .... biocViews: ImmunoOncology, BiologicalQuestion, GeneSetEnrichment, WorkflowStep, GO, StatisticalMethod, GeneExpression, Transcription, RNASeq, Microarray Author: Markus Boenn Maintainer: Markus Boenn SystemRequirements: Rcpp git_url: https://git.bioconductor.org/packages/geecc git_branch: RELEASE_3_10 git_last_commit: d5113c2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/geecc_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/geecc_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/geecc_1.20.0.tgz vignettes: vignettes/geecc/inst/doc/geecc.pdf vignetteTitles: geecc User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geecc/inst/doc/geecc.R dependencyCount: 14 Package: GEM Version: 1.12.0 Depends: R (>= 3.3) Imports: tcltk, ggplot2, methods, stats, grDevices, graphics, utils Suggests: knitr, RUnit, testthat, BiocGenerics License: Artistic-2.0 MD5sum: 610d496c0d3a5262756fdf2a03dc2c80 NeedsCompilation: no Title: GEM: fast association study for the interplay of Gene, Environment and Methylation Description: Tools for analyzing EWAS, methQTL and GxE genome widely. biocViews: MethylSeq, MethylationArray, GenomeWideAssociation, Regression, DNAMethylation, SNP, GeneExpression, GUI Author: Hong Pan, Joanna D Holbrook, Neerja Karnani, Chee-Keong Kwoh Maintainer: Hong Pan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GEM git_branch: RELEASE_3_10 git_last_commit: c9112e6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GEM_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GEM_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GEM_1.12.0.tgz vignettes: vignettes/GEM/inst/doc/user_guide.html vignetteTitles: The GEM User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEM/inst/doc/user_guide.R dependencyCount: 55 Package: gemini Version: 1.0.0 Depends: R (>= 3.6.0) Imports: dplyr, grDevices, ggplot2, magrittr, mixtools, scales, pbmcapply, parallel, stats, utils Suggests: knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE Archs: i386, x64 MD5sum: 8ac72cd8ca83e11d225e5a40e302ad5a NeedsCompilation: no Title: GEMINI: Variational inference approach to infer genetic interactions from pairwise CRISPR screens Description: GEMINI uses log-fold changes to model sample-dependent and independent effects, and uses a variational Bayes approach to infer these effects. The inferred effects are used to score and identify genetic interactions, such as lethality and recovery. More details can be found in Zamanighomi et al. 2019 (in press). biocViews: Software, CRISPR, Bayesian, DataImport Author: Mahdi Zamanighomi [aut], Sidharth Jain [aut, cre] Maintainer: Sidharth Jain VignetteBuilder: knitr BugReports: https://github.com/sellerslab/gemini/issues git_url: https://git.bioconductor.org/packages/gemini git_branch: RELEASE_3_10 git_last_commit: 44b4098 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gemini_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gemini_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gemini_1.0.0.tgz vignettes: vignettes/gemini/inst/doc/gemini-quickstart.html vignetteTitles: QuickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gemini/inst/doc/gemini-quickstart.R dependencyCount: 65 Package: genArise Version: 1.62.0 Depends: R (>= 1.7.1), locfit, tkrplot, methods Imports: graphics, grDevices, methods, stats, tcltk, utils, xtable License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: cec96e4b7460a6cf185faae83971eafa NeedsCompilation: no Title: Microarray Analysis tool Description: genArise is an easy to use tool for dual color microarray data. Its GUI-Tk based environment let any non-experienced user performs a basic, but not simple, data analysis just following a wizard. In addition it provides some tools for the developer. biocViews: Microarray, TwoChannel, Preprocessing Author: Ana Patricia Gomez Mayen ,\\ Gustavo Corral Guille , \\ Lina Riego Ruiz ,\\ Gerardo Coello Coutino Maintainer: IFC Development Team URL: http://www.ifc.unam.mx/genarise git_url: https://git.bioconductor.org/packages/genArise git_branch: RELEASE_3_10 git_last_commit: 4c468bc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/genArise_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/genArise_1.62.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/genArise_1.62.0.tgz vignettes: vignettes/genArise/inst/doc/genArise.pdf vignetteTitles: genAriseGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/genArise/inst/doc/genArise.R dependencyCount: 11 Package: genbankr Version: 1.14.0 Depends: methods Imports: BiocGenerics, IRanges (>= 2.13.15), GenomicRanges (>= 1.31.10), GenomicFeatures (>= 1.31.5), Biostrings, VariantAnnotation, rtracklayer, S4Vectors (>= 0.17.28), GenomeInfoDb, Biobase Suggests: RUnit, rentrez, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: a887f502e8773fe732edc88ab6c7558c NeedsCompilation: no Title: Parsing GenBank files into semantically useful objects Description: Reads Genbank files. biocViews: Infrastructure, DataImport Author: Gabriel Becker [aut, cre], Michael Lawrence [aut] Maintainer: Gabriel Becker VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genbankr git_branch: RELEASE_3_10 git_last_commit: bd4c3cb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/genbankr_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/genbankr_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/genbankr_1.14.0.tgz vignettes: vignettes/genbankr/inst/doc/genbankr.html vignetteTitles: An introduction to genbankr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genbankr/inst/doc/genbankr.R dependencyCount: 85 Package: GeneAccord Version: 1.4.0 Depends: R (>= 3.5) Imports: biomaRt, caTools, dplyr, ggplot2, graphics, grDevices, gtools, ggpubr, magrittr, maxLik, RColorBrewer, reshape2, stats, tibble, utils Suggests: assertthat, BiocStyle, devtools, knitr, rmarkdown, testthat License: file LICENSE MD5sum: df6ada64b96331cabc28477e24c26132 NeedsCompilation: no Title: Detection of clonally exclusive gene or pathway pairs in a cohort of cancer patients Description: A statistical framework to examine the combinations of clones that co-exist in tumors. More precisely, the algorithm finds pairs of genes that are mutated in the same tumor but in different clones, i.e. their subclonal mutation profiles are mutually exclusive. We refer to this as clonally exclusive. It means that the mutations occurred in different branches of the tumor phylogeny, indicating parallel evolution of the clones. Our statistical framework assesses whether a pattern of clonal exclusivity occurs more often than expected by chance alone across a cohort of patients. The required input data are the mutated gene-to-clone assignments from a cohort of cancer patients, which were obtained by running phylogenetic tree inference methods. Reconstructing the evolutionary history of a tumor and detecting the clones is challenging. For nondeterministic algorithms, repeated tree inference runs may lead to slightly different mutation-to-clone assignments. Therefore, our algorithm was designed to allow the input of multiple gene-to-clone assignments per patient. They may have been generated by repeatedly performing the tree inference, or by sampling from the posterior distribution of trees. The tree inference methods designate the mutations to individual clones. The mutations can then be mapped to genes or pathways. Hence our statistical framework can be applied on the gene level, or on the pathway level to detect clonally exclusive pairs of pathways. If a pair is significantly clonally exclusive, it points towards the fact that this specific clone configuration confers a selective advantage, possibly through synergies between the clones with these mutations. biocViews: BiomedicalInformatics, GeneticVariability, GenomicVariation, SomaticMutation, FunctionalGenomics, Genetics, MathematicalBiology, SystemsBiology, FeatureExtraction, PatternLogic, Pathways Author: Ariane L. Moore, Jack Kuipers and Niko Beerenwinkel Maintainer: Ariane L. Moore URL: https://github.com/cbg-ethz/GeneAccord VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneAccord git_branch: RELEASE_3_10 git_last_commit: ad6ab9d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GeneAccord_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GeneAccord_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GeneAccord_1.4.0.tgz vignettes: vignettes/GeneAccord/inst/doc/GeneAccord.html vignetteTitles: GeneAccord hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneAccord/inst/doc/GeneAccord.R dependencyCount: 105 Package: GeneAnswers Version: 2.28.0 Depends: R (>= 3.0.0), igraph, RCurl, annotate, Biobase (>= 1.12.0), methods, XML, RSQLite, MASS, Heatplus, RColorBrewer Imports: RBGL, annotate, downloader Suggests: GO.db, KEGG.db, reactome.db, biomaRt, AnnotationDbi, org.Hs.eg.db, org.Rn.eg.db, org.Mm.eg.db, org.Dm.eg.db, graph License: LGPL (>= 2) MD5sum: 18bd205ee60670c5af1f25d7962f378a NeedsCompilation: no Title: Integrated Interpretation of Genes Description: GeneAnswers provides an integrated tool for biological or medical interpretation of the given one or more groups of genes by means of statistical test. biocViews: Infrastructure, DataRepresentation, Visualization, GraphsAndNetworks Author: Lei Huang, Gang Feng, Pan Du, Tian Xia, Xishu Wang, Jing, Wen, Warren Kibbe and Simon Lin Maintainer: Lei Huang and Gang Feng git_url: https://git.bioconductor.org/packages/GeneAnswers git_branch: RELEASE_3_10 git_last_commit: a499f2f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GeneAnswers_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GeneAnswers_2.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GeneAnswers_2.28.0.tgz vignettes: vignettes/GeneAnswers/inst/doc/geneAnswers.pdf, vignettes/GeneAnswers/inst/doc/getListGIF.pdf vignetteTitles: GeneAnswers, GeneAnswers web-based visualization module hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneAnswers/inst/doc/geneAnswers.R, vignettes/GeneAnswers/inst/doc/getListGIF.R suggestsMe: InterMineR dependencyCount: 43 Package: geneAttribution Version: 1.12.0 Imports: utils, GenomicRanges, org.Hs.eg.db, BiocGenerics, GenomeInfoDb, GenomicFeatures, IRanges, rtracklayer Suggests: TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: e81d6f83fce4728c87d0a5d343aa41e2 NeedsCompilation: no Title: Identification of candidate genes associated with genetic variation Description: Identification of the most likely gene or genes through which variation at a given genomic locus in the human genome acts. The most basic functionality assumes that the closer gene is to the input locus, the more likely the gene is to be causative. Additionally, any empirical data that links genomic regions to genes (e.g. eQTL or genome conformation data) can be used if it is supplied in the UCSC .BED file format. biocViews: SNP, GenePrediction, GenomeWideAssociation, VariantAnnotation, GenomicVariation Author: Arthur Wuster Maintainer: Arthur Wuster VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneAttribution git_branch: RELEASE_3_10 git_last_commit: 8037d9c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/geneAttribution_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/geneAttribution_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/geneAttribution_1.12.0.tgz vignettes: vignettes/geneAttribution/inst/doc/geneAttribution.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 84 Package: GeneBreak Version: 1.16.0 Depends: R(>= 3.2), QDNAseq, CGHcall, CGHbase, GenomicRanges Imports: graphics, methods License: GPL-2 MD5sum: 33bd1ffc50de66e3152d86f26a4f2093 NeedsCompilation: no Title: Gene Break Detection Description: Recurrent breakpoint gene detection on copy number aberration profiles. biocViews: aCGH, CopyNumberVariation, DNASeq, Genetics, Sequencing, WholeGenome, Visualization Author: Evert van den Broek, Stef van Lieshout Maintainer: Evert van den Broek URL: https://github.com/stefvanlieshout/GeneBreak git_url: https://git.bioconductor.org/packages/GeneBreak git_branch: RELEASE_3_10 git_last_commit: 55bb827 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GeneBreak_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GeneBreak_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GeneBreak_1.16.0.tgz vignettes: vignettes/GeneBreak/inst/doc/GeneBreak.pdf vignetteTitles: GeneBreak hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneBreak/inst/doc/GeneBreak.R dependencyCount: 47 Package: geneClassifiers Version: 1.10.0 Depends: R (>= 3.6.0) Imports: utils, methods, stats, Biobase, BiocGenerics Suggests: testthat License: GPL-2 MD5sum: 245cd31e22585b33eab5e5ec6de96aae NeedsCompilation: no Title: Application of gene classifiers Description: This packages aims for easy accessible application of classifiers which have been published in literature using an ExpressionSet as input. biocViews: GeneExpression, BiomedicalInformatics, Classification, Survival, Microarray Author: R Kuiper [cre, aut] () Maintainer: R Kuiper URL: https://doi.org/doi:10.18129/B9.bioc.geneClassifiers BugReports: https://github.com/rkuiper/geneClassifiers/issues git_url: https://git.bioconductor.org/packages/geneClassifiers git_branch: RELEASE_3_10 git_last_commit: 3355567 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/geneClassifiers_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/geneClassifiers_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/geneClassifiers_1.10.0.tgz vignettes: vignettes/geneClassifiers/inst/doc/geneClassifiers.pdf, vignettes/geneClassifiers/inst/doc/MissingCovariates.pdf vignetteTitles: geneClassifiers introduction, geneClassifiers and missing probesets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneClassifiers/inst/doc/geneClassifiers.R dependencyCount: 7 Package: GeneExpressionSignature Version: 1.32.0 Depends: R (>= 2.13), Biobase, PGSEA Suggests: apcluster,GEOquery License: GPL-2 MD5sum: 7bcd17aad804b94684e8d35659efcacf NeedsCompilation: no Title: Gene Expression Signature based Similarity Metric Description: This package gives the implementations of the gene expression signature and its distance to each. Gene expression signature is represented as a list of genes whose expression is correlated with a biological state of interest. And its distance is defined using a nonparametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov statistic. Gene expression signature and its distance can be used to detect similarities among the signatures of drugs, diseases, and biological states of interest. biocViews: GeneExpression Author: Yang Cao Maintainer: Yang Cao , Fei Li ,Lu Han git_url: https://git.bioconductor.org/packages/GeneExpressionSignature git_branch: RELEASE_3_10 git_last_commit: 6afd2c0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GeneExpressionSignature_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GeneExpressionSignature_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GeneExpressionSignature_1.32.0.tgz vignettes: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.pdf vignetteTitles: GeneExpressionSignature hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.R dependencyCount: 30 Package: genefilter Version: 1.68.0 Imports: BiocGenerics (>= 0.31.2), AnnotationDbi, annotate, Biobase, graphics, methods, stats, survival Suggests: class, hgu95av2.db, tkWidgets, ALL, ROC, DESeq, pasilla, RColorBrewer, BiocStyle, knitr License: Artistic-2.0 MD5sum: 7230c58b5b6d7ea99296a1a5e1da96c9 NeedsCompilation: yes Title: genefilter: methods for filtering genes from high-throughput experiments Description: Some basic functions for filtering genes. biocViews: Microarray Author: R. Gentleman, V. Carey, W. Huber, F. Hahne Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genefilter git_branch: RELEASE_3_10 git_last_commit: cba90ff git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/genefilter_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/genefilter_1.68.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/genefilter_1.68.0.tgz vignettes: vignettes/genefilter/inst/doc/howtogenefilter.pdf, vignettes/genefilter/inst/doc/howtogenefinder.pdf, vignettes/genefilter/inst/doc/independent_filtering_plots.pdf, vignettes/genefilter/inst/doc/independent_filtering.pdf vignetteTitles: Using the genefilter function to filter genes from a microarray dataset, How to find genes whose expression profile is similar to that of specified genes, Additional plots for: Independent filtering increases power for detecting differentially expressed genes,, Bourgon et al.,, PNAS (2010), Diagnostics for independent filtering hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefilter/inst/doc/howtogenefilter.R, vignettes/genefilter/inst/doc/howtogenefinder.R, vignettes/genefilter/inst/doc/independent_filtering_plots.R, vignettes/genefilter/inst/doc/independent_filtering.R dependsOnMe: a4Base, cellHTS2, CNTools, GeneMeta, simpleaffy, sva importsMe: affyQCReport, ALPS, annmap, arrayQualityMetrics, Category, cbaf, countsimQC, covRNA, DESeq, DESeq2, DEXSeq, eisa, gCMAP, GGBase, GISPA, GSRI, JunctionSeq, methyAnalysis, methylCC, methylumi, minfi, MLInterfaces, mogsa, NBAMSeq, pcaExplorer, PECA, phenoTest, pwrEWAS, Ringo, simpleaffy, TCGAbiolinks, tilingArray, XDE, zinbwave suggestsMe: AffyExpress, annotate, ArrayTools, BiocCaseStudies, BioNet, categoryCompare, ClassifyR, clusterStab, codelink, cola, compcodeR, DelayedArray, EnrichedHeatmap, factDesign, ffpe, GenoGAM, GenomicFiles, GOstats, GSAR, GSEAlm, GSVA, logicFS, lumi, MCRestimate, MMUPHin, npGSEA, oligo, phyloseq, pvac, qpgraph, rtracklayer, siggenes, SSPA, topGO dependencyCount: 37 Package: genefu Version: 2.18.1 Depends: survcomp, mclust, limma, biomaRt, iC10, AIMS, R (>= 2.10) Imports: amap, impute Suggests: GeneMeta, breastCancerVDX, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerUNT, breastCancerNKI, rmeta, Biobase, xtable, knitr, caret, survival License: Artistic-2.0 MD5sum: d9fdd36bd1a91aed72df6ec23701f23c NeedsCompilation: no Title: Computation of Gene Expression-Based Signatures in Breast Cancer Description: Description: This package contains functions implementing various tasks usually required by gene expression analysis, especially in breast cancer studies: gene mapping between different microarray platforms, identification of molecular subtypes, implementation of published gene signatures, gene selection, and survival analysis. biocViews: DifferentialExpression, GeneExpression, Visualization, Clustering, Classification Author: Deena M.A. Gendoo, Natchar Ratanasirigulchai, Markus S. Schroeder, Laia Pare, Joel S. Parker, Aleix Prat, and Benjamin Haibe-Kains Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/software/genefu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genefu git_branch: RELEASE_3_10 git_last_commit: 41b962e git_last_commit_date: 2020-01-31 Date/Publication: 2020-01-31 source.ver: src/contrib/genefu_2.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/genefu_2.18.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/genefu_2.18.1.tgz vignettes: vignettes/genefu/inst/doc/genefu.pdf vignetteTitles: genefu An Introduction (HowTo) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefu/inst/doc/genefu.R importsMe: consensusOV dependencyCount: 88 Package: GeneGA Version: 1.36.0 Depends: seqinr, hash, methods License: GPL version 2 MD5sum: 5a0862300f31b66f65b80d87b7c9e544 NeedsCompilation: no Title: Design gene based on both mRNA secondary structure and codon usage bias using Genetic algorithm Description: R based Genetic algorithm for gene expression optimization by considering both mRNA secondary structure and codon usage bias, GeneGA includes the information of highly expressed genes of almost 200 genomes. Meanwhile, Vienna RNA Package is needed to ensure GeneGA to function properly. biocViews: GeneExpression Author: Zhenpeng Li and Haixiu Huang Maintainer: Zhenpeng Li URL: http://www.tbi.univie.ac.at/~ivo/RNA/ git_url: https://git.bioconductor.org/packages/GeneGA git_branch: RELEASE_3_10 git_last_commit: e3d3b88 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GeneGA_1.36.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GeneGA_1.36.0.tgz vignettes: vignettes/GeneGA/inst/doc/GeneGA.pdf vignetteTitles: GeneGA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneGA/inst/doc/GeneGA.R dependencyCount: 14 Package: GeneGeneInteR Version: 1.12.0 Depends: R (>= 3.5) Imports: snpStats, mvtnorm, GGtools, Rsamtools, igraph, kernlab, FactoMineR, plspm, IRanges, GenomicRanges, data.table,grDevices, graphics,stats, utils, methods License: GPL (>= 2) MD5sum: 3062693e1333045c529a55dfefdec343 NeedsCompilation: yes Title: Tools for Testing Gene-Gene Interaction at the Gene Level Description: The aim of this package is to propose several methods for testing gene-gene interaction in case-control association studies. Such a test can be done by aggregating SNP-SNP interaction tests performed at the SNP level (SSI) or by using gene-gene multidimensionnal methods (GGI) methods. The package also proposes tools for a graphic display of the results. biocViews: GenomeWideAssociation, SNP, Genetics, GeneticVariability Author: Mathieu Emily, Nicolas Sounac, Florian Kroell, Magalie Houee-Bigot Maintainer: Mathieu Emily , Magalie Houee-Bigot git_url: https://git.bioconductor.org/packages/GeneGeneInteR git_branch: RELEASE_3_10 git_last_commit: 5429235 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GeneGeneInteR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GeneGeneInteR_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GeneGeneInteR_1.12.0.tgz vignettes: vignettes/GeneGeneInteR/inst/doc/GenePair.pdf, vignettes/GeneGeneInteR/inst/doc/VignetteGeneGeneInteR_Introduction.pdf vignetteTitles: Pairwise interaction tests, GeneGeneInteR Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneGeneInteR/inst/doc/GenePair.R, vignettes/GeneGeneInteR/inst/doc/VignetteGeneGeneInteR_Introduction.R dependencyCount: 210 Package: GeneMeta Version: 1.58.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), genefilter Imports: methods, Biobase (>= 2.5.5) Suggests: RColorBrewer License: Artistic-2.0 Archs: i386, x64 MD5sum: 71fb0261fd33d97125f787bc5515de73 NeedsCompilation: no Title: MetaAnalysis for High Throughput Experiments Description: A collection of meta-analysis tools for analysing high throughput experimental data biocViews: Sequencing, GeneExpression, Microarray Author: Lara Lusa , R. Gentleman, M. Ruschhaupt Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/GeneMeta git_branch: RELEASE_3_10 git_last_commit: e7e32ae git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GeneMeta_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GeneMeta_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GeneMeta_1.58.0.tgz vignettes: vignettes/GeneMeta/inst/doc/GeneMeta.pdf vignetteTitles: GeneMeta Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneMeta/inst/doc/GeneMeta.R importsMe: XDE suggestsMe: genefu dependencyCount: 38 Package: GeneNetworkBuilder Version: 1.28.1 Depends: R (>= 2.15.1), Rcpp (>= 0.9.13) Imports: plyr, graph, htmlwidgets, Rgraphviz, rjson, XML, methods, grDevices, stats, graphics LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, RBGL, knitr, simpIntLists, shiny, STRINGdb, BiocStyle, magick License: GPL (>= 2) MD5sum: 7a57de00fd6d0a7d62a32a03415ec3ca NeedsCompilation: yes Title: GeneNetworkBuilder: a bioconductor package for building regulatory network using ChIP-chip/ChIP-seq data and Gene Expression Data Description: Appliation for discovering direct or indirect targets of transcription factors using ChIP-chip or ChIP-seq, and microarray or RNA-seq gene expression data. Inputting a list of genes of potential targets of one TF from ChIP-chip or ChIP-seq, and the gene expression results, GeneNetworkBuilder generates a regulatory network of the TF. biocViews: Sequencing, Microarray, GraphAndNetwork Author: Jianhong Ou, Haibo Liu, Heidi A Tissenbaum and Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneNetworkBuilder git_branch: RELEASE_3_10 git_last_commit: cbec4f6 git_last_commit_date: 2020-01-18 Date/Publication: 2020-01-18 source.ver: src/contrib/GeneNetworkBuilder_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/GeneNetworkBuilder_1.28.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GeneNetworkBuilder_1.28.1.tgz vignettes: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.html, vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.html, vignettes/GeneNetworkBuilder/inst/doc/with.BioGRID.STRING.html vignetteTitles: GeneNetworkBuilder Vignette, Generate Network from a list of gene, Working with BioGRID,, STRING hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.R, vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.R, vignettes/GeneNetworkBuilder/inst/doc/with.BioGRID.STRING.R dependencyCount: 21 Package: GeneOverlap Version: 1.22.0 Imports: stats, RColorBrewer, gplots, methods Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 3e4ff2e8c15e92a45154bb53fc294227 NeedsCompilation: no Title: Test and visualize gene overlaps Description: Test two sets of gene lists and visualize the results. biocViews: MultipleComparison, Visualization Author: Li Shen, Mount Sinai Maintainer: Li Shen, Mount Sinai URL: http://shenlab-sinai.github.io/shenlab-sinai/ git_url: https://git.bioconductor.org/packages/GeneOverlap git_branch: RELEASE_3_10 git_last_commit: 84ded08 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GeneOverlap_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GeneOverlap_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GeneOverlap_1.22.0.tgz vignettes: vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf vignetteTitles: Testing and visualizing gene overlaps with the "GeneOverlap" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneOverlap/inst/doc/GeneOverlap.R dependencyCount: 10 Package: geneplast Version: 1.12.2 Depends: R (>= 3.3), methods Imports: igraph, snow, ape, grDevices, graphics, stats, utils, data.table Suggests: RTN, RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown, Fletcher2013b, geneplast.data.string.v91, ggplot2, ggpubr, plyr License: GPL (>= 2) MD5sum: 7bb06e9cbedc1412c956bc0d7e77fa9d NeedsCompilation: no Title: Evolutionary and plasticity analysis of orthologous groups Description: Geneplast is designed for evolutionary and plasticity analysis based on orthologous groups distribution in a given species tree. It uses Shannon information theory and orthologs abundance to estimate the Evolutionary Plasticity Index. Additionally, it implements the Bridge algorithm to determine the evolutionary root of a given gene based on its orthologs distribution. biocViews: Genetics, GeneRegulation, SystemsBiology Author: Rodrigo Dalmolin, Mauro Castro Maintainer: Mauro Castro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneplast git_branch: RELEASE_3_10 git_last_commit: cb33d73 git_last_commit_date: 2019-11-05 Date/Publication: 2019-11-06 source.ver: src/contrib/geneplast_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/geneplast_1.12.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/geneplast_1.12.2.tgz vignettes: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.html, vignettes/geneplast/inst/doc/geneplast.html vignetteTitles: "Supporting Material for Trefflich2019.", "Geneplast: evolutionary rooting and plasticity analysis." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.R, vignettes/geneplast/inst/doc/geneplast.R dependencyCount: 18 Package: geneplotter Version: 1.64.0 Depends: R (>= 2.10), methods, Biobase, BiocGenerics, lattice, annotate Imports: AnnotationDbi, graphics, grDevices, grid, RColorBrewer, stats, utils Suggests: Rgraphviz, fibroEset, hgu95av2.db, hu6800.db, hgu133a.db License: Artistic-2.0 Archs: i386, x64 MD5sum: 44ee8c53a3daa6e0564b7cf13988a488 NeedsCompilation: no Title: Graphics related functions for Bioconductor Description: Functions for plotting genomic data biocViews: Visualization Author: R. Gentleman, Biocore Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/geneplotter git_branch: RELEASE_3_10 git_last_commit: 4dd2f1c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/geneplotter_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/geneplotter_1.64.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/geneplotter_1.64.0.tgz vignettes: vignettes/geneplotter/inst/doc/byChroms.pdf, vignettes/geneplotter/inst/doc/visualize.pdf vignetteTitles: How to assemble a chromLocation object, Visualization of Microarray Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplotter/inst/doc/byChroms.R, vignettes/geneplotter/inst/doc/visualize.R importsMe: biocGraph, DESeq, DESeq2, DEXSeq, EnrichmentBrowser, GSVA, IsoGeneGUI, JunctionSeq, MethylSeekR, RNAinteract, RNAither suggestsMe: BiocCaseStudies, biocGraph, Category, chimera, GOstats dependencyCount: 35 Package: geneRecommender Version: 1.58.0 Depends: R (>= 1.8.0), Biobase (>= 1.4.22), methods Imports: Biobase, methods, stats License: GPL (>= 2) MD5sum: ed64747892fbd596f6415f7ce4c3754d NeedsCompilation: no Title: A gene recommender algorithm to identify genes coexpressed with a query set of genes Description: This package contains a targeted clustering algorithm for the analysis of microarray data. The algorithm can aid in the discovery of new genes with similar functions to a given list of genes already known to have closely related functions. biocViews: Microarray, Clustering Author: Gregory J. Hather , with contributions from Art B. Owen and Terence P. Speed Maintainer: Greg Hather git_url: https://git.bioconductor.org/packages/geneRecommender git_branch: RELEASE_3_10 git_last_commit: 837fe5e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/geneRecommender_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/geneRecommender_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/geneRecommender_1.58.0.tgz vignettes: vignettes/geneRecommender/inst/doc/geneRecommender.pdf vignetteTitles: Using the geneRecommender Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneRecommender/inst/doc/geneRecommender.R dependencyCount: 7 Package: GeneRegionScan Version: 1.42.0 Depends: methods, Biobase (>= 2.5.5), Biostrings Imports: S4Vectors (>= 0.9.25), Biobase (>= 2.5.5), affxparser, RColorBrewer, Biostrings Suggests: BSgenome, affy, AnnotationDbi License: GPL (>= 2) MD5sum: 85dedd2e473ceadc1c1e43f89dffdaa6 NeedsCompilation: no Title: GeneRegionScan Description: A package with focus on analysis of discrete regions of the genome. This package is useful for investigation of one or a few genes using Affymetrix data, since it will extract probe level data using the Affymetrix Power Tools application and wrap these data into a ProbeLevelSet. A ProbeLevelSet directly extends the expressionSet, but includes additional information about the sequence of each probe and the probe set it is derived from. The package includes a number of functions used for plotting these probe level data as a function of location along sequences of mRNA-strands. This can be used for analysis of variable splicing, and is especially well suited for use with exon-array data. biocViews: Microarray, DataImport, SNP, OneChannel, Visualization Author: Lasse Folkersen, Diego Diez Maintainer: Lasse Folkersen git_url: https://git.bioconductor.org/packages/GeneRegionScan git_branch: RELEASE_3_10 git_last_commit: 4b291c4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GeneRegionScan_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GeneRegionScan_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GeneRegionScan_1.42.0.tgz vignettes: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.pdf vignetteTitles: GeneRegionScan hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.R dependencyCount: 15 Package: geneRxCluster Version: 1.22.0 Depends: GenomicRanges,IRanges Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: f89c95e7e0662bd0a0acca2c1466e6a4 NeedsCompilation: yes Title: gRx Differential Clustering Description: Detect Differential Clustering of Genomic Sites such as gene therapy integrations. The package provides some functions for exploring genomic insertion sites originating from two different sources. Possibly, the two sources are two different gene therapy vectors. Vectors are preferred that target sensitive regions less frequently, motivating the search for localized clusters of insertions and comparison of the clusters formed by integration of different vectors. Scan statistics allow the discovery of spatial differences in clustering and calculation of False Discovery Rates (FDRs) providing statistical methods for comparing retroviral vectors. A scan statistic for comparing two vectors using multiple window widths to detect clustering differentials and compute FDRs is implemented here. biocViews: Sequencing, Clustering, Genetics Author: Charles Berry Maintainer: Charles Berry git_url: https://git.bioconductor.org/packages/geneRxCluster git_branch: RELEASE_3_10 git_last_commit: 6c90a6d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/geneRxCluster_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/geneRxCluster_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/geneRxCluster_1.22.0.tgz vignettes: vignettes/geneRxCluster/inst/doc/tutorial.pdf vignetteTitles: Using geneRxCluster hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneRxCluster/inst/doc/tutorial.R dependencyCount: 16 Package: GeneSelectMMD Version: 2.30.0 Depends: R (>= 2.13.2), Biobase Imports: Biobase, MASS, graphics, stats, survival, limma Suggests: ALL License: GPL (>= 2) MD5sum: 3b5f1202c5260e7a49d68da35341fe57 NeedsCompilation: yes Title: Gene selection based on the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions Description: Gene selection based on a mixture of marginal distributions biocViews: DifferentialExpression Author: Jarrett Morrow , Weiliang Qiu , Wenqing He , Xiaogang Wang , Ross Lazarus . Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/GeneSelectMMD git_branch: RELEASE_3_10 git_last_commit: 4070b7b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GeneSelectMMD_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GeneSelectMMD_2.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GeneSelectMMD_2.30.0.tgz vignettes: vignettes/GeneSelectMMD/inst/doc/gsMMD.pdf vignetteTitles: Gene Selection based on a mixture of marginal distributions hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneSelectMMD/inst/doc/gsMMD.R importsMe: iCheck dependencyCount: 15 Package: GENESIS Version: 2.16.1 Imports: Biobase, BiocGenerics, GWASTools, gdsfmt, GenomicRanges, IRanges, S4Vectors, SeqArray, SeqVarTools, SNPRelate, data.table, dplyr, foreach, graphics, grDevices, igraph, Matrix, methods, reshape2, stats, utils Suggests: CompQuadForm, poibin, SPAtest, survey, testthat, BiocStyle, knitr, rmarkdown, GWASdata, ggplot2, GGally, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: d7474e6dae7d8082b793753598dc2ed7 NeedsCompilation: yes Title: GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness Description: The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes. biocViews: SNP, GeneticVariability, Genetics, StatisticalMethod, DimensionReduction, PrincipalComponent, GenomeWideAssociation, QualityControl, BiocViews Author: Matthew P. Conomos, Stephanie M. Gogarten, Lisa Brown, Han Chen, Thomas Lumley, Ken Rice, Tamar Sofer, Timothy Thornton, Chaoyu Yu Maintainer: Stephanie M. Gogarten URL: https://github.com/UW-GAC/GENESIS VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GENESIS git_branch: RELEASE_3_10 git_last_commit: 31387c5 git_last_commit_date: 2019-11-01 Date/Publication: 2019-11-02 source.ver: src/contrib/GENESIS_2.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/GENESIS_2.16.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GENESIS_2.16.1.tgz vignettes: vignettes/GENESIS/inst/doc/assoc_test_seq.html, vignettes/GENESIS/inst/doc/assoc_test.html, vignettes/GENESIS/inst/doc/pcair.html vignetteTitles: Analyzing Sequence Data using the GENESIS Package, Genetic Association Testing using the GENESIS Package, Population Structure and Relatedness Inference using the GENESIS Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENESIS/inst/doc/assoc_test_seq.R, vignettes/GENESIS/inst/doc/assoc_test.R, vignettes/GENESIS/inst/doc/pcair.R dependencyCount: 84 Package: GeneStructureTools Version: 1.6.0 Imports: Biostrings,GenomicRanges,IRanges,data.table,plyr,stringdist,stringr,S4Vectors,BSgenome.Mmusculus.UCSC.mm10,stats,utils,Gviz,rtracklayer,methods Suggests: BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE Archs: i386, x64 MD5sum: 7573793df01224cf512a313eea14e5e9 NeedsCompilation: no Title: Tools for spliced gene structure manipulation and analysis Description: GeneStructureTools can be used to create in silico alternative splicing events, and analyse potential effects this has on functional gene products. biocViews: ImmunoOncology, Software, DifferentialSplicing, FunctionalPrediction, Transcriptomics, AlternativeSplicing, RNASeq Author: Beth Signal Maintainer: Beth Signal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneStructureTools git_branch: RELEASE_3_10 git_last_commit: 94b91cf git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GeneStructureTools_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GeneStructureTools_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GeneStructureTools_1.6.0.tgz vignettes: vignettes/GeneStructureTools/inst/doc/Vignette.html vignetteTitles: Introduction to GeneStructureTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneStructureTools/inst/doc/Vignette.R dependencyCount: 146 Package: geNetClassifier Version: 1.26.0 Depends: R (>= 2.10.1), Biobase (>= 2.5.5), EBarrays, minet, methods Imports: e1071, graphics Suggests: leukemiasEset, RUnit, BiocGenerics Enhances: RColorBrewer, igraph, infotheo License: GPL (>= 2) Archs: i386, x64 MD5sum: 4c8f609e9c8292aa8d1c3155c0413e5c NeedsCompilation: no Title: Classify diseases and build associated gene networks using gene expression profiles Description: Comprehensive package to automatically train and validate a multi-class SVM classifier based on gene expression data. Provides transparent selection of gene markers, their coexpression networks, and an interface to query the classifier. biocViews: Classification, DifferentialExpression, Microarray Author: Sara Aibar, Celia Fontanillo and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain. Maintainer: Sara Aibar URL: http://www.cicancer.org git_url: https://git.bioconductor.org/packages/geNetClassifier git_branch: RELEASE_3_10 git_last_commit: 4eddd50 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/geNetClassifier_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/geNetClassifier_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/geNetClassifier_1.26.0.tgz vignettes: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.pdf vignetteTitles: geNetClassifier-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.R importsMe: bioCancer, canceR dependencyCount: 17 Package: GeneticsDesign Version: 1.54.0 Imports: gmodels, graphics, gtools (>= 2.4.0), mvtnorm, stats License: GPL-2 Archs: i386, x64 MD5sum: 544e1731be7163ac7106ea6af379759c NeedsCompilation: no Title: Functions for designing genetics studies Description: This package contains functions useful for designing genetics studies, including power and sample-size calculations. biocViews: Genetics Author: Gregory Warnes David Duffy , Michael Man Weiliang Qiu Ross Lazarus Maintainer: The R Genetics Project git_url: https://git.bioconductor.org/packages/GeneticsDesign git_branch: RELEASE_3_10 git_last_commit: 11095c2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GeneticsDesign_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GeneticsDesign_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GeneticsDesign_1.54.0.tgz vignettes: vignettes/GeneticsDesign/inst/doc/GPC.pdf vignetteTitles: Power Calculation for Testing If Disease is Associated a Marker in a Case-Control Study Using the \Rpackage{GeneticsDesign} Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneticsDesign/inst/doc/GPC.R dependencyCount: 10 Package: GeneticsPed Version: 1.48.0 Depends: R (>= 2.4.0), MASS Imports: gdata, genetics Suggests: RUnit, gtools License: LGPL (>= 2.1) | file LICENSE MD5sum: 0eed7a10dc5820cd22d0dbad1dfdf0c7 NeedsCompilation: yes Title: Pedigree and genetic relationship functions Description: Classes and methods for handling pedigree data. It also includes functions to calculate genetic relationship measures as relationship and inbreeding coefficients and other utilities. Note that package is not yet stable. Use it with care! biocViews: Genetics Author: Gregor Gorjanc and David A. Henderson , with code contributions by Brian Kinghorn and Andrew Percy (see file COPYING) Maintainer: David Henderson URL: http://rgenetics.org git_url: https://git.bioconductor.org/packages/GeneticsPed git_branch: RELEASE_3_10 git_last_commit: 9d4a1f0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GeneticsPed_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GeneticsPed_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GeneticsPed_1.48.0.tgz vignettes: vignettes/GeneticsPed/inst/doc/geneticRelatedness.pdf, vignettes/GeneticsPed/inst/doc/pedigreeHandling.pdf, vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.pdf vignetteTitles: Calculation of genetic relatedness/relationship between individuals in the pedigree, Pedigree handling, Quantitative genetic (animal) model example in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneticsPed/inst/doc/geneticRelatedness.R, vignettes/GeneticsPed/inst/doc/pedigreeHandling.R, vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.R dependencyCount: 11 Package: geneXtendeR Version: 1.12.0 Depends: rtracklayer, GO.db, R (>= 3.5.0) Imports: data.table, dplyr, graphics, networkD3, RColorBrewer, SnowballC, tm, utils, wordcloud, AnnotationDbi, BiocStyle, org.Rn.eg.db Suggests: knitr, rmarkdown, testthat, org.Ag.eg.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Pt.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, rtracklayer License: GPL (>= 3) MD5sum: ff35d4bc4dbfa17698de0b02493b42de NeedsCompilation: yes Title: Optimized Functional Annotation Of ChIP-seq Data Description: geneXtendeR optimizes the functional annotation of ChIP-seq peaks by exploring relative differences in annotating ChIP-seq peak sets to variable-length gene bodies. In contrast to prior techniques, geneXtendeR considers peak annotations beyond just the closest gene, allowing users to see peak summary statistics for the first-closest gene, second-closest gene, ..., n-closest gene whilst ranking the output according to biologically relevant events and iteratively comparing the fidelity of peak-to-gene overlap across a user-defined range of upstream and downstream extensions on the original boundaries of each gene's coordinates. Since different ChIP-seq peak callers produce different differentially enriched peaks with a large variance in peak length distribution and total peak count, annotating peak lists with their nearest genes can often be a noisy process. As such, the goal of geneXtendeR is to robustly link differentially enriched peaks with their respective genes, thereby aiding experimental follow-up and validation in designing primers for a set of prospective gene candidates during qPCR. biocViews: ChIPSeq, Genetics, Annotation, GenomeAnnotation, DifferentialPeakCalling, Coverage, PeakDetection, ChipOnChip, HistoneModification, DataImport, NaturalLanguageProcessing, Visualization, GO, Software Author: Bohdan Khomtchouk [aut, cre], William Koehler [aut] Maintainer: Bohdan Khomtchouk URL: https://github.com/Bohdan-Khomtchouk/geneXtendeR VignetteBuilder: knitr BugReports: https://github.com/Bohdan-Khomtchouk/geneXtendeR/issues git_url: https://git.bioconductor.org/packages/geneXtendeR git_branch: RELEASE_3_10 git_last_commit: 2b766f5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-11-02 source.ver: src/contrib/geneXtendeR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/geneXtendeR_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/geneXtendeR_1.12.0.tgz vignettes: vignettes/geneXtendeR/inst/doc/geneXtendeR.pdf vignetteTitles: geneXtendeR.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 96 Package: GENIE3 Version: 1.8.0 Imports: stats, reshape2 Suggests: knitr, rmarkdown, foreach, doRNG, doParallel, Biobase, SummarizedExperiment, testthat, methods License: GPL (>= 2) MD5sum: 229e576a21ba8c55d9f43a4955afbe85 NeedsCompilation: yes Title: GEne Network Inference with Ensemble of trees Description: This package implements the GENIE3 algorithm for inferring gene regulatory networks from expression data. biocViews: NetworkInference, SystemsBiology, DecisionTree, Regression, Network, GraphAndNetwork, GeneExpression Author: Van Anh Huynh-Thu, Sara Aibar, Pierre Geurts Maintainer: Van Anh Huynh-Thu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GENIE3 git_branch: RELEASE_3_10 git_last_commit: 9a07ede git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GENIE3_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GENIE3_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GENIE3_1.8.0.tgz vignettes: vignettes/GENIE3/inst/doc/GENIE3.html vignetteTitles: GENIE3 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENIE3/inst/doc/GENIE3.R importsMe: MetNet, netbenchmark dependencyCount: 11 Package: genoCN Version: 1.38.0 Imports: graphics, stats, utils License: GPL (>=2) Archs: i386, x64 MD5sum: 3da29c5c79318ecec77567b3d05b1612 NeedsCompilation: yes Title: genotyping and copy number study tools Description: Simultaneous identification of copy number states and genotype calls for regions of either copy number variations or copy number aberrations biocViews: Microarray, Genetics Author: Wei Sun and ZhengZheng Tang Maintainer: Wei Sun git_url: https://git.bioconductor.org/packages/genoCN git_branch: RELEASE_3_10 git_last_commit: 1e036f1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/genoCN_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/genoCN_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/genoCN_1.38.0.tgz vignettes: vignettes/genoCN/inst/doc/genoCN.pdf vignetteTitles: add stuff hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genoCN/inst/doc/genoCN.R dependencyCount: 3 Package: GenoGAM Version: 2.4.0 Depends: R (>= 3.5), SummarizedExperiment (>= 1.1.19), HDF5Array (>= 1.8.0), rhdf5 (>= 2.21.6), S4Vectors (>= 0.23.18), Matrix (>= 1.2-8), data.table (>= 1.9.4) Imports: Rcpp (>= 0.12.14), sparseinv (>= 0.1.1), Rsamtools (>= 1.18.2), GenomicRanges (>= 1.23.16), BiocParallel (>= 1.5.17), DESeq2 (>= 1.11.23), futile.logger (>= 1.4.1), GenomeInfoDb (>= 1.7.6), GenomicAlignments (>= 1.7.17), IRanges (>= 2.5.30), Biostrings (>= 2.39.14), DelayedArray (>= 0.3.19), methods, stats LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, chipseq (>= 1.21.2), LSD (>= 3.0.0), genefilter (>= 1.54.2), ggplot2 (>= 2.1.0), testthat, knitr, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 3ef3d724ec8e9f92de3a781a01343a69 NeedsCompilation: yes Title: A GAM based framework for analysis of ChIP-Seq data Description: This package allows statistical analysis of genome-wide data with smooth functions using generalized additive models based on the implementation from the R-package 'mgcv'. It provides methods for the statistical analysis of ChIP-Seq data including inference of protein occupancy, and pointwise and region-wise differential analysis. Estimation of dispersion and smoothing parameters is performed by cross-validation. Scaling of generalized additive model fitting to whole chromosomes is achieved by parallelization over overlapping genomic intervals. biocViews: Regression, DifferentialPeakCalling, ChIPSeq, DifferentialExpression, Genetics, Epigenetics, WholeGenome, ChipOnChip, ImmunoOncology Author: Georg Stricker [aut, cre], Alexander Engelhardt [aut], Julien Gagneur [aut] Maintainer: Georg Stricker URL: https://github.com/gstricker/GenoGAM VignetteBuilder: knitr BugReports: https://github.com/gstricker/GenoGAM/issues git_url: https://git.bioconductor.org/packages/GenoGAM git_branch: RELEASE_3_10 git_last_commit: 6753a93 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GenoGAM_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GenoGAM_2.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GenoGAM_2.4.0.tgz vignettes: vignettes/GenoGAM/inst/doc/GenoGAM.html vignetteTitles: "Modeling ChIP-Seq data with GenoGAM 2.0: A Genome-wide generalized additive model" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenoGAM/inst/doc/GenoGAM.R dependencyCount: 131 Package: genomation Version: 1.18.0 Depends: R (>= 3.0.0),grid Imports: Biostrings (>= 2.47.6), BSgenome (>= 1.47.3), data.table, GenomeInfoDb, GenomicRanges (>= 1.31.8), GenomicAlignments (>= 1.15.6), S4Vectors (>= 0.17.25), ggplot2, gridBase, impute, IRanges (>= 2.13.12), matrixStats, methods, parallel, plotrix, plyr, readr, reshape2, Rsamtools (>= 1.31.2), seqPattern, rtracklayer (>= 1.39.7), RUnit, Rcpp (>= 0.12.14) LinkingTo: Rcpp Suggests: BiocGenerics, genomationData, knitr, RColorBrewer, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 67f92320f24a839fc870940775330607 NeedsCompilation: yes Title: Summary, annotation and visualization of genomic data Description: A package for summary and annotation of genomic intervals. Users can visualize and quantify genomic intervals over pre-defined functional regions, such as promoters, exons, introns, etc. The genomic intervals represent regions with a defined chromosome position, which may be associated with a score, such as aligned reads from HT-seq experiments, TF binding sites, methylation scores, etc. The package can use any tabular genomic feature data as long as it has minimal information on the locations of genomic intervals. In addition, It can use BAM or BigWig files as input. biocViews: Annotation, Sequencing, Visualization, CpGIsland Author: Altuna Akalin [aut, cre], Vedran Franke [aut, cre], Katarzyna Wreczycka [aut], Alexander Gosdschan [ctb], Liz Ing-Simmons [ctb], Bozena Mika-Gospodorz [ctb] Maintainer: Altuna Akalin , Vedran Franke , Katarzyna Wreczycka URL: http://bioinformatics.mdc-berlin.de/genomation/ VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/genomation/issues git_url: https://git.bioconductor.org/packages/genomation git_branch: RELEASE_3_10 git_last_commit: 4a51cb9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/genomation_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/genomation_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/genomation_1.18.0.tgz vignettes: vignettes/genomation/inst/doc/GenomationManual.html vignetteTitles: genomation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomation/inst/doc/GenomationManual.R importsMe: CexoR, fCCAC, RCAS suggestsMe: methylKit dependencyCount: 98 Package: GenomeGraphs Version: 1.46.0 Depends: R (>= 2.10), methods, biomaRt, grid License: Artistic-2.0 Archs: i386, x64 MD5sum: 431337c4b88464c4ad8d2753db69e546 NeedsCompilation: no Title: Plotting genomic information from Ensembl Description: Genomic data analyses requires integrated visualization of known genomic information and new experimental data. GenomeGraphs uses the biomaRt package to perform live annotation queries to Ensembl and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data. Another strength of GenomeGraphs is to plot different data types such as array CGH, gene expression, sequencing and other data, together in one plot using the same genome coordinate system. biocViews: Visualization, Microarray Author: Steffen Durinck , James Bullard Maintainer: Steffen Durinck PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/GenomeGraphs git_branch: RELEASE_3_10 git_last_commit: 8104468 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GenomeGraphs_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GenomeGraphs_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GenomeGraphs_1.46.0.tgz vignettes: vignettes/GenomeGraphs/inst/doc/GenomeGraphs.pdf vignetteTitles: The GenomeGraphs users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomeGraphs/inst/doc/GenomeGraphs.R dependsOnMe: Genominator, waveTiling suggestsMe: oligo dependencyCount: 59 Package: GenomeInfoDb Version: 1.22.1 Depends: R (>= 3.1), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12) Imports: stats, stats4, utils, RCurl, GenomeInfoDbData Suggests: GenomicRanges, Rsamtools, GenomicAlignments, BSgenome, GenomicFeatures, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Dmelanogaster.UCSC.dm3.ensGene, RUnit, BiocStyle, knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: 3fdc0c86b77fd0ac96f17ff37d974f03 NeedsCompilation: no Title: Utilities for manipulating chromosome names, including modifying them to follow a particular naming style Description: Contains data and functions that define and allow translation between different chromosome sequence naming conventions (e.g., "chr1" versus "1"), including a function that attempts to place sequence names in their natural, rather than lexicographic, order. biocViews: Genetics, DataRepresentation, Annotation, GenomeAnnotation Author: Sonali Arora, Martin Morgan, Marc Carlson, H. Pagès Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr Video: http://youtu.be/wdEjCYSXa7w git_url: https://git.bioconductor.org/packages/GenomeInfoDb git_branch: RELEASE_3_10 git_last_commit: a4109ce git_last_commit_date: 2020-03-27 Date/Publication: 2020-03-27 source.ver: src/contrib/GenomeInfoDb_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/GenomeInfoDb_1.22.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GenomeInfoDb_1.22.1.tgz vignettes: vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.pdf, vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.pdf vignetteTitles: GenomeInfoDb: Submitting your organism to GenomeInfoDb, GenomeInfoDb: Introduction to GenomeInfoDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.R, vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.R dependsOnMe: BSgenome, bumphunter, CODEX, CSAR, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicTuples, gmapR, groHMM, HelloRanges, methyAnalysis, MTseeker, Rsamtools, VariantAnnotation importsMe: AllelicImbalance, alpine, amplican, AneuFinder, AnnotationHubData, annotatr, ASpediaFI, ATACseqQC, BaalChIP, ballgown, biovizBase, biscuiteer, BiSeq, bnbc, branchpointer, breakpointR, BSgenome, bsseq, BUSpaRse, CAGEfightR, CAGEr, casper, CexoR, chimeraviz, chipenrich, ChIPexoQual, ChIPpeakAnno, ChIPseeker, chromstaR, chromVAR, circRNAprofiler, cleanUpdTSeq, cn.mops, CNEr, CNVfilteR, CNVPanelizer, CNVRanger, compEpiTools, consensusSeekeR, conumee, CopyNumberPlots, CopywriteR, CrispRVariants, csaw, customProDB, DeepBlueR, derfinder, derfinderPlot, DEScan2, DEWSeq, diffHic, diffloop, DMRcate, DMRScan, dmrseq, DominoEffect, easyRNASeq, ELMER, ENCODExplorer, enrichTF, ensembldb, ensemblVEP, epigenomix, epivizrData, epivizrStandalone, erma, esATAC, EventPointer, exomeCopy, FunChIP, funtooNorm, GA4GHclient, GA4GHshiny, gcapc, genbankr, geneAttribution, GenoGAM, genomation, genomeIntervals, GenomicFiles, GenomicInteractions, GenomicOZone, GenomicScores, genoset, genotypeeval, GenVisR, ggbio, GGtools, GOTHiC, gQTLstats, GreyListChIP, GUIDEseq, Gviz, gwascat, h5vc, heatmaps, HiCBricks, HiTC, HTSeqGenie, idr2d, IMAS, InPAS, INSPEcT, InteractionSet, IsoformSwitchAnalyzeR, IVAS, karyoploteR, ldblock, MACPET, MADSEQ, maser, metagene, metagene2, metavizr, MethCP, methimpute, methInheritSim, methylKit, methylPipe, methylumi, methyvim, minfi, MinimumDistance, MMAPPR2, mosaics, motifbreakR, motifmatchr, msgbsR, multiHiCcompare, MutationalPatterns, myvariant, NADfinder, NarrowPeaks, normr, nucleR, OMICsPCA, ORFik, Organism.dplyr, panelcn.mops, Pi, pipeFrame, plyranges, podkat, pram, prebs, profileplyr, ProteomicsAnnotationHubData, PureCN, qpgraph, qsea, QuasR, R3CPET, r3Cseq, RaggedExperiment, RareVariantVis, Rariant, Rcade, RCAS, recount, regioneR, regionReport, REMP, Repitools, RiboProfiling, riboSeqR, RJMCMCNucleosomes, RNAmodR, roar, RTCGAToolbox, rtracklayer, scmeth, scruff, segmentSeq, SeqArray, seqCAT, seqplots, seqsetvis, sevenC, SGSeq, ShortRead, signeR, SigsPack, SNPchip, SNPhood, soGGi, SomaticSignatures, SparseSignatures, SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR, SummarizedExperiment, TarSeqQC, TCGAbiolinks, TCGAutils, TFBSTools, TitanCNA, TnT, trackViewer, transcriptR, tRNAscanImport, TSRchitect, TVTB, tximeta, TxRegInfra, Ularcirc, VanillaICE, VariantFiltering, VariantTools, wiggleplotr, YAPSA suggestsMe: AnnotationForge, AnnotationHub, BiocOncoTK, chromswitch, ExperimentHubData, gQTLBase, methrix, parglms, QDNAseq, recoup, StructuralVariantAnnotation, TFutils dependencyCount: 12 Package: genomeIntervals Version: 1.42.0 Depends: R (>= 2.15.0), methods, intervals (>= 0.14.0), BiocGenerics (>= 0.15.2) Imports: GenomeInfoDb (>= 1.5.8), GenomicRanges (>= 1.21.16), IRanges(>= 2.3.14), S4Vectors (>= 0.7.10) License: Artistic-2.0 Archs: i386, x64 MD5sum: 01f41a46768b8c4f72557eeb3634c984 NeedsCompilation: no Title: Operations on genomic intervals Description: This package defines classes for representing genomic intervals and provides functions and methods for working with these. Note: The package provides the basic infrastructure for and is enhanced by the package 'girafe'. biocViews: DataImport, Infrastructure, Genetics Author: Julien Gagneur , Joern Toedling, Richard Bourgon, Nicolas Delhomme Maintainer: Julien Gagneur git_url: https://git.bioconductor.org/packages/genomeIntervals git_branch: RELEASE_3_10 git_last_commit: c91c96c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/genomeIntervals_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/genomeIntervals_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/genomeIntervals_1.42.0.tgz vignettes: vignettes/genomeIntervals/inst/doc/genomeIntervals.pdf vignetteTitles: Overview of the genomeIntervals package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomeIntervals/inst/doc/genomeIntervals.R dependsOnMe: girafe importsMe: easyRNASeq dependencyCount: 17 Package: genomes Version: 3.16.0 Depends: readr, curl License: GPL-3 MD5sum: 8dfe7fa325bf5d234643ad8136c1e131 NeedsCompilation: no Title: Genome sequencing project metadata Description: Download genome and assembly reports from NCBI biocViews: Annotation, Genetics Author: Chris Stubben Maintainer: Chris Stubben git_url: https://git.bioconductor.org/packages/genomes git_branch: RELEASE_3_10 git_last_commit: 0edeb90 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/genomes_3.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/genomes_3.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/genomes_3.16.0.tgz vignettes: vignettes/genomes/inst/doc/genomes.pdf vignetteTitles: Genome metadata hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomes/inst/doc/genomes.R dependencyCount: 26 Package: GenomicAlignments Version: 1.22.1 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.15.3), S4Vectors (>= 0.23.19), IRanges (>= 2.15.12), GenomeInfoDb (>= 1.13.1), GenomicRanges (>= 1.37.2), SummarizedExperiment (>= 1.9.13), Biostrings (>= 2.47.6), Rsamtools (>= 1.31.2) Imports: methods, utils, stats, BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings, Rsamtools, BiocParallel LinkingTo: S4Vectors, IRanges Suggests: ShortRead, rtracklayer, BSgenome, GenomicFeatures, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Hsapiens.UCSC.hg19, DESeq2, edgeR, RUnit, BiocStyle License: Artistic-2.0 MD5sum: 6fb92061c3ffbeb89423f731f662f9d1 NeedsCompilation: yes Title: Representation and manipulation of short genomic alignments Description: Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments. biocViews: ImmunoOncology, Genetics, Infrastructure, DataImport, Sequencing, RNASeq, SNP, Coverage, Alignment Author: Hervé Pagès, Valerie Obenchain, Martin Morgan Maintainer: Bioconductor Package Maintainer Video: https://www.youtube.com/watch?v=2KqBSbkfhRo , https://www.youtube.com/watch?v=3PK_jx44QTs git_url: https://git.bioconductor.org/packages/GenomicAlignments git_branch: RELEASE_3_10 git_last_commit: 6db98e3 git_last_commit_date: 2019-11-12 Date/Publication: 2019-11-12 source.ver: src/contrib/GenomicAlignments_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/GenomicAlignments_1.22.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GenomicAlignments_1.22.1.tgz vignettes: vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.pdf, vignettes/GenomicAlignments/inst/doc/OverlapEncodings.pdf, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.pdf, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.pdf vignetteTitles: An Introduction to the GenomicAlignments Package, Overlap encodings, Counting reads with summarizeOverlaps, Working with aligned nucleotides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.R, vignettes/GenomicAlignments/inst/doc/OverlapEncodings.R, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.R, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.R dependsOnMe: AllelicImbalance, Basic4Cseq, chimera, ChIPexoQual, groHMM, HelloRanges, hiReadsProcessor, igvR, MTseeker, ORFik, prebs, recoup, RIPSeeker, rnaSeqMap, ShortRead, SplicingGraphs importsMe: alpine, AneuFinder, APAlyzer, ASpediaFI, ASpli, ATACseqQC, BaalChIP, biovizBase, breakpointR, CAGEr, chimeraviz, ChIPpeakAnno, ChIPQC, chromstaR, CNEr, consensusDE, contiBAIT, CopywriteR, CoverageView, CrispRVariants, customProDB, derfinder, DEScan2, DiffBind, easyRNASeq, FourCSeq, FunChIP, gcapc, GenoGAM, genomation, GenomicFiles, ggbio, gmapR, GreyListChIP, GUIDEseq, Gviz, HTSeqGenie, icetea, IMAS, INSPEcT, IntEREst, MACPET, MADSEQ, MDTS, metagene, metagene2, methylPipe, mosaics, msgbsR, NADfinder, PICS, plyranges, pram, QuasR, ramwas, Rcade, Repitools, RiboProfiling, RNAmodR, RNAprobR, roar, Rqc, rtracklayer, scruff, seqplots, seqsetvis, SGSeq, soGGi, SplicingGraphs, SPLINTER, srnadiff, strandCheckR, TarSeqQC, TCseq, trackViewer, transcriptR, TSRchitect, Ularcirc suggestsMe: amplican, BiocParallel, csaw, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, IRanges, Rsamtools, similaRpeak, Streamer dependencyCount: 35 Package: GenomicDataCommons Version: 1.10.0 Depends: R (>= 3.4.0), magrittr Imports: stats, httr, xml2, jsonlite, utils, rlang, readr, GenomicRanges, IRanges, dplyr, rappdirs, SummarizedExperiment, S4Vectors, tibble Suggests: BiocStyle, knitr, rmarkdown, DT, testthat, listviewer, ggplot2, GenomicAlignments, Rsamtools License: Artistic-2.0 MD5sum: 46239b3fc502a1f5464f7bdad0cdb1b9 NeedsCompilation: no Title: NIH / NCI Genomic Data Commons Access Description: Programmatically access the NIH / NCI Genomic Data Commons RESTful service. biocViews: DataImport, Sequencing Author: Martin Morgan [aut], Sean Davis [aut, cre] Maintainer: Sean Davis URL: https://bioconductor.org/packages/GenomicDataCommons, http://github.com/Bioconductor/GenomicDataCommons VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicDataCommons/issues/new git_url: https://git.bioconductor.org/packages/GenomicDataCommons git_branch: RELEASE_3_10 git_last_commit: 75d565f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-30 source.ver: src/contrib/GenomicDataCommons_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GenomicDataCommons_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GenomicDataCommons_1.10.0.tgz vignettes: vignettes/GenomicDataCommons/inst/doc/overview.html vignetteTitles: Introduction to Accessing the NCI Genomic Data Commons hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicDataCommons/inst/doc/overview.R importsMe: GDCRNATools, TCGAutils dependencyCount: 65 Package: GenomicFeatures Version: 1.38.2 Depends: BiocGenerics (>= 0.1.0), S4Vectors (>= 0.17.29), IRanges (>= 2.13.23), GenomeInfoDb (>= 1.15.4), GenomicRanges (>= 1.31.17), AnnotationDbi (>= 1.41.4) Imports: methods, utils, stats, tools, DBI, RSQLite (>= 2.0), RCurl, XVector (>= 0.19.7), Biostrings (>= 2.47.6), rtracklayer (>= 1.39.7), biomaRt (>= 2.17.1), Biobase (>= 2.15.1) Suggests: RMariaDB, org.Mm.eg.db, org.Hs.eg.db, BSgenome, BSgenome.Hsapiens.UCSC.hg19 (>= 1.3.17), BSgenome.Celegans.UCSC.ce2, BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.17), mirbase.db, FDb.UCSC.tRNAs, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene (>= 2.7.1), TxDb.Mmusculus.UCSC.mm10.knownGene (>= 3.4.7), TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene (>= 3.4.6), SNPlocs.Hsapiens.dbSNP144.GRCh38, Rsamtools, pasillaBamSubset (>= 0.0.5), GenomicAlignments (>= 1.15.7), ensembldb, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: b4afee3b93fd793d848f03c494411462 NeedsCompilation: no Title: Conveniently import and query gene models Description: A set of tools and methods for making and manipulating transcript centric annotations. With these tools the user can easily download the genomic locations of the transcripts, exons and cds of a given organism, from either the UCSC Genome Browser or a BioMart database (more sources will be supported in the future). This information is then stored in a local database that keeps track of the relationship between transcripts, exons, cds and genes. Flexible methods are provided for extracting the desired features in a convenient format. biocViews: Genetics, Infrastructure, Annotation, Sequencing, GenomeAnnotation Author: M. Carlson, H. Pagès, P. Aboyoun, S. Falcon, M. Morgan, D. Sarkar, M. Lawrence, V. Obenchain Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenomicFeatures git_branch: RELEASE_3_10 git_last_commit: 091006c git_last_commit_date: 2020-02-14 Date/Publication: 2020-02-15 source.ver: src/contrib/GenomicFeatures_1.38.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/GenomicFeatures_1.38.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GenomicFeatures_1.38.2.tgz vignettes: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.pdf vignetteTitles: Making and Utilizing TxDb Objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.R dependsOnMe: cpvSNP, ensembldb, GSReg, Guitar, HelloRanges, InPAS, OrganismDbi, OUTRIDER, RareVariantVis, RNAprobR, SplicingGraphs importsMe: AllelicImbalance, alpine, AnnotationHubData, annotatr, APAlyzer, appreci8R, ASpediaFI, ASpli, BiocOncoTK, biovizBase, bumphunter, BUSpaRse, CAGEfightR, casper, ChIPpeakAnno, ChIPQC, ChIPseeker, compEpiTools, CompGO, consensusDE, crisprseekplus, csaw, customProDB, decompTumor2Sig, derfinder, derfinderPlot, EDASeq, ELMER, epivizrData, epivizrStandalone, esATAC, EventPointer, GA4GHshiny, genbankr, geneAttribution, GenVisR, ggbio, gmapR, gQTLstats, Gviz, gwascat, HiLDA, HTSeqGenie, icetea, INSPEcT, IntEREst, karyoploteR, lumi, mCSEA, metagene, methyAnalysis, msgbsR, MTseeker, ORFik, Organism.dplyr, PGA, proBAMr, PureCN, qpgraph, QuasR, RCAS, Rhisat2, RiboProfiling, RNAmodR, scruff, SGSeq, SplicingGraphs, SPLINTER, srnadiff, systemPipeR, TCGAbiolinks, TCGAutils, TFEA.ChIP, trackViewer, transcriptR, tximeta, Ularcirc, VariantAnnotation, VariantFiltering, VariantTools, wavClusteR suggestsMe: AnnotationHub, BANDITS, biomvRCNS, Biostrings, chipseq, chromPlot, CrispRVariants, cummeRbund, DEXSeq, GenomeInfoDb, GenomicAlignments, GenomicRanges, groHMM, HDF5Array, IRanges, MiRaGE, recount, RIPSeeker, RNAmodR.ML, Rsamtools, rtracklayer, ShortRead, SummarizedExperiment, TFutils, TnT, wiggleplotr, flipflop dependencyCount: 82 Package: GenomicFiles Version: 1.22.0 Depends: R (>= 3.1.0), methods, BiocGenerics (>= 0.11.2), GenomicRanges (>= 1.31.16), SummarizedExperiment, BiocParallel (>= 1.1.0), Rsamtools (>= 1.17.29), rtracklayer (>= 1.25.3) Imports: GenomicAlignments (>= 1.7.7), IRanges, S4Vectors (>= 0.9.25), VariantAnnotation (>= 1.27.9), GenomeInfoDb Suggests: BiocStyle, RUnit, genefilter, deepSNV, RNAseqData.HNRNPC.bam.chr14, Biostrings, Homo.sapiens License: Artistic-2.0 Archs: i386, x64 MD5sum: d3e841e39f7505dfc9c8aa2711b09802 NeedsCompilation: no Title: Distributed computing by file or by range Description: This package provides infrastructure for parallel computations distributed 'by file' or 'by range'. User defined MAPPER and REDUCER functions provide added flexibility for data combination and manipulation. biocViews: Genetics, Infrastructure, DataImport, Sequencing, Coverage Author: Bioconductor Package Maintainer [aut, cre], Valerie Obenchain [aut], Michael Love [aut], Lori Shepherd [aut], Martin Morgan [aut] Maintainer: Bioconductor Package Maintainer Video: https://www.youtube.com/watch?v=3PK_jx44QTs git_url: https://git.bioconductor.org/packages/GenomicFiles git_branch: RELEASE_3_10 git_last_commit: ef2b830 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GenomicFiles_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GenomicFiles_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GenomicFiles_1.22.0.tgz vignettes: vignettes/GenomicFiles/inst/doc/GenomicFiles.pdf vignetteTitles: Introduction to GenomicFiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFiles/inst/doc/GenomicFiles.R dependsOnMe: erma importsMe: CAGEfightR, contiBAIT, derfinder, gQTLBase, gQTLstats, ldblock, QuasR, Rqc, VCFArray suggestsMe: TFutils, TxRegInfra dependencyCount: 85 Package: GenomicInteractions Version: 1.20.3 Depends: R (>= 3.5), InteractionSet Imports: Rsamtools, rtracklayer, GenomicRanges (>= 1.29.6), IRanges, BiocGenerics (>= 0.15.3), data.table, stringr, GenomeInfoDb, ggplot2, grid, gridExtra, methods, igraph, S4Vectors (>= 0.13.13), dplyr, Gviz, Biobase, graphics, stats, utils, grDevices Suggests: knitr, BiocStyle, testthat License: GPL-3 MD5sum: 642e819a0d7df5c5da8b2f2bac3c4851 NeedsCompilation: no Title: R package for handling genomic interaction data Description: R package for handling Genomic interaction data, such as ChIA-PET/Hi-C, annotating genomic features with interaction information and producing various plots / statistics. biocViews: Software,Infrastructure,DataImport,DataRepresentation,HiC Author: Harmston, N., Ing-Simmons, E., Perry, M., Baresic, A., Lenhard, B. Maintainer: Liz Ing-Simmons URL: https://github.com/ComputationalRegulatoryGenomicsICL/GenomicInteractions/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenomicInteractions git_branch: RELEASE_3_10 git_last_commit: 941a58c git_last_commit_date: 2020-04-10 Date/Publication: 2020-04-11 source.ver: src/contrib/GenomicInteractions_1.20.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/GenomicInteractions_1.20.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GenomicInteractions_1.20.3.tgz vignettes: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.html, vignettes/GenomicInteractions/inst/doc/hic_vignette.html vignetteTitles: chiapet_vignette.html, GenomicInteractions-HiC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.R, vignettes/GenomicInteractions/inst/doc/hic_vignette.R importsMe: CAGEfightR suggestsMe: Chicago, ELMER, sevenC dependencyCount: 145 Package: GenomicOZone Version: 1.0.0 Depends: R (>= 3.6), Ckmeans.1d.dp (>= 4.3.0), GenomicRanges, biomaRt, ggplot2 Imports: grDevices, stats, utils, plyr, gridExtra, sjstats, parallel, ggbio, S4Vectors, IRanges, GenomeInfoDb, Rdpack Suggests: readxl, GEOquery, knitr, rmarkdown License: LGPL (>=3) MD5sum: 00f14e9476303d3dfd60e98674038ec4 NeedsCompilation: no Title: Delineate outstanding genomic zones of differential gene activity Description: The package clusters gene activity along chromosome into zones, detects differential zones as outstanding, and visualizes maps of outstanding zones across the genome. The method guarantees cluster optimality, linear runtime to sample size, and reproducibility. It enables new characterization of effects due to genome reorganization, structural variation, and epigenome alteration. biocViews: Software, GeneExpression, Transcription, DifferentialExpression, FunctionalPrediction, GeneRegulation, BiomedicalInformatics, CellBiology, FunctionalGenomics, Genetics, SystemsBiology, Transcriptomics, Clustering, Regression, RNASeq, Annotation, Visualization, Sequencing, Coverage, DifferentialMethylation, GenomicVariation, StructuralVariation Author: Hua Zhong, Mingzhou Song Maintainer: Hua Zhong, Mingzhou Song VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenomicOZone git_branch: RELEASE_3_10 git_last_commit: 5cd2322 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GenomicOZone_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GenomicOZone_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GenomicOZone_1.0.0.tgz vignettes: vignettes/GenomicOZone/inst/doc/GenomicOZone.html vignetteTitles: GenomicOZone hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicOZone/inst/doc/GenomicOZone.R dependencyCount: 182 Package: GenomicRanges Version: 1.38.0 Depends: R (>= 2.10), methods, stats4, BiocGenerics (>= 0.25.3), S4Vectors (>= 0.23.19), IRanges (>= 2.19.9), GenomeInfoDb (>= 1.15.2) Imports: utils, stats, XVector (>= 0.19.8) LinkingTo: S4Vectors, IRanges Suggests: Matrix, Biobase, AnnotationDbi, annotate, Biostrings (>= 2.25.3), SummarizedExperiment (>= 0.1.5), Rsamtools (>= 1.13.53), GenomicAlignments, rtracklayer, BSgenome, GenomicFeatures, Gviz, VariantAnnotation, AnnotationHub, DESeq2, DEXSeq, edgeR, KEGGgraph, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, KEGG.db, hgu95av2.db, hgu95av2probe, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, RUnit, digest, knitr, BiocStyle License: Artistic-2.0 MD5sum: 1cee38e912a367f89297097a1cdbfdb2 NeedsCompilation: yes Title: Representation and manipulation of genomic intervals Description: The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome. More specialized containers for representing and manipulating short alignments against a reference genome, or a matrix-like summarization of an experiment, are defined in the GenomicAlignments and SummarizedExperiment packages, respectively. Both packages build on top of the GenomicRanges infrastructure. biocViews: Genetics, Infrastructure, Sequencing, Annotation, Coverage, GenomeAnnotation Author: P. Aboyoun, H. Pagès, and M. Lawrence Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenomicRanges git_branch: RELEASE_3_10 git_last_commit: 0a8bf1d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GenomicRanges_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GenomicRanges_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GenomicRanges_1.38.0.tgz vignettes: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.pdf, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.pdf, vignettes/GenomicRanges/inst/doc/Ten_things_slides.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.html vignetteTitles: 5. Extending GenomicRanges, 2. GenomicRanges HOWTOs, 3. A quick introduction to GRanges and GRangesList objects (slides), 4. Ten Things You Didn't Know (slides from BioC 2016), 1. An Introduction to the GenomicRanges Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.R, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.R, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.R, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.R, vignettes/GenomicRanges/inst/doc/Ten_things_slides.R dependsOnMe: AllelicImbalance, AneuFinder, annmap, AnnotationHubData, BaalChIP, Basic4Cseq, baySeq, biomvRCNS, BiSeq, bnbc, BPRMeth, breakpointR, BSgenome, bsseq, BubbleTree, bumphunter, CAFE, CAGEfightR, casper, CHARGE, chimera, chimeraviz, ChIPpeakAnno, ChIPQC, chipseq, chroGPS, chromPlot, chromstaR, chromswitch, CINdex, cn.mops, cnvGSA, CNVPanelizer, CNVRanger, COCOA, compEpiTools, consensusSeekeR, CSAR, csaw, deepSNV, DEScan2, DESeq2, DEXSeq, DiffBind, diffHic, DMCFB, DMCHMM, DMRcaller, DMRforPairs, DNAshapeR, EnrichedHeatmap, ensembldb, ensemblVEP, epigenomix, epihet, esATAC, ExCluster, exomeCopy, fastseg, fCCAC, FourCSeq, FunChIP, GeneBreak, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicOZone, GenomicScores, GenomicTuples, genoset, gmapR, GOTHiC, GreyListChIP, groHMM, gtrellis, GUIDEseq, Guitar, Gviz, HelloRanges, hiAnnotator, HiTC, IdeoViz, igvR, InPAS, InTAD, intansv, InteractionSet, IntEREst, IWTomics, karyoploteR, maser, MBASED, Melissa, metagene, metagene2, methimpute, methyAnalysis, methylKit, methylPipe, minfi, MotIV, msgbsR, MutationalPatterns, NADfinder, ORFik, PGA, PING, plyranges, podkat, QuasR, r3Cseq, RaggedExperiment, Rariant, Rcade, recoup, regioneR, RepViz, rfPred, rGREAT, riboSeqR, RIPSeeker, RJMCMCNucleosomes, RNAmodR, RnBeads, Rsamtools, RSVSim, rtracklayer, Scale4C, segmentSeq, seqbias, seqCAT, SGSeq, SICtools, SigFuge, SMITE, SNPhood, SomaticSignatures, StructuralVariantAnnotation, SummarizedExperiment, TarSeqQC, TnT, trackViewer, TransView, tRNA, tRNAdbImport, tRNAscanImport, VanillaICE, VariantAnnotation, VariantExperiment, VariantTools, vtpnet, vulcan, wavClusteR, YAPSA importsMe: ACE, ALDEx2, alpine, ALPS, amplican, AnnotationFilter, annotatr, APAlyzer, apeglm, appreci8R, ArrayExpressHTS, ASpediaFI, ASpli, AssessORF, ATACseqQC, BadRegionFinder, ballgown, bamsignals, BBCAnalyzer, beadarray, BEAT, BiFET, BiocOncoTK, BioTIP, biovizBase, biscuiteer, BiSeq, brainflowprobes, branchpointer, BSgenome, BUSpaRse, CAGEr, CexoR, chipenrich, ChIPexoQual, ChIPseeker, chipseq, ChIPseqR, ChIPSeqSpike, chromDraw, ChromHeatMap, chromVAR, cicero, circRNAprofiler, cleanUpdTSeq, CNEr, CNVfilteR, coMET, compartmap, contiBAIT, conumee, copynumber, CopyNumberPlots, CopywriteR, CoverageView, crisprseekplus, CrispRVariants, customProDB, DChIPRep, debrowser, decompTumor2Sig, DeepBlueR, DEFormats, deltaCaptureC, derfinder, derfinderPlot, DEWSeq, diffloop, DMRcate, dmrseq, DominoEffect, DRIMSeq, easyRNASeq, EDASeq, ELMER, ENCODExplorer, enrichTF, epivizr, epivizrData, erma, EventPointer, fcScan, FourCSeq, FunciSNP, GA4GHclient, gcapc, genbankr, geneAttribution, GeneGeneInteR, GENESIS, GenoGAM, genomation, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicInteractions, genotypeeval, GenVisR, GGBase, ggbio, GGtools, GOfuncR, gpart, gQTLBase, gQTLstats, gwascat, h5vc, heatmaps, HiCBricks, HiCcompare, HilbertCurve, HiLDA, hiReadsProcessor, HTSeqGenie, icetea, ideal, idr2d, IMAS, INSPEcT, InterMineR, ipdDb, IsoformSwitchAnalyzeR, isomiRs, iteremoval, IVAS, karyoploteR, loci2path, LOLA, LoomExperiment, lumi, M3D, MACPET, MADSEQ, mCSEA, MDTS, MEAL, MEDIPS, MetaNeighbor, MethCP, methInheritSim, methyAnalysis, methylCC, methylInheritance, MethylSeekR, methylumi, MinimumDistance, MIRA, MMAPPR2, MMDiff2, Modstrings, mosaics, motifbreakR, motifmatchr, MTseeker, MultiAssayExperiment, MultiDataSet, multiHiCcompare, NarrowPeaks, normr, nucleR, oligoClasses, OmaDB, openPrimeR, Organism.dplyr, OrganismDbi, OUTRIDER, panelcn.mops, PAST, Pbase, pcaExplorer, pepStat, Pi, PICS, pqsfinder, pram, prebs, PrecisionTrialDrawer, primirTSS, proBAMr, profileplyr, PureCN, Pviz, pwOmics, QDNAseq, qpgraph, qPLEXanalyzer, qsea, Qtlizer, R3CPET, R453Plus1Toolbox, RareVariantVis, RCAS, recount, regioneR, regionReport, REMP, Repitools, rGADEM, RGMQL, Rhisat2, RiboProfiling, Rmmquant, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RNAprobR, rnaSeqMap, roar, RTCGAToolbox, scmeth, scoreInvHap, scPipe, scruff, seq2pathway, SeqArray, seqPattern, seqplots, seqsetvis, SeqSQC, SeqVarTools, sesame, sevenC, ShortRead, signeR, SigsPack, simulatorZ, SNPchip, soGGi, SparseSignatures, SpectralTAD, SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR, systemPipeR, target, TCGAbiolinks, TCGAutils, TCseq, TFARM, TFBSTools, TFEA.ChIP, TFHAZ, TitanCNA, tracktables, transcriptR, transite, trena, triplex, TSRchitect, TVTB, tximeta, TxRegInfra, Ularcirc, Uniquorn, VariantFiltering, VCFArray, waveTiling, wiggleplotr, flipflop suggestsMe: AnnotationHub, biobroom, BiocGenerics, BiocParallel, Chicago, ComplexHeatmap, cummeRbund, epivizrChart, GenomeInfoDb, Glimma, GSReg, GWASTools, HDF5Array, interactiveDisplay, IRanges, metaseqR, methrix, MiRaGE, NarrowPeaks, omicsPrint, Onassis, parglms, RTCGA, S4Vectors, SeqGSEA, TFutils dependencyCount: 15 Package: GenomicScores Version: 1.10.0 Depends: R (>= 3.5), S4Vectors (>= 0.7.21), GenomicRanges, methods, BiocGenerics (>= 0.13.8) Imports: utils, XML, Biobase, IRanges (>= 2.3.23), Biostrings, BSgenome, GenomeInfoDb, AnnotationHub Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19, phastCons100way.UCSC.hg19, MafDb.1Kgenomes.phase1.hs37d5, SNPlocs.Hsapiens.dbSNP144.GRCh37, VariantAnnotation, TxDb.Hsapiens.UCSC.hg19.knownGene, gwascat, RColorBrewer License: Artistic-2.0 MD5sum: c21fbb5652aa5017f81352ff4e20e914 NeedsCompilation: no Title: Infrastructure to work with genomewide position-specific scores Description: Provide infrastructure to store and access genomewide position-specific scores within R and Bioconductor. biocViews: Infrastructure, Genetics, Annotation, Sequencing, Coverage Author: Robert Castelo [aut, cre], Pau Puigdevall [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/GenomicScores VignetteBuilder: knitr BugReports: https://github.com/rcastelo/GenomicScores/issues git_url: https://git.bioconductor.org/packages/GenomicScores git_branch: RELEASE_3_10 git_last_commit: c6ce709 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GenomicScores_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GenomicScores_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GenomicScores_1.10.0.tgz vignettes: vignettes/GenomicScores/inst/doc/GenomicScores.html vignetteTitles: An introduction to the GenomicScores package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicScores/inst/doc/GenomicScores.R importsMe: appreci8R, ATACseqQC, primirTSS, RareVariantVis, VariantFiltering suggestsMe: methrix dependencyCount: 90 Package: GenomicTuples Version: 1.20.0 Depends: R (>= 3.3.0), GenomicRanges (>= 1.37.4), GenomeInfoDb (>= 1.15.2), S4Vectors (>= 0.17.25) Imports: methods, BiocGenerics (>= 0.21.2), Rcpp (>= 0.11.2), IRanges (>= 2.19.13), data.table, stats4 LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: e3b932960895f76747ee54d5cbcd1751 NeedsCompilation: yes Title: Representation and Manipulation of Genomic Tuples Description: GenomicTuples defines general purpose containers for storing genomic tuples. It aims to provide functionality for tuples of genomic co-ordinates that are analogous to those available for genomic ranges in the GenomicRanges Bioconductor package. biocViews: Infrastructure, DataRepresentation, Sequencing Author: Peter Hickey , with contributions from Marcin Cieslik and Hervé Pagès. Maintainer: Peter Hickey URL: www.github.com/PeteHaitch/GenomicTuples VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/GenomicTuples/issues git_url: https://git.bioconductor.org/packages/GenomicTuples git_branch: RELEASE_3_10 git_last_commit: 46b3bad git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GenomicTuples_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GenomicTuples_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GenomicTuples_1.20.0.tgz vignettes: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.html vignetteTitles: GenomicTuplesIntroduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.R dependencyCount: 18 Package: Genominator Version: 1.40.0 Depends: R (>= 2.10), methods, RSQLite, DBI (>= 0.2-5), BiocGenerics (>= 0.1.0), IRanges (>= 2.5.27), GenomeGraphs Imports: graphics, stats, utils Suggests: biomaRt, ShortRead, yeastRNASeq License: Artistic-2.0 Archs: i386, x64 MD5sum: 6a2344af8d7d6d8e5d8306ecb12260d7 NeedsCompilation: no Title: Analyze, manage and store genomic data Description: Tools for storing, accessing, analyzing and visualizing genomic data. biocViews: Infrastructure Author: James Bullard, Kasper Daniel Hansen Maintainer: James Bullard git_url: https://git.bioconductor.org/packages/Genominator git_branch: RELEASE_3_10 git_last_commit: 475c8da git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Genominator_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Genominator_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Genominator_1.40.0.tgz vignettes: vignettes/Genominator/inst/doc/Genominator.pdf, vignettes/Genominator/inst/doc/plotting.pdf, vignettes/Genominator/inst/doc/withShortRead.pdf vignetteTitles: The Genominator User Guide, Plotting with Genominator, Working with the ShortRead Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Genominator/inst/doc/Genominator.R, vignettes/Genominator/inst/doc/plotting.R, vignettes/Genominator/inst/doc/withShortRead.R dependencyCount: 60 Package: genoset Version: 1.42.0 Depends: R (>= 2.10), BiocGenerics (>= 0.11.3), GenomicRanges (>= 1.17.19), SummarizedExperiment (>= 1.1.6) Imports: S4Vectors (>= 0.23.18), GenomeInfoDb (>= 1.1.3), IRanges (>= 2.5.12), methods, graphics Suggests: testthat, knitr, BiocStyle, rmarkdown, DNAcopy, stats, BSgenome, Biostrings Enhances: parallel License: Artistic-2.0 MD5sum: 0b156cf6d3a2794a02fd4a6ae9ef60de NeedsCompilation: yes Title: A RangedSummarizedExperiment with methods for copy number analysis Description: GenoSet provides an extension of the RangedSummarizedExperiment class with additional API features. This class provides convenient and fast methods for working with segmented genomic data. Additionally, GenoSet provides the class RleDataFrame which stores runs of data along the genome for multiple samples and provides very fast summaries of arbitrary row sets (regions of the genome). biocViews: Infrastructure, DataRepresentation, Microarray, SNP, CopyNumberVariation Author: Peter M. Haverty Maintainer: Peter M. Haverty URL: https://github.com/phaverty/genoset VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genoset git_branch: RELEASE_3_10 git_last_commit: 8bd8fd5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/genoset_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/genoset_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/genoset_1.42.0.tgz vignettes: vignettes/genoset/inst/doc/genoset.html vignetteTitles: genoset hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genoset/inst/doc/genoset.R importsMe: methyAnalysis, VegaMC dependencyCount: 32 Package: genotypeeval Version: 1.18.0 Depends: R (>= 3.4.0), VariantAnnotation Imports: ggplot2, rtracklayer, BiocGenerics, GenomicRanges, GenomeInfoDb, IRanges, methods, BiocParallel, graphics, stats Suggests: rmarkdown, testthat, SNPlocs.Hsapiens.dbSNP141.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene License: file LICENSE Archs: i386, x64 MD5sum: b196ca7302a91ddfbf5af12223313a2b NeedsCompilation: no Title: QA/QC of a gVCF or VCF file Description: Takes in a gVCF or VCF and reports metrics to assess quality of calls. biocViews: Genetics, BatchEffect, Sequencing, SNP, VariantAnnotation, DataImport Author: Jennifer Tom [aut, cre] Maintainer: Jennifer Tom VignetteBuilder: rmarkdown git_url: https://git.bioconductor.org/packages/genotypeeval git_branch: RELEASE_3_10 git_last_commit: 4ff4ced git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/genotypeeval_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/genotypeeval_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/genotypeeval_1.18.0.tgz vignettes: vignettes/genotypeeval/inst/doc/genotypeeval_vignette.html vignetteTitles: genotypeeval_vignette.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 112 Package: genphen Version: 1.14.0 Depends: R (>= 3.5.0), Rcpp (>= 0.12.17), methods, stats, graphics Imports: rstan (>= 2.17.3), ranger, parallel, foreach, doParallel, e1071, Biostrings, rPref Suggests: testthat, ggplot2, gridExtra, ape, ggrepel, knitr, reshape, xtable License: GPL (>= 2) MD5sum: 11dfb801e7622369d7783a23938dd7dd NeedsCompilation: no Title: A tool for quantification of associations between genotypes and phenotypes in genome wide association studies (GWAS) with Bayesian inference and statistical learning Description: Genetic association studies are an essential tool for studying the relationship between genotypes and phenotypes. With genphen we can jointly study multiple phenotypes of different types, by quantifying the association between different genotypes and each phenotype using a hybrid method which uses statistical learning techniques such as random forest and support vector machines, and Bayesian inference using hierarchical models. biocViews: GenomeWideAssociation, Regression, Classification, SupportVectorMachine, Genetics, SequenceMatching, Bayesian, FeatureExtraction, Sequencing Author: Simo Kitanovski [aut, cre] Maintainer: Simo Kitanovski BugReports: https://github.com/snaketron/genphen/issues git_url: https://git.bioconductor.org/packages/genphen git_branch: RELEASE_3_10 git_last_commit: 8c43b14 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/genphen_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/genphen_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/genphen_1.14.0.tgz vignettes: vignettes/genphen/inst/doc/genphenManual.pdf vignetteTitles: genphen overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genphen/inst/doc/genphenManual.R dependencyCount: 86 Package: GenRank Version: 1.14.0 Depends: R (>= 3.2.3) Imports: matrixStats, reshape2, survcomp Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 6ef2edd846f9e051960741bed3c71f29 NeedsCompilation: no Title: Candidate gene prioritization based on convergent evidence Description: Methods for ranking genes based on convergent evidence obtained from multiple independent evidence layers. This package adapts three methods that are popular for meta-analysis. biocViews: GeneExpression, SNP, CopyNumberVariation, Microarray, Sequencing, Software, Genetics Author: Chakravarthi Kanduri Maintainer: Chakravarthi Kanduri URL: https://github.com/chakri9/GenRank VignetteBuilder: knitr BugReports: https://github.com/chakri9/GenRank/issues git_url: https://git.bioconductor.org/packages/GenRank git_branch: RELEASE_3_10 git_last_commit: 3ad33db git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GenRank_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GenRank_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GenRank_1.14.0.tgz vignettes: vignettes/GenRank/inst/doc/GenRank_Vignette.html vignetteTitles: Introduction to GenRank Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenRank/inst/doc/GenRank_Vignette.R dependencyCount: 34 Package: GenVisR Version: 1.18.1 Depends: R (>= 3.3.0), methods Imports: AnnotationDbi, biomaRt, BiocGenerics, Biostrings, DBI, FField, GenomicFeatures, GenomicRanges (>= 1.25.4), ggplot2 (>= 2.1.0), grid, gridExtra (>= 2.0.0), gtable, gtools, IRanges (>= 2.7.5), plyr (>= 1.8.3), reshape2, Rsamtools, scales, stats, utils, viridis, data.table, BSgenome, grDevices, GenomeInfoDb, VariantAnnotation Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, knitr, RMySQL, roxygen2, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, rmarkdown, vdiffr, formatR, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg38 License: GPL-3 + file LICENSE MD5sum: 10d6170755acfdfe634ebf409a036aad NeedsCompilation: no Title: Genomic Visualizations in R Description: Produce highly customizable publication quality graphics for genomic data primarily at the cohort level. biocViews: Infrastructure, DataRepresentation, Classification, DNASeq Author: Zachary Skidmore [aut, cre], Alex Wagner [aut], Robert Lesurf [aut], Katie Campbell [aut], Jason Kunisaki [aut], Obi Griffith [aut], Malachi Griffith [aut] Maintainer: Zachary Skidmore VignetteBuilder: knitr BugReports: https://github.com/griffithlab/GenVisR/issues git_url: https://git.bioconductor.org/packages/GenVisR git_branch: RELEASE_3_10 git_last_commit: 827ef01 git_last_commit_date: 2019-10-30 Date/Publication: 2019-11-01 source.ver: src/contrib/GenVisR_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/GenVisR_1.18.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GenVisR_1.18.1.tgz vignettes: vignettes/GenVisR/inst/doc/Intro.html, vignettes/GenVisR/inst/doc/Upcoming_Features.html, vignettes/GenVisR/inst/doc/waterfall_introduction.html vignetteTitles: GenVisR: An introduction, Visualizing Small Variants, waterfall: function introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenVisR/inst/doc/Intro.R, vignettes/GenVisR/inst/doc/Upcoming_Features.R, vignettes/GenVisR/inst/doc/waterfall_introduction.R dependencyCount: 119 Package: GEOmetadb Version: 1.48.0 Depends: GEOquery,RSQLite Suggests: knitr, rmarkdown, dplyr, tm, wordcloud License: Artistic-2.0 MD5sum: 81fa87021bf28ed54504952d4d5d2805 NeedsCompilation: no Title: A compilation of metadata from NCBI GEO Description: The NCBI Gene Expression Omnibus (GEO) represents the largest public repository of microarray data. However, finding data of interest can be challenging using current tools. GEOmetadb is an attempt to make access to the metadata associated with samples, platforms, and datasets much more feasible. This is accomplished by parsing all the NCBI GEO metadata into a SQLite database that can be stored and queried locally. GEOmetadb is simply a thin wrapper around the SQLite database along with associated documentation. Finally, the SQLite database is updated regularly as new data is added to GEO and can be downloaded at will for the most up-to-date metadata. GEOmetadb paper: http://bioinformatics.oxfordjournals.org/cgi/content/short/24/23/2798 . biocViews: Infrastructure Author: Jack Zhu and Sean Davis Maintainer: Jack Zhu URL: http://gbnci.abcc.ncifcrf.gov/geo/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GEOmetadb git_branch: RELEASE_3_10 git_last_commit: 4177e85 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GEOmetadb_1.48.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GEOmetadb_1.48.0.tgz vignettes: vignettes/GEOmetadb/inst/doc/GEOmetadb.html vignetteTitles: GEOmetadb hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOmetadb/inst/doc/GEOmetadb.R importsMe: Onassis dependencyCount: 52 Package: GEOquery Version: 2.54.1 Depends: methods, Biobase Imports: httr, readr (>= 1.3.1), xml2, dplyr, tidyr, magrittr, limma Suggests: knitr, rmarkdown, BiocGenerics, testthat, covr License: GPL-2 MD5sum: 83b7e177214cc471e8a9265317aa4dde NeedsCompilation: no Title: Get data from NCBI Gene Expression Omnibus (GEO) Description: The NCBI Gene Expression Omnibus (GEO) is a public repository of microarray data. Given the rich and varied nature of this resource, it is only natural to want to apply BioConductor tools to these data. GEOquery is the bridge between GEO and BioConductor. biocViews: Microarray, DataImport, OneChannel, TwoChannel, SAGE Author: Sean Davis Maintainer: Sean Davis URL: https://github.com/seandavi/GEOquery VignetteBuilder: knitr BugReports: https://github.com/seandavi/GEOquery/issues/new git_url: https://git.bioconductor.org/packages/GEOquery git_branch: RELEASE_3_10 git_last_commit: abbe180 git_last_commit_date: 2019-11-18 Date/Publication: 2019-11-18 source.ver: src/contrib/GEOquery_2.54.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/GEOquery_2.54.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GEOquery_2.54.1.tgz vignettes: vignettes/GEOquery/inst/doc/GEOquery.html vignetteTitles: Using GEOquery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOquery/inst/doc/GEOquery.R dependsOnMe: DrugVsDisease, SCAN.UPC importsMe: bigmelon, ChIPXpress, coexnet, crossmeta, EGAD, GAPGOM, MACPET, minfi, MoonlightR, phantasus, recount, SRAdb suggestsMe: AUCell, CAMTHC, ctsGE, debCAM, diffcoexp, dyebias, ELBOW, EpiDISH, fgsea, GenomicOZone, GSEABenchmarkeR, multiClust, MultiDataSet, omicsPrint, pathprint, PCAtools, PGSEA, RGSEA, Rnits, runibic, skewr, TargetScore, zFPKM dependencyCount: 45 Package: GEOsubmission Version: 1.38.0 Imports: affy, Biobase, utils License: GPL (>= 2) MD5sum: 0cc56604a506fe5d12f80d742d4ff3c2 NeedsCompilation: no Title: Prepares microarray data for submission to GEO Description: Helps to easily submit a microarray dataset and the associated sample information to GEO by preparing a single file for upload (direct deposit). biocViews: Microarray Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/GEOsubmission git_branch: RELEASE_3_10 git_last_commit: fa09977 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GEOsubmission_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GEOsubmission_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GEOsubmission_1.38.0.tgz vignettes: vignettes/GEOsubmission/inst/doc/GEOsubmission.pdf vignetteTitles: GEOsubmission Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOsubmission/inst/doc/GEOsubmission.R dependencyCount: 13 Package: gep2pep Version: 1.6.0 Imports: repo (>= 2.1.1), foreach, stats, utils, GSEABase, methods, Biobase, XML, rhdf5, digest, iterators Suggests: WriteXLS, testthat, knitr, rmarkdown License: GPL-3 MD5sum: 2773a95f5bbe50a1e81bc46d7b2dc634 NeedsCompilation: no Title: Creation and Analysis of Pathway Expression Profiles (PEPs) Description: Pathway Expression Profiles (PEPs) are based on the expression of pathways (defined as sets of genes) as opposed to individual genes. This package converts gene expression profiles to PEPs and performs enrichment analysis of both pathways and experimental conditions, such as "drug set enrichment analysis" and "gene2drug" drug discovery analysis respectively. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DimensionReduction, Pathways, GO Author: Francesco Napolitano Maintainer: Francesco Napolitano VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gep2pep git_branch: RELEASE_3_10 git_last_commit: cce30b8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gep2pep_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gep2pep_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gep2pep_1.6.0.tgz vignettes: vignettes/gep2pep/inst/doc/vignette.html vignetteTitles: Introduction to gep2pep hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gep2pep/inst/doc/vignette.R dependencyCount: 40 Package: gespeR Version: 1.18.0 Depends: methods, graphics, ggplot2, R(>= 2.10) Imports: Matrix, glmnet, cellHTS2, Biobase, biomaRt, doParallel, parallel, foreach, reshape2, dplyr Suggests: knitr License: GPL-3 MD5sum: dc27a63afa1adefb273115109519bbf8 NeedsCompilation: no Title: Gene-Specific Phenotype EstimatoR Description: Estimates gene-specific phenotypes from off-target confounded RNAi screens. The phenotype of each siRNA is modeled based on on-targeted and off-targeted genes, using a regularized linear regression model. biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, GeneTarget, Regression, Visualization Author: Fabian Schmich Maintainer: Fabian Schmich URL: http://www.cbg.ethz.ch/software/gespeR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gespeR git_branch: RELEASE_3_10 git_last_commit: 011074e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gespeR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gespeR_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gespeR_1.18.0.tgz vignettes: vignettes/gespeR/inst/doc/gespeR.pdf vignetteTitles: An R package for deconvoluting off-target confounded RNAi screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gespeR/inst/doc/gespeR.R dependencyCount: 123 Package: GEWIST Version: 1.30.0 Depends: R (>= 2.10), car License: GPL-2 MD5sum: 08eb40e85cb57f0051230c9520427b35 NeedsCompilation: no Title: Gene Environment Wide Interaction Search Threshold Description: This 'GEWIST' package provides statistical tools to efficiently optimize SNP prioritization for gene-gene and gene-environment interactions. biocViews: MultipleComparison, Genetics Author: Wei Q. Deng, Guillaume Pare Maintainer: Wei Q. Deng git_url: https://git.bioconductor.org/packages/GEWIST git_branch: RELEASE_3_10 git_last_commit: ac1a6d4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GEWIST_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GEWIST_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GEWIST_1.30.0.tgz vignettes: vignettes/GEWIST/inst/doc/GEWIST.pdf vignetteTitles: GEWIST.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEWIST/inst/doc/GEWIST.R dependencyCount: 67 Package: GGBase Version: 3.48.0 Depends: R (>= 2.14), methods, snpStats Imports: limma, genefilter, Biobase, BiocGenerics, S4Vectors, IRanges, Matrix, AnnotationDbi, digest, GenomicRanges, SummarizedExperiment Suggests: GGtools, illuminaHumanv1.db, knitr License: Artistic-2.0 MD5sum: 951b3a2da50ad58ff26011e29ce0c797 NeedsCompilation: no Title: GGBase infrastructure for genetics of gene expression package GGtools Description: infrastructure biocViews: Genetics, Infrastructure Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GGBase git_branch: RELEASE_3_10 git_last_commit: bfcc4bd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GGBase_3.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GGBase_3.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GGBase_3.48.0.tgz vignettes: vignettes/GGBase/inst/doc/ggbase.html vignetteTitles: GGBase -- infrastructure for GGtools,, genetics of gene expression hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GGBase/inst/doc/ggbase.R dependsOnMe: GGtools dependencyCount: 55 Package: ggbio Version: 1.34.0 Depends: methods, BiocGenerics, ggplot2 (>= 1.0.0) Imports: grid, grDevices, graphics, stats, utils, gridExtra, scales, reshape2, gtable, Hmisc, biovizBase (>= 1.29.2), Biobase, S4Vectors (>= 0.13.13), IRanges (>= 2.11.16), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.29.14), SummarizedExperiment, Biostrings, Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), BSgenome, VariantAnnotation (>= 1.11.4), rtracklayer (>= 1.25.16), GenomicFeatures (>= 1.29.11), OrganismDbi, GGally, ensembldb (>= 1.99.13), AnnotationDbi, AnnotationFilter, rlang Suggests: vsn, BSgenome.Hsapiens.UCSC.hg19, Homo.sapiens, TxDb.Hsapiens.UCSC.hg19.knownGene, chipseq, TxDb.Mmusculus.UCSC.mm9.knownGene, knitr, BiocStyle, testthat, EnsDb.Hsapiens.v75, tinytex License: Artistic-2.0 MD5sum: c14b3c14020fb81a529d6c5c1d213368 NeedsCompilation: no Title: Visualization tools for genomic data Description: The ggbio package extends and specializes the grammar of graphics for biological data. The graphics are designed to answer common scientific questions, in particular those often asked of high throughput genomics data. All core Bioconductor data structures are supported, where appropriate. The package supports detailed views of particular genomic regions, as well as genome-wide overviews. Supported overviews include ideograms and grand linear views. High-level plots include sequence fragment length, edge-linked interval to data view, mismatch pileup, and several splicing summaries. biocViews: Infrastructure, Visualization Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne Cook [aut, ths], Johannes Rainer [ctb] Maintainer: Michael Lawrence URL: http://tengfei.github.com/ggbio/ VignetteBuilder: knitr BugReports: https://github.com/tengfei/ggbio/issues git_url: https://git.bioconductor.org/packages/ggbio git_branch: RELEASE_3_10 git_last_commit: 15346db git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ggbio_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ggbio_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ggbio_1.34.0.tgz vignettes: vignettes/ggbio/inst/doc/ggbio.pdf vignetteTitles: Part 0: Introduction and quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CAFE, intansv importsMe: derfinderPlot, FourCSeq, GenomicOZone, msgbsR, Pi, R3CPET, Rariant, ReportingTools, RiboProfiling, scruff, SomaticSignatures suggestsMe: beadarray, ensembldb, gQTLstats, gwascat, interactiveDisplay, regionReport, RnBeads, StructuralVariantAnnotation dependencyCount: 150 Package: ggcyto Version: 1.14.1 Depends: methods, ggplot2(>= 3.3.0), flowCore(>= 1.41.5), ncdfFlow(>= 2.17.1), flowWorkspace(>= 3.33.1) Imports: plyr, scales, data.table, RColorBrewer, gridExtra, rlang Suggests: testthat, flowWorkspaceData, knitr, rmarkdown, flowStats, openCyto, flowViz, ggridges, vdiffr License: Artistic-2.0 MD5sum: 6dc7dcfb30f3337a4c31b45a96574c74 NeedsCompilation: no Title: Visualize Cytometry data with ggplot Description: With the dedicated fortify method implemented for flowSet, ncdfFlowSet and GatingSet classes, both raw and gated flow cytometry data can be plotted directly with ggplot. ggcyto wrapper and some customed layers also make it easy to add gates and population statistics to the plot. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Infrastructure, Visualization Author: Mike Jiang Maintainer: Mike Jiang URL: https://github.com/RGLab/ggcyto/issues VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggcyto git_branch: RELEASE_3_10 git_last_commit: 295ca54 git_last_commit_date: 2020-03-06 Date/Publication: 2020-03-07 source.ver: src/contrib/ggcyto_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/ggcyto_1.14.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ggcyto_1.14.1.tgz vignettes: vignettes/ggcyto/inst/doc/autoplot.html, vignettes/ggcyto/inst/doc/ggcyto.flowSet.html, vignettes/ggcyto/inst/doc/ggcyto.GatingSet.html, vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.html vignetteTitles: Quick plot for cytometry data, Visualize flowSet with ggcyto, Visualize GatingSet with ggcyto, Feature summary of ggcyto hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggcyto/inst/doc/autoplot.R, vignettes/ggcyto/inst/doc/ggcyto.flowSet.R, vignettes/ggcyto/inst/doc/ggcyto.GatingSet.R, vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.R importsMe: CytoML suggestsMe: flowCore, flowWorkspace, openCyto dependencyCount: 88 Package: GGtools Version: 5.22.0 Depends: R (>= 2.14), GGBase (>= 3.19.7), data.table, parallel, Homo.sapiens Imports: methods, utils, stats, BiocGenerics (>= 0.25.1), snpStats, ff, Rsamtools, AnnotationDbi, Biobase, bit, VariantAnnotation, hexbin, rtracklayer, Gviz, stats4, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges (>= 1.29.6), iterators, Biostrings, ROCR, biglm, ggplot2, reshape2 Suggests: GGdata, illuminaHumanv1.db, SNPlocs.Hsapiens.dbSNP144.GRCh37, multtest, aod, rmeta Enhances: MatrixEQTL, foreach, doParallel, gwascat License: Artistic-2.0 MD5sum: 82eeed4df6faa345e8f9e350d57f74ca NeedsCompilation: no Title: software and data for analyses in genetics of gene expression Description: software and data for analyses in genetics of gene expression and/or DNA methylation biocViews: Genetics, GeneExpression, GeneticVariability, SNP Author: VJ Carey Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/GGtools git_branch: RELEASE_3_10 git_last_commit: 01aaefc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GGtools_5.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GGtools_5.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GGtools_5.22.0.tgz vignettes: vignettes/GGtools/inst/doc/GGtools.pdf vignetteTitles: GGtools: software for eQTL identification hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GGtools/inst/doc/GGtools.R importsMe: GeneGeneInteR suggestsMe: GGBase, gQTLBase dependencyCount: 169 Package: ggtree Version: 2.0.4 Depends: R (>= 3.4.0) Imports: ape, dplyr, ggplot2 (>= 3.0.0), grid, magrittr, methods, purrr, rlang, rvcheck, tidyr, tidytree (>= 0.2.6), treeio (>= 1.8.0), utils Suggests: emojifont, ggimage, ggplotify, grDevices, knitr, prettydoc, rmarkdown, scales, stats, testthat, tibble License: Artistic-2.0 MD5sum: e41db4ee9a7bab9012e97bffe294d40f NeedsCompilation: no Title: an R package for visualization of tree and annotation data Description: 'ggtree' extends the 'ggplot2' plotting system which implemented the grammar of graphics. 'ggtree' is designed for visualization and annotation of phylogenetic trees and other tree-like structures with their annotation data. biocViews: Alignment, Annotation, Clustering, DataImport, MultipleSequenceAlignment, ReproducibleResearch, Software, Visualization Author: Guangchuang Yu [aut, cre, cph] (), Tommy Tsan-Yuk Lam [aut, ths], Justin Silverman [ctb], Bradley Jones [ctb], Watal M. Iwasaki [ctb], Ruizhu Huang [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.github.io/treedata-book/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggtree/issues git_url: https://git.bioconductor.org/packages/ggtree git_branch: RELEASE_3_10 git_last_commit: e6958bd git_last_commit_date: 2020-04-13 Date/Publication: 2020-04-13 source.ver: src/contrib/ggtree_2.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/ggtree_2.0.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ggtree_2.0.4.tgz vignettes: vignettes/ggtree/inst/doc/ggtree.html vignetteTitles: ggtree hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtree/inst/doc/ggtree.R importsMe: LINC, LymphoSeq, philr, singleCellTK, universalmotif suggestsMe: metagenomeFeatures, treeio, TreeSummarizedExperiment dependencyCount: 69 Package: GIGSEA Version: 1.4.0 Depends: R (>= 3.5), Matrix, MASS, locfdr, stats, utils Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: 202dc84a2e54b9e073dfcf1cc426ff8a NeedsCompilation: no Title: Genotype Imputed Gene Set Enrichment Analysis Description: We presented the Genotype-imputed Gene Set Enrichment Analysis (GIGSEA), a novel method that uses GWAS-and-eQTL-imputed trait-associated differential gene expression to interrogate gene set enrichment for the trait-associated SNPs. By incorporating eQTL from large gene expression studies, e.g. GTEx, GIGSEA appropriately addresses such challenges for SNP enrichment as gene size, gene boundary, SNP distal regulation, and multiple-marker regulation. The weighted linear regression model, taking as weights both imputation accuracy and model completeness, was used to perform the enrichment test, properly adjusting the bias due to redundancy in different gene sets. The permutation test, furthermore, is used to evaluate the significance of enrichment, whose efficiency can be largely elevated by expressing the computational intensive part in terms of large matrix operation. We have shown the appropriate type I error rates for GIGSEA (<5%), and the preliminary results also demonstrate its good performance to uncover the real signal. biocViews: GeneSetEnrichment,SNP,VariantAnnotation,GeneExpression,GeneRegulation,Regression,DifferentialExpression Author: Shijia Zhu Maintainer: Shijia Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GIGSEA git_branch: RELEASE_3_10 git_last_commit: b929e03 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GIGSEA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GIGSEA_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GIGSEA_1.4.0.tgz vignettes: vignettes/GIGSEA/inst/doc/GIGSEA_tutorial.pdf vignetteTitles: GIGSEA: Genotype Imputed Gene Set Enrichment Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GIGSEA/inst/doc/GIGSEA_tutorial.R dependencyCount: 11 Package: girafe Version: 1.38.0 Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.17.25), Rsamtools (>= 1.31.2), intervals (>= 0.13.1), ShortRead (>= 1.37.1), genomeIntervals (>= 1.25.1), grid Imports: methods, Biobase, Biostrings (>= 2.47.6), graphics, grDevices, stats, utils, IRanges (>= 2.13.12) Suggests: MASS, org.Mm.eg.db, RColorBrewer Enhances: genomeIntervals License: Artistic-2.0 MD5sum: f7a140bbe1ce6a52b090d5196df00129 NeedsCompilation: yes Title: Genome Intervals and Read Alignments for Functional Exploration Description: The package 'girafe' deals with the genome-level representation of aligned reads from next-generation sequencing data. It contains an object class for enabling a detailed description of genome intervals with aligned reads and functions for comparing, visualising, exporting and working with such intervals and the aligned reads. As such, the package interacts with and provides a link between the packages ShortRead, IRanges and genomeIntervals. biocViews: Sequencing Author: Joern Toedling, with contributions from Constance Ciaudo, Olivier Voinnet, Edith Heard, Emmanuel Barillot, and Wolfgang Huber Maintainer: J. Toedling git_url: https://git.bioconductor.org/packages/girafe git_branch: RELEASE_3_10 git_last_commit: 2ee1a3a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/girafe_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/girafe_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/girafe_1.38.0.tgz vignettes: vignettes/girafe/inst/doc/girafe.pdf vignetteTitles: Genome intervals and read alignments for functional exploration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/girafe/inst/doc/girafe.R dependencyCount: 44 Package: GISPA Version: 1.10.0 Depends: R (>= 3.3.2) Imports: Biobase, changepoint, data.table, genefilter, graphics, GSEABase, HH, lattice, latticeExtra, plyr, scatterplot3d, stats Suggests: knitr License: GPL-2 MD5sum: 542aa47b58946e5ee40262d610cac904 NeedsCompilation: no Title: GISPA: Method for Gene Integrated Set Profile Analysis Description: GISPA is a method intended for the researchers who are interested in defining gene sets with similar, a priori specified molecular profile. GISPA method has been previously published in Nucleic Acid Research (Kowalski et al., 2016; PMID: 26826710). biocViews: StatisticalMethod,GeneSetEnrichment,GenomeWideAssociation Author: Bhakti Dwivedi and Jeanne Kowalski Maintainer: Bhakti Dwivedi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GISPA git_branch: RELEASE_3_10 git_last_commit: cf84369 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GISPA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GISPA_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GISPA_1.10.0.tgz vignettes: vignettes/GISPA/inst/doc/GISPA_manual.html vignetteTitles: GISPA:Method for Gene Integrated Set Profile Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GISPA/inst/doc/GISPA_manual.R dependencyCount: 128 Package: GLAD Version: 2.50.0 Depends: R (>= 2.10) Imports: aws License: GPL-2 MD5sum: 20f690eaa7a178117b04ad58f63a862d NeedsCompilation: yes Title: Gain and Loss Analysis of DNA Description: Analysis of array CGH data : detection of breakpoints in genomic profiles and assignment of a status (gain, normal or loss) to each chromosomal regions identified. biocViews: Microarray, CopyNumberVariation Author: Philippe Hupe Maintainer: Philippe Hupe URL: http://bioinfo.curie.fr SystemRequirements: gsl. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. git_url: https://git.bioconductor.org/packages/GLAD git_branch: RELEASE_3_10 git_last_commit: 34b2862 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GLAD_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GLAD_2.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GLAD_2.50.0.tgz vignettes: vignettes/GLAD/inst/doc/GLAD.pdf vignetteTitles: GLAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GLAD/inst/doc/GLAD.R dependsOnMe: ADaCGH2, ITALICS, MANOR, seqCNA importsMe: ITALICS, MANOR, snapCGH suggestsMe: RnBeads dependencyCount: 4 Package: GladiaTOX Version: 1.2.0 Depends: R (>= 3.6.0), data.table (>= 1.9.4) Imports: DBI, RMySQL, RSQLite, numDeriv, RColorBrewer, parallel, stats, methods, graphics, grDevices, xtable, tools, brew, stringr, RJSONIO, ggplot2, ggrepel, tidyr, utils, RCurl, XML Suggests: roxygen2, knitr, rmarkdown, testthat, BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: d50fc47c7603f926b7835bb81b6b8095 NeedsCompilation: no Title: R Package for Processing High Content Screening data Description: GladiaTOX R package is an open-source, flexible solution to high-content screening data processing and reporting in biomedical research. GladiaTOX takes advantage of the tcpl core functionalities and provides a number of extensions: it provides a web-service solution to fetch raw data; it computes severity scores and exports ToxPi formatted files; furthermore it contains a suite of functionalities to generate pdf reports for quality control and data processing. biocViews: Software, WorkflowStep, Normalization, Preprocessing, QualityControl Author: Vincenzo Belcastro [aut, cre], Dayne L Filer [aut], Stephane Cano [aut] Maintainer: PMP S.A. R Support VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GladiaTOX git_branch: RELEASE_3_10 git_last_commit: 2715ef9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GladiaTOX_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GladiaTOX_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GladiaTOX_1.2.0.tgz vignettes: vignettes/GladiaTOX/inst/doc/GladiaTOX.html vignetteTitles: GladiaTOX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GladiaTOX/inst/doc/GladiaTOX.R dependencyCount: 79 Package: Glimma Version: 1.14.0 Depends: R (>= 3.4.0) Imports: edgeR, grDevices, jsonlite, methods, stats, S4Vectors, utils Suggests: BiocStyle, IRanges, GenomicRanges, SummarizedExperiment, DESeq2, limma, testthat, knitr, rmarkdown, pryr License: GPL-3 | file LICENSE MD5sum: cbaccdb212bd00b5fc62ff41e9426145 NeedsCompilation: no Title: Interactive HTML graphics Description: This package generates interactive visualisations for analysis of RNA-sequencing data using output from limma, edgeR or DESeq2 packages in an HTML page. The interactions are built on top of the popular static representations of analysis results in order to provide additional information. biocViews: ImmunoOncology, DifferentialExpression, GeneExpression, Microarray, ReportWriting, RNASeq, Sequencing, Visualization Author: Shian Su [aut, cre], Matthew Ritchie [aut], Charity Law [aut], Stuart Lee [ctb] Maintainer: Shian Su URL: https://github.com/Shians/Glimma VignetteBuilder: knitr BugReports: https://github.com/Shians/Glimma/issues git_url: https://git.bioconductor.org/packages/Glimma git_branch: RELEASE_3_10 git_last_commit: 236f365 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Glimma_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Glimma_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Glimma_1.14.0.tgz vignettes: vignettes/Glimma/inst/doc/Glimma.pdf vignetteTitles: Glimma Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Glimma/inst/doc/Glimma.R importsMe: affycoretools, EGSEA dependencyCount: 16 Package: glmSparseNet Version: 1.4.0 Depends: R (>= 3.5), Matrix, MultiAssayExperiment, glmnet Imports: SummarizedExperiment, STRINGdb, biomaRt, futile.logger, sparsebn, sparsebnUtils, forcats, dplyr, readr, ggplot2, survminer, reshape2, stats, stringr, rlang, parallel, methods, loose.rock (>= 1.0.12) Suggests: testthat, knitr, rmarkdown, survival, survcomp, pROC, VennDiagram, BiocStyle, curatedTCGAData, TCGAutils License: GPL (>=3) Archs: i386, x64 MD5sum: aaef0f9d13d40b540c61b3c96c0eae4f NeedsCompilation: no Title: Network Centrality Metrics for Elastic-Net Regularized Models Description: glmSparseNet is an R-package that generalizes sparse regression models when the features (e.g. genes) have a graph structure (e.g. protein-protein interactions), by including network-based regularizers. glmSparseNet uses the glmnet R-package, by including centrality measures of the network as penalty weights in the regularization. The current version implements regularization based on node degree, i.e. the strength and/or number of its associated edges, either by promoting hubs in the solution or orphan genes in the solution. All the glmnet distribution families are supported, namely "gaussian", "poisson", "binomial", "multinomial", "cox", and "mgaussian". biocViews: Software, StatisticalMethod, DimensionReduction, Regression, Classification, Survival, Network, GraphAndNetwork Author: André Veríssimo [aut, cre], Susana Vinga [aut], Eunice Carrasquinha [ctb], Marta Lopes [ctb] Maintainer: André Veríssimo URL: https://www.github.com/sysbiomed/glmSparseNet VignetteBuilder: knitr BugReports: https://www.github.com/sysbiomed/glmSparseNet/issues git_url: https://git.bioconductor.org/packages/glmSparseNet git_branch: RELEASE_3_10 git_last_commit: 91e41c0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/glmSparseNet_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/glmSparseNet_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/glmSparseNet_1.4.0.tgz vignettes: vignettes/glmSparseNet/inst/doc/example_brca_logistic.html, vignettes/glmSparseNet/inst/doc/example_brca_protein-protein-interactions_survival.html, vignettes/glmSparseNet/inst/doc/example_brca_survival.html, vignettes/glmSparseNet/inst/doc/example_prad_survival.html, vignettes/glmSparseNet/inst/doc/example_skcm_survival.html vignetteTitles: Example for Classification -- Breast Invasive Carcinoma, Breast survival dataset using network from STRING DB, Example for Survival Data -- Breast Invasive Carcinoma, Example for Survival Data -- Prostate Adenocarcinoma, Example for Survival Data -- Skin Melanoma hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/glmSparseNet/inst/doc/example_brca_logistic.R, vignettes/glmSparseNet/inst/doc/example_brca_protein-protein-interactions_survival.R, vignettes/glmSparseNet/inst/doc/example_brca_survival.R, vignettes/glmSparseNet/inst/doc/example_prad_survival.R, vignettes/glmSparseNet/inst/doc/example_skcm_survival.R dependencyCount: 162 Package: GlobalAncova Version: 4.4.0 Depends: methods, corpcor, globaltest Imports: annotate, AnnotationDbi, Biobase, dendextend, GSEABase, VGAM Suggests: GO.db, KEGG.db, golubEsets, hu6800.db, vsn, Rgraphviz License: GPL (>= 2) MD5sum: 756487a87919a0a913b6003560fc57f4 NeedsCompilation: yes Title: Global test for groups of variables via model comparisons Description: The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany. biocViews: Microarray, OneChannel, DifferentialExpression, Pathways, Regression Author: U. Mansmann, R. Meister, M. Hummel, R. Scheufele, with contributions from S. Knueppel Maintainer: Manuela Hummel git_url: https://git.bioconductor.org/packages/GlobalAncova git_branch: RELEASE_3_10 git_last_commit: 40f7cd2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GlobalAncova_4.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GlobalAncova_4.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GlobalAncova_4.4.0.tgz vignettes: vignettes/GlobalAncova/inst/doc/GlobalAncova.pdf, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.pdf vignetteTitles: GlobalAncova.pdf, GlobalAncovaDecomp.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GlobalAncova/inst/doc/GlobalAncova.R, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.R dependencyCount: 84 Package: globalSeq Version: 1.14.0 Depends: R (>= 3.5.0) Suggests: knitr, testthat, SummarizedExperiment, S4Vectors License: GPL-3 MD5sum: aad82a1fb2429ee03092cf81b3074de8 NeedsCompilation: no Title: Global Test for Counts Description: The method may be conceptualised as a test of overall significance in regression analysis, where the response variable is overdispersed and the number of explanatory variables exceeds the sample size. Useful for testing for association between RNA-Seq and high-dimensional data. biocViews: GeneExpression, ExonArray, DifferentialExpression, GenomeWideAssociation, Transcriptomics, DimensionReduction, Regression, Sequencing, WholeGenome, RNASeq, ExomeSeq, miRNA, MultipleComparison Author: Armin Rauschenberger [aut, cre] Maintainer: Armin Rauschenberger URL: https://github.com/rauschenberger/globalSeq VignetteBuilder: knitr BugReports: https://github.com/rauschenberger/globalSeq/issues git_url: https://git.bioconductor.org/packages/globalSeq git_branch: RELEASE_3_10 git_last_commit: da278e8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/globalSeq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/globalSeq_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/globalSeq_1.14.0.tgz vignettes: vignettes/globalSeq/inst/doc/globalSeq.pdf, vignettes/globalSeq/inst/doc/article.html, vignettes/globalSeq/inst/doc/vignette.html vignetteTitles: vignette source, article frame, vignette frame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globalSeq/inst/doc/globalSeq.R dependencyCount: 0 Package: globaltest Version: 5.40.0 Depends: methods, survival Imports: Biobase, AnnotationDbi, annotate, graphics Suggests: vsn, golubEsets, KEGG.db, hu6800.db, Rgraphviz, GO.db, lungExpression, org.Hs.eg.db, GSEABase, penalized, gss, MASS, boot, rpart, mstate License: GPL (>= 2) MD5sum: 07a7f70811bb9210ebac185a8be0e040 NeedsCompilation: no Title: Testing Groups of Covariates/Features for Association with a Response Variable, with Applications to Gene Set Testing Description: The global test tests groups of covariates (or features) for association with a response variable. This package implements the test with diagnostic plots and multiple testing utilities, along with several functions to facilitate the use of this test for gene set testing of GO and KEGG terms. biocViews: Microarray, OneChannel, Bioinformatics, DifferentialExpression, GO, Pathways Author: Jelle Goeman and Jan Oosting, with contributions by Livio Finos, Aldo Solari and Dominic Edelmann Maintainer: Jelle Goeman git_url: https://git.bioconductor.org/packages/globaltest git_branch: RELEASE_3_10 git_last_commit: 14fb0aa git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/globaltest_5.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/globaltest_5.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/globaltest_5.40.0.tgz vignettes: vignettes/globaltest/inst/doc/GlobalTest.pdf vignetteTitles: Global Test hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globaltest/inst/doc/GlobalTest.R dependsOnMe: GlobalAncova importsMe: BiSeq, EGSEA, SIM suggestsMe: topGO dependencyCount: 37 Package: gmapR Version: 1.28.0 Depends: R (>= 2.15.0), methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.31.8), Rsamtools (>= 1.31.2) Imports: S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), BiocGenerics (>= 0.25.1), rtracklayer (>= 1.39.7), GenomicFeatures (>= 1.31.3), Biostrings, VariantAnnotation (>= 1.25.11), tools, Biobase, BSgenome, GenomicAlignments (>= 1.15.6), BiocParallel Suggests: RUnit, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Scerevisiae.UCSC.sacCer3, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, LungCancerLines License: Artistic-2.0 MD5sum: 9c4474292a223a706376374d03a2b429 NeedsCompilation: yes Title: An R interface to the GMAP/GSNAP/GSTRUCT suite Description: GSNAP and GMAP are a pair of tools to align short-read data written by Tom Wu. This package provides convenience methods to work with GMAP and GSNAP from within R. In addition, it provides methods to tally alignment results on a per-nucleotide basis using the bam_tally tool. biocViews: Alignment Author: Cory Barr, Thomas Wu, Michael Lawrence Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/gmapR git_branch: RELEASE_3_10 git_last_commit: 70a270e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gmapR_1.28.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gmapR_1.28.0.tgz vignettes: vignettes/gmapR/inst/doc/gmapR.pdf vignetteTitles: gmapR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gmapR/inst/doc/gmapR.R dependsOnMe: HTSeqGenie importsMe: MMAPPR2, MTseeker suggestsMe: VariantTools dependencyCount: 85 Package: GmicR Version: 1.0.6 Imports: AnnotationDbi, ape, bnlearn, Category, DT, gRbase, GSEABase, gRain, GOstats, org.Hs.eg.db, org.Mm.eg.db, reshape2, shiny, WGCNA, data.table, grDevices, graphics, stats, utils Suggests: knitr, rmarkdown, testthat License: GPL-2 + file LICENSE Archs: i386, x64 MD5sum: 995b96feaaef0a2c30092e3490bf1fab NeedsCompilation: no Title: Combines WGCNA and xCell readouts with bayesian network learrning to generate a Gene-Module Immune-Cell network (GMIC) Description: This package uses bayesian network learning to detect relationships between Gene Modules detected by WGCNA and immune cell signatures defined by xCell. It is a hypothesis generating tool. biocViews: Software, SystemsBiology, GraphAndNetwork, Network, NetworkInference, GUI, ImmunoOncology, GeneExpression, QualityControl, Bayesian, Clustering Author: Richard Virgen-Slane Maintainer: Richard Virgen-Slane VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GmicR git_branch: RELEASE_3_10 git_last_commit: 0618485 git_last_commit_date: 2020-02-13 Date/Publication: 2020-02-14 source.ver: src/contrib/GmicR_1.0.6.tar.gz win.binary.ver: bin/windows/contrib/3.6/GmicR_1.0.6.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GmicR_1.0.6.tgz vignettes: vignettes/GmicR/inst/doc/GmicR_vignette.html vignetteTitles: GmicR_vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GmicR/inst/doc/GmicR_vignette.R dependencyCount: 141 Package: GMRP Version: 1.14.0 Depends: R(>= 3.3.0),stats,utils,graphics, grDevices, diagram, plotrix, base,GenomicRanges Suggests: BiocStyle, BiocGenerics License: GPL (>= 2) MD5sum: b894f4d088c05901860ddee4e950374c NeedsCompilation: no Title: GWAS-based Mendelian Randomization and Path Analyses Description: Perform Mendelian randomization analysis of multiple SNPs to determine risk factors causing disease of study and to exclude confounding variabels and perform path analysis to construct path of risk factors to the disease. biocViews: Sequencing, Regression, SNP Author: Yuan-De Tan Maintainer: Yuan-De Tan git_url: https://git.bioconductor.org/packages/GMRP git_branch: RELEASE_3_10 git_last_commit: 38be328 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GMRP_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GMRP_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GMRP_1.14.0.tgz vignettes: vignettes/GMRP/inst/doc/GMRP-manual.pdf, vignettes/GMRP/inst/doc/GMRP.pdf vignetteTitles: GMRP-manual.pdf, Causal Effect Analysis of Risk Factors for Disease with the "GMRP" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GMRP/inst/doc/GMRP.R dependencyCount: 21 Package: GNET2 Version: 1.2.0 Depends: R (>= 3.6) Imports: ggplot2,xgboost,Rcpp,reshape2,grid,DiagrammeR,methods,stats,matrixStats,graphics,SummarizedExperiment LinkingTo: Rcpp Suggests: knitr, rmarkdown License: Apache License 2.0 MD5sum: c00561da14adbb304f9c989638a86776 NeedsCompilation: yes Title: Constructing gene regulatory networks from expression data through functional module inference Description: Cluster genes to functional groups with E-M process. Iteratively perform TF assigning and Gene assigning, until the assignment of genes did not change, or max number of iterations is reached. biocViews: GeneExpression, Regression, Network, NetworkInference, Software Author: Chen Chen, Jie Hou and Jianlin Cheng Maintainer: Chen Chen URL: https://github.com/chrischen1/GNET2 VignetteBuilder: knitr BugReports: https://github.com/chrischen1/GNET2/issues git_url: https://git.bioconductor.org/packages/GNET2 git_branch: RELEASE_3_10 git_last_commit: d99bcef git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GNET2_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GNET2_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GNET2_1.2.0.tgz vignettes: vignettes/GNET2/inst/doc/run_gnet2.html vignetteTitles: Build functional gene modules with GNET2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GNET2/inst/doc/run_gnet2.R dependencyCount: 102 Package: GOexpress Version: 1.20.0 Depends: R (>= 3.4), grid, stats, graphics, Biobase (>= 2.22.0) Imports: biomaRt (>= 2.18.0), stringr (>= 0.6.2), ggplot2 (>= 0.9.0), RColorBrewer (>= 1.0), gplots (>= 2.13.0), randomForest (>= 4.6), RCurl (>= 1.95) Suggests: BiocStyle License: GPL (>= 3) MD5sum: 5a95efa38bf433106ad3cd0f4448fbb3 NeedsCompilation: no Title: Visualise microarray and RNAseq data using gene ontology annotations Description: The package contains methods to visualise the expression profile of genes from a microarray or RNA-seq experiment, and offers a supervised clustering approach to identify GO terms containing genes with expression levels that best classify two or more predefined groups of samples. Annotations for the genes present in the expression dataset may be obtained from Ensembl through the biomaRt package, if not provided by the user. The default random forest framework is used to evaluate the capacity of each gene to cluster samples according to the factor of interest. Finally, GO terms are scored by averaging the rank (alternatively, score) of their respective gene sets to cluster the samples. P-values may be computed to assess the significance of GO term ranking. Visualisation function include gene expression profile, gene ontology-based heatmaps, and hierarchical clustering of experimental samples using gene expression data. biocViews: Software, GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, DataRepresentation, Clustering, TimeCourse, Microarray, Sequencing, RNASeq, Annotation, MultipleComparison, Pathways, GO, Visualization, ImmunoOncology Author: Kevin Rue-Albrecht [aut, cre], Tharvesh M.L. Ali [ctb], Paul A. McGettigan [ctb], Belinda Hernandez [ctb], David A. Magee [ctb], Nicolas C. Nalpas [ctb], Andrew Parnell [ctb], Stephen V. Gordon [ths], David E. MacHugh [ths] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/GOexpress git_url: https://git.bioconductor.org/packages/GOexpress git_branch: RELEASE_3_10 git_last_commit: ed4991f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GOexpress_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GOexpress_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GOexpress_1.20.0.tgz vignettes: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.pdf vignetteTitles: UsersGuide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.R dependencyCount: 96 Package: GOfuncR Version: 1.6.1 Depends: R (>= 3.4), vioplot (>= 0.2), Imports: Rcpp (>= 0.11.5), mapplots (>= 1.5), gtools (>= 3.5.0), GenomicRanges (>= 1.28.4), IRanges, AnnotationDbi, utils, grDevices, graphics, stats, LinkingTo: Rcpp Suggests: Homo.sapiens, BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: 85ee3eec48ddf68938c5c7c51757fde9 NeedsCompilation: yes Title: Gene ontology enrichment using FUNC Description: GOfuncR performs a gene ontology enrichment analysis based on the ontology enrichment software FUNC. GO-annotations are obtained from OrganismDb or OrgDb packages ('Homo.sapiens' by default); the GO-graph is included in the package and updated regularly (07-Oct-2019). GOfuncR provides the standard candidate vs. background enrichment analysis using the hypergeometric test, as well as three additional tests: (i) the Wilcoxon rank-sum test that is used when genes are ranked, (ii) a binomial test that is used when genes are associated with two counts and (iii) a Chi-square or Fisher's exact test that is used in cases when genes are associated with four counts. To correct for multiple testing and interdependency of the tests, family-wise error rates are computed based on random permutations of the gene-associated variables. GOfuncR also provides tools for exploring the ontology graph and the annotations, and options to take gene-length or spatial clustering of genes into account. It is also possible to provide custom gene coordinates, annotations and ontologies. biocViews: GeneSetEnrichment, GO Author: Steffi Grote Maintainer: Steffi Grote VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GOfuncR git_branch: RELEASE_3_10 git_last_commit: a485fbd git_last_commit_date: 2020-03-29 Date/Publication: 2020-03-29 source.ver: src/contrib/GOfuncR_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/GOfuncR_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GOfuncR_1.6.1.tgz vignettes: vignettes/GOfuncR/inst/doc/GOfuncR.html vignetteTitles: Introduction to GOfuncR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOfuncR/inst/doc/GOfuncR.R importsMe: ABAEnrichment dependencyCount: 41 Package: GOFunction Version: 1.34.0 Depends: R (>= 2.11.0), methods, Biobase (>= 2.8.0), graph (>= 1.26.0), Rgraphviz (>= 1.26.0), GO.db (>= 2.4.1), AnnotationDbi (>= 1.10.2), SparseM (>= 0.85) Imports: methods, Biobase, graph, Rgraphviz, GO.db, AnnotationDbi, DBI, SparseM License: GPL (>= 2) Archs: i386, x64 MD5sum: 6aad7d5d5c7b16f5eda9b1ff195e1e8d NeedsCompilation: no Title: GO-function: deriving biologcially relevant functions from statistically significant functions Description: The GO-function package provides a tool to address the redundancy that result from the GO structure or multiple annotation genes and derive biologically relevant functions from the statistically significant functions based on some intuitive assumption and statistical testing. biocViews: GO, Pathways, Microarray, GeneSetEnrichment Author: Jing Wang Maintainer: Jing Wang git_url: https://git.bioconductor.org/packages/GOFunction git_branch: RELEASE_3_10 git_last_commit: f05c08e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GOFunction_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GOFunction_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GOFunction_1.34.0.tgz vignettes: vignettes/GOFunction/inst/doc/GOFunction.pdf vignetteTitles: GO-function hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOFunction/inst/doc/GOFunction.R dependencyCount: 32 Package: GOpro Version: 1.12.0 Depends: R (>= 3.4) Imports: AnnotationDbi, dendextend, doParallel, foreach, parallel, org.Hs.eg.db, GO.db, Rcpp, stats, graphics, MultiAssayExperiment, IRanges, S4Vectors LinkingTo: Rcpp, BH Suggests: knitr, rmarkdown, RTCGA.PANCAN12, BiocStyle, testthat License: GPL-3 MD5sum: 4e7052da47f62339372573f78994be75 NeedsCompilation: yes Title: Find the most characteristic gene ontology terms for groups of human genes Description: Find the most characteristic gene ontology terms for groups of human genes. This package was created as a part of the thesis which was developed under the auspices of MI^2 Group (http://mi2.mini.pw.edu.pl/, https://github.com/geneticsMiNIng). biocViews: Annotation, Clustering, GO, GeneExpression, GeneSetEnrichment, MultipleComparison Author: Lidia Chrabaszcz Maintainer: Lidia Chrabaszcz URL: https://github.com/mi2-warsaw/GOpro VignetteBuilder: knitr BugReports: https://github.com/mi2-warsaw/GOpro/issues git_url: https://git.bioconductor.org/packages/GOpro git_branch: RELEASE_3_10 git_last_commit: da1b78b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GOpro_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GOpro_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GOpro_1.12.0.tgz vignettes: vignettes/GOpro/inst/doc/GOpro_vignette.html vignetteTitles: GOpro: Determine groups of genes and find their characteristic GO term hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOpro/inst/doc/GOpro_vignette.R dependencyCount: 101 Package: goProfiles Version: 1.48.0 Depends: Biobase, AnnotationDbi, GO.db, CompQuadForm, stringr Suggests: org.Hs.eg.db License: GPL-2 Archs: i386, x64 MD5sum: 365fbbdce68290c7a75bf19789f69eb4 NeedsCompilation: no Title: goProfiles: an R package for the statistical analysis of functional profiles Description: The package implements methods to compare lists of genes based on comparing the corresponding 'functional profiles'. biocViews: Annotation, GO, GeneExpression, GeneSetEnrichment, GraphAndNetwork, Microarray, MultipleComparison, Pathways, Software Author: Alex Sanchez, Jordi Ocana and Miquel Salicru Maintainer: Alex Sanchez git_url: https://git.bioconductor.org/packages/goProfiles git_branch: RELEASE_3_10 git_last_commit: 4aa76ed git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/goProfiles_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/goProfiles_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/goProfiles_1.48.0.tgz vignettes: vignettes/goProfiles/inst/doc/goProfiles-comparevisual.pdf, vignettes/goProfiles/inst/doc/goProfiles-plotProfileMF.pdf, vignettes/goProfiles/inst/doc/goProfiles.pdf vignetteTitles: goProfiles-comparevisual.pdf, goProfiles-plotProfileMF.pdf, goProfiles Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goProfiles/inst/doc/goProfiles.R dependencyCount: 32 Package: GOSemSim Version: 2.12.1 Depends: R (>= 3.4.0) Imports: AnnotationDbi, GO.db, methods, utils LinkingTo: Rcpp Suggests: AnnotationHub, BiocManager, clusterProfiler, DOSE, knitr, org.Hs.eg.db, prettydoc, testthat License: Artistic-2.0 MD5sum: 9f8945c8578a7c3d22a8c1d4b0a4c7d6 NeedsCompilation: yes Title: GO-terms Semantic Similarity Measures Description: The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters. GOSemSim implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively. biocViews: Annotation, GO, Clustering, Pathways, Network, Software Author: Guangchuang Yu [aut, cre], Alexey Stukalov [ctb], Chuanle Xiao [ctb], Lluís Revilla Sancho [ctb] Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/software/GOSemSim VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/GOSemSim/issues git_url: https://git.bioconductor.org/packages/GOSemSim git_branch: RELEASE_3_10 git_last_commit: 35fce78 git_last_commit_date: 2020-03-18 Date/Publication: 2020-03-19 source.ver: src/contrib/GOSemSim_2.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/GOSemSim_2.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GOSemSim_2.12.1.tgz vignettes: vignettes/GOSemSim/inst/doc/GOSemSim.html vignetteTitles: An introduction to GOSemSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSemSim/inst/doc/GOSemSim.R dependsOnMe: tRanslatome importsMe: clusterProfiler, DOSE, enrichplot, GAPGOM, meshes, Rcpi, ViSEAGO suggestsMe: BioCor, epiNEM, FELLA, SemDist dependencyCount: 27 Package: goseq Version: 1.38.0 Depends: R (>= 2.11.0), BiasedUrn, geneLenDataBase (>= 1.9.2) Imports: mgcv, graphics, stats, utils, AnnotationDbi, GO.db,BiocGenerics Suggests: edgeR, org.Hs.eg.db, rtracklayer License: LGPL (>= 2) Archs: i386, x64 MD5sum: 6efc8bb0bde5418163e263a45a7c3360 NeedsCompilation: no Title: Gene Ontology analyser for RNA-seq and other length biased data Description: Detects Gene Ontology and/or other user defined categories which are over/under represented in RNA-seq data biocViews: ImmunoOncology, Sequencing, GO, GeneExpression, Transcription, RNASeq Author: Matthew Young Maintainer: Matthew Young , Nadia Davidson git_url: https://git.bioconductor.org/packages/goseq git_branch: RELEASE_3_10 git_last_commit: 525067a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/goseq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/goseq_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/goseq_1.38.0.tgz vignettes: vignettes/goseq/inst/doc/goseq.pdf vignetteTitles: goseq User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goseq/inst/doc/goseq.R dependsOnMe: rgsepd importsMe: ideal, PathwaySplice, SMITE dependencyCount: 89 Package: GOSim Version: 1.24.0 Depends: GO.db, annotate Imports: org.Hs.eg.db, AnnotationDbi, topGO, cluster, flexmix, RBGL, graph, Matrix, corpcor, Rcpp LinkingTo: Rcpp Enhances: igraph License: GPL (>= 2) MD5sum: c37eb15201cd3e2d8a94725db6068507 NeedsCompilation: yes Title: Computation of functional similarities between GO terms and gene products; GO enrichment analysis Description: This package implements several functions useful for computing similarities between GO terms and gene products based on their GO annotation. Moreover it allows for computing a GO enrichment analysis biocViews: GO, Clustering, Software, Pathways Author: Holger Froehlich Maintainer: Holger Froehlich git_url: https://git.bioconductor.org/packages/GOSim git_branch: RELEASE_3_10 git_last_commit: 2174c49 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GOSim_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GOSim_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GOSim_1.24.0.tgz vignettes: vignettes/GOSim/inst/doc/GOSim.pdf vignetteTitles: GOsim hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSim/inst/doc/GOSim.R dependencyCount: 47 Package: goSTAG Version: 1.10.0 Depends: R (>= 3.4) Imports: AnnotationDbi, biomaRt, GO.db, graphics, memoise, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 Archs: i386, x64 MD5sum: 7cd0a22a6ae5e9ea94e42365e7a6d733 NeedsCompilation: no Title: A tool to use GO Subtrees to Tag and Annotate Genes within a set Description: Gene lists derived from the results of genomic analyses are rich in biological information. For instance, differentially expressed genes (DEGs) from a microarray or RNA-Seq analysis are related functionally in terms of their response to a treatment or condition. Gene lists can vary in size, up to several thousand genes, depending on the robustness of the perturbations or how widely different the conditions are biologically. Having a way to associate biological relatedness between hundreds and thousands of genes systematically is impractical by manually curating the annotation and function of each gene. Over-representation analysis (ORA) of genes was developed to identify biological themes. Given a Gene Ontology (GO) and an annotation of genes that indicate the categories each one fits into, significance of the over-representation of the genes within the ontological categories is determined by a Fisher's exact test or modeling according to a hypergeometric distribution. Comparing a small number of enriched biological categories for a few samples is manageable using Venn diagrams or other means for assessing overlaps. However, with hundreds of enriched categories and many samples, the comparisons are laborious. Furthermore, if there are enriched categories that are shared between samples, trying to represent a common theme across them is highly subjective. goSTAG uses GO subtrees to tag and annotate genes within a set. goSTAG visualizes the similarities between the over-representation of DEGs by clustering the p-values from the enrichment statistical tests and labels clusters with the GO term that has the most paths to the root within the subtree generated from all the GO terms in the cluster. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, Clustering, Microarray, mRNAMicroarray, RNASeq, Visualization, GO, ImmunoOncology Author: Brian D. Bennett and Pierre R. Bushel Maintainer: Brian D. Bennett VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/goSTAG git_branch: RELEASE_3_10 git_last_commit: 89b8403 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/goSTAG_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/goSTAG_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/goSTAG_1.10.0.tgz vignettes: vignettes/goSTAG/inst/doc/goSTAG.html vignetteTitles: The goSTAG User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goSTAG/inst/doc/goSTAG.R dependencyCount: 59 Package: GOstats Version: 2.52.0 Depends: R (>= 2.10), Biobase (>= 1.15.29), Category (>= 2.43.2), graph Imports: methods, stats, stats4, AnnotationDbi (>= 0.0.89), GO.db (>= 1.13.0), RBGL, annotate (>= 1.13.2), AnnotationForge, Rgraphviz Suggests: hgu95av2.db (>= 1.13.0), ALL, multtest, genefilter, RColorBrewer, xtable, SparseM, GSEABase, geneplotter, org.Hs.eg.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 3f1afb8eb259aae0d5e27a85350b720b NeedsCompilation: no Title: Tools for manipulating GO and microarrays Description: A set of tools for interacting with GO and microarray data. A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations. biocViews: Annotation, GO, MultipleComparison, GeneExpression, Microarray, Pathways, GeneSetEnrichment, GraphAndNetwork Author: Robert Gentleman [aut], Seth Falcon [ctb], Robert Castelo [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/GOstats git_branch: RELEASE_3_10 git_last_commit: 3d1396d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GOstats_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GOstats_2.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GOstats_2.52.0.tgz vignettes: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.pdf, vignettes/GOstats/inst/doc/GOstatsHyperG.pdf, vignettes/GOstats/inst/doc/GOvis.pdf vignetteTitles: Hypergeometric tests for less common model organisms, Hypergeometric Tests Using GOstats, Visualizing Data Using GOstats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.R, vignettes/GOstats/inst/doc/GOstatsHyperG.R, vignettes/GOstats/inst/doc/GOvis.R dependsOnMe: MineICA, RDAVIDWebService importsMe: affycoretools, attract, categoryCompare, GmicR, ideal, MIGSA, miRLAB, pcaExplorer, scTensor, systemPipeR suggestsMe: BiocCaseStudies, Category, eisa, fastLiquidAssociation, GSEAlm, HTSanalyzeR, interactiveDisplay, MineICA, MLP, MmPalateMiRNA, qpgraph, RnBeads, safe dependencyCount: 45 Package: GOsummaries Version: 2.22.0 Depends: R (>= 2.15), Rcpp Imports: plyr, grid, gProfileR, reshape2, limma, ggplot2, gtable LinkingTo: Rcpp Suggests: vegan License: GPL (>= 2) Archs: i386, x64 MD5sum: 97052bca413101d1c7f0013fada2d54a NeedsCompilation: yes Title: Word cloud summaries of GO enrichment analysis Description: A package to visualise Gene Ontology (GO) enrichment analysis results on gene lists arising from different analyses such clustering or PCA. The significant GO categories are visualised as word clouds that can be combined with different plots summarising the underlying data. biocViews: GeneExpression, Clustering, GO, Visualization Author: Raivo Kolde Maintainer: Raivo Kolde URL: https://github.com/raivokolde/GOsummaries git_url: https://git.bioconductor.org/packages/GOsummaries git_branch: RELEASE_3_10 git_last_commit: 680f373 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GOsummaries_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GOsummaries_2.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GOsummaries_2.22.0.tgz vignettes: vignettes/GOsummaries/inst/doc/GOsummaries-basics.pdf vignetteTitles: GOsummaries basics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOsummaries/inst/doc/GOsummaries-basics.R dependencyCount: 62 Package: GOTHiC Version: 1.22.0 Depends: R (>= 3.5.0), methods, GenomicRanges, Biostrings, BSgenome, data.table Imports: BiocGenerics, S4Vectors (>= 0.9.38), IRanges, Rsamtools, ShortRead, rtracklayer, ggplot2, BiocManager, grDevices, utils, stats, GenomeInfoDb Suggests: HiCDataLymphoblast Enhances: parallel License: GPL-3 MD5sum: 9beb825b02b2e94bcf1509b516792136 NeedsCompilation: no Title: Binomial test for Hi-C data analysis Description: This is a Hi-C analysis package using a cumulative binomial test to detect interactions between distal genomic loci that have significantly more reads than expected by chance in Hi-C experiments. It takes mapped paired NGS reads as input and gives back the list of significant interactions for a given bin size in the genome. biocViews: ImmunoOncology, Sequencing, Preprocessing, Epigenetics, HiC Author: Borbala Mifsud and Robert Sugar Maintainer: Borbala Mifsud git_url: https://git.bioconductor.org/packages/GOTHiC git_branch: RELEASE_3_10 git_last_commit: 30d16e9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GOTHiC_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GOTHiC_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GOTHiC_1.22.0.tgz vignettes: vignettes/GOTHiC/inst/doc/package_vignettes.pdf vignetteTitles: package_vignettes.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOTHiC/inst/doc/package_vignettes.R dependencyCount: 91 Package: goTools Version: 1.60.0 Depends: GO.db Imports: AnnotationDbi, GO.db, graphics, grDevices Suggests: hgu133a.db License: GPL-2 MD5sum: 40e3ed2d6a0aaa62ea49e9a6471d43ad NeedsCompilation: no Title: Functions for Gene Ontology database Description: Wraper functions for description/comparison of oligo ID list using Gene Ontology database biocViews: Microarray,GO,Visualization Author: Yee Hwa (Jean) Yang , Agnes Paquet Maintainer: Agnes Paquet git_url: https://git.bioconductor.org/packages/goTools git_branch: RELEASE_3_10 git_last_commit: 32d2be0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/goTools_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/goTools_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/goTools_1.60.0.tgz vignettes: vignettes/goTools/inst/doc/goTools.pdf vignetteTitles: goTools overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/goTools/inst/doc/goTools.R dependencyCount: 28 Package: gpart Version: 1.4.0 Depends: R (>= 3.5.0), grid, Homo.sapiens, TxDb.Hsapiens.UCSC.hg38.knownGene, Imports: igraph, biomaRt, Rcpp, data.table, OrganismDbi, AnnotationDbi, grDevices, stats, utils, GenomicRanges, IRanges LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat License: MIT + file LICENSE Archs: i386, x64 MD5sum: 31ed1a5c8b4b1300d42ef17cb5415ac2 NeedsCompilation: yes Title: Human genome partitioning of dense sequencing data by identifying haplotype blocks Description: we provide a new SNP sequence partitioning method which partitions the whole SNP sequence based on not only LD block structures but also gene location information. The LD block construction for GPART is performed using Big-LD algorithm, with additional improvement from previous version reported in Kim et al.(2017). We also add a visualization tool to show the LD heatmap with the information of LD block boundaries and gene locations in the package. biocViews: Software, Clustering Author: Sun Ah Kim [aut, cre, cph], Yun Joo Yoo [aut, cph] Maintainer: Sun Ah Kim VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gpart git_branch: RELEASE_3_10 git_last_commit: d7f275e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gpart_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gpart_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gpart_1.4.0.tgz vignettes: vignettes/gpart/inst/doc/gpart.html vignetteTitles: Your Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gpart/inst/doc/gpart.R dependencyCount: 94 Package: gpls Version: 1.58.0 Imports: stats Suggests: MASS License: Artistic-2.0 MD5sum: d06b2d565bae224541a87685a85b4149 NeedsCompilation: no Title: Classification using generalized partial least squares Description: Classification using generalized partial least squares for two-group and multi-group (more than 2 group) classification. biocViews: Classification, Microarray, Regression Author: Beiying Ding Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/gpls git_branch: RELEASE_3_10 git_last_commit: 1599100 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gpls_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gpls_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gpls_1.58.0.tgz vignettes: vignettes/gpls/inst/doc/gpls.pdf vignetteTitles: gpls Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gpls/inst/doc/gpls.R suggestsMe: MCRestimate, MLInterfaces dependencyCount: 1 Package: gprege Version: 1.30.0 Depends: R (>= 2.10), gptk Suggests: spam License: AGPL-3 MD5sum: 7e09902bcd183fa9475752a8e90b5b5d NeedsCompilation: no Title: Gaussian Process Ranking and Estimation of Gene Expression time-series Description: The gprege package implements the methodology described in Kalaitzis & Lawrence (2011) "A simple approach to ranking differentially expressed gene expression time-courses through Gaussian process regression". The software fits two GPs with the an RBF (+ noise diagonal) kernel on each profile. One GP kernel is initialised wih a short lengthscale hyperparameter, signal variance as the observed variance and a zero noise variance. It is optimised via scaled conjugate gradients (netlab). A second GP has fixed hyperparameters: zero inverse-width, zero signal variance and noise variance as the observed variance. The log-ratio of marginal likelihoods of the two hypotheses acts as a score of differential expression for the profile. Comparison via ROC curves is performed against BATS (Angelini et.al, 2007). A detailed discussion of the ranking approach and dataset used can be found in the paper (http://www.biomedcentral.com/1471-2105/12/180). biocViews: Microarray, Preprocessing, Bioinformatics, DifferentialExpression, TimeCourse Author: Alfredo Kalaitzis Maintainer: Alfredo Kalaitzis BugReports: alkalait@gmail.com git_url: https://git.bioconductor.org/packages/gprege git_branch: RELEASE_3_10 git_last_commit: df32c00 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gprege_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gprege_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gprege_1.30.0.tgz vignettes: vignettes/gprege/inst/doc/gprege_quick.pdf vignetteTitles: gprege Quick Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gprege/inst/doc/gprege_quick.R dependencyCount: 13 Package: gQTLBase Version: 1.18.0 Imports: GenomicRanges, methods, BatchJobs, BBmisc, S4Vectors, BiocGenerics, foreach, doParallel, bit, ff, rtracklayer, ffbase, GenomicFiles, SummarizedExperiment Suggests: geuvStore2, knitr, rmarkdown, BiocStyle, RUnit, GGtools, Homo.sapiens, IRanges, erma, GenomeInfoDb, gwascat, geuvPack License: Artistic-2.0 Archs: i386, x64 MD5sum: 191261e2ca486bc541118536afaf5337 NeedsCompilation: no Title: gQTLBase: infrastructure for eQTL, mQTL and similar studies Description: Infrastructure for eQTL, mQTL and similar studies. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gQTLBase git_branch: RELEASE_3_10 git_last_commit: b15bef8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gQTLBase_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gQTLBase_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gQTLBase_1.18.0.tgz vignettes: vignettes/gQTLBase/inst/doc/gQTLBase.html vignetteTitles: gQTLBase infrastructure for eQTL archives hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gQTLBase/inst/doc/gQTLBase.R importsMe: gQTLstats suggestsMe: parglms dependencyCount: 101 Package: gQTLstats Version: 1.18.0 Depends: R (>= 3.5.0), Homo.sapiens Imports: methods, snpStats, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicFiles, GenomicRanges, SummarizedExperiment, VariantAnnotation, Biobase, BatchJobs, gQTLBase, limma, mgcv, dplyr, AnnotationDbi, GenomicFeatures, ggplot2, reshape2, doParallel, foreach, ffbase, BBmisc, beeswarm, HardyWeinberg, graphics, stats, utils, shiny, plotly, erma, ggbeeswarm Suggests: geuvPack, geuvStore2, Rsamtools, knitr, rmarkdown, ggbio, BiocStyle, RUnit, multtest, gwascat, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, ldblock License: Artistic-2.0 MD5sum: 39ee74ceff66b6054b56d64c913e1c58 NeedsCompilation: no Title: gQTLstats: computationally efficient analysis for eQTL and allied studies Description: computationally efficient analysis of eQTL, mQTL, dsQTL, etc. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gQTLstats git_branch: RELEASE_3_10 git_last_commit: fe72a50 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gQTLstats_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gQTLstats_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gQTLstats_1.18.0.tgz vignettes: vignettes/gQTLstats/inst/doc/gQTLstats.html vignetteTitles: gQTLstats: statistics for genetics of genomic features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gQTLstats/inst/doc/gQTLstats.R suggestsMe: gwascat, parglms dependencyCount: 166 Package: gramm4R Version: 1.0.0 Depends: R (>= 3.6.0) Imports: basicTrendline,investr,minerva,psych,grDevices, graphics, stats,DelayedArray,SummarizedExperiment,DMwR,phyloseq Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 76e0296b22f828c907b0de2b6af7a4cd NeedsCompilation: no Title: Generalized correlation analysis and model construction strategy for metabolome and microbiome Description: Generalized Correlation Analysis for Metabolome and Microbiome (GRaMM), for inter-correlation pairs discovery among metabolome and microbiome. biocViews: GraphAndNetwork,Microbiome Author: Mengci Li, Dandan Liang, Tianlu Chen and Wei Jia Maintainer: Tianlu Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gramm4R git_branch: RELEASE_3_10 git_last_commit: f0c9bc5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gramm4R_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gramm4R_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gramm4R_1.0.0.tgz vignettes: vignettes/gramm4R/inst/doc/gramm4R.html vignetteTitles: gramm4R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gramm4R/inst/doc/gramm4R.R dependencyCount: 123 Package: graper Version: 1.2.0 Depends: R (>= 3.6) Imports: Matrix, Rcpp, stats, ggplot2, methods, cowplot, matrixStats LinkingTo: Rcpp, RcppArmadillo, BH Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) MD5sum: 3716a4f82648343a97b05f85f29d5afa NeedsCompilation: yes Title: Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes Description: This package enables regression and classification on high-dimensional data with different relative strengths of penalization for different feature groups, such as different assays or omic types. The optimal relative strengths are chosen adaptively. Optimisation is performed using a variational Bayes approach. biocViews: Regression, Bayesian, Classification Author: Britta Velten [aut, cre], Wolfgang Huber [aut] Maintainer: Britta Velten VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/graper git_branch: RELEASE_3_10 git_last_commit: f14bae8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/graper_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/graper_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/graper_1.2.0.tgz vignettes: vignettes/graper/inst/doc/example_linear.html, vignettes/graper/inst/doc/example_logistic.html vignetteTitles: example_linear, example_logistic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graper/inst/doc/example_linear.R, vignettes/graper/inst/doc/example_logistic.R dependencyCount: 58 Package: graph Version: 1.64.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.11) Imports: stats, stats4, utils Suggests: SparseM (>= 0.36), XML, RBGL, RUnit, cluster Enhances: Rgraphviz License: Artistic-2.0 MD5sum: 31f92dbe60c8a6972cf4885ec71a98cd NeedsCompilation: yes Title: graph: A package to handle graph data structures Description: A package that implements some simple graph handling capabilities. biocViews: GraphAndNetwork Author: R. Gentleman, Elizabeth Whalen, W. Huber, S. Falcon Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/graph git_branch: RELEASE_3_10 git_last_commit: 3d35921 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/graph_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/graph_1.64.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/graph_1.64.0.tgz vignettes: vignettes/graph/inst/doc/clusterGraph.pdf, vignettes/graph/inst/doc/graph.pdf, vignettes/graph/inst/doc/graphAttributes.pdf, vignettes/graph/inst/doc/GraphClass.pdf, vignettes/graph/inst/doc/MultiGraphClass.pdf vignetteTitles: clusterGraph and distGraph, Graph, Attributes for Graph Objects, Graph Design, graphBAM and MultiGraph classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graph/inst/doc/clusterGraph.R, vignettes/graph/inst/doc/graph.R, vignettes/graph/inst/doc/graphAttributes.R, vignettes/graph/inst/doc/GraphClass.R, vignettes/graph/inst/doc/MultiGraphClass.R dependsOnMe: apComplex, biocGraph, BioMVCClass, BioNet, BLMA, CellNOptR, clipper, CNORfeeder, EnrichmentBrowser, gaggle, GOFunction, GOstats, GraphAT, GSEABase, hypergraph, maigesPack, MineICA, NetSAM, pathRender, Pigengene, pkgDepTools, PoTRA, RbcBook1, RBGL, RBioinf, RCyjs, RDAVIDWebService, Rgraphviz, ROntoTools, RpsiXML, SRAdb, topGO, vtpnet importsMe: alpine, AnalysisPageServer, BgeeDB, BiocCheck, biocGraph, BiocOncoTK, BiocPkgTools, biocViews, CAMERA, Category, categoryCompare, chimeraviz, ChIPpeakAnno, CHRONOS, CytoML, DAPAR, DEGraph, DEsubs, epiNEM, EventPointer, ExperimentHubData, FEM, flowCL, flowClust, flowUtils, flowWorkspace, gage, GAPGOM, GeneNetworkBuilder, GOFunction, GOSim, GraphAT, graphite, HTSanalyzeR, hyperdraw, KEGGgraph, keggorthology, MAGeCKFlute, MIGSA, mnem, NCIgraph, NeighborNet, nem, netresponse, OncoSimulR, ontoProc, OrganismDbi, pathview, PCpheno, PhenStat, pkgDepTools, ppiStats, pwOmics, qpgraph, RchyOptimyx, RCy3, RGraph2js, rsbml, Rtreemix, signet, SplicingGraphs, Streamer, ToPASeq, trackViewer, VariantFiltering suggestsMe: AnnotationDbi, BiocCaseStudies, DEGraph, EBcoexpress, ecolitk, GeneAnswers, gwascat, KEGGlincs, MmPalateMiRNA, netbenchmark, NetPathMiner, rBiopaxParser, rTRM, S4Vectors, SPIA, TxRegInfra, VariantTools dependencyCount: 7 Package: GraphAlignment Version: 1.50.0 License: file LICENSE License_restricts_use: yes MD5sum: 48d23eab6fc1a5bfda627062e4772beb NeedsCompilation: yes Title: GraphAlignment Description: Graph alignment is an extension package for the R programming environment which provides functions for finding an alignment between two networks based on link and node similarity scores. (J. Berg and M. Laessig, "Cross-species analysis of biological networks by Bayesian alignment", PNAS 103 (29), 10967-10972 (2006)) biocViews: GraphAndNetwork, Network Author: Joern P. Meier , Michal Kolar, Ville Mustonen, Michael Laessig, and Johannes Berg. Maintainer: Joern P. Meier URL: http://www.thp.uni-koeln.de/~berg/GraphAlignment/ git_url: https://git.bioconductor.org/packages/GraphAlignment git_branch: RELEASE_3_10 git_last_commit: 38aff9e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GraphAlignment_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GraphAlignment_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GraphAlignment_1.50.0.tgz vignettes: vignettes/GraphAlignment/inst/doc/GraphAlignment.pdf vignetteTitles: GraphAlignment hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GraphAlignment/inst/doc/GraphAlignment.R dependencyCount: 0 Package: GraphAT Version: 1.58.0 Depends: R (>= 2.10), graph, methods Imports: graph, MCMCpack, methods, stats License: LGPL Archs: i386, x64 MD5sum: 6e6093a8a6d7cb424d08903541b56de1 NeedsCompilation: no Title: Graph Theoretic Association Tests Description: Functions and data used in Balasubramanian, et al. (2004) biocViews: Network, GraphAndNetwork Author: R. Balasubramanian, T. LaFramboise, D. Scholtens Maintainer: Thomas LaFramboise git_url: https://git.bioconductor.org/packages/GraphAT git_branch: RELEASE_3_10 git_last_commit: 3a7a5f4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GraphAT_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GraphAT_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GraphAT_1.58.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 19 Package: graphite Version: 1.32.0 Depends: R (>= 2.10), methods Imports: AnnotationDbi, checkmate, graph, httr, rappdirs, stats, utils Suggests: a4Preproc, ALL, BiocStyle, clipper, codetools, hgu133plus2.db, hgu95av2.db, impute, knitr, org.Hs.eg.db, parallel, R.rsp, RCy3, rmarkdown, SPIA (>= 2.2), testthat, topologyGSA (>= 1.4.0) License: AGPL-3 Archs: i386, x64 MD5sum: 828080683364be145dde282bb78aeaa1 NeedsCompilation: no Title: GRAPH Interaction from pathway Topological Environment Description: Graph objects from pathway topology derived from Biocarta, HumanCyc, KEGG, NCI, Panther, PathBank, PharmGKB, Reactome and SMPDB databases. biocViews: Pathways, ThirdPartyClient, GraphAndNetwork, Network, Reactome, KEGG, BioCarta, Metabolomics Author: Gabriele Sales , Enrica Calura , Chiara Romualdi Maintainer: Gabriele Sales VignetteBuilder: R.rsp git_url: https://git.bioconductor.org/packages/graphite git_branch: RELEASE_3_10 git_last_commit: 023c632 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/graphite_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/graphite_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/graphite_1.32.0.tgz vignettes: vignettes/graphite/inst/doc/graphite.pdf, vignettes/graphite/inst/doc/metabolites.pdf vignetteTitles: GRAPH Interaction from pathway Topological Environment, metabolites.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graphite/inst/doc/graphite.R dependsOnMe: PoTRA, ToPASeq importsMe: EnrichmentBrowser, mogsa, ReactomePA, StarBioTrek suggestsMe: clipper, signet dependencyCount: 39 Package: GraphPAC Version: 1.28.0 Depends: R(>= 2.15),iPAC, igraph, TSP, RMallow Suggests: RUnit, BiocGenerics License: GPL-2 Archs: i386, x64 MD5sum: 521b3fe0ef2c631e65aba2364553e735 NeedsCompilation: no Title: Identification of Mutational Clusters in Proteins via a Graph Theoretical Approach. Description: Identifies mutational clusters of amino acids in a protein while utilizing the proteins tertiary structure via a graph theoretical model. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/GraphPAC git_branch: RELEASE_3_10 git_last_commit: 1b386f5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GraphPAC_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GraphPAC_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GraphPAC_1.28.0.tgz vignettes: vignettes/GraphPAC/inst/doc/GraphPAC.pdf vignetteTitles: iPAC: identification of Protein Amino acid Mutations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GraphPAC/inst/doc/GraphPAC.R dependsOnMe: QuartPAC dependencyCount: 34 Package: GRENITS Version: 1.38.0 Depends: R (>= 2.12.0), Rcpp (>= 0.8.6), RcppArmadillo (>= 0.2.8), ggplot2 (>= 0.9.0) Imports: graphics, grDevices, reshape2, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: network License: GPL (>= 2) MD5sum: e836c11b62b1f5bfed1128eb75465125 NeedsCompilation: yes Title: Gene Regulatory Network Inference Using Time Series Description: The package offers four network inference statistical models using Dynamic Bayesian Networks and Gibbs Variable Selection: a linear interaction model, two linear interaction models with added experimental noise (Gaussian and Student distributed) for the case where replicates are available and a non-linear interaction model. biocViews: NetworkInference, GeneRegulation, TimeCourse, GraphAndNetwork, GeneExpression, Network, Bayesian Author: Edward Morrissey Maintainer: Edward Morrissey git_url: https://git.bioconductor.org/packages/GRENITS git_branch: RELEASE_3_10 git_last_commit: 27d7055 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GRENITS_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GRENITS_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GRENITS_1.38.0.tgz vignettes: vignettes/GRENITS/inst/doc/GRENITS_package.pdf vignetteTitles: GRENITS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRENITS/inst/doc/GRENITS_package.R dependencyCount: 59 Package: GreyListChIP Version: 1.18.0 Depends: R (>= 3.5), methods, GenomicRanges Imports: GenomicAlignments, BSgenome, Rsamtools, rtracklayer, MASS, parallel, GenomeInfoDb, SummarizedExperiment, stats, utils Suggests: BiocStyle, BiocGenerics, RUnit Enhances: BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: 3691a970d0d80e3cf6aeca167376b74b NeedsCompilation: no Title: Grey Lists -- Mask Artefact Regions Based on ChIP Inputs Description: Identify regions of ChIP experiments with high signal in the input, that lead to spurious peaks during peak calling. Remove reads aligning to these regions prior to peak calling, for cleaner ChIP analysis. biocViews: ChIPSeq, Alignment, Preprocessing, DifferentialPeakCalling, Sequencing, GenomeAnnotation, Coverage Author: Gord Brown Maintainer: Gordon Brown git_url: https://git.bioconductor.org/packages/GreyListChIP git_branch: RELEASE_3_10 git_last_commit: 0e26590 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GreyListChIP_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GreyListChIP_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GreyListChIP_1.18.0.tgz vignettes: vignettes/GreyListChIP/inst/doc/GreyList-demo.pdf vignetteTitles: Generating Grey Lists from Input Libraries hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GreyListChIP/inst/doc/GreyList-demo.R dependencyCount: 40 Package: GRmetrics Version: 1.12.2 Depends: R (>= 3.6), SummarizedExperiment Imports: drc, plotly, ggplot2, S4Vectors, stats Suggests: knitr, rmarkdown, BiocStyle, tinytex License: GPL-3 MD5sum: dbe661f27de256fcae412f6dd29db566 NeedsCompilation: no Title: Calculate growth-rate inhibition (GR) metrics Description: Functions for calculating and visualizing growth-rate inhibition (GR) metrics. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Software, TimeCourse, Visualization Author: Nicholas Clark Maintainer: Nicholas Clark , Mario Medvedovic URL: https://github.com/uc-bd2k/GRmetrics VignetteBuilder: knitr BugReports: https://github.com/uc-bd2k/GRmetrics/issues git_url: https://git.bioconductor.org/packages/GRmetrics git_branch: RELEASE_3_10 git_last_commit: 799d154 git_last_commit_date: 2020-04-12 Date/Publication: 2020-04-13 source.ver: src/contrib/GRmetrics_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/GRmetrics_1.12.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GRmetrics_1.12.2.tgz vignettes: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.html vignetteTitles: GRmetrics: an R package for calculation and visualization of dose-response metrics based on growth rate inhibition hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.R dependencyCount: 140 Package: groHMM Version: 1.20.0 Depends: R (>= 3.0.2), MASS, parallel, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8), GenomicAlignments (>= 1.15.6), rtracklayer (>= 1.39.7) Suggests: BiocStyle, GenomicFeatures, edgeR, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 Archs: i386, x64 MD5sum: 0d6c469bea5dae3382e8f4782fe22bdc NeedsCompilation: yes Title: GRO-seq Analysis Pipeline Description: A pipeline for the analysis of GRO-seq data. biocViews: Sequencing, Software Author: Charles G. Danko, Minho Chae, Andre Martins, W. Lee Kraus Maintainer: Anusha Nagari , Tulip Nandu , W. Lee Kraus URL: https://github.com/Kraus-Lab/groHMM BugReports: https://github.com/Kraus-Lab/groHMM/issues git_url: https://git.bioconductor.org/packages/groHMM git_branch: RELEASE_3_10 git_last_commit: 68d48de git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/groHMM_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/groHMM_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/groHMM_1.20.0.tgz vignettes: vignettes/groHMM/inst/doc/groHMM.pdf vignetteTitles: groHMM tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/groHMM/inst/doc/groHMM.R dependencyCount: 39 Package: GRridge Version: 1.10.0 Depends: R (>= 3.2), penalized, Iso, survival, methods, graph,stats,glmnet,mvtnorm Suggests: testthat License: GPL-3 MD5sum: 6a51e11485c74e2a7163d95b0ce4268a NeedsCompilation: no Title: Better prediction by use of co-data: Adaptive group-regularized ridge regression Description: This package allows the use of multiple sources of co-data (e.g. external p-values, gene lists, annotation) to improve prediction of binary, continuous and survival response using (logistic, linear or Cox) group-regularized ridge regression. It also facilitates post-hoc variable selection and prediction diagnostics by cross-validation using ROC curves and AUC. biocViews: Classification, Regression, Survival, Bayesian, RNASeq, GenePrediction, GeneExpression, Pathways, GeneSetEnrichment, GO, KEGG, GraphAndNetwork, ImmunoOncology Author: Mark A. van de Wiel , Putri W. Novianti Maintainer: Mark A. van de Wiel git_url: https://git.bioconductor.org/packages/GRridge git_branch: RELEASE_3_10 git_last_commit: 4a0c5b8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GRridge_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GRridge_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GRridge_1.10.0.tgz vignettes: vignettes/GRridge/inst/doc/GRridge.pdf vignetteTitles: GRridge hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRridge/inst/doc/GRridge.R dependencyCount: 24 Package: GSALightning Version: 1.14.0 Depends: R (>= 3.3.0) Imports: Matrix, data.table, stats Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: fdfe39c926ff51d83b917865b6e17abb NeedsCompilation: no Title: Fast Permutation-based Gene Set Analysis Description: GSALightning provides a fast implementation of permutation-based gene set analysis for two-sample problem. This package is particularly useful when testing simultaneously a large number of gene sets, or when a large number of permutations is necessary for more accurate p-values estimation. biocViews: Software, BiologicalQuestion, GeneSetEnrichment, DifferentialExpression, GeneExpression, Transcription Author: Billy Heung Wing Chang Maintainer: Billy Heung Wing Chang URL: https://github.com/billyhw/GSALightning VignetteBuilder: knitr BugReports: https://github.com/billyhw/GSALightning/issues git_url: https://git.bioconductor.org/packages/GSALightning git_branch: RELEASE_3_10 git_last_commit: 694b065 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GSALightning_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GSALightning_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GSALightning_1.14.0.tgz vignettes: vignettes/GSALightning/inst/doc/vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSALightning/inst/doc/vignette.R dependencyCount: 9 Package: GSAR Version: 1.20.0 Depends: R (>= 3.0.1), igraph (>= 0.7.1) Imports: stats, graphics Suggests: MASS, GSVAdata, ALL, tweeDEseqCountData, GSEABase, annotate, org.Hs.eg.db, Biobase, genefilter, hgu95av2.db, edgeR, BiocStyle License: GPL (>=2) Archs: i386, x64 MD5sum: f779f9c812f6fafd9e5f67ab120516cd NeedsCompilation: no Title: Gene Set Analysis in R Description: Gene set analysis using specific alternative hypotheses. Tests for differential expression, scale and net correlation structure. biocViews: Software, StatisticalMethod, DifferentialExpression Author: Yasir Rahmatallah , Galina Glazko Maintainer: Yasir Rahmatallah , Galina Glazko git_url: https://git.bioconductor.org/packages/GSAR git_branch: RELEASE_3_10 git_last_commit: 26722c0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GSAR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GSAR_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GSAR_1.20.0.tgz vignettes: vignettes/GSAR/inst/doc/GSAR.pdf vignetteTitles: Gene Set Analysis in R -- the GSAR Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSAR/inst/doc/GSAR.R dependencyCount: 11 Package: GSCA Version: 2.16.0 Depends: shiny, sp, gplots, ggplot2, reshape2, RColorBrewer, rhdf5, R(>= 2.10.0) Imports: graphics Suggests: Affyhgu133aExpr, Affymoe4302Expr, Affyhgu133A2Expr, Affyhgu133Plus2Expr License: GPL(>=2) MD5sum: 720cdb95e3d6234e3d427787dc836480 NeedsCompilation: no Title: GSCA: Gene Set Context Analysis Description: GSCA takes as input several lists of activated and repressed genes. GSCA then searches through a compendium of publicly available gene expression profiles for biological contexts that are enriched with a specified pattern of gene expression. GSCA provides both traditional R functions and interactive, user-friendly user interface. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji, Hongkai Ji Maintainer: Zhicheng Ji git_url: https://git.bioconductor.org/packages/GSCA git_branch: RELEASE_3_10 git_last_commit: 01ae2a2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GSCA_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GSCA_2.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GSCA_2.16.0.tgz vignettes: vignettes/GSCA/inst/doc/GSCA.pdf vignetteTitles: GSCA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSCA/inst/doc/GSCA.R dependencyCount: 78 Package: gscreend Version: 1.0.0 Depends: R (>= 3.6) Imports: SummarizedExperiment, nloptr, fGarch, methods, BiocParallel, graphics Suggests: knitr, testthat License: GPL-3 MD5sum: 2e2548b54a767901ce0e84f52bc95e0f NeedsCompilation: no Title: Analysis of pooled genetic screens Description: Package for the analysis of pooled genetic screens (e.g. CRISPR-KO). The analysis of such screens is based on the comparison of gRNA abundances before and after a cell proliferation phase. The gscreend packages takes gRNA counts as input and allows detection of genes whose knockout decreases or increases cell proliferation. biocViews: Software, StatisticalMethod, PooledScreens, CRISPR Author: Katharina Imkeller [cre, aut], Wolfgang Huber [aut] Maintainer: Katharina Imkeller URL: https://github.com/imkeller/gscreend VignetteBuilder: knitr BugReports: https://github.com/imkeller/gscreend/issues git_url: https://git.bioconductor.org/packages/gscreend git_branch: RELEASE_3_10 git_last_commit: 9f117dd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gscreend_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gscreend_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gscreend_1.0.0.tgz vignettes: vignettes/gscreend/inst/doc/gscreend_simulated_data.html vignetteTitles: Example_simulated hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gscreend/inst/doc/gscreend_simulated_data.R dependencyCount: 42 Package: GSEABase Version: 1.48.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.13.8), Biobase (>= 2.17.8), annotate (>= 1.45.3), methods, graph (>= 1.37.2) Imports: AnnotationDbi, XML Suggests: hgu95av2.db, GO.db, org.Hs.eg.db, Rgraphviz, ReportingTools, testthat, BiocStyle, knitr License: Artistic-2.0 MD5sum: 333ab85bf8c3e689e55004e0ebbee691 NeedsCompilation: no Title: Gene set enrichment data structures and methods Description: This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA). biocViews: GeneExpression, GeneSetEnrichment, GraphAndNetwork, GO, KEGG Author: Martin Morgan, Seth Falcon, Robert Gentleman Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GSEABase git_branch: RELEASE_3_10 git_last_commit: bad6847 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GSEABase_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GSEABase_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GSEABase_1.48.0.tgz vignettes: vignettes/GSEABase/inst/doc/GSEABase.pdf vignetteTitles: An introduction to GSEABase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEABase/inst/doc/GSEABase.R dependsOnMe: AGDEX, BicARE, CCPROMISE, cpvSNP, gCMAP, npGSEA, PROMISE, splineTimeR, TissueEnrich importsMe: AUCell, BioCor, canceR, Category, categoryCompare, cellHTS2, EnrichmentBrowser, gCMAPWeb, gep2pep, GISPA, GlobalAncova, GmicR, GSRI, GSVA, HTSanalyzeR, MIGSA, miRSM, mogsa, oppar, PCpheno, phenoTest, POST, PROMISE, RcisTarget, ReportingTools, scTGIF, signatureSearch, slalom, TFutils suggestsMe: BiocCaseStudies, BiocSet, gage, globaltest, GOstats, GSAR, MAST, PGSEA, phenoTest, singleCellTK, TFEA.ChIP dependencyCount: 32 Package: GSEABenchmarkeR Version: 1.6.4 Depends: Biobase, SummarizedExperiment Imports: AnnotationDbi, AnnotationHub, BiocFileCache, BiocParallel, edgeR, EnrichmentBrowser, ExperimentHub, grDevices, graphics, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, methods, rappdirs, S4Vectors, stats, utils Suggests: BiocStyle, GEOquery, knitr, rmarkdown License: Artistic-2.0 MD5sum: 9b39958a59cdeff88b194e80c7260e53 NeedsCompilation: no Title: Reproducible GSEA Benchmarking Description: The GSEABenchmarkeR package implements an extendable framework for reproducible evaluation of set- and network-based methods for enrichment analysis of gene expression data. This includes support for the efficient execution of these methods on comprehensive real data compendia (microarray and RNA-seq) using parallel computation on standard workstations and institutional computer grids. Methods can then be assessed with respect to runtime, statistical significance, and relevance of the results for the phenotypes investigated. biocViews: ImmunoOncology, Microarray, RNASeq, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara Santarelli [ctb], Lucas Schiffer [ctb], Marcel Ramos [ctb], Ralf Zimmer [aut], Levi Waldron [aut] Maintainer: Ludwig Geistlinger URL: https://github.com/waldronlab/GSEABenchmarkeR VignetteBuilder: knitr BugReports: https://github.com/waldronlab/GSEABenchmarkeR/issues git_url: https://git.bioconductor.org/packages/GSEABenchmarkeR git_branch: RELEASE_3_10 git_last_commit: e6e51fb git_last_commit_date: 2020-02-25 Date/Publication: 2020-02-26 source.ver: src/contrib/GSEABenchmarkeR_1.6.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/GSEABenchmarkeR_1.6.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GSEABenchmarkeR_1.6.4.tgz vignettes: vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.html vignetteTitles: Reproducible GSEA Benchmarking hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.R dependencyCount: 122 Package: GSEAlm Version: 1.46.0 Depends: Biobase Suggests: GSEABase,Category, multtest, ALL, annotate, hgu95av2.db, genefilter, GOstats, RColorBrewer License: Artistic-2.0 MD5sum: 9167dba90b2fe4033e083f2dc3f53645 NeedsCompilation: no Title: Linear Model Toolset for Gene Set Enrichment Analysis Description: Models and methods for fitting linear models to gene expression data, together with tools for computing and using various regression diagnostics. biocViews: Microarray Author: Assaf Oron, Robert Gentleman (with contributions from S. Falcon and Z. Jiang) Maintainer: Assaf Oron git_url: https://git.bioconductor.org/packages/GSEAlm git_branch: RELEASE_3_10 git_last_commit: 4400c27 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GSEAlm_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GSEAlm_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GSEAlm_1.46.0.tgz vignettes: vignettes/GSEAlm/inst/doc/GSEAlm.pdf vignetteTitles: Linear models in GSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEAlm/inst/doc/GSEAlm.R importsMe: gCMAP dependencyCount: 7 Package: gsean Version: 1.6.0 Depends: R (>= 3.5), fgsea, PPInfer Suggests: SummarizedExperiment, knitr, plotly, RANKS, WGCNA License: Artistic-2.0 MD5sum: 73d8dfa91ee1bab3484bace682d9f5e6 NeedsCompilation: no Title: Gene Set Enrichment Analysis with Networks Description: Biological molecules in a living organism seldom work individually. They usually interact each other in a cooperative way. Biological process is too complicated to understand without considering such interactions. Thus, network-based procedures can be seen as powerful methods for studying complex process. However, many methods are devised for analyzing individual genes. It is said that techniques based on biological networks such as gene co-expression are more precise ways to represent information than those using lists of genes only. This package is aimed to integrate the gene expression and biological network. A biological network is constructed from gene expression data and it is used for Gene Set Enrichment Analysis. biocViews: Software, StatisticalMethod, Network, GraphAndNetwork, GeneSetEnrichment, GeneExpression, NetworkEnrichment, Pathways, DifferentialExpression Author: Dongmin Jung Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gsean git_branch: RELEASE_3_10 git_last_commit: 58769c5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gsean_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gsean_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gsean_1.6.0.tgz vignettes: vignettes/gsean/inst/doc/gsean.html vignetteTitles: gsean hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gsean/inst/doc/gsean.R dependencyCount: 119 Package: GSReg Version: 1.20.0 Depends: R (>= 2.13.1), Homo.sapiens, org.Hs.eg.db, GenomicFeatures, AnnotationDbi Suggests: GenomicRanges, GSBenchMark License: GPL-2 MD5sum: cd3cf25d7348b7b7d1f61f754820dc97 NeedsCompilation: yes Title: Gene Set Regulation (GS-Reg) Description: A package for gene set analysis based on the variability of expressions as well as a method to detect Alternative Splicing Events . It implements DIfferential RAnk Conservation (DIRAC) and gene set Expression Variation Analysis (EVA) methods. For detecting Differentially Spliced genes, it provides an implementation of the Spliced-EVA (SEVA). biocViews: GeneRegulation, Pathways, GeneExpression, GeneticVariability, GeneSetEnrichment, AlternativeSplicing Author: Bahman Afsari , Elana J. Fertig Maintainer: Bahman Afsari , Elana J. Fertig git_url: https://git.bioconductor.org/packages/GSReg git_branch: RELEASE_3_10 git_last_commit: 49efade git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GSReg_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GSReg_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GSReg_1.20.0.tgz vignettes: vignettes/GSReg/inst/doc/GSReg.pdf vignetteTitles: Working with the GSReg package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSReg/inst/doc/GSReg.R dependencyCount: 91 Package: GSRI Version: 2.34.0 Depends: R (>= 2.14.2), fdrtool Imports: methods, graphics, stats, utils, genefilter, Biobase, GSEABase, les (>= 1.1.6) Suggests: limma, hgu95av2.db Enhances: parallel License: GPL-3 MD5sum: 79937361a098f7f43be593409fa453d6 NeedsCompilation: no Title: Gene Set Regulation Index Description: The GSRI package estimates the number of differentially expressed genes in gene sets, utilizing the concept of the Gene Set Regulation Index (GSRI). biocViews: Microarray, Transcription, DifferentialExpression, GeneSetEnrichment, GeneRegulation Author: Julian Gehring, Kilian Bartholome, Clemens Kreutz, Jens Timmer Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/GSRI git_branch: RELEASE_3_10 git_last_commit: 91d87fe git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GSRI_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GSRI_2.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GSRI_2.34.0.tgz vignettes: vignettes/GSRI/inst/doc/gsri.pdf vignetteTitles: Introduction to the GSRI package: Estimating Regulatory Effects utilizing the Gene Set Regulation Index hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSRI/inst/doc/gsri.R dependencyCount: 49 Package: GSVA Version: 1.34.0 Depends: R (>= 3.5.0) Imports: methods, BiocGenerics, Biobase, GSEABase (>= 1.17.4), geneplotter, shiny, shinythemes Suggests: limma, RColorBrewer, genefilter, edgeR, snow, parallel, GSVAdata License: GPL (>= 2) MD5sum: 9c43cdbb2698ca6bb361eaccaf98a064 NeedsCompilation: yes Title: Gene Set Variation Analysis for microarray and RNA-seq data Description: Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner. biocViews: Microarray, Pathways, GeneSetEnrichment Author: Justin Guinney [aut, cre], Robert Castelo [aut], Joan Fernandez [ctb] Maintainer: Justin Guinney URL: https://github.com/rcastelo/GSVA BugReports: https://github.com/rcastelo/GSVA/issues git_url: https://git.bioconductor.org/packages/GSVA git_branch: RELEASE_3_10 git_last_commit: 04d0b94 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GSVA_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GSVA_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GSVA_1.34.0.tgz vignettes: vignettes/GSVA/inst/doc/GSVA.pdf vignetteTitles: Gene Set Variation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSVA/inst/doc/GSVA.R importsMe: consensusOV, EGSEA, oppar, singleCellTK, TNBC.CMS suggestsMe: MCbiclust dependencyCount: 52 Package: gtrellis Version: 1.18.0 Depends: R (>= 3.1.2), grid, IRanges, GenomicRanges Imports: circlize (>= 0.3.3), GetoptLong, grDevices, utils Suggests: testthat (>= 1.0.0), knitr, RColorBrewer, markdown, ComplexHeatmap (>= 1.99.0), Cairo, png, jpeg, tiff License: MIT + file LICENSE Archs: i386, x64 MD5sum: 6c27a30acc8bc7fd3cc9309ba927fada NeedsCompilation: no Title: Genome Level Trellis Layout Description: Genome level Trellis graph visualizes genomic data conditioned by genomic categories (e.g. chromosomes). For each genomic category, multiple dimensional data which are represented as tracks describe different features from different aspects. This package provides high flexibility to arrange genomic categories and to add self-defined graphics in the plot. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/gtrellis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gtrellis git_branch: RELEASE_3_10 git_last_commit: ed895f8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gtrellis_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gtrellis_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gtrellis_1.18.0.tgz vignettes: vignettes/gtrellis/inst/doc/gtrellis.html vignetteTitles: Make Genome-level Trellis Graph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gtrellis/inst/doc/gtrellis.R importsMe: YAPSA dependencyCount: 24 Package: GUIDEseq Version: 1.16.0 Depends: R (>= 3.2.0), GenomicRanges, BiocGenerics Imports: BiocParallel, Biostrings, CRISPRseek, ChIPpeakAnno, data.table, matrixStats, BSgenome, parallel, IRanges (>= 2.5.5), S4Vectors (>= 0.9.6), GenomicAlignments (>= 1.7.3), GenomeInfoDb, Rsamtools, hash, limma Suggests: knitr, RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: GPL (>= 2) MD5sum: 8fe4602cf87835860a9cab7b2059f156 NeedsCompilation: no Title: GUIDE-seq analysis pipeline Description: The package implements GUIDE-seq analysis workflow including functions for obtaining unique insertion sites (proxy of cleavage sites), estimating the locations of the insertion sites, aka, peaks, merging estimated insertion sites from plus and minus strand, and performing off target search of the extended regions around insertion sites. biocViews: ImmunoOncology, GeneRegulation, Sequencing, WorkflowStep, CRISPR Author: Lihua Julie Zhu, Michael Lawrence, Ankit Gupta, Hervé Pagès , Alper Kucukural, Manuel Garber, Scot A. Wolfe Maintainer: Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GUIDEseq git_branch: RELEASE_3_10 git_last_commit: 952ecf6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GUIDEseq_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GUIDEseq_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GUIDEseq_1.16.0.tgz vignettes: vignettes/GUIDEseq/inst/doc/GUIDEseq.pdf vignetteTitles: GUIDEseq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GUIDEseq/inst/doc/GUIDEseq.R importsMe: crisprseekplus dependencyCount: 109 Package: Guitar Version: 2.2.0 Depends: GenomicFeatures, rtracklayer,AnnotationDbi, GenomicRanges, magrittr, ggplot2, methods, stats,utils ,knitr,dplyr License: GPL-2 Archs: i386, x64 MD5sum: 97a0c98414ea86dd5a0a60699418dc61 NeedsCompilation: no Title: Guitar Description: The package is designed for visualization of RNA-related genomic features with respect to the landmarks of RNA transcripts, i.e., transcription starting site, start codon, stop codon and transcription ending site. biocViews: Sequencing, SplicedAlignment, Alignment, DataImport, RNASeq, MethylSeq, QualityControl, Transcription Author: Xiao Du, Hui Liu, Lin Zhang, Jia Meng Maintainer: Jia Meng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Guitar git_branch: RELEASE_3_10 git_last_commit: ea9c88c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Guitar_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Guitar_2.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Guitar_2.2.0.tgz vignettes: vignettes/Guitar/inst/doc/Guitar-Overview.pdf vignetteTitles: Guitar hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Guitar/inst/doc/Guitar-Overview.R dependencyCount: 115 Package: Gviz Version: 1.30.3 Depends: R (>= 2.10.0), methods, S4Vectors (>= 0.9.25), IRanges (>= 1.99.18), GenomicRanges (>= 1.17.20), grid Imports: XVector (>= 0.5.7), rtracklayer (>= 1.25.13), lattice, RColorBrewer, biomaRt (>= 2.11.0), AnnotationDbi (>= 1.27.5), Biobase (>= 2.15.3), GenomicFeatures (>= 1.17.22), BSgenome (>= 1.33.1), Biostrings (>= 2.33.11), biovizBase (>= 1.13.8), Rsamtools (>= 1.17.28), latticeExtra (>= 0.6-26), matrixStats (>= 0.8.14), GenomicAlignments (>= 1.1.16), GenomeInfoDb (>= 1.1.3), BiocGenerics (>= 0.11.3), digest(>= 0.6.8) Suggests: BSgenome.Hsapiens.UCSC.hg19, BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: c657fab644afbac73fbde590ffcd685f NeedsCompilation: no Title: Plotting data and annotation information along genomic coordinates Description: Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data. biocViews: Visualization, Microarray, Sequencing Author: Florian Hahne [aut], Steffen Durinck [aut], Robert Ivanek [aut, cre] (), Arne Mueller [aut], Steve Lianoglou [aut], Ge Tan [aut], Lance Parsons [aut], Shraddha Pai [aut] Maintainer: Robert Ivanek URL: https://github.com/ivanek/Gviz VignetteBuilder: knitr BugReports: https://github.com/ivanek/Gviz/issues git_url: https://git.bioconductor.org/packages/Gviz git_branch: RELEASE_3_10 git_last_commit: 01406e6 git_last_commit_date: 2020-02-17 Date/Publication: 2020-02-17 source.ver: src/contrib/Gviz_1.30.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/Gviz_1.30.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Gviz_1.30.3.tgz vignettes: vignettes/Gviz/inst/doc/Gviz.html vignetteTitles: The Gviz User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Gviz/inst/doc/Gviz.R dependsOnMe: biomvRCNS, chimeraviz, cicero, coMET, cummeRbund, DMRforPairs, Pbase, Pviz importsMe: AllelicImbalance, ALPS, ASpediaFI, ASpli, CAGEfightR, DMRcate, ELMER, GenomicInteractions, GGtools, InPAS, maser, mCSEA, MEAL, methyAnalysis, methylPipe, motifbreakR, Pi, PING, primirTSS, RNAmodR, RNAmodR.AlkAnilineSeq, RNAmodR.RiboMethSeq, SPLINTER, srnadiff, STAN, trackViewer, TVTB, VariantFiltering suggestsMe: annmap, cellbaseR, CNEr, CNVRanger, DeepBlueR, ensembldb, GenomicRanges, gwascat, interactiveDisplay, InterMineR, pqsfinder, QuasR, RnBeads, SplicingGraphs, TFutils, TxRegInfra dependencyCount: 142 Package: gwascat Version: 2.18.0 Depends: R (>= 3.5.0), Homo.sapiens Imports: methods, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges (>= 1.29.6), GenomicFeatures, Biostrings, Rsamtools, rtracklayer, AnnotationDbi, utils Suggests: DO.db, DT, knitr, RBGL, RUnit, snpStats, Gviz, VariantAnnotation, AnnotationHub, gQTLstats, graph, ggbio, ggplot2, DelayedArray Enhances: SNPlocs.Hsapiens.dbSNP144.GRCh37 License: Artistic-2.0 MD5sum: 8dc6a30604ecdc70bc796549ef840e16 NeedsCompilation: no Title: representing and modeling data in the EMBL-EBI GWAS catalog Description: Represent and model data in the EMBL-EBI GWAS catalog. biocViews: Genetics Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: utils, knitr git_url: https://git.bioconductor.org/packages/gwascat git_branch: RELEASE_3_10 git_last_commit: e9cf69e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gwascat_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gwascat_2.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gwascat_2.18.0.tgz vignettes: vignettes/gwascat/inst/doc/gwascat.pdf, vignettes/gwascat/inst/doc/gwascatOnt.html vignetteTitles: gwascat -- exploring NHGRI GWAS catalog, gwascat -- GRanges for GWAS hits in EBI catalog hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwascat/inst/doc/gwascat.R, vignettes/gwascat/inst/doc/gwascatOnt.R dependsOnMe: circRNAprofiler, vtpnet suggestsMe: GenomicScores, gQTLBase, gQTLstats, hmdbQuery, ldblock, parglms, TFutils dependencyCount: 91 Package: GWASTools Version: 1.32.0 Depends: Biobase Imports: graphics, stats, utils, methods, gdsfmt, DBI, RSQLite, GWASExactHW, DNAcopy, survival, sandwich, lmtest, logistf, quantsmooth Suggests: ncdf4, GWASdata, BiocGenerics, RUnit, Biostrings, GenomicRanges, IRanges, SNPRelate, snpStats, S4Vectors, VariantAnnotation License: Artistic-2.0 MD5sum: bcd518b45bde314d9611d4aed6b8b237 NeedsCompilation: no Title: Tools for Genome Wide Association Studies Description: Classes for storing very large GWAS data sets and annotation, and functions for GWAS data cleaning and analysis. biocViews: SNP, GeneticVariability, QualityControl, Microarray Author: Stephanie M. Gogarten, Cathy Laurie, Tushar Bhangale, Matthew P. Conomos, Cecelia Laurie, Michael Lawrence, Caitlin McHugh, Ian Painter, Xiuwen Zheng, Jess Shen, Rohit Swarnkar, Adrienne Stilp, Sarah Nelson Maintainer: Stephanie M. Gogarten URL: https://github.com/smgogarten/GWASTools git_url: https://git.bioconductor.org/packages/GWASTools git_branch: RELEASE_3_10 git_last_commit: 8f581fe git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/GWASTools_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/GWASTools_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/GWASTools_1.32.0.tgz vignettes: vignettes/GWASTools/inst/doc/Affymetrix.pdf, vignettes/GWASTools/inst/doc/DataCleaning.pdf, vignettes/GWASTools/inst/doc/Formats.pdf vignetteTitles: Preparing Affymetrix Data, GWAS Data Cleaning, Data formats in GWASTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GWASTools/inst/doc/Affymetrix.R, vignettes/GWASTools/inst/doc/DataCleaning.R, vignettes/GWASTools/inst/doc/Formats.R importsMe: GENESIS, gwasurvivr suggestsMe: podkat dependencyCount: 64 Package: gwasurvivr Version: 1.4.0 Depends: R (>= 3.4.0) Imports: GWASTools, survival, VariantAnnotation, parallel, matrixStats, SummarizedExperiment, stats, utils, SNPRelate Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 7f6b97ea8958290b2a9fb414a112c990 NeedsCompilation: no Title: gwasurvivr: an R package for genome wide survival analysis Description: gwasurvivr is a package to perform survival analysis using Cox proportional hazard models on imputed genetic data. biocViews: GenomeWideAssociation, Survival, Regression, Genetics, SNP, GeneticVariability, Pharmacogenomics, BiomedicalInformatics Author: Abbas Rizvi, Ezgi Karaesmen, Martin Morgan, Lara Sucheston-Campbell Maintainer: Abbas Rizvi URL: https://github.com/suchestoncampbelllab/gwasurvivr VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gwasurvivr git_branch: RELEASE_3_10 git_last_commit: 60b8e84 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/gwasurvivr_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/gwasurvivr_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gwasurvivr_1.4.0.tgz vignettes: vignettes/gwasurvivr/inst/doc/gwasurvivr_Introduction.html vignetteTitles: gwasurvivr Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwasurvivr/inst/doc/gwasurvivr_Introduction.R dependencyCount: 109 Package: h5vc Version: 2.20.0 Depends: grid, gridExtra, ggplot2 Imports: rhdf5, reshape, S4Vectors, IRanges, Biostrings, Rsamtools (>= 1.99.1), methods, GenomicRanges, abind, BiocParallel, BatchJobs, h5vcData, GenomeInfoDb LinkingTo: Rhtslib (>= 1.15.3) Suggests: knitr, locfit, BSgenome.Hsapiens.UCSC.hg19, biomaRt, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocGenerics License: GPL (>= 3) MD5sum: 292e81abbd2da38f2e42ae318ab53d5f NeedsCompilation: yes Title: Managing alignment tallies using a hdf5 backend Description: This package contains functions to interact with tally data from NGS experiments that is stored in HDF5 files. Author: Paul Theodor Pyl Maintainer: Paul Theodor Pyl SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/h5vc git_branch: RELEASE_3_10 git_last_commit: 0190512 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/h5vc_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/h5vc_2.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/h5vc_2.20.0.tgz vignettes: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.html, vignettes/h5vc/inst/doc/h5vc.tour.html vignetteTitles: Building a minimal genome browser with h5vc and shiny, h5vc -- Tour hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.R, vignettes/h5vc/inst/doc/h5vc.tour.R dependencyCount: 98 Package: hapFabia Version: 1.28.0 Depends: R (>= 3.6.0), Biobase, fabia (>= 2.3.1) Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) MD5sum: 77c92d919e57d0b052c361cf6592d405 NeedsCompilation: yes Title: hapFabia: Identification of very short segments of identity by descent (IBD) characterized by rare variants in large sequencing data Description: A package to identify very short IBD segments in large sequencing data by FABIA biclustering. Two haplotypes are identical by descent (IBD) if they share a segment that both inherited from a common ancestor. Current IBD methods reliably detect long IBD segments because many minor alleles in the segment are concordant between the two haplotypes. However, many cohort studies contain unrelated individuals which share only short IBD segments. This package provides software to identify short IBD segments in sequencing data. Knowledge of short IBD segments are relevant for phasing of genotyping data, association studies, and for population genetics, where they shed light on the evolutionary history of humans. The package supports VCF formats, is based on sparse matrix operations, and provides visualization of haplotype clusters in different formats. biocViews: Genetics, GeneticVariability, SNP, Sequencing, Sequencing, Visualization, Clustering, SequenceMatching, Software Author: Sepp Hochreiter Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/hapFabia/hapFabia.html git_url: https://git.bioconductor.org/packages/hapFabia git_branch: RELEASE_3_10 git_last_commit: 840df47 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/hapFabia_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/hapFabia_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/hapFabia_1.28.0.tgz vignettes: vignettes/hapFabia/inst/doc/hapfabia.pdf vignetteTitles: hapFabia: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hapFabia/inst/doc/hapfabia.R dependencyCount: 9 Package: Harman Version: 1.14.0 Depends: R (>= 3.5) Imports: Rcpp (>= 0.11.2), graphics, stats LinkingTo: Rcpp Suggests: HarmanData, BiocGenerics, BiocStyle, knitr, rmarkdown, RUnit, RColorBrewer, bladderbatch, limma, minfi, lumi, msmsEDA, affydata, minfiData, sva License: GPL-3 + file LICENCE MD5sum: 143dbee8ffaa73680e5df1288775be49 NeedsCompilation: yes Title: The removal of batch effects from datasets using a PCA and constrained optimisation based technique Description: Harman is a PCA and constrained optimisation based technique that maximises the removal of batch effects from datasets, with the constraint that the probability of overcorrection (i.e. removing genuine biological signal along with batch noise) is kept to a fraction which is set by the end-user. biocViews: BatchEffect, Microarray, MultipleComparison, PrincipalComponent, Normalization, Preprocessing, DNAMethylation, Transcription, Software, StatisticalMethod Author: Josh Bowden [aut], Jason Ross [aut, cre], Yalchin Oytam [aut] Maintainer: Jason Ross URL: http://www.bioinformatics.csiro.au/harman/ VignetteBuilder: knitr BugReports: https://github.com/JasonR055/Harman/issues git_url: https://git.bioconductor.org/packages/Harman git_branch: RELEASE_3_10 git_last_commit: 0696de1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Harman_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Harman_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Harman_1.14.0.tgz vignettes: vignettes/Harman/inst/doc/IntroductionToHarman.html vignetteTitles: IntroductionToHarman hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Harman/inst/doc/IntroductionToHarman.R importsMe: debrowser dependencyCount: 5 Package: Harshlight Version: 1.58.0 Depends: R (>= 2.10) Imports: affy, altcdfenvs, Biobase, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 6ffd19901e6bfc2dba65647adcdf1d7b NeedsCompilation: yes Title: A "corrective make-up" program for microarray chips Description: The package is used to detect extended, diffuse and compact blemishes on microarray chips. Harshlight automatically marks the areas in a collection of chips (affybatch objects) and a corrected AffyBatch object is returned, in which the defected areas are substituted with NAs or the median of the values of the same probe in the other chips in the collection. The new version handle the substitute value as whole matrix to solve the memory problem. biocViews: Microarray, QualityControl, Preprocessing, OneChannel, ReportWriting Author: Mayte Suarez-Farinas, Maurizio Pellegrino, Knut M. Wittkowski, Marcelo O. Magnasco Maintainer: Maurizio Pellegrino URL: http://asterion.rockefeller.edu/Harshlight/ git_url: https://git.bioconductor.org/packages/Harshlight git_branch: RELEASE_3_10 git_last_commit: 65a98d4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Harshlight_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Harshlight_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Harshlight_1.58.0.tgz vignettes: vignettes/Harshlight/inst/doc/Harshlight.pdf vignetteTitles: Harshlight hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Harshlight/inst/doc/Harshlight.R dependencyCount: 22 Package: HCABrowser Version: 1.2.0 Depends: R (>= 3.6.0), dplyr Imports: BiocFileCache, S4Vectors, curl, googleAuthR, httr, jsonlite, methods, plyr, readr, rlang, stringr, tibble, tidygraph, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 9b388c068b5952c3bf74231b10325e22 NeedsCompilation: no Title: Browse the Human Cell Atlas data portal Description: Search, browse, reference, and download resources from the Human Cell Atlas data portal. Development of this package is supported through funds from the Chan / Zuckerberg initiative. biocViews: DataImport, Sequencing Author: Daniel Van Twisk [aut], Martin Morgan [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/HCABrowser VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/HCABrowser/issues git_url: https://git.bioconductor.org/packages/HCABrowser git_branch: RELEASE_3_10 git_last_commit: ec235b0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HCABrowser_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HCABrowser_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HCABrowser_1.2.0.tgz vignettes: vignettes/HCABrowser/inst/doc/HCABrowser.html vignetteTitles: HCABrowser hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HCABrowser/inst/doc/HCABrowser.R dependencyCount: 64 Package: HCAExplorer Version: 1.0.0 Depends: R (>= 3.6.0), dplyr Imports: S4Vectors, vctrs, curl, httr, jsonlite, methods, plyr, rlang, tibble, tidygraph, utils Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0) License: Artistic-2.0 Archs: i386, x64 MD5sum: f3ccc6f0a03a229cd8fbcedcdde737e7 NeedsCompilation: no Title: Browse the Human Cell Atlas data portal Description: Search, browse, reference, and download resources from the Human Cell Atlas data portal. Development of this package is supported through funds from the Chan / Zuckerberg initiative. biocViews: DataImport, Sequencing Author: Daniel Van Twisk [aut], Martin Morgan [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/HCABrowser VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/HCABrowser/issues git_url: https://git.bioconductor.org/packages/HCAExplorer git_branch: RELEASE_3_10 git_last_commit: 79ed75d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HCAExplorer_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HCAExplorer_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HCAExplorer_1.0.0.tgz vignettes: vignettes/HCAExplorer/inst/doc/HCAExplorer.html vignetteTitles: HCAExplorer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HCAExplorer/inst/doc/HCAExplorer.R dependencyCount: 47 Package: HDF5Array Version: 1.14.4 Depends: R (>= 3.4), methods, DelayedArray (>= 0.12.3), rhdf5 (>= 2.30.1) Imports: utils, tools, Matrix, BiocGenerics (>= 0.32.0), S4Vectors, IRanges LinkingTo: S4Vectors (>= 0.24.4), Rhdf5lib Suggests: h5vcData, SummarizedExperiment (>= 1.15.1), GenomicRanges, ExperimentHub, TENxBrainData, BiocParallel, GenomicFeatures, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 101d7bcbefa761a3f909d6042b3e8152 NeedsCompilation: yes Title: HDF5 backend for DelayedArray objects Description: Implements the HDF5Array and TENxMatrix classes, 2 convenient and memory-efficient array-like containers for on-disk representation of HDF5 datasets. HDF5Array is for datasets that use the conventional (i.e. dense) HDF5 representation. TENxMatrix is for datasets that use the HDF5-based sparse matrix representation from 10x Genomics (e.g. the 1.3 Million Brain Cell Dataset). Both containers being DelayedArray extensions, they support all operations supported by DelayedArray objects. These operations can be either delayed or block-processed. biocViews: Infrastructure, DataRepresentation, DataImport, Sequencing, RNASeq, Coverage, Annotation, GenomeAnnotation, SingleCell, ImmunoOncology Author: Hervé Pagès Maintainer: Hervé Pagès SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/HDF5Array git_branch: RELEASE_3_10 git_last_commit: 5e6ab9a git_last_commit_date: 2020-04-04 Date/Publication: 2020-04-13 source.ver: src/contrib/HDF5Array_1.14.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/HDF5Array_1.14.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HDF5Array_1.14.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: GenoGAM importsMe: biscuiteer, bsseq, clusterExperiment, DelayedMatrixStats, DropletUtils, LoomExperiment, methrix, minfi, netSmooth, scmeth, signatureSearch suggestsMe: BiocSklearn, DelayedArray, MAST, mbkmeans, MultiAssayExperiment, scMerge, scran, SummarizedExperiment dependencyCount: 25 Package: HDTD Version: 1.20.0 Depends: R (>= 3.6) Imports: stats, Rcpp (>= 1.0.1) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, markdown License: GPL-3 MD5sum: 6d01279c0c47f06509f5860027e7855f NeedsCompilation: yes Title: Statistical Inference about the Mean Matrix and the Covariance Matrices in High-Dimensional Transposable Data (HDTD) Description: Characterization of intra-individual variability using physiologically relevant measurements provides important insights into fundamental biological questions ranging from cell type identity to tumor development. For each individual, the data measurements can be written as a matrix with the different subsamples of the individual recorded in the columns and the different phenotypic units recorded in the rows. Datasets of this type are called high-dimensional transposable data. The HDTD package provides functions for conducting statistical inference for the mean relationship between the row and column variables and for the covariance structure within and between the row and column variables. biocViews: DifferentialExpression, Genetics, GeneExpression, Microarray, Sequencing, StatisticalMethod, Software Author: Anestis Touloumis [cre, aut], John C. Marioni [aut], Simon Tavar\'{e} [aut] Maintainer: Anestis Touloumis URL: http://github.com/AnestisTouloumis/HDTD VignetteBuilder: knitr BugReports: http://github.com/AnestisTouloumis/HDTD/issues git_url: https://git.bioconductor.org/packages/HDTD git_branch: RELEASE_3_10 git_last_commit: f8dac1e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HDTD_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HDTD_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HDTD_1.20.0.tgz vignettes: vignettes/HDTD/inst/doc/HDTD.html vignetteTitles: HDTD to Analyze High-Dimensional Transposable Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HDTD/inst/doc/HDTD.R dependencyCount: 5 Package: heatmaps Version: 1.10.0 Depends: R (>= 3.4) Imports: methods, grDevices, graphics, stats, Biostrings, GenomicRanges, IRanges, KernSmooth, plotrix, Matrix, EBImage, RColorBrewer, BiocGenerics, GenomeInfoDb Suggests: BSgenome.Drerio.UCSC.danRer7, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 43e8b8d98912309d22a7d8141d820e82 NeedsCompilation: no Title: Flexible Heatmaps for Functional Genomics and Sequence Features Description: This package provides functions for plotting heatmaps of genome-wide data across genomic intervals, such as ChIP-seq signals at peaks or across promoters. Many functions are also provided for investigating sequence features. biocViews: Visualization, SequenceMatching, FunctionalGenomics Author: Malcolm Perry Maintainer: Malcolm Perry VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/heatmaps git_branch: RELEASE_3_10 git_last_commit: cf7312e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/heatmaps_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/heatmaps_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/heatmaps_1.10.0.tgz vignettes: vignettes/heatmaps/inst/doc/heatmaps.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/heatmaps/inst/doc/heatmaps.R dependencyCount: 38 Package: Heatplus Version: 2.32.1 Imports: graphics, grDevices, stats, RColorBrewer Suggests: Biobase, hgu95av2.db, limma License: GPL (>= 2) Archs: i386, x64 MD5sum: b14955259dc1bc5a826a5694a4e388a9 NeedsCompilation: no Title: Heatmaps with row and/or column covariates and colored clusters Description: Display a rectangular heatmap (intensity plot) of a data matrix. By default, both samples (columns) and features (row) of the matrix are sorted according to a hierarchical clustering, and the corresponding dendrogram is plotted. Optionally, panels with additional information about samples and features can be added to the plot. biocViews: Microarray, Visualization Author: Alexander Ploner Maintainer: Alexander Ploner URL: https://github.com/alexploner/Heatplus BugReports: https://github.com/alexploner/Heatplus/issues git_url: https://git.bioconductor.org/packages/Heatplus git_branch: RELEASE_3_10 git_last_commit: d1a8324 git_last_commit_date: 2020-02-11 Date/Publication: 2020-02-11 source.ver: src/contrib/Heatplus_2.32.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/Heatplus_2.32.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Heatplus_2.32.1.tgz vignettes: vignettes/Heatplus/inst/doc/annHeatmap.pdf, vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.pdf, vignettes/Heatplus/inst/doc/oldHeatplus.pdf vignetteTitles: Annotated and regular heatmaps, Commented package source, Old functions (deprecated) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Heatplus/inst/doc/annHeatmap.R, vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.R, vignettes/Heatplus/inst/doc/oldHeatplus.R dependsOnMe: GeneAnswers, phenoTest, tRanslatome dependencyCount: 4 Package: HelloRanges Version: 1.12.0 Depends: methods, BiocGenerics, S4Vectors (>= 0.17.39), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.10), Biostrings (>= 2.41.3), BSgenome, GenomicFeatures (>= 1.31.5), VariantAnnotation (>= 1.19.3), Rsamtools, GenomicAlignments (>= 1.15.7), rtracklayer (>= 1.33.8), GenomeInfoDb, SummarizedExperiment Imports: docopt, stats, tools, utils Suggests: HelloRangesData, BiocStyle License: GPL (>= 2) Archs: i386, x64 MD5sum: ce158562284d459fd2cbc5aa2c791bfb NeedsCompilation: no Title: Introduce *Ranges to bedtools users Description: Translates bedtools command-line invocations to R code calling functions from the Bioconductor *Ranges infrastructure. This is intended to educate novice Bioconductor users and to compare the syntax and semantics of the two frameworks. biocViews: Sequencing, Annotation, Coverage, GenomeAnnotation, DataImport, SequenceMatching, VariantAnnotation Author: Michael Lawrence Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/HelloRanges git_branch: RELEASE_3_10 git_last_commit: b9f3913 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HelloRanges_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HelloRanges_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HelloRanges_1.12.0.tgz vignettes: vignettes/HelloRanges/inst/doc/tutorial.pdf vignetteTitles: HelloRanges Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HelloRanges/inst/doc/tutorial.R importsMe: OMICsPCA suggestsMe: plyranges dependencyCount: 86 Package: HELP Version: 1.44.0 Depends: R (>= 2.8.0), stats, graphics, grDevices, Biobase, methods License: GPL (>= 2) MD5sum: 70cc2c48c8b128e5d10307ae7e518301 NeedsCompilation: no Title: Tools for HELP data analysis Description: The package contains a modular pipeline for analysis of HELP microarray data, and includes graphical and mathematical tools with more general applications. biocViews: CpGIsland, DNAMethylation, Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, Visualization Author: Reid F. Thompson , John M. Greally , with contributions from Mark Reimers Maintainer: Reid F. Thompson git_url: https://git.bioconductor.org/packages/HELP git_branch: RELEASE_3_10 git_last_commit: 2d4bbd4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HELP_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HELP_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HELP_1.44.0.tgz vignettes: vignettes/HELP/inst/doc/HELP.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HELP/inst/doc/HELP.R dependencyCount: 8 Package: HEM Version: 1.58.0 Depends: R (>= 2.1.0) Imports: Biobase, grDevices, stats, utils License: GPL (>= 2) MD5sum: 60b9e5eae11b3099a7f14d645fbf1767 NeedsCompilation: yes Title: Heterogeneous error model for identification of differentially expressed genes under multiple conditions Description: This package fits heterogeneous error models for analysis of microarray data biocViews: Microarray, DifferentialExpression Author: HyungJun Cho and Jae K. Lee Maintainer: HyungJun Cho URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ git_url: https://git.bioconductor.org/packages/HEM git_branch: RELEASE_3_10 git_last_commit: c1070f0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HEM_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HEM_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HEM_1.58.0.tgz vignettes: vignettes/HEM/inst/doc/HEM.pdf vignetteTitles: HEM Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: hiAnnotator Version: 1.20.0 Depends: GenomicRanges, R (>= 2.10) Imports: foreach, iterators, rtracklayer, dplyr, BSgenome, ggplot2, scales, methods Suggests: knitr, doParallel, testthat, BiocGenerics License: GPL (>= 2) MD5sum: 64b1b15ef8eaa2613773c6cf2ecdebfb NeedsCompilation: no Title: Functions for annotating GRanges objects Description: hiAnnotator contains set of functions which allow users to annotate a GRanges object with custom set of annotations. The basic philosophy of this package is to take two GRanges objects (query & subject) with common set of seqnames (i.e. chromosomes) and return associated annotation per seqnames and rows from the query matching seqnames and rows from the subject (i.e. genes or cpg islands). The package comes with three types of annotation functions which calculates if a position from query is: within a feature, near a feature, or count features in defined window sizes. Moreover, each function is equipped with parallel backend to utilize the foreach package. In addition, the package is equipped with wrapper functions, which finds appropriate columns needed to make a GRanges object from a common data frame. biocViews: Software, Annotation Author: Nirav V Malani Maintainer: Nirav V Malani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hiAnnotator git_branch: RELEASE_3_10 git_last_commit: 5540ff6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/hiAnnotator_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/hiAnnotator_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/hiAnnotator_1.20.0.tgz vignettes: vignettes/hiAnnotator/inst/doc/Intro.html vignetteTitles: Using hiAnnotator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiAnnotator/inst/doc/Intro.R dependsOnMe: hiReadsProcessor dependencyCount: 91 Package: HIBAG Version: 1.22.0 Depends: R (>= 3.2.0) Imports: methods Suggests: parallel, knitr, gdsfmt (>= 1.2.2), SNPRelate (>= 1.1.6), ggplot2, reshape2 License: GPL-3 MD5sum: 2b5d5aab6d112075f1e36f482848283a NeedsCompilation: yes Title: HLA Genotype Imputation with Attribute Bagging Description: It is a software package for imputing HLA types using SNP data, and relies on a training set of HLA and SNP genotypes. HIBAG can be used by researchers with published parameter estimates instead of requiring access to large training sample datasets. It combines the concepts of attribute bagging, an ensemble classifier method, with haplotype inference for SNPs and HLA types. Attribute bagging is a technique which improves the accuracy and stability of classifier ensembles using bootstrap aggregating and random variable selection. biocViews: Genetics, StatisticalMethod Author: Xiuwen Zheng [aut, cre, cph] (), Bruce Weir [ctb, ths] () Maintainer: Xiuwen Zheng URL: http://github.com/zhengxwen/HIBAG, http://www.biostat.washington.edu/~bsweir/HIBAG/, https://hibag.s3.amazonaws.com/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HIBAG git_branch: RELEASE_3_10 git_last_commit: a63fd73 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HIBAG_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HIBAG_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HIBAG_1.22.0.tgz vignettes: vignettes/HIBAG/inst/doc/HIBAG.html vignetteTitles: HIBAG vignette html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIBAG/inst/doc/HIBAG.R dependencyCount: 1 Package: HiCBricks Version: 1.4.0 Depends: R (>= 3.6), utils, curl, rhdf5, R6, grid Imports: ggplot2, viridis, RColorBrewer, scales, reshape2, stringr, data.table, GenomeInfoDb, GenomicRanges, stats, IRanges, grDevices, S4Vectors, digest, tibble, jsonlite, BiocParallel, R.utils, readr, methods Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE Archs: i386, x64 MD5sum: d8c0423f7481db4896d503f7c6738711 NeedsCompilation: no Title: Framework for Storing and Accessing Hi-C Data Through HDF Files Description: HiCBricks is a library designed for handling large high-resolution Hi-C datasets. Over the years, the Hi-C field has experienced a rapid increase in the size and complexity of datasets. HiCBricks is meant to overcome the challenges related to the analysis of such large datasets within the R environment. HiCBricks offers user-friendly and efficient solutions for handling large high-resolution Hi-C datasets. The package provides an R/Bioconductor framework with the bricks to build more complex data analysis pipelines and algorithms. HiCBricks already incorporates example algorithms for calling domain boundaries and functions for high quality data visualization. biocViews: DataImport, Infrastructure, Software, Technology, Sequencing, HiC Author: Koustav Pal [aut, cre], Carmen Livi [ctb], Ilario Tagliaferri [ctb] Maintainer: Koustav Pal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HiCBricks git_branch: RELEASE_3_10 git_last_commit: cf9b736 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HiCBricks_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HiCBricks_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HiCBricks_1.4.0.tgz vignettes: vignettes/HiCBricks/inst/doc/IntroductionToHiCBricks.html vignetteTitles: IntroductionToHiCBricks.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCBricks/inst/doc/IntroductionToHiCBricks.R dependencyCount: 90 Package: HiCcompare Version: 1.8.0 Depends: R (>= 3.4.0), dplyr Imports: data.table, ggplot2, gridExtra, mgcv, stats, InteractionSet, GenomicRanges, IRanges, S4Vectors, BiocParallel, QDNAseq, KernSmooth, methods, utils, graphics, pheatmap, gtools, rhdf5 Suggests: knitr, rmarkdown, testthat, multiHiCcompare License: MIT + file LICENSE MD5sum: 176fe3648e53d9588fab9d558dcd1cff NeedsCompilation: no Title: HiCcompare: Joint normalization and comparative analysis of multiple Hi-C datasets Description: HiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. HiCcompare operates on processed Hi-C data in the form of chromosome-specific chromatin interaction matrices. It accepts three-column tab-separated text files storing chromatin interaction matrices in a sparse matrix format which are available from several sources. HiCcompare is designed to give the user the ability to perform a comparative analysis on the 3-Dimensional structure of the genomes of cells in different biological states.`HiCcompare` differs from other packages that attempt to compare Hi-C data in that it works on processed data in chromatin interaction matrix format instead of pre-processed sequencing data. In addition, `HiCcompare` provides a non-parametric method for the joint normalization and removal of biases between two Hi-C datasets for the purpose of comparative analysis. `HiCcompare` also provides a simple yet robust method for detecting differences between Hi-C datasets. biocViews: Software, HiC, Sequencing, Normalization Author: John Stansfield [aut, cre], Kellen Cresswell [aut], Mikhail Dozmorov [aut] Maintainer: John Stansfield VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HiCcompare git_branch: RELEASE_3_10 git_last_commit: 127baf5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HiCcompare_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HiCcompare_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HiCcompare_1.8.0.tgz vignettes: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.html vignetteTitles: HiCcompare Usage Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.R importsMe: multiHiCcompare, SpectralTAD dependencyCount: 108 Package: hicrep Version: 1.10.0 Depends: R (>= 3.4) Imports: stats Suggests: knitr, rmarkdown, testthat License: GPL (>= 2.0) Archs: i386, x64 MD5sum: 058248ff890d45a46bd7cfbf01b829ca NeedsCompilation: no Title: Measuring the reproducibility of Hi-C data Description: Hi-C is a powerful technology for studying genome-wide chromatin interactions. However, current methods for assessing Hi-C data reproducibility can produce misleading results because they ignore spatial features in Hi-C data, such as domain structure and distance-dependence. We present a novel reproducibility measure that systematically takes these features into consideration. This measure can assess pairwise differences between Hi-C matrices under a wide range of settings, and can be used to determine optimal sequencing depth. Compared to existing approaches, it consistently shows higher accuracy in distinguishing subtle differences in reproducibility and depicting interrelationships of cell lineages than existing approaches. This R package `hicrep` implements our approach. biocViews: Sequencing, HiC, QualityControl Author: Tao Yang [aut, cre] Maintainer: Tao Yang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hicrep git_branch: RELEASE_3_10 git_last_commit: b0bc7e9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/hicrep_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/hicrep_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/hicrep_1.10.0.tgz vignettes: vignettes/hicrep/inst/doc/hicrep-vigenette.html vignetteTitles: Evaluate reproducibility of Hi-C data with `hicrep` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hicrep/inst/doc/hicrep-vigenette.R dependencyCount: 1 Package: hierGWAS Version: 1.16.0 Depends: R (>= 3.2.0) Imports: fastcluster,glmnet, fmsb Suggests: BiocGenerics, RUnit, MASS License: GPL-3 MD5sum: dbf41123a1621a0ee716872c650ae2aa NeedsCompilation: no Title: Asessing statistical significance in predictive GWA studies Description: Testing individual SNPs, as well as arbitrarily large groups of SNPs in GWA studies, using a joint model of all SNPs. The method controls the FWER, and provides an automatic, data-driven refinement of the SNP clusters to smaller groups or single markers. biocViews: SNP, LinkageDisequilibrium, Clustering Author: Laura Buzdugan Maintainer: Laura Buzdugan git_url: https://git.bioconductor.org/packages/hierGWAS git_branch: RELEASE_3_10 git_last_commit: 4ff5276 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/hierGWAS_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/hierGWAS_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/hierGWAS_1.16.0.tgz vignettes: vignettes/hierGWAS/inst/doc/hierGWAS.pdf vignetteTitles: User manual for R-Package hierGWAS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hierGWAS/inst/doc/hierGWAS.R dependencyCount: 15 Package: hierinf Version: 1.4.0 Depends: R (>= 3.6.0) Imports: fmsb, glmnet, methods, parallel, stats Suggests: knitr, MASS, testthat License: GPL-3 | file LICENSE MD5sum: f5af213eca9e347b77b74ae67bd33129 NeedsCompilation: no Title: Hierarchical Inference Description: Tools to perform hierarchical inference for one or multiple studies / data sets based on high-dimensional multivariate (generalised) linear models. A possible application is to perform hierarchical inference for GWA studies to find significant groups or single SNPs (if the signal is strong) in a data-driven and automated procedure. The method is based on an efficient hierarchical multiple testing correction and controls the FWER. The functions can easily be run in parallel. biocViews: Clustering, GenomeWideAssociation, LinkageDisequilibrium, Regression, SNP Author: Claude Renaux [aut, cre], Laura Buzdugan [aut], Markus Kalisch [aut], Peter Bühlmann [aut] Maintainer: Claude Renaux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hierinf git_branch: RELEASE_3_10 git_last_commit: 371e2ba git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/hierinf_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/hierinf_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/hierinf_1.4.0.tgz vignettes: vignettes/hierinf/inst/doc/vignette-hierinf.pdf vignetteTitles: vignette-hierinf.Rnw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hierinf/inst/doc/vignette-hierinf.R dependencyCount: 15 Package: HilbertCurve Version: 1.16.0 Depends: R (>= 3.1.2), grid Imports: methods, utils, HilbertVis, png, grDevices, circlize (>= 0.3.3), IRanges, GenomicRanges, polylabelr Suggests: knitr, testthat (>= 1.0.0), ComplexHeatmap (>= 1.99.0), markdown, RColorBrewer, RCurl, GetoptLong License: MIT + file LICENSE MD5sum: 8648a289b21a6aa257565abe73c1348f NeedsCompilation: no Title: Making 2D Hilbert Curve Description: Hilbert curve is a type of space-filling curves that fold one dimensional axis into a two dimensional space, but with still preserves the locality. This package aims to provide an easy and flexible way to visualize data through Hilbert curve. biocViews: Software, Visualization, Sequencing, Coverage, GenomeAnnotation Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/HilbertCurve VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HilbertCurve git_branch: RELEASE_3_10 git_last_commit: 0ea1a95 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HilbertCurve_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HilbertCurve_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HilbertCurve_1.16.0.tgz vignettes: vignettes/HilbertCurve/inst/doc/HilbertCurve.html vignetteTitles: Making 2D Hilbert Curve hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HilbertCurve/inst/doc/HilbertCurve.R dependencyCount: 27 Package: HilbertVis Version: 1.44.0 Depends: R (>= 2.6.0), grid, lattice Suggests: IRanges, EBImage License: GPL (>= 3) MD5sum: 06f80c668bde88ec96c68374dca8919f NeedsCompilation: yes Title: Hilbert curve visualization Description: Functions to visualize long vectors of integer data by means of Hilbert curves biocViews: Visualization Author: Simon Anders Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert git_url: https://git.bioconductor.org/packages/HilbertVis git_branch: RELEASE_3_10 git_last_commit: fde2932 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HilbertVis_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HilbertVis_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HilbertVis_1.44.0.tgz vignettes: vignettes/HilbertVis/inst/doc/HilbertVis.pdf vignetteTitles: Visualising very long data vectors with the Hilbert curve hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HilbertVis/inst/doc/HilbertVis.R dependsOnMe: HilbertVisGUI importsMe: ChIPseqR, HilbertCurve dependencyCount: 6 Package: HilbertVisGUI Version: 1.44.0 Depends: R (>= 2.6.0), HilbertVis (>= 1.1.6) Suggests: lattice, IRanges License: GPL (>= 3) MD5sum: c12203d6ec9ffaf4159d00485686d412 NeedsCompilation: yes Title: HilbertVisGUI Description: An interactive tool to visualize long vectors of integer data by means of Hilbert curves biocViews: Visualization Author: Simon Anders Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert SystemRequirements: gtkmm-2.4, GNU make git_url: https://git.bioconductor.org/packages/HilbertVisGUI git_branch: RELEASE_3_10 git_last_commit: d4e14c0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HilbertVisGUI_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HilbertVisGUI_1.44.0.zip vignettes: vignettes/HilbertVisGUI/inst/doc/HilbertVisGUI.pdf vignetteTitles: See vignette in package HilbertVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE dependencyCount: 7 Package: HiLDA Version: 1.0.0 Depends: R(>= 3.6), ggplot2 Imports: R2jags, abind, cowplot, grid, forcats, stringr, GenomicRanges, S4Vectors, XVector, Biostrings, GenomicFeatures, BSgenome.Hsapiens.UCSC.hg19, BiocGenerics, tidyr, grDevices, stats, TxDb.Hsapiens.UCSC.hg19.knownGene, utils, methods, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: e89a6c3bbcb5f1c89701363aa3e8d39b NeedsCompilation: yes Title: Conducting statistical inference on comparing the mutational exposures of mutational signatures by using hierarchical latent Dirichlet allocation Description: A package built under the Bayesian framework of applying hierarchical latent Dirichlet allocation to statistically test whether the mutational exposures of mutational signatures (Shiraishi-model signatures) are different between two groups. biocViews: Software, SomaticMutation, Sequencing, StatisticalMethod, Bayesian Author: Zhi Yang [aut, cre], Yuichi Shiraishi [ctb] Maintainer: Zhi Yang URL: https://github.com/USCbiostats/HiLDA, https://doi.org/10.1101/577452 SystemRequirements: JAGS 4.2.0 VignetteBuilder: knitr BugReports: https://github.com/USCbiostats/HiLDA/issues git_url: https://git.bioconductor.org/packages/HiLDA git_branch: RELEASE_3_10 git_last_commit: 45ea7e6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HiLDA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HiLDA_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HiLDA_1.0.0.tgz vignettes: vignettes/HiLDA/inst/doc/HiLDA.html vignetteTitles: An introduction to HiLDA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/HiLDA/inst/doc/HiLDA.R dependencyCount: 122 Package: hipathia Version: 2.2.1 Depends: R (>= 3.6), igraph (>= 1.0.1), AnnotationHub(>= 2.6.5), MultiAssayExperiment(>= 1.4.9), SummarizedExperiment(>= 1.8.1) Imports: coin, stats, limma, grDevices, utils, graphics, preprocessCore, servr, DelayedArray, matrixStats, methods, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 Archs: i386, x64 MD5sum: 712e0dbe55a7ee5fc58cf779c2a9cb4d NeedsCompilation: no Title: HiPathia: High-throughput Pathway Analysis Description: Hipathia is a method for the computation of signal transduction along signaling pathways from transcriptomic data. The method is based on an iterative algorithm which is able to compute the signal intensity passing through the nodes of a network by taking into account the level of expression of each gene and the intensity of the signal arriving to it. It also provides a new approach to functional analysis allowing to compute the signal arriving to the functions annotated to each pathway. biocViews: Pathways, GraphAndNetwork, GeneExpression, GeneSignaling, GO Author: Marta R. Hidalgo [aut, cre], José Carbonell-Caballero [ctb], Francisco Salavert [ctb], Alicia Amadoz [ctb], Çankut Cubuk [ctb], Joaquin Dopazo [ctb] Maintainer: Marta R. Hidalgo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hipathia git_branch: RELEASE_3_10 git_last_commit: 4e96525 git_last_commit_date: 2019-12-02 Date/Publication: 2019-12-06 source.ver: src/contrib/hipathia_2.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/hipathia_2.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/hipathia_2.2.1.tgz vignettes: vignettes/hipathia/inst/doc/hipathia-vignette.pdf vignetteTitles: Hipathia Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hipathia/inst/doc/hipathia-vignette.R dependencyCount: 103 Package: hiReadsProcessor Version: 1.22.0 Depends: Biostrings, GenomicAlignments, BiocParallel, hiAnnotator, R (>= 3.0) Imports: sonicLength, dplyr, BiocGenerics, GenomicRanges, readxl, methods Suggests: knitr, testthat License: GPL-3 MD5sum: 72f4c6a201f976ef748ae1fe6592cfee NeedsCompilation: no Title: Functions to process LM-PCR reads from 454/Illumina data Description: hiReadsProcessor contains set of functions which allow users to process LM-PCR products sequenced using any platform. Given an excel/txt file containing parameters for demultiplexing and sample metadata, the functions automate trimming of adaptors and identification of the genomic product. Genomic products are further processed for QC and abundance quantification. biocViews: Sequencing, Preprocessing Author: Nirav V Malani Maintainer: Nirav V Malani SystemRequirements: BLAT, UCSC hg18 in 2bit format for BLAT VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hiReadsProcessor git_branch: RELEASE_3_10 git_last_commit: 326ba63 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/hiReadsProcessor_1.22.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/hiReadsProcessor_1.22.0.tgz vignettes: vignettes/hiReadsProcessor/inst/doc/Tutorial.html vignetteTitles: Using hiReadsProcessor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiReadsProcessor/inst/doc/Tutorial.R dependencyCount: 98 Package: HIREewas Version: 1.4.0 Depends: R (>= 3.5.0) Imports: quadprog, gplots, grDevices, stats Suggests: BiocStyle, knitr, BiocGenerics License: GPL (>= 2) Archs: x64 MD5sum: 7c6176651b66a810db2a483968dc1deb NeedsCompilation: yes Title: Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies Description: In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. The current approaches to the association detection only claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not, but they cannot determine the cell type in which the risk-CpG site is affected by the phenotype. We propose a solid statistical method, HIgh REsolution (HIRE), which not only substantially improves the power of association detection at the aggregated level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types. The "HIREewas" R package is to implement HIRE model in R. biocViews: DNAMethylation, DifferentialMethylation, FeatureExtraction Author: Xiangyu Luo , Can Yang , Yingying Wei Maintainer: Xiangyu Luo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HIREewas git_branch: RELEASE_3_10 git_last_commit: 4e29abd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HIREewas_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HIREewas_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HIREewas_1.4.0.tgz vignettes: vignettes/HIREewas/inst/doc/HIREewas.pdf vignetteTitles: HIREewas hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIREewas/inst/doc/HIREewas.R dependencyCount: 11 Package: HiTC Version: 1.30.0 Depends: R (>= 2.15.0), methods, IRanges, GenomicRanges Imports: Biostrings, graphics, grDevices, rtracklayer, RColorBrewer, Matrix, parallel, GenomeInfoDb Suggests: BiocStyle, HiCDataHumanIMR90 License: Artistic-2.0 Archs: i386, x64 MD5sum: a559f58f96924284ca7fd19c02896fe4 NeedsCompilation: no Title: High Throughput Chromosome Conformation Capture analysis Description: The HiTC package was developed to explore high-throughput 'C' data such as 5C or Hi-C. Dedicated R classes as well as standard methods for quality controls, normalization, visualization, and further analysis are also provided. biocViews: Sequencing, HighThroughputSequencing, HiC Author: Nicolas Servant Maintainer: Nicolas Servant git_url: https://git.bioconductor.org/packages/HiTC git_branch: RELEASE_3_10 git_last_commit: a7e9d85 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HiTC_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HiTC_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HiTC_1.30.0.tgz vignettes: vignettes/HiTC/inst/doc/HiC_analysis.pdf, vignettes/HiTC/inst/doc/HiTC.pdf vignetteTitles: Hi-C data analysis using HiTC, Introduction to HiTC package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiTC/inst/doc/HiC_analysis.R, vignettes/HiTC/inst/doc/HiTC.R dependencyCount: 39 Package: hmdbQuery Version: 1.6.1 Depends: R (>= 3.5), XML Imports: S4Vectors, methods, utils Suggests: knitr, annotate, gwascat, testthat License: Artistic-2.0 MD5sum: f06a9b2ddca906b06c34faf0b80e4846 NeedsCompilation: no Title: utilities for exploration of human metabolome database Description: Define utilities for exploration of human metabolome database, including functions to retrieve specific metabolite entries and data snapshots with pairwise associations (metabolite-gene,-protein,-disease). biocViews: Metabolomics, Infrastructure Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hmdbQuery git_branch: RELEASE_3_10 git_last_commit: ea6d6a7 git_last_commit_date: 2020-03-04 Date/Publication: 2020-03-05 source.ver: src/contrib/hmdbQuery_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/hmdbQuery_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/hmdbQuery_1.6.1.tgz vignettes: vignettes/hmdbQuery/inst/doc/hmdbQuery.html vignetteTitles: hmdbQuery: working with Human Metabolome Database (hmdb.ca) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hmdbQuery/inst/doc/hmdbQuery.R dependencyCount: 9 Package: HMMcopy Version: 1.28.1 Depends: R (>= 2.10.0), data.table (>= 1.11.8) License: GPL-3 Archs: i386, x64 MD5sum: 5890d05d3f95301b681459f29c29fc96 NeedsCompilation: yes Title: Copy number prediction with correction for GC and mappability bias for HTS data Description: Corrects GC and mappability biases for readcounts (i.e. coverage) in non-overlapping windows of fixed length for single whole genome samples, yielding a rough estimate of copy number for furthur analysis. Designed for rapid correction of high coverage whole genome tumour and normal samples. biocViews: Sequencing, Preprocessing, Visualization, CopyNumberVariation, Microarray Author: Daniel Lai, Gavin Ha, Sohrab Shah Maintainer: Daniel Lai , Sohrab Shah git_url: https://git.bioconductor.org/packages/HMMcopy git_branch: RELEASE_3_10 git_last_commit: 79ce9d7 git_last_commit_date: 2020-03-30 Date/Publication: 2020-03-31 source.ver: src/contrib/HMMcopy_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/HMMcopy_1.28.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HMMcopy_1.28.1.tgz vignettes: vignettes/HMMcopy/inst/doc/HMMcopy.pdf vignetteTitles: HMMcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HMMcopy/inst/doc/HMMcopy.R importsMe: qsea dependencyCount: 2 Package: hopach Version: 2.46.0 Depends: R (>= 2.11.0), cluster, Biobase, methods Imports: graphics, grDevices, stats, utils, BiocGenerics License: GPL (>= 2) MD5sum: 513ae14abc10de06d21b041910aba2c0 NeedsCompilation: yes Title: Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH) Description: The HOPACH clustering algorithm builds a hierarchical tree of clusters by recursively partitioning a data set, while ordering and possibly collapsing clusters at each level. The algorithm uses the Mean/Median Split Silhouette (MSS) criteria to identify the level of the tree with maximally homogeneous clusters. It also runs the tree down to produce a final ordered list of the elements. The non-parametric bootstrap allows one to estimate the probability that each element belongs to each cluster (fuzzy clustering). biocViews: Clustering Author: Katherine S. Pollard, with Mark J. van der Laan and Greg Wall Maintainer: Katherine S. Pollard URL: http://www.stat.berkeley.edu/~laan/, http://docpollard.org/ git_url: https://git.bioconductor.org/packages/hopach git_branch: RELEASE_3_10 git_last_commit: 5ce7636 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/hopach_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/hopach_2.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/hopach_2.46.0.tgz vignettes: vignettes/hopach/inst/doc/hopach.pdf vignetteTitles: hopach hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hopach/inst/doc/hopach.R importsMe: phenoTest suggestsMe: BiocCaseStudies dependencyCount: 9 Package: HPAanalyze Version: 1.4.3 Depends: R (>= 3.5.0) Imports: dplyr, openxlsx, ggplot2, tibble, xml2, stats, utils, gridExtra Suggests: knitr, rmarkdown, devtools, BiocStyle License: GPL-3 + file LICENSE MD5sum: 00669af1c8732b356c8dccb07265a768 NeedsCompilation: no Title: Retrieve and analyze data from the Human Protein Atlas Description: Provide functions for retrieving, exploratory analyzing and visualizing the Human Protein Atlas data. biocViews: Proteomics, CellBiology, Visualization, Software Author: Anh Nhat Tran [aut, cre] Maintainer: Anh Nhat Tran VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HPAanalyze git_branch: RELEASE_3_10 git_last_commit: 6c129f5 git_last_commit_date: 2020-01-17 Date/Publication: 2020-01-17 source.ver: src/contrib/HPAanalyze_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/HPAanalyze_1.4.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HPAanalyze_1.4.3.tgz vignettes: vignettes/HPAanalyze/inst/doc/a_HPAanalyze_quick_start.html, vignettes/HPAanalyze/inst/doc/b_HPAanalyze_indepth.html, vignettes/HPAanalyze/inst/doc/c_HPAanalyze_case_query.html, vignettes/HPAanalyze/inst/doc/d_HPAanalyze_case_offline_xml.html, vignettes/HPAanalyze/inst/doc/e_HPAanalyze_case_json.html, vignettes/HPAanalyze/inst/doc/f_HPAanalyze_case_images.html, vignettes/HPAanalyze/inst/doc/z_HPAanalyze_paper_figures.html vignetteTitles: "1. HPAanalyze quick start guide: Acquire and visualize the Human Protein Atlas (HPA) data in one function", "2. HPAanalyze in-depth: Working with Human Protein Atlas (HPA) data in R", "3. HPAanalyze use case: Combine with your Human Protein Atlas (HPA) queries", "4. HPAanalyze use case: Working with Human Protein Atlas (HPA) xml files offline", "5. HPAanalyze use case: Export Human Protein Atlas (HPA) data as JSON", "6. HPAanalyze use case: Download histology images from the Human Protein Atlas", "99. Code for figures from HPAanalyze paper" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HPAanalyze/inst/doc/a_HPAanalyze_quick_start.R, vignettes/HPAanalyze/inst/doc/b_HPAanalyze_indepth.R, vignettes/HPAanalyze/inst/doc/c_HPAanalyze_case_query.R, vignettes/HPAanalyze/inst/doc/d_HPAanalyze_case_offline_xml.R, vignettes/HPAanalyze/inst/doc/e_HPAanalyze_case_json.R, vignettes/HPAanalyze/inst/doc/f_HPAanalyze_case_images.R, vignettes/HPAanalyze/inst/doc/z_HPAanalyze_paper_figures.R dependencyCount: 64 Package: hpar Version: 1.28.0 Depends: R (>= 2.15) Imports: utils Suggests: org.Hs.eg.db, GO.db, knitr, BiocStyle, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 1446695a52e111ef2864712f12b586a0 NeedsCompilation: no Title: Human Protein Atlas in R Description: The hpar package provides a simple R interface to and data from the Human Protein Atlas project. biocViews: Proteomics, Homo_sapiens, CellBiology Author: Laurent Gatto Maintainer: Laurent Gatto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hpar git_branch: RELEASE_3_10 git_last_commit: 8f153be git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/hpar_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/hpar_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/hpar_1.28.0.tgz vignettes: vignettes/hpar/inst/doc/hpar.html vignetteTitles: Human Protein Atlas in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hpar/inst/doc/hpar.R importsMe: MetaboSignal suggestsMe: pRoloc dependencyCount: 1 Package: HTqPCR Version: 1.40.0 Depends: Biobase, RColorBrewer, limma Imports: affy, Biobase, gplots, graphics, grDevices, limma, methods, RColorBrewer, stats, stats4, utils Suggests: statmod License: Artistic-2.0 Archs: i386, x64 MD5sum: f479cbc105b304e03cdf5e603c5d3673 NeedsCompilation: no Title: Automated analysis of high-throughput qPCR data Description: Analysis of Ct values from high throughput quantitative real-time PCR (qPCR) assays across multiple conditions or replicates. The input data can be from spatially-defined formats such ABI TaqMan Low Density Arrays or OpenArray; LightCycler from Roche Applied Science; the CFX plates from Bio-Rad Laboratories; conventional 96- or 384-well plates; or microfluidic devices such as the Dynamic Arrays from Fluidigm Corporation. HTqPCR handles data loading, quality assessment, normalization, visualization and parametric or non-parametric testing for statistical significance in Ct values between features (e.g. genes, microRNAs). biocViews: MicrotitrePlateAssay, DifferentialExpression, GeneExpression, DataImport, QualityControl, Preprocessing, Visualization, MultipleComparison, qPCR Author: Heidi Dvinge, Paul Bertone Maintainer: Heidi Dvinge URL: http://www.ebi.ac.uk/bertone/software git_url: https://git.bioconductor.org/packages/HTqPCR git_branch: RELEASE_3_10 git_last_commit: a8aea66 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HTqPCR_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HTqPCR_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HTqPCR_1.40.0.tgz vignettes: vignettes/HTqPCR/inst/doc/HTqPCR.pdf vignetteTitles: qPCR analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTqPCR/inst/doc/HTqPCR.R importsMe: nondetects, unifiedWMWqPCR dependencyCount: 22 Package: HTSanalyzeR Version: 2.38.0 Depends: R (>= 2.15), igraph, methods Imports: graph, igraph, GSEABase, BioNet, cellHTS2, AnnotationDbi, biomaRt, RankProd Suggests: KEGG.db, GO.db, org.Dm.eg.db, GOstats, org.Ce.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Hs.eg.db, snow License: Artistic-2.0 MD5sum: a508f2e503361159e39346dd6b36f23c NeedsCompilation: no Title: Gene set over-representation, enrichment and network analyses for high-throughput screens Description: This package provides classes and methods for gene set over-representation, enrichment and network analyses on high-throughput screens. The over-representation analysis is performed based on hypergeometric tests. The enrichment analysis is based on the GSEA algorithm (Subramanian et al. PNAS 2005). The network analysis identifies enriched subnetworks based on algorithms from the BioNet package (Beisser et al., Bioinformatics 2010). A pipeline is also specifically designed for cellHTS2 object to perform integrative network analyses of high-throughput RNA interference screens. The users can build their own analysis pipeline for their own data set based on this package. biocViews: ImmunoOncology, CellBasedAssays, MultipleComparison Author: Xin Wang , Camille Terfve , John C. Rose , Florian Markowetz Maintainer: Xin Wang PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/HTSanalyzeR git_branch: RELEASE_3_10 git_last_commit: eec27b7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HTSanalyzeR_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HTSanalyzeR_2.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HTSanalyzeR_2.38.0.tgz vignettes: vignettes/HTSanalyzeR/inst/doc/HTSanalyzeR-Vignette.pdf vignetteTitles: Main vignette:Gene set enrichment and network analysis of high-throughput RNAi screen data using HTSanalyzeR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSanalyzeR/inst/doc/HTSanalyzeR-Vignette.R importsMe: phenoTest dependencyCount: 120 Package: HTSeqGenie Version: 4.16.0 Depends: R (>= 3.0.0), gmapR (>= 1.8.0), ShortRead (>= 1.19.13), VariantAnnotation (>= 1.8.3) Imports: BiocGenerics (>= 0.2.0), S4Vectors (>= 0.9.25), IRanges (>= 1.21.39), GenomicRanges (>= 1.23.21), Rsamtools (>= 1.8.5), Biostrings (>= 2.24.1), chipseq (>= 1.6.1), hwriter (>= 1.3.0), Cairo (>= 1.5.5), GenomicFeatures (>= 1.9.31), BiocParallel, parallel, tools, rtracklayer (>= 1.17.19), GenomicAlignments, VariantTools (>= 1.7.7), GenomeInfoDb, SummarizedExperiment, methods Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, LungCancerLines, org.Hs.eg.db License: Artistic-2.0 MD5sum: eead176138c31c601ad7083e33ff211f NeedsCompilation: no Title: A NGS analysis pipeline. Description: Libraries to perform NGS analysis. Author: Gregoire Pau, Jens Reeder Maintainer: Jens Reeder git_url: https://git.bioconductor.org/packages/HTSeqGenie git_branch: RELEASE_3_10 git_last_commit: 481e232 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HTSeqGenie_4.16.0.tar.gz vignettes: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.pdf vignetteTitles: HTSeqGenie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.R dependencyCount: 95 Package: HTSFilter Version: 1.26.0 Depends: R (>= 3.4) Imports: edgeR (>= 3.9.14), DESeq2 (>= 1.10.1), DESeq (>= 1.22.1), BiocParallel (>= 1.4.3), Biobase, utils, stats, grDevices, graphics, methods Suggests: EDASeq (>= 2.1.4), BiocStyle, testthat License: Artistic-2.0 MD5sum: 41dcd33a06064c6425956e4e65ecb754 NeedsCompilation: no Title: Filter replicated high-throughput transcriptome sequencing data Description: This package implements a filtering procedure for replicated transcriptome sequencing data based on a global Jaccard similarity index in order to identify genes with low, constant levels of expression across one or more experimental conditions. biocViews: Sequencing, RNASeq, Preprocessing, DifferentialExpression, GeneExpression, Normalization, ImmunoOncology Author: Andrea Rau, Melina Gallopin, Gilles Celeux, and Florence Jaffrezic Maintainer: Andrea Rau git_url: https://git.bioconductor.org/packages/HTSFilter git_branch: RELEASE_3_10 git_last_commit: 10dc37e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HTSFilter_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HTSFilter_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HTSFilter_1.26.0.tgz vignettes: vignettes/HTSFilter/inst/doc/HTSFilter.pdf vignetteTitles: HTSFilter Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSFilter/inst/doc/HTSFilter.R importsMe: coseq dependencyCount: 124 Package: HumanTranscriptomeCompendium Version: 1.2.0 Depends: R (>= 3.6) Imports: shiny, ssrch, S4Vectors, SummarizedExperiment, utils Suggests: knitr, BiocStyle, beeswarm, tximportData, DT, tximport, dplyr, magrittr, BiocFileCache, testthat License: Artistic-2.0 MD5sum: 6e5d4615af9a25bc88743f6089638bf5 NeedsCompilation: no Title: Tools to work with a Compendium of 181000 human transcriptome sequencing studies Description: Provide tools for working with a compendium of human transcriptome sequences (originally htxcomp). biocViews: Transcriptomics, Infrastructure Author: Sean Davis, Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HumanTranscriptomeCompendium git_branch: RELEASE_3_10 git_last_commit: 82a6295 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HumanTranscriptomeCompendium_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HumanTranscriptomeCompendium_1.1.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HumanTranscriptomeCompendium_1.2.0.tgz vignettes: vignettes/HumanTranscriptomeCompendium/inst/doc/htxcomp.html vignetteTitles: HumanTranscriptomeCompendium -- a cloud-resident collection of sequenced human transcriptomes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HumanTranscriptomeCompendium/inst/doc/htxcomp.R dependencyCount: 54 Package: HybridMTest Version: 1.30.0 Depends: R (>= 2.9.0), Biobase, fdrtool, MASS, survival Imports: stats License: GPL Version 2 or later MD5sum: 87b9d921ea8981bb8d5010f950d82a8c NeedsCompilation: no Title: Hybrid Multiple Testing Description: Performs hybrid multiple testing that incorporates method selection and assumption evaluations into the analysis using empirical Bayes probability (EBP) estimates obtained by Grenander density estimation. For instance, for 3-group comparison analysis, Hybrid Multiple testing considers EBPs as weighted EBPs between F-test and H-test with EBPs from Shapiro Wilk test of normality as weigth. Instead of just using EBPs from F-test only or using H-test only, this methodology combines both types of EBPs through EBPs from Shapiro Wilk test of normality. This methodology uses then the law of total EBPs. biocViews: GeneExpression, Genetics, Microarray Author: Stan Pounds , Demba Fofana Maintainer: Demba Fofana git_url: https://git.bioconductor.org/packages/HybridMTest git_branch: RELEASE_3_10 git_last_commit: 181595e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/HybridMTest_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/HybridMTest_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/HybridMTest_1.30.0.tgz vignettes: vignettes/HybridMTest/inst/doc/HybridMTest.pdf vignetteTitles: Hybrid Multiple Testing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HybridMTest/inst/doc/HybridMTest.R dependencyCount: 15 Package: hypeR Version: 1.2.0 Depends: R (>= 3.6.0) Imports: ggplot2, ggforce, DT, R6, magrittr, dplyr, purrr, stats, scales, rlang, openxlsx, msigdbr, kableExtra, rmarkdown, gh, igraph, visNetwork Suggests: devtools, testthat, knitr, httr License: GPL-3 + file LICENSE MD5sum: 54ad00d8e433c068f53ce7fa11f6cebe NeedsCompilation: no Title: Hyper Enrichment Description: An R Package for Geneset Enrichment Workflows. biocViews: GeneSetEnrichment, Annotation, Pathways Author: Anthony Federico [aut, cre], Stefano Monti [aut] Maintainer: Anthony Federico URL: https://github.com/montilab/hypeR VignetteBuilder: knitr BugReports: https://github.com/montilab/hypeR/issues git_url: https://git.bioconductor.org/packages/hypeR git_branch: RELEASE_3_10 git_last_commit: 96a640b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/hypeR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/hypeR_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/hypeR_1.2.0.tgz vignettes: vignettes/hypeR/inst/doc/hypeR.html vignetteTitles: hypeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hypeR/inst/doc/hypeR.R dependencyCount: 102 Package: hyperdraw Version: 1.38.0 Depends: R (>= 2.9.0) Imports: methods, grid, graph, hypergraph, Rgraphviz, stats4 License: GPL (>= 2) MD5sum: 4f599bae5020151751cd910eb2836251 NeedsCompilation: no Title: Visualizing Hypergaphs Description: Functions for visualizing hypergraphs. biocViews: Visualization, GraphAndNetwork Author: Paul Murrell Maintainer: Paul Murrell SystemRequirements: graphviz git_url: https://git.bioconductor.org/packages/hyperdraw git_branch: RELEASE_3_10 git_last_commit: 4157968 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/hyperdraw_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/hyperdraw_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/hyperdraw_1.38.0.tgz vignettes: vignettes/hyperdraw/inst/doc/hyperdraw.pdf vignetteTitles: Hyperdraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hyperdraw/inst/doc/hyperdraw.R dependsOnMe: BiGGR dependencyCount: 12 Package: hypergraph Version: 1.58.0 Depends: R (>= 2.1.0), methods, utils, graph Suggests: BiocGenerics, RUnit License: Artistic-2.0 MD5sum: 81a1924c036984e59d805823e8643b3f NeedsCompilation: no Title: A package providing hypergraph data structures Description: A package that implements some simple capabilities for representing and manipulating hypergraphs. biocViews: GraphAndNetwork Author: Seth Falcon, Robert Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/hypergraph git_branch: RELEASE_3_10 git_last_commit: c71b8a1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/hypergraph_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/hypergraph_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/hypergraph_1.58.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: altcdfenvs, RpsiXML importsMe: BiGGR, hyperdraw dependencyCount: 8 Package: iASeq Version: 1.30.0 Depends: R (>= 2.14.1) Imports: graphics, grDevices License: GPL-2 MD5sum: 7f86fc16245f280d842d186874fd464f NeedsCompilation: no Title: iASeq: integrating multiple sequencing datasets for detecting allele-specific events Description: It fits correlation motif model to multiple RNAseq or ChIPseq studies to improve detection of allele-specific events and describe correlation patterns across studies. biocViews: ImmunoOncology, SNP, RNASeq, ChIPSeq Author: Yingying Wei, Hongkai Ji Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/iASeq git_branch: RELEASE_3_10 git_last_commit: fc08bf9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/iASeq_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/iASeq_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/iASeq_1.30.0.tgz vignettes: vignettes/iASeq/inst/doc/iASeqVignette.pdf vignetteTitles: iASeq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iASeq/inst/doc/iASeqVignette.R dependencyCount: 2 Package: iasva Version: 1.4.0 Depends: R (>= 3.5), Imports: irlba, stats, cluster, graphics, SummarizedExperiment, BiocParallel Suggests: knitr, testthat, rmarkdown, sva, Rtsne, pheatmap, corrplot, DescTools, RColorBrewer License: GPL-2 MD5sum: 4f06f0ccf208b6f9aa8e50fd772f4a9b NeedsCompilation: no Title: Iteratively Adjusted Surrogate Variable Analysis Description: Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated. IA-SVA provides a flexible methodology to i) identify a hidden factor for unwanted heterogeneity while adjusting for all known factors; ii) test the significance of the putative hidden factor for explaining the unmodeled variation in the data; and iii), if significant, use the estimated factor as an additional known factor in the next iteration to uncover further hidden factors. biocViews: Preprocessing, QualityControl, BatchEffect, RNASeq, Software, StatisticalMethod, FeatureExtraction, ImmunoOncology Author: Donghyung Lee [aut, cre], Anthony Cheng [aut], Nathan Lawlor [aut], Duygu Ucar [aut] Maintainer: Donghyung Lee , Anthony Cheng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/iasva git_branch: RELEASE_3_10 git_last_commit: bfe75fc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/iasva_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/iasva_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/iasva_1.4.0.tgz vignettes: vignettes/iasva/inst/doc/detecting_hidden_heterogeneity_iasvaV0.95.html vignetteTitles: "Detecting hidden heterogeneity in single cell RNA-Seq data" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iasva/inst/doc/detecting_hidden_heterogeneity_iasvaV0.95.R dependencyCount: 34 Package: iBBiG Version: 1.30.0 Depends: biclust Imports: stats4,xtable,ade4 Suggests: methods License: Artistic-2.0 MD5sum: e113763572c733f555fd7cd80c18fe42 NeedsCompilation: yes Title: Iterative Binary Biclustering of Genesets Description: iBBiG is a bi-clustering algorithm which is optimizes for binary data analysis. We apply it to meta-gene set analysis of large numbers of gene expression datasets. The iterative algorithm extracts groups of phenotypes from multiple studies that are associated with similar gene sets. iBBiG does not require prior knowledge of the number or scale of clusters and allows discovery of clusters with diverse sizes biocViews: Clustering, Annotation, GeneSetEnrichment Author: Daniel Gusenleitner, Aedin Culhane Maintainer: Aedin Culhane URL: http://bcb.dfci.harvard.edu/~aedin/publications/ git_url: https://git.bioconductor.org/packages/iBBiG git_branch: RELEASE_3_10 git_last_commit: 916b261 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/iBBiG_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/iBBiG_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/iBBiG_1.30.0.tgz vignettes: vignettes/iBBiG/inst/doc/tutorial.pdf vignetteTitles: iBBiG User Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iBBiG/inst/doc/tutorial.R importsMe: miRSM dependencyCount: 72 Package: ibh Version: 1.34.0 Depends: simpIntLists Suggests: yeastCC, stats License: GPL (>= 2) MD5sum: 8c92f5c705ce8332e8b59226aee1cefe NeedsCompilation: no Title: Interaction Based Homogeneity for Evaluating Gene Lists Description: This package contains methods for calculating Interaction Based Homogeneity to evaluate fitness of gene lists to an interaction network which is useful for evaluation of clustering results and gene list analysis. BioGRID interactions are used in the calculation. The user can also provide their own interactions. biocViews: QualityControl, DataImport, GraphAndNetwork, NetworkEnrichment Author: Kircicegi Korkmaz, Volkan Atalay, Rengul Cetin Atalay. Maintainer: Kircicegi Korkmaz git_url: https://git.bioconductor.org/packages/ibh git_branch: RELEASE_3_10 git_last_commit: 0403f19 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ibh_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ibh_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ibh_1.34.0.tgz vignettes: vignettes/ibh/inst/doc/ibh.pdf vignetteTitles: ibh hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ibh/inst/doc/ibh.R dependencyCount: 1 Package: iBMQ Version: 1.26.0 Depends: R(>= 2.15.0),Biobase (>= 2.16.0), ggplot2 (>= 0.9.2) License: Artistic-2.0 MD5sum: fc5bbee1e6ed3ac5e22cbef5ea4e251f NeedsCompilation: yes Title: integrated Bayesian Modeling of eQTL data Description: integrated Bayesian Modeling of eQTL data biocViews: Microarray, Preprocessing, GeneExpression, SNP Author: Marie-Pier Scott-Boyer and Greg Imholte Maintainer: Greg Imholte URL: http://www.rglab.org SystemRequirements: GSL and OpenMP git_url: https://git.bioconductor.org/packages/iBMQ git_branch: RELEASE_3_10 git_last_commit: 15769c7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/iBMQ_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/iBMQ_1.26.0.zip vignettes: vignettes/iBMQ/inst/doc/iBMQ.pdf vignetteTitles: iBMQ: An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iBMQ/inst/doc/iBMQ.R dependencyCount: 57 Package: iCARE Version: 1.14.0 Depends: R (>= 3.3.0), plotrix, gtools, Hmisc Suggests: RUnit, BiocGenerics License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: 4881e34e7ba87a7143447e67577fc82c NeedsCompilation: yes Title: A Tool for Individualized Coherent Absolute Risk Estimation (iCARE) Description: An R package to compute Individualized Coherent Absolute Risk Estimators. biocViews: Software, StatisticalMethod, GenomeWideAssociation Author: Paige Maas, Parichoy Pal Choudhury, Nilanjan Chatterjee and William Wheeler Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/iCARE git_branch: RELEASE_3_10 git_last_commit: 1701702 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/iCARE_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/iCARE_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/iCARE_1.14.0.tgz vignettes: vignettes/iCARE/inst/doc/vignette_model_validation.pdf, vignettes/iCARE/inst/doc/vignette.pdf vignetteTitles: iCARE Vignette Model Validation, iCARE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iCARE/inst/doc/vignette_model_validation.R, vignettes/iCARE/inst/doc/vignette.R dependencyCount: 84 Package: Icens Version: 1.58.0 Depends: survival Imports: graphics License: Artistic-2.0 MD5sum: 72cd00f33e07081ec927b33fede2afd3 NeedsCompilation: no Title: NPMLE for Censored and Truncated Data Description: Many functions for computing the NPMLE for censored and truncated data. biocViews: Infrastructure Author: R. Gentleman and Alain Vandal Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/Icens git_branch: RELEASE_3_10 git_last_commit: 4e961c7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Icens_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Icens_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Icens_1.58.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PROcess importsMe: PROcess dependencyCount: 10 Package: icetea Version: 1.4.0 Depends: R (>= 3.6) Imports: stats, utils, methods, graphics, grDevices, ggplot2, GenomicFeatures, ShortRead, BiocParallel, Biostrings, S4Vectors, Rsamtools, BiocGenerics, IRanges, GenomicAlignments, GenomicRanges, rtracklayer, SummarizedExperiment, VariantAnnotation, limma, edgeR, csaw, DESeq2, TxDb.Dmelanogaster.UCSC.dm6.ensGene Suggests: knitr, rmarkdown, Rsubread (>= 1.29.0), testthat License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: bbee88fe735a72b378d6576ccb3d4c3a NeedsCompilation: no Title: Integrating Cap Enrichment with Transcript Expression Analysis Description: icetea (Integrating Cap Enrichment with Transcript Expression Analysis) provides functions for end-to-end analysis of multiple 5'-profiling methods such as CAGE, RAMPAGE and MAPCap, beginning from raw reads to detection of transcription start sites using replicates. It also allows performing differential TSS detection between group of samples, therefore, integrating the mRNA cap enrichment information with transcript expression analysis. biocViews: ImmunoOncology, Transcription, GeneExpression, Sequencing, RNASeq, Transcriptomics, DifferentialExpression Author: Vivek Bhardwaj [aut, cre] Maintainer: Vivek Bhardwaj URL: https://github.com/vivekbhr/icetea VignetteBuilder: knitr BugReports: https://github.com/vivekbhr/icetea/issues git_url: https://git.bioconductor.org/packages/icetea git_branch: RELEASE_3_10 git_last_commit: fd079d9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/icetea_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/icetea_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/icetea_1.4.0.tgz vignettes: vignettes/icetea/inst/doc/mapcap_analysis.html vignetteTitles: Analysing transcript 5'-profiling data using icetea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/icetea/inst/doc/mapcap_analysis.R dependencyCount: 149 Package: iCheck Version: 1.16.0 Depends: R (>= 3.2.0), Biobase, lumi, gplots Imports: stats, graphics, preprocessCore, grDevices, randomForest, affy, limma, parallel, GeneSelectMMD, rgl, MASS, lmtest, scatterplot3d, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 7abedb8f8356def656a9315d4fd4e393 NeedsCompilation: no Title: QC Pipeline and Data Analysis Tools for High-Dimensional Illumina mRNA Expression Data Description: QC pipeline and data analysis tools for high-dimensional Illumina mRNA expression data. biocViews: GeneExpression, DifferentialExpression, Microarray, Preprocessing, DNAMethylation, OneChannel, TwoChannel, QualityControl Author: Weiliang Qiu [aut, cre], Brandon Guo [aut, ctb], Christopher Anderson [aut, ctb], Barbara Klanderman [aut, ctb], Vincent Carey [aut, ctb], Benjamin Raby [aut, ctb] Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/iCheck git_branch: RELEASE_3_10 git_last_commit: 7550b3e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/iCheck_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/iCheck_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/iCheck_1.16.0.tgz vignettes: vignettes/iCheck/inst/doc/iCheck.pdf vignetteTitles: iCheck hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCheck/inst/doc/iCheck.R dependencyCount: 185 Package: iChip Version: 1.40.0 Depends: R (>= 2.10.0) Imports: limma License: GPL (>= 2) MD5sum: d5e322d313458db3e50ee4d510df7992 NeedsCompilation: yes Title: Bayesian Modeling of ChIP-chip Data Through Hidden Ising Models Description: Hidden Ising models are implemented to identify enriched genomic regions in ChIP-chip data. They can be used to analyze the data from multiple platforms (e.g., Affymetrix, Agilent, and NimbleGen), and the data with single to multiple replicates. biocViews: ChIPchip, OneChannel, AgilentChip, Microarray Author: Qianxing Mo Maintainer: Qianxing Mo git_url: https://git.bioconductor.org/packages/iChip git_branch: RELEASE_3_10 git_last_commit: 0434114 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/iChip_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/iChip_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/iChip_1.40.0.tgz vignettes: vignettes/iChip/inst/doc/iChip.pdf vignetteTitles: iChip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iChip/inst/doc/iChip.R dependencyCount: 6 Package: iClusterPlus Version: 1.22.0 Depends: R (>= 3.3.0), parallel Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 0207662f9c22530faf26d7066a7b96fb NeedsCompilation: yes Title: Integrative clustering of multi-type genomic data Description: Integrative clustering of multiple genomic data using a joint latent variable model. biocViews: Microarray, Clustering Author: Qianxing Mo, Ronglai Shen Maintainer: Qianxing Mo , Ronglai Shen git_url: https://git.bioconductor.org/packages/iClusterPlus git_branch: RELEASE_3_10 git_last_commit: 7e57b2c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/iClusterPlus_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/iClusterPlus_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/iClusterPlus_1.22.0.tgz vignettes: vignettes/iClusterPlus/inst/doc/iClusterPlus.pdf, vignettes/iClusterPlus/inst/doc/iManual.pdf vignetteTitles: iClusterPlus, iManual.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: MultiDataSet dependencyCount: 1 Package: iCNV Version: 1.6.0 Depends: R (>= 3.3.1), CODEX Imports: fields, ggplot2, truncnorm, tidyr, data.table, dplyr, grDevices, graphics, stats, utils, rlang Suggests: knitr, rmarkdown, WES.1KG.WUGSC License: GPL-2 MD5sum: 7b9e471c2219373d07333e1a85c743d8 NeedsCompilation: no Title: Integrated Copy Number Variation detection Description: Integrative copy number variation (CNV) detection from multiple platform and experimental design. biocViews: ImmunoOncology, ExomeSeq, WholeGenome, SNP, CopyNumberVariation, HiddenMarkovModel Author: Zilu Zhou, Nancy Zhang Maintainer: Zilu Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/iCNV git_branch: RELEASE_3_10 git_last_commit: d1e773a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/iCNV_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/iCNV_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/iCNV_1.6.0.tgz vignettes: vignettes/iCNV/inst/doc/iCNV-vignette.html vignetteTitles: iCNV Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCNV/inst/doc/iCNV-vignette.R dependencyCount: 98 Package: iCOBRA Version: 1.14.0 Depends: R (>= 3.4) Imports: shiny (>= 0.9.1.9008), shinydashboard, shinyBS, reshape2, ggplot2 (>= 2.0.0), scales, ROCR, dplyr, DT, limma, methods, UpSetR Suggests: knitr, testthat License: GPL (>=2) Archs: i386, x64 MD5sum: a107aaca8eb9b4e730c37b55b537a8ce NeedsCompilation: no Title: Comparison and Visualization of Ranking and Assignment Methods Description: This package provides functions for calculation and visualization of performance metrics for evaluation of ranking and binary classification (assignment) methods. It also contains a shiny application for interactive exploration of results. biocViews: Classification Author: Charlotte Soneson [aut, cre] () Maintainer: Charlotte Soneson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/iCOBRA git_branch: RELEASE_3_10 git_last_commit: 2d2d57a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/iCOBRA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/iCOBRA_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/iCOBRA_1.14.0.tgz vignettes: vignettes/iCOBRA/inst/doc/iCOBRA.html vignetteTitles: iCOBRA User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCOBRA/inst/doc/iCOBRA.R suggestsMe: SummarizedBenchmark dependencyCount: 90 Package: ideal Version: 1.10.0 Depends: topGO Imports: DESeq2, SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, ggplot2 (>= 2.0.0), d3heatmap, pheatmap, pcaExplorer, IHW, gplots, UpSetR, goseq, stringr, dplyr, limma, GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0), shinydashboard, shinyBS, DT, rentrez, rintrojs, ggrepel, knitr, rmarkdown, shinyAce, BiocParallel, grDevices, base64enc, methods Suggests: testthat, BiocStyle, airway, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, DEFormats, edgeR License: MIT + file LICENSE Archs: i386, x64 MD5sum: bd8708b147fbde697c93fbf7be302faf NeedsCompilation: no Title: Interactive Differential Expression AnaLysis Description: This package provides functions for an Interactive Differential Expression AnaLysis of RNA-sequencing datasets, to extract quickly and effectively information downstream the step of differential expression. A Shiny application encapsulates the whole package. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, RNASeq, Sequencing, Visualization, QualityControl, GUI, GeneSetEnrichment, ReportWriting Author: Federico Marini [aut, cre] () Maintainer: Federico Marini URL: https://github.com/federicomarini/ideal, https://federicomarini.github.io/ideal/ VignetteBuilder: knitr BugReports: https://github.com/federicomarini/ideal/issues git_url: https://git.bioconductor.org/packages/ideal git_branch: RELEASE_3_10 git_last_commit: 842157c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ideal_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ideal_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ideal_1.10.0.tgz vignettes: vignettes/ideal/inst/doc/ideal-usersguide.html vignetteTitles: ideal User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ideal/inst/doc/ideal-usersguide.R dependencyCount: 203 Package: IdeoViz Version: 1.22.0 Depends: Biobase, IRanges, GenomicRanges, RColorBrewer, rtracklayer,graphics,GenomeInfoDb License: GPL-2 MD5sum: 9fa2e4e8864e11572efd3a9f4378f8db NeedsCompilation: no Title: Plots data (continuous/discrete) along chromosomal ideogram Description: Plots data associated with arbitrary genomic intervals along chromosomal ideogram. biocViews: Visualization,Microarray Author: Shraddha Pai , Jingliang Ren Maintainer: Shraddha Pai git_url: https://git.bioconductor.org/packages/IdeoViz git_branch: RELEASE_3_10 git_last_commit: 5ab7ecf git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/IdeoViz_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/IdeoViz_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IdeoViz_1.22.0.tgz vignettes: vignettes/IdeoViz/inst/doc/Vignette.pdf vignetteTitles: IdeoViz: a package for plotting simple data along ideograms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IdeoViz/inst/doc/Vignette.R dependencyCount: 39 Package: idiogram Version: 1.62.0 Depends: R (>= 2.10), methods, Biobase, annotate, plotrix Suggests: hu6800.db, hgu95av2.db, golubEsets License: GPL-2 MD5sum: fbc280c9b6aedada6a339e29edb7166e NeedsCompilation: no Title: idiogram Description: A package for plotting genomic data by chromosomal location biocViews: Visualization Author: Karl J. Dykema Maintainer: Karl J. Dykema git_url: https://git.bioconductor.org/packages/idiogram git_branch: RELEASE_3_10 git_last_commit: c942d75 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/idiogram_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/idiogram_1.62.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/idiogram_1.62.0.tgz vignettes: vignettes/idiogram/inst/doc/idiogram.pdf vignetteTitles: HOWTO: idiogram hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/idiogram/inst/doc/idiogram.R dependsOnMe: reb dependencyCount: 33 Package: IdMappingAnalysis Version: 1.30.0 Depends: R (>= 2.14), R.oo (>= 1.13.0), rChoiceDialogs Imports: boot, mclust, RColorBrewer, Biobase License: GPL-2 MD5sum: afaa1ce733d68fd9a5ca194ad775da85 NeedsCompilation: no Title: ID Mapping Analysis Description: Identifier mapping performance analysis biocViews: Annotation, MultipleComparison Author: Alex Lisovich, Roger Day Maintainer: Alex Lisovich , Roger Day git_url: https://git.bioconductor.org/packages/IdMappingAnalysis git_branch: RELEASE_3_10 git_last_commit: 09eecf9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/IdMappingAnalysis_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/IdMappingAnalysis_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IdMappingAnalysis_1.30.0.tgz vignettes: vignettes/IdMappingAnalysis/inst/doc/IdMappingAnalysis.pdf vignetteTitles: Critically comparing identifier maps retrieved from bioinformatics annotation resources. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IdMappingAnalysis/inst/doc/IdMappingAnalysis.R dependencyCount: 16 Package: IdMappingRetrieval Version: 1.34.0 Depends: R.oo, XML, RCurl, rChoiceDialogs Imports: biomaRt, ENVISIONQuery, AffyCompatible, R.methodsS3, utils License: GPL-2 MD5sum: 8268318e69528a120ef9a5d2d22bb0dc NeedsCompilation: no Title: ID Mapping Data Retrieval Description: Data retrieval for identifier mapping performance analysis biocViews: Annotation, MultipleComparison Author: Alex Lisovich, Roger Day Maintainer: Alex Lisovich , Roger Day git_url: https://git.bioconductor.org/packages/IdMappingRetrieval git_branch: RELEASE_3_10 git_last_commit: 44caf88 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/IdMappingRetrieval_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/IdMappingRetrieval_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IdMappingRetrieval_1.34.0.tgz vignettes: vignettes/IdMappingRetrieval/inst/doc/IdMappingRetrieval.pdf vignetteTitles: Collection and subsequent fast retrieval of identifier mapping related information from various online sources. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IdMappingRetrieval/inst/doc/IdMappingRetrieval.R dependencyCount: 70 Package: idr2d Version: 1.0.4 Depends: R (>= 3.6) Imports: dplyr (>= 0.7.6), futile.logger (>= 1.4.3), GenomeInfoDb (>= 1.14.0), GenomicRanges (>= 1.30), ggplot2 (>= 3.1.1), grDevices, idr (>= 1.2), IRanges (>= 2.18.0), magrittr (>= 1.5), methods, reticulate (>= 1.13), scales (>= 1.0.0), stats, stringr (>= 1.3.1), utils Suggests: DT (>= 0.4), htmltools (>= 0.3.6), knitr (>= 1.20), rmarkdown (>= 1.10), roxygen2 (>= 6.1.0), testthat (>= 2.0.1) License: MIT + file LICENSE MD5sum: 727ee4c7b0873ff25843d9c7fa05458e NeedsCompilation: no Title: Irreproducible Discovery Rate for Genomic Interactions Data Description: A tool to measure reproducibility between genomic experiments that produce two-dimensional peaks (interactions between peaks), such as ChIA-PET, HiChIP, and HiC. idr2d is an extension of the original idr package, which is intended for (one-dimensional) ChIP-seq peaks. biocViews: DNA3DStructure, GeneRegulation, PeakDetection, Epigenetics, FunctionalGenomics, Classification, HiC Author: Konstantin Krismer [aut, cre, cph] (), David Gifford [ths, cph] () Maintainer: Konstantin Krismer URL: https://idr2d.mit.edu SystemRequirements: Python (>= 3.5.0), hic-straw VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/idr2d git_branch: RELEASE_3_10 git_last_commit: 484fd72 git_last_commit_date: 2020-03-28 Date/Publication: 2020-03-29 source.ver: src/contrib/idr2d_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/idr2d_1.0.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/idr2d_1.0.4.tgz vignettes: vignettes/idr2d/inst/doc/idr1d.html, vignettes/idr2d/inst/doc/idr2d.html vignetteTitles: Identify reproducible genomic peaks from replicate ChIP-seq experiments, Identify reproducible genomic interactions from replicate ChIA-PET experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/idr2d/inst/doc/idr1d.R, vignettes/idr2d/inst/doc/idr2d.R dependencyCount: 81 Package: iGC Version: 1.16.0 Depends: R (>= 3.2.0) Imports: plyr, data.table Suggests: BiocStyle, knitr, rmarkdown Enhances: doMC License: GPL-2 MD5sum: df7b5b306f6ca8b7ed8344e20aa20aff NeedsCompilation: no Title: An integrated analysis package of Gene expression and Copy number alteration Description: This package is intended to identify differentially expressed genes driven by Copy Number Alterations from samples with both gene expression and CNA data. biocViews: Software, Biological Question, DifferentialExpression, GenomicVariation, AssayDomain, CopyNumberVariation, GeneExpression, ResearchField, Genetics, Technology, Microarray, Sequencing, WorkflowStep, MultipleComparison Author: Yi-Pin Lai [aut], Liang-Bo Wang [aut, cre], Tzu-Pin Lu [aut], Eric Y. Chuang [aut] Maintainer: Liang-Bo Wang URL: http://github.com/ccwang002/iGC VignetteBuilder: knitr BugReports: http://github.com/ccwang002/iGC/issues git_url: https://git.bioconductor.org/packages/iGC git_branch: RELEASE_3_10 git_last_commit: e19c7dc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/iGC_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/iGC_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/iGC_1.16.0.tgz vignettes: vignettes/iGC/inst/doc/Introduction.html vignetteTitles: Introduction to iGC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iGC/inst/doc/Introduction.R dependencyCount: 5 Package: IgGeneUsage Version: 1.0.1 Depends: methods, R (>= 3.6.0), Rcpp (>= 0.12.0), SummarizedExperiment, StanHeaders (> 2.18.1) Imports: rstan (>= 2.19.2), reshape2 (>= 1.4.3) Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), ggplot2, ggforce, gridExtra, ggrepel License: file LICENSE MD5sum: 14d35538c30603bfcdf3a2dae93d9aac NeedsCompilation: no Title: Differential gene usage in immune repertoires Description: Decoding the properties of immune repertoires is key in understanding the response of adaptive immunity to challenges such as viral infection. One important task in immune repertoire profiling is the detection of biases in Ig gene usage between biological conditions. IgGeneUsage is a computational tool for the analysis of differential gene usage in immune repertoires. It employs Bayesian hierarchical models to fit complex gene usage data from immune repertoire sequencing experiments and quantifies Ig gene usage biases as probabilities. biocViews: DifferentialExpression, Regression, Genetics, Bayesian Author: Simo Kitanovski [aut, cre] Maintainer: Simo Kitanovski VignetteBuilder: knitr BugReports: https://github.com/snaketron/IgGeneUsage/issues git_url: https://git.bioconductor.org/packages/IgGeneUsage git_branch: RELEASE_3_10 git_last_commit: ca78a4e git_last_commit_date: 2020-04-04 Date/Publication: 2020-04-04 source.ver: src/contrib/IgGeneUsage_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/IgGeneUsage_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IgGeneUsage_1.0.1.tgz vignettes: vignettes/IgGeneUsage/inst/doc/IgUsageCaseStudies.html vignetteTitles: User Manual: IgGeneUsage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IgGeneUsage/inst/doc/IgUsageCaseStudies.R dependencyCount: 88 Package: igvR Version: 1.6.1 Depends: R (>= 3.5.0), GenomicRanges, GenomicAlignments, BrowserViz (>= 2.7.20) Imports: methods, BiocGenerics, httpuv, utils, MotifDb, seqLogo, rtracklayer, VariantAnnotation, randomcoloR Suggests: RUnit, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 280ea52a589c35dd41626ce56891ec82 NeedsCompilation: no Title: igvR: integrative genomics viewer Description: Access to igv.js, the Integrative Genomics Viewer running in a web browser. biocViews: Visualization, ThirdPartyClient, GenomeBrowsers Author: Paul Shannon Maintainer: Paul Shannon URL: https://paul-shannon.github.io/igvR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/igvR git_branch: RELEASE_3_10 git_last_commit: 98f0da5 git_last_commit_date: 2019-11-01 Date/Publication: 2019-11-01 source.ver: src/contrib/igvR_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/igvR_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/igvR_1.6.1.tgz vignettes: vignettes/igvR/inst/doc/alzheimersVariantsNearMEF2C.html, vignettes/igvR/inst/doc/basicIntro.html, vignettes/igvR/inst/doc/ctcfChipSeq.html vignetteTitles: "Explore VCF variants,, GWAS snps,, promoters and histone marks around the MEF2C gene in Alzheimers Disease", "Introduction: a simple demo", "Explore ChIP-seq alignments from a bam file,, MACS2 narrowPeaks,, conservation,, H3K4me3 methylation and motif matching" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/igvR/inst/doc/alzheimersVariantsNearMEF2C.R, vignettes/igvR/inst/doc/basicIntro.R, vignettes/igvR/inst/doc/ctcfChipSeq.R dependencyCount: 104 Package: IHW Version: 1.14.0 Depends: R (>= 3.3.0) Imports: methods, slam, lpsymphony, fdrtool, BiocGenerics Suggests: ggplot2, dplyr, gridExtra, scales, DESeq2, airway, testthat, Matrix, BiocStyle, knitr, rmarkdown, devtools License: Artistic-2.0 MD5sum: 5b6f97b947afb689abcc23b43734aa07 NeedsCompilation: no Title: Independent Hypothesis Weighting Description: Independent hypothesis weighting (IHW) is a multiple testing procedure that increases power compared to the method of Benjamini and Hochberg by assigning data-driven weights to each hypothesis. The input to IHW is a two-column table of p-values and covariates. The covariate can be any continuous-valued or categorical variable that is thought to be informative on the statistical properties of each hypothesis test, while it is independent of the p-value under the null hypothesis. biocViews: ImmunoOncology, MultipleComparison, RNASeq Author: Nikos Ignatiadis [aut, cre], Wolfgang Huber [aut] Maintainer: Nikos Ignatiadis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IHW git_branch: RELEASE_3_10 git_last_commit: d4942e6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/IHW_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/IHW_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IHW_1.14.0.tgz vignettes: vignettes/IHW/inst/doc/introduction_to_ihw.html vignetteTitles: "Introduction to IHW" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IHW/inst/doc/introduction_to_ihw.R importsMe: ideal, metagenomeSeq suggestsMe: DESeq2, DEWSeq, SummarizedBenchmark dependencyCount: 10 Package: illuminaio Version: 0.28.0 Imports: base64 Suggests: RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2), BiocStyle License: GPL-2 MD5sum: ff4c031a3acee98df0ed6ae85c9231c2 NeedsCompilation: yes Title: Parsing Illumina Microarray Output Files Description: Tools for parsing Illumina's microarray output files, including IDAT. biocViews: Infrastructure, DataImport, Microarray, ProprietaryPlatforms Author: Keith Baggerly [aut], Henrik Bengtsson [aut], Kasper Daniel Hansen [aut, cre], Matt Ritchie [aut], Mike L. Smith [aut], Tim Triche Jr. [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/illuminaio BugReports: https://github.com/HenrikBengtsson/illuminaio/issues git_url: https://git.bioconductor.org/packages/illuminaio git_branch: RELEASE_3_10 git_last_commit: d54936a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/illuminaio_0.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/illuminaio_0.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/illuminaio_0.28.0.tgz vignettes: vignettes/illuminaio/inst/doc/EncryptedFormat.pdf, vignettes/illuminaio/inst/doc/illuminaio.pdf vignetteTitles: Description of Encrypted IDAT Format, Introduction to illuminaio hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/illuminaio/inst/doc/illuminaio.R dependsOnMe: normalize450K, RnBeads, wateRmelon importsMe: beadarray, crlmm, methylumi, minfi, sesame suggestsMe: limma dependencyCount: 4 Package: imageHTS Version: 1.36.0 Depends: R (>= 2.9.0), EBImage (>= 4.3.12), cellHTS2 (>= 2.10.0) Imports: tools, Biobase, hwriter, methods, vsn, stats, utils, e1071 Suggests: BiocStyle, MASS License: LGPL-2.1 MD5sum: 5aa24c3201bdbf26e8f7da764c49d6ca NeedsCompilation: no Title: Analysis of high-throughput microscopy-based screens Description: imageHTS is an R package dedicated to the analysis of high-throughput microscopy-based screens. The package provides a modular and extensible framework to segment cells, extract quantitative cell features, predict cell types and browse screen data through web interfaces. Designed to operate in distributed environments, imageHTS provides a standardized access to remote data and facilitates the dissemination of high-throughput microscopy-based datasets. biocViews: ImmunoOncology, Software, CellBasedAssays, Preprocessing, Visualization Author: Gregoire Pau, Xian Zhang, Michael Boutros, Wolfgang Huber Maintainer: Joseph Barry git_url: https://git.bioconductor.org/packages/imageHTS git_branch: RELEASE_3_10 git_last_commit: ecbbd2d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/imageHTS_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/imageHTS_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/imageHTS_1.36.0.tgz vignettes: vignettes/imageHTS/inst/doc/imageHTS-introduction.pdf vignetteTitles: Analysis of high-throughput microscopy-based screens with imageHTS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/imageHTS/inst/doc/imageHTS-introduction.R dependencyCount: 109 Package: IMAS Version: 1.10.0 Depends: R (> 3.0.0),GenomicFeatures, ggplot2, IVAS Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, GenomeInfoDb, stats, ggfortify, grDevices, methods, Matrix, utils, graphics, gridExtra, grid, lattice, Rsamtools, survival, BiocParallel, GenomicAlignments, parallel Suggests: BiocStyle, RUnit License: GPL-2 MD5sum: f7a97d09af352e1091159056cfacd850 NeedsCompilation: no Title: Integrative analysis of Multi-omics data for Alternative Splicing Description: Integrative analysis of Multi-omics data for Alternative splicing. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, GeneRegulation, Regression, RNASeq, Sequencing, SNP, Software, Transcription Author: Seonggyun Han, Younghee Lee Maintainer: Seonggyun Han git_url: https://git.bioconductor.org/packages/IMAS git_branch: RELEASE_3_10 git_last_commit: d3bd3ea git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/IMAS_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/IMAS_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IMAS_1.10.0.tgz vignettes: vignettes/IMAS/inst/doc/IMAS.pdf vignetteTitles: IMAS : Integrative analysis of Multi-omics data for Alternative Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IMAS/inst/doc/IMAS.R dependencyCount: 125 Package: Imetagene Version: 1.16.0 Depends: R (>= 3.2.0), metagene, shiny Imports: d3heatmap, shinyBS, shinyFiles, shinythemes, ggplot2 Suggests: knitr, BiocStyle, rmarkdown License: Artistic-2.0 | file LICENSE Archs: i386, x64 MD5sum: 82778daa5c4fa60eec40e460ab757ffe NeedsCompilation: no Title: A graphical interface for the metagene package Description: This package provide a graphical user interface to the metagene package. This will allow people with minimal R experience to easily complete metagene analysis. biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment, Sequencing Author: Audrey Lemacon , Charles Joly Beauparlant , Arnaud Droit Maintainer: Audrey Lemacon VignetteBuilder: knitr BugReports: https://github.com/andronekomimi/Imetagene/issues git_url: https://git.bioconductor.org/packages/Imetagene git_branch: RELEASE_3_10 git_last_commit: 9f34467 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Imetagene_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Imetagene_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Imetagene_1.16.0.tgz vignettes: vignettes/Imetagene/inst/doc/imetagene.html vignetteTitles: Presentation of Imetagene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Imetagene/inst/doc/imetagene.R dependencyCount: 153 Package: IMMAN Version: 1.6.0 Imports: STRINGdb, Biostrings, igraph, graphics, utils, seqinr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 20e836fcd5b15959b3d2704eb3a7d825 NeedsCompilation: no Title: Interlog protein network reconstruction by Mapping and Mining ANalysis Description: Reconstructing Interlog Protein Network (IPN) integrated from several Protein protein Interaction Networks (PPINs). Using this package, overlaying different PPINs to mine conserved common networks between diverse species will be applicable. biocViews: SequenceMatching, Alignment, SystemsBiology, GraphAndNetwork, Network, Proteomics Author: Minoo Ashtiani, Payman Nickchi, Abdollah Safari, Mehdi Mirzaie, Mohieddin Jafari Maintainer: Minoo Ashtiani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IMMAN git_branch: RELEASE_3_10 git_last_commit: f6243fb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/IMMAN_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/IMMAN_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IMMAN_1.6.0.tgz vignettes: vignettes/IMMAN/inst/doc/IMMAN.html vignetteTitles: IMMAN hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IMMAN/inst/doc/IMMAN.R dependencyCount: 56 Package: ImmuneSpaceR Version: 1.14.1 Depends: R (>= 3.5.0) Imports: utils, R6, data.table, curl, httr, Rlabkey (>= 2.3.1), Biobase, pheatmap, ggplot2 (>= 3.2.0), scales, stats, gplots, plotly, heatmaply (>= 0.7.0), jsonlite, rmarkdown, preprocessCore, flowCore, flowWorkspace, digest Suggests: knitr, testthat License: GPL-2 MD5sum: ad1987788be3fbbe415208a5c1525bb4 NeedsCompilation: no Title: A Thin Wrapper around the ImmuneSpace Database Description: Provides a convenient API for accessing data sets within ImmuneSpace (www.immunespace.org), the data repository and analysis platform of the Human Immunology Project Consortium (HIPC). biocViews: DataImport, DataRepresentation, ThirdPartyClient Author: Greg Finak [aut], Renan Sauteraud [aut], Mike Jiang [aut], Gil Guday [aut], Leo Dashevskiy [aut], Evan Henrich [aut], Ju Yeong Kim [aut], Lauren Wolfe [aut], Helen Miller [aut], Raphael Gottardo [aut], ImmuneSpace Package Maintainer [cre, cph] Maintainer: ImmuneSpace Package Maintainer URL: https://github.com/RGLab/ImmuneSpaceR VignetteBuilder: knitr BugReports: https://github.com/RGLab/ImmuneSpaceR/issues git_url: https://git.bioconductor.org/packages/ImmuneSpaceR git_branch: RELEASE_3_10 git_last_commit: ef8edfc git_last_commit_date: 2020-01-14 Date/Publication: 2020-01-15 source.ver: src/contrib/ImmuneSpaceR_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/ImmuneSpaceR_1.14.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ImmuneSpaceR_1.14.1.tgz vignettes: vignettes/ImmuneSpaceR/inst/doc/getDataset.html, vignettes/ImmuneSpaceR/inst/doc/getGEMatrix.html, vignettes/ImmuneSpaceR/inst/doc/interactiveNetrc.html, vignettes/ImmuneSpaceR/inst/doc/Intro_to_ImmuneSpaceR.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY144.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY180.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY269.html vignetteTitles: Downloading Datasets with getDataset, Handling Expression Matrices with ImmuneSpaceR, interactive_netrc() Function Walkthrough, An Introduction to the ImmuneSpaceR Package, SDY144: Correlation of HAI/Virus Neutralizition Titer and Cell Counts, SDY180: Abundance of Plasmablasts Measured by Multiparameter Flow Cytometry, SDY269: Correlating HAI with Flow Cytometry and ELISPOT Results hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ImmuneSpaceR/inst/doc/getDataset.R, vignettes/ImmuneSpaceR/inst/doc/getGEMatrix.R, vignettes/ImmuneSpaceR/inst/doc/interactiveNetrc.R, vignettes/ImmuneSpaceR/inst/doc/Intro_to_ImmuneSpaceR.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY144.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY180.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY269.R dependencyCount: 135 Package: immunoClust Version: 1.18.1 Depends: R(>= 3.6), methods, stats, graphics, grid, lattice, grDevices, flowCore Suggests: BiocStyle, utils License: Artistic-2.0 MD5sum: b92383f33fe25d720880a40eb0ccdf0b NeedsCompilation: yes Title: immunoClust - Automated Pipeline for Population Detection in Flow Cytometry Description: Model based clustering and meta-clustering of Flow Cytometry Data biocViews: Clustering, FlowCytometry, CellBasedAssays, ImmunoOncology Author: Till Soerensen Maintainer: Till Soerensen git_url: https://git.bioconductor.org/packages/immunoClust git_branch: RELEASE_3_10 git_last_commit: b8b8173 git_last_commit_date: 2020-03-25 Date/Publication: 2020-03-25 source.ver: src/contrib/immunoClust_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/immunoClust_1.18.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/immunoClust_1.18.1.tgz vignettes: vignettes/immunoClust/inst/doc/immunoClust.pdf vignetteTitles: immunoClust package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/immunoClust/inst/doc/immunoClust.R dependencyCount: 16 Package: IMPCdata Version: 1.22.0 Depends: R (>= 2.3.0) Imports: rjson License: file LICENSE MD5sum: 05e8aa41420e1c5ade8e6661be833668 NeedsCompilation: no Title: Retrieves data from IMPC database Description: Package contains methods for data retrieval from IMPC Database. biocViews: ExperimentData Author: Natalja Kurbatova, Jeremy Mason Maintainer: Jeremy Mason git_url: https://git.bioconductor.org/packages/IMPCdata git_branch: RELEASE_3_10 git_last_commit: eef4270 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/IMPCdata_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/IMPCdata_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IMPCdata_1.22.0.tgz vignettes: vignettes/IMPCdata/inst/doc/IMPCdata.pdf vignetteTitles: IMPCdata Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IMPCdata/inst/doc/IMPCdata.R dependencyCount: 1 Package: ImpulseDE Version: 1.12.0 Depends: graphics, grDevices, stats, utils, parallel, compiler, R (>= 3.2.3) Imports: amap, boot Suggests: longitudinal, knitr License: GPL-3 MD5sum: 3b1473585a0d92f36d5e05ce34a7405a NeedsCompilation: no Title: Detection of DE genes in time series data using impulse models Description: ImpulseDE is suited to capture single impulse-like patterns in high throughput time series datasets. By fitting a representative impulse model to each gene, it reports differentially expressed genes whether across time points in a single experiment or between two time courses from two experiments. To optimize the running time, the code makes use of clustering steps and multi-threading. biocViews: Software, StatisticalMethod, TimeCourse Author: Jil Sander [aut, cre], Nir Yosef [aut] Maintainer: Jil Sander , Nir Yosef URL: https://github.com/YosefLab/ImpulseDE VignetteBuilder: knitr BugReports: https://github.com/YosefLab/ImpulseDE/issues git_url: https://git.bioconductor.org/packages/ImpulseDE git_branch: RELEASE_3_10 git_last_commit: 780d53b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ImpulseDE_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ImpulseDE_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ImpulseDE_1.12.0.tgz vignettes: vignettes/ImpulseDE/inst/doc/ImpulseDE.pdf vignetteTitles: ImpulseDE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ImpulseDE/inst/doc/ImpulseDE.R dependencyCount: 8 Package: ImpulseDE2 Version: 1.10.0 Imports: Biobase, BiocParallel, ComplexHeatmap, circlize, compiler, cowplot, DESeq2, ggplot2, grDevices, knitr, Matrix, methods, S4Vectors, stats, SummarizedExperiment, utils License: Artistic-2.0 Archs: i386, x64 MD5sum: 41f27ceb038040eee4e04c59e13a6939 NeedsCompilation: no Title: Differential expression analysis of longitudinal count data sets Description: ImpulseDE2 is a differential expression algorithm for longitudinal count data sets which arise in sequencing experiments such as RNA-seq, ChIP-seq, ATAC-seq and DNaseI-seq. ImpulseDE2 is based on a negative binomial noise model with dispersion trend smoothing by DESeq2 and uses the impulse model to constrain the mean expression trajectory of each gene. The impulse model was empirically found to fit global expression changes in cells after environmental and developmental stimuli and is therefore appropriate in most cell biological scenarios. The constraint on the mean expression trajectory prevents overfitting to small expression fluctuations. Secondly, ImpulseDE2 has higher statistical testing power than generalized linear model-based differential expression algorithms which fit time as a categorial variable if more than six time points are sampled because of the fixed number of parameters. biocViews: ImmunoOncology, Software, StatisticalMethod, TimeCourse, Sequencing, DifferentialExpression, GeneExpression, CellBiology, CellBasedAssays Author: David S Fischer [aut, cre], Fabian J Theis [ctb], Nir Yosef [ctb] Maintainer: David S Fischer VignetteBuilder: knitr BugReports: https://github.com/YosefLab/ImpulseDE2/issues git_url: https://git.bioconductor.org/packages/ImpulseDE2 git_branch: RELEASE_3_10 git_last_commit: 1cc86d1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ImpulseDE2_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ImpulseDE2_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ImpulseDE2_1.10.0.tgz vignettes: vignettes/ImpulseDE2/inst/doc/ImpulseDE2_Tutorial.html vignetteTitles: ImpulseDE2 Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ImpulseDE2/inst/doc/ImpulseDE2_Tutorial.R dependencyCount: 130 Package: impute Version: 1.60.0 Depends: R (>= 2.10) License: GPL-2 MD5sum: 7dbfa72a7ea02da1ddd4bb8e302acd1c NeedsCompilation: yes Title: impute: Imputation for microarray data Description: Imputation for microarray data (currently KNN only) biocViews: Microarray Author: Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, Gilbert Chu Maintainer: Balasubramanian Narasimhan git_url: https://git.bioconductor.org/packages/impute git_branch: RELEASE_3_10 git_last_commit: 559cacd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/impute_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/impute_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/impute_1.60.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: AMARETTO, CGHcall, TIN importsMe: biscuiteer, CancerSubtypes, cola, doppelgangR, EGAD, fastLiquidAssociation, genefu, genomation, MethylMix, miRLAB, MSnbase, netboost, Pigengene, REMP, Rnits suggestsMe: BioNet, graphite, MethPed, RnBeads, TCGAutils dependencyCount: 0 Package: INDEED Version: 2.0.0 Depends: glasso (>= 1.8), R (>= 3.5) Imports: devtools (>= 1.13.0), graphics (>= 3.3.1), stats (>= 3.3.1), utils (>= 3.3.1), igraph (>= 1.2.4), visNetwork(>= 2.0.6) Suggests: knitr (>= 1.19), rmarkdown (>= 1.8), testthat (>= 2.0.0) License: Artistic-2.0 MD5sum: 59ad5e07f0664af17e646f9fa197532a NeedsCompilation: no Title: Interactive Visualization of Integrated Differential Expression and Differential Network Analysis for Biomarker Candidate Selection Package Description: An R package for integrated differential expression and differential network analysis based on omic data for cancer biomarker discovery. Both correlation and partial correlation can be used to generate differential network to aid the traditional differential expression analysis to identify changes between biomolecules on both their expression and pairwise association levels. A detailed description of the methodology has been published in Methods journal (PMID: 27592383). An interactive visualization feature allows for the exploration and selection of candidate biomarkers. biocViews: ImmunoOncology, Software, ResearchField, BiologicalQuestion, StatisticalMethod, DifferentialExpression, MassSpectrometry, Metabolomics Author: Yiming Zuo , Kian Ghaffari , Zhenzhi Li Maintainer: Ressom group , Yiming Zuo URL: http://github.com/ressomlab/INDEED VignetteBuilder: knitr BugReports: http://github.com/ressomlab/INDEED/issues git_url: https://git.bioconductor.org/packages/INDEED git_branch: RELEASE_3_10 git_last_commit: 3773b41 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/INDEED_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/INDEED_2.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/INDEED_2.0.0.tgz vignettes: vignettes/INDEED/inst/doc/Introduction_to_INDEED.pdf vignetteTitles: INDEED R package for cancer biomarker discovery hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/INDEED/inst/doc/Introduction_to_INDEED.R dependencyCount: 87 Package: infercnv Version: 1.2.1 Depends: R(>= 3.6) Imports: graphics, grDevices, RColorBrewer, gplots, futile.logger, stats, utils, methods, ape, Matrix, fastcluster, dplyr, HiddenMarkov, ggplot2, edgeR, coin, caTools, digest, reshape, rjags, fitdistrplus, future, foreach, doParallel, BiocGenerics, SummarizedExperiment, SingleCellExperiment, tidyr, parallel, coda, gridExtra, argparse Suggests: BiocStyle, knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: c8e1298731e040b5b12074875e1fd389 NeedsCompilation: no Title: Infer Copy Number Variation from Single-Cell RNA-Seq Data Description: Using single-cell RNA-Seq expression to visualize CNV in cells. biocViews: Software, CopyNumberVariation, VariantDetection, StructuralVariation, GenomicVariation, Genetics, Transcriptomics, StatisticalMethod, Bayesian, HiddenMarkovModel, SingleCell Author: Timothy Tickle [aut], Itay Tirosh [aut], Christophe Georgescu [aut, cre], Maxwell Brown [aut], Brian Haas [aut] Maintainer: Christophe Georgescu URL: https://github.com/broadinstitute/inferCNV/wiki SystemRequirements: JAGS 4.x.y VignetteBuilder: knitr BugReports: https://github.com/broadinstitute/inferCNV/issues git_url: https://git.bioconductor.org/packages/infercnv git_branch: RELEASE_3_10 git_last_commit: 42f3b0f git_last_commit_date: 2019-11-14 Date/Publication: 2019-11-15 source.ver: src/contrib/infercnv_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/infercnv_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/infercnv_1.2.1.tgz vignettes: vignettes/infercnv/inst/doc/inferCNV.html vignetteTitles: Visualizing Large-scale Copy Number Variation in Single-Cell RNA-Seq Expression Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/infercnv/inst/doc/inferCNV.R dependencyCount: 122 Package: InPAS Version: 1.18.0 Depends: R (>= 3.1), methods, Biobase, GenomicRanges, GenomicFeatures, S4Vectors Imports: AnnotationDbi, BSgenome, cleanUpdTSeq, Gviz, seqinr, preprocessCore, IRanges, GenomeInfoDb, depmixS4, limma, BiocParallel Suggests: RUnit, BiocGenerics, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, org.Hs.eg.db, org.Mm.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, rtracklayer, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: bf20ddcb0466ee29462b5713afa9eacd NeedsCompilation: no Title: InPAS: a bioconductor package for the identification of novel alternative PolyAdenylation Sites (PAS) using RNA-seq data Description: Alternative polyadenylation (APA) is one of the important post-transcriptional regulation mechanisms which occurs in most human genes. InPAS facilitates the discovery of novel APA sites and the differential usage of APA sites from RNA-Seq data. It leverages cleanUpdTSeq to fine tune identified APA sites by removing false sites due to internal-priming. biocViews: RNASeq, Sequencing, AlternativeSplicing, Coverage, DifferentialSplicing, GeneRegulation, Transcription, ImmunoOncology Author: Jianhong Ou, Sungmi M. Park, Michael R. Green and Lihua Julie Zhu Maintainer: Jianhong Ou , Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InPAS git_branch: RELEASE_3_10 git_last_commit: 205b776 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/InPAS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/InPAS_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/InPAS_1.18.0.tgz vignettes: vignettes/InPAS/inst/doc/InPAS.html vignetteTitles: InPAS Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InPAS/inst/doc/InPAS.R dependencyCount: 157 Package: INPower Version: 1.22.0 Depends: R (>= 3.1.0), mvtnorm Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: a35f5454143708c965e59db73928cc4d NeedsCompilation: no Title: An R package for computing the number of susceptibility SNPs Description: An R package for computing the number of susceptibility SNPs and power of future studies biocViews: SNP Author: Ju-Hyun Park Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/INPower git_branch: RELEASE_3_10 git_last_commit: 147ec8c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/INPower_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/INPower_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/INPower_1.22.0.tgz vignettes: vignettes/INPower/inst/doc/vignette.pdf vignetteTitles: INPower Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/INPower/inst/doc/vignette.R dependencyCount: 3 Package: INSPEcT Version: 1.16.3 Depends: R (>= 3.5), methods, Biobase, BiocParallel Imports: pROC, deSolve, rootSolve, KernSmooth, gdata, GenomicFeatures, GenomicRanges, IRanges, BiocGenerics, GenomicAlignments, Rsamtools, S4Vectors, GenomeInfoDb, DESeq2, plgem, SummarizedExperiment, TxDb.Mmusculus.UCSC.mm9.knownGene, shiny Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 26e4d8e695ab6f913040b08cd75fe088 NeedsCompilation: no Title: Modeling RNA synthesis, processing and degradation with RNA-seq data Description: INSPEcT (INference of Synthesis, Processing and dEgradation rates from Transcriptomic data) RNA-seq data in time-course experiments or steady-state conditions, with or without the support of nascent RNA data. biocViews: Sequencing, RNASeq, GeneRegulation, TimeCourse, SystemsBiology Author: Stefano de Pretis Maintainer: Stefano de Pretis , Mattia Furlan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/INSPEcT git_branch: RELEASE_3_10 git_last_commit: 57a0ad7 git_last_commit_date: 2020-02-03 Date/Publication: 2020-02-03 source.ver: src/contrib/INSPEcT_1.16.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/INSPEcT_1.16.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/INSPEcT_1.16.3.tgz vignettes: vignettes/INSPEcT/inst/doc/INSPEcT_GUI.html, vignettes/INSPEcT/inst/doc/INSPEcT.html vignetteTitles: INSPEcT_GUI.html, INSPEcT - INference of Synthesis,, Processing and dEgradation rates from Transcriptomic data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/INSPEcT/inst/doc/INSPEcT_GUI.R, vignettes/INSPEcT/inst/doc/INSPEcT.R dependencyCount: 156 Package: InTAD Version: 1.6.0 Depends: R (>= 3.5), methods, S4Vectors, IRanges, GenomicRanges, MultiAssayExperiment, SummarizedExperiment,stats Imports: BiocGenerics,Biobase,rtracklayer,parallel,graphics,mclust,qvalue, ggplot2,utils,ggpubr Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: d85d62cf62352a569e40a5f971fddf2a NeedsCompilation: no Title: Search for correlation between epigenetic signals and gene expression in TADs Description: The package is focused on the detection of correlation between expressed genes and selected epigenomic signals i.e. enhancers obtained from ChIP-seq data within topologically associated domains (TADs). Various parameters can be controlled to investigate the influence of external factors and visualization plots are available for each analysis step. biocViews: Epigenetics, Sequencing, ChIPSeq, RNASeq, HiC, GeneExpression,ImmunoOncology Author: Konstantin Okonechnikov, Serap Erkek, Lukas Chavez Maintainer: Konstantin Okonechnikov VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InTAD git_branch: RELEASE_3_10 git_last_commit: fd08bb0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/InTAD_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/InTAD_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/InTAD_1.6.0.tgz vignettes: vignettes/InTAD/inst/doc/InTAD.html vignetteTitles: Correlation of epigenetic signals and genes in TADs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InTAD/inst/doc/InTAD.R dependencyCount: 102 Package: intansv Version: 1.26.1 Depends: R (>= 2.14.0), plyr, ggbio, GenomicRanges Imports: BiocGenerics, IRanges License: MIT + file LICENSE MD5sum: cbaecaa757110082ad4e2f5a86645143 NeedsCompilation: no Title: Integrative analysis of structural variations Description: This package provides efficient tools to read and integrate structural variations predicted by popular softwares. Annotation and visulation of structural variations are also implemented in the package. biocViews: Genetics, Annotation, Sequencing, Software Author: Wen Yao Maintainer: Wen Yao git_url: https://git.bioconductor.org/packages/intansv git_branch: RELEASE_3_10 git_last_commit: 8c93ffe git_last_commit_date: 2020-04-14 Date/Publication: 2020-04-14 source.ver: src/contrib/intansv_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/intansv_1.26.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/intansv_1.26.1.tgz vignettes: vignettes/intansv/inst/doc/intansvOverview.pdf vignetteTitles: An Introduction to intansv hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/intansv/inst/doc/intansvOverview.R dependencyCount: 151 Package: InteractionSet Version: 1.14.0 Depends: GenomicRanges, SummarizedExperiment Imports: IRanges, S4Vectors, GenomeInfoDb, BiocGenerics, methods, Matrix, Rcpp LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: dc8f695047667e13337b2acedefbf835 NeedsCompilation: yes Title: Base Classes for Storing Genomic Interaction Data Description: Provides the GInteractions, InteractionSet and ContactMatrix objects and associated methods for storing and manipulating genomic interaction data from Hi-C and ChIA-PET experiments. biocViews: Infrastructure, DataRepresentation, Software, HiC Author: Aaron Lun [aut, cre], Malcolm Perry [aut], Elizabeth Ing-Simmons [aut] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InteractionSet git_branch: RELEASE_3_10 git_last_commit: 7c00ae4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/InteractionSet_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/InteractionSet_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/InteractionSet_1.14.0.tgz vignettes: vignettes/InteractionSet/inst/doc/interactions.html vignetteTitles: Interacting with InteractionSet classes for genomic interaction data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InteractionSet/inst/doc/interactions.R dependsOnMe: diffHic, GenomicInteractions, MACPET, sevenC importsMe: CAGEfightR, HiCcompare, trackViewer dependencyCount: 33 Package: interactiveDisplay Version: 1.24.0 Depends: R (>= 2.10), methods, BiocGenerics, grid Imports: interactiveDisplayBase (>= 1.7.3), shiny, RColorBrewer, ggplot2, reshape2, plyr, gridSVG, XML, Category, AnnotationDbi Suggests: RUnit, hgu95av2.db, knitr, GenomicRanges, SummarizedExperiment, GOstats, ggbio, GO.db, Gviz, rtracklayer, metagenomeSeq, gplots, vegan, Biobase Enhances: rstudio License: Artistic-2.0 MD5sum: b3c93dbb117b1488a2456dca84edbdf7 NeedsCompilation: no Title: Package for enabling powerful shiny web displays of Bioconductor objects Description: The interactiveDisplay package contains the methods needed to generate interactive Shiny based display methods for Bioconductor objects. biocViews: GO, GeneExpression, Microarray, Sequencing, Classification, Network, QualityControl, Visualization, Visualization, Genetics, DataRepresentation, GUI, AnnotationData Author: Shawn Balcome, Marc Carlson Maintainer: Shawn Balcome VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interactiveDisplay git_branch: RELEASE_3_10 git_last_commit: 085d758 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/interactiveDisplay_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/interactiveDisplay_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/interactiveDisplay_1.24.0.tgz vignettes: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.pdf vignetteTitles: interactiveDisplay: A package for enabling interactive visualization of Bioconductor objects hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.R suggestsMe: metagenomeSeq dependencyCount: 95 Package: interactiveDisplayBase Version: 1.24.0 Depends: R (>= 2.10), methods, BiocGenerics Imports: shiny Suggests: knitr Enhances: rstudioapi License: Artistic-2.0 MD5sum: c0c12ee30f198c1db5a24955efda2f5d NeedsCompilation: no Title: Base package for enabling powerful shiny web displays of Bioconductor objects Description: The interactiveDisplayBase package contains the the basic methods needed to generate interactive Shiny based display methods for Bioconductor objects. biocViews: GO, GeneExpression, Microarray, Sequencing, Classification, Network, QualityControl, Visualization, Visualization, Genetics, DataRepresentation, GUI, AnnotationData Author: Shawn Balcome, Marc Carlson Maintainer: Shawn Balcome VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interactiveDisplayBase git_branch: RELEASE_3_10 git_last_commit: f85c344 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/interactiveDisplayBase_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/interactiveDisplayBase_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/interactiveDisplayBase_1.24.0.tgz vignettes: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.html vignetteTitles: Using interactiveDisplayBase for Bioconductor object visualization and modification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.R importsMe: AnnotationHub, interactiveDisplay dependencyCount: 25 Package: IntEREst Version: 1.10.2 Depends: R (>= 3.4), GenomicRanges, Rsamtools, SummarizedExperiment, edgeR, S4Vectors Imports: seqLogo, Biostrings, GenomicFeatures, IRanges, seqinr, graphics, grDevices, stats, utils, grid, methods, DBI, RMySQL, GenomicAlignments, BiocParallel, BiocGenerics, DEXSeq, DESeq2 Suggests: clinfun, knitr, BSgenome.Hsapiens.UCSC.hg19 License: GPL-2 Archs: i386, x64 MD5sum: 32a3edb92fc61047f3013fd959144322 NeedsCompilation: no Title: Intron-Exon Retention Estimator Description: This package performs Intron-Exon Retention analysis on RNA-seq data (.bam files). biocViews: Software, AlternativeSplicing, Coverage, DifferentialSplicing, Sequencing, RNASeq, Alignment, Normalization, DifferentialExpression, ImmunoOncology Author: Ali Oghabian , Dario Greco , Mikko Frilander Maintainer: Ali Oghabian , Mikko Frilander VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IntEREst git_branch: RELEASE_3_10 git_last_commit: 9af72d5 git_last_commit_date: 2019-12-17 Date/Publication: 2019-12-17 source.ver: src/contrib/IntEREst_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/IntEREst_1.10.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IntEREst_1.10.2.tgz vignettes: vignettes/IntEREst/inst/doc/IntEREst.html vignetteTitles: IntEREst,, Intron Exon Retention Estimator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IntEREst/inst/doc/IntEREst.R dependencyCount: 153 Package: InterMineR Version: 1.8.1 Depends: R (>= 3.4.1) Imports: Biostrings, RCurl, XML, xml2, RJSONIO, sqldf, igraph, httr, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, methods Suggests: BiocStyle, Gviz, knitr, rmarkdown, GeneAnswers, GO.db, org.Hs.eg.db License: LGPL MD5sum: 1e992cede6634cd0e2efab10159a140b NeedsCompilation: no Title: R Interface with InterMine-Powered Databases Description: Databases based on the InterMine platform such as FlyMine, modMine (modENCODE), RatMine, YeastMine, HumanMine and TargetMine are integrated databases of genomic, expression and protein data for various organisms. Integrating data makes it possible to run sophisticated data mining queries that span domains of biological knowledge. This R package provides interfaces with these databases through webservices. It makes most from the correspondence of the data frame object in R and the table object in databases, while hiding the details of data exchange through XML or JSON. biocViews: GeneExpression, SNP, GeneSetEnrichment, DifferentialExpression, GeneRegulation, GenomeAnnotation, GenomeWideAssociation, FunctionalPrediction, AlternativeSplicing, ComparativeGenomics, FunctionalGenomics, Proteomics, SystemsBiology, Microarray, MultipleComparison, Pathways, GO, KEGG, Reactome, Visualization Author: Bing Wang, Julie Sullivan, Rachel Lyne, Konstantinos Kyritsis Maintainer: InterMine Team VignetteBuilder: knitr BugReports: https://github.com/intermine/intermineR/issues git_url: https://git.bioconductor.org/packages/InterMineR git_branch: RELEASE_3_10 git_last_commit: 9c604d7 git_last_commit_date: 2020-02-11 Date/Publication: 2020-02-16 source.ver: src/contrib/InterMineR_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/InterMineR_1.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/InterMineR_1.8.1.tgz vignettes: vignettes/InterMineR/inst/doc/Enrichment_Analysis_and_Visualization.html, vignettes/InterMineR/inst/doc/FlyMine_Genomic_Visualizations.html, vignettes/InterMineR/inst/doc/InterMineR.html vignetteTitles: Enrichment Analysis and Visualization, FlyMine Genomic Visualizations, InterMineR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/InterMineR/inst/doc/Enrichment_Analysis_and_Visualization.R, vignettes/InterMineR/inst/doc/FlyMine_Genomic_Visualizations.R, vignettes/InterMineR/inst/doc/InterMineR.R dependencyCount: 64 Package: IntramiRExploreR Version: 1.8.0 Depends: R (>= 3.4) Imports: igraph (>= 1.0.1), FGNet (>= 3.0.7), knitr (>= 1.12.3), stats, utils, grDevices, graphics Suggests: RDAVIDWebService, gProfileR, topGO, KEGGprofile, org.Dm.eg.db, rmarkdown, testthat License: GPL-2 MD5sum: 463865673972a978373f243bc9285247 NeedsCompilation: no Title: Predicting Targets for Drosophila Intragenic miRNAs Description: Intra-miR-ExploreR, an integrative miRNA target prediction bioinformatics tool, identifies targets combining expression and biophysical interactions of a given microRNA (miR). Using the tool, we have identified targets for 92 intragenic miRs in D. melanogaster, using available microarray expression data, from Affymetrix 1 and Affymetrix2 microarray array platforms, providing a global perspective of intragenic miR targets in Drosophila. Predicted targets are grouped according to biological functions using the DAVID Gene Ontology tool and are ranked based on a biologically relevant scoring system, enabling the user to identify functionally relevant targets for a given miR. biocViews: Software, Microarray, GeneTarget, StatisticalMethod, GeneExpression, GenePrediction Author: Surajit Bhattacharya and Daniel Cox Maintainer: Surajit Bhattacharya URL: https://github.com/sbhattacharya3/IntramiRExploreR/ VignetteBuilder: knitr BugReports: https://github.com/sbhattacharya3/IntramiRExploreR/issues git_url: https://git.bioconductor.org/packages/IntramiRExploreR git_branch: RELEASE_3_10 git_last_commit: dd83d0d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/IntramiRExploreR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/IntramiRExploreR_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IntramiRExploreR_1.8.0.tgz vignettes: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.html vignetteTitles: IntramiRExploreR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.R dependencyCount: 34 Package: inveRsion Version: 1.34.0 Depends: methods, haplo.stats Imports: graphics, methods, utils License: GPL (>= 2) MD5sum: 6a676b2454fb7e3a99275dbb4a9fc2ec NeedsCompilation: yes Title: Inversions in genotype data Description: Package to find genetic inversions in genotype (SNP array) data. biocViews: Microarray, SNP Author: Alejandro Caceres Maintainer: Alejandro Caceres git_url: https://git.bioconductor.org/packages/inveRsion git_branch: RELEASE_3_10 git_last_commit: 1520bcd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/inveRsion_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/inveRsion_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/inveRsion_1.34.0.tgz vignettes: vignettes/inveRsion/inst/doc/inveRsion.pdf vignetteTitles: Quick start guide for inveRsion package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/inveRsion/inst/doc/inveRsion.R dependencyCount: 94 Package: IONiseR Version: 2.10.0 Depends: R (>= 3.4) Imports: rhdf5, dplyr, magrittr, tidyr, ShortRead, Biostrings, ggplot2, methods, BiocGenerics, XVector, tibble, stats, BiocParallel, bit64, stringr, utils Suggests: BiocStyle, knitr, rmarkdown, gridExtra, testthat, minionSummaryData License: MIT + file LICENSE MD5sum: 9e170d6d6704abc53ee7614a25bc092f NeedsCompilation: no Title: Quality Assessment Tools for Oxford Nanopore MinION data Description: IONiseR provides tools for the quality assessment of Oxford Nanopore MinION data. It extracts summary statistics from a set of fast5 files and can be used either before or after base calling. In addition to standard summaries of the read-types produced, it provides a number of plots for visualising metrics relative to experiment run time or spatially over the surface of a flowcell. biocViews: QualityControl, DataImport, Sequencing Author: Mike Smith [aut, cre] Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IONiseR git_branch: RELEASE_3_10 git_last_commit: ecd890f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/IONiseR_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/IONiseR_2.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IONiseR_2.10.0.tgz vignettes: vignettes/IONiseR/inst/doc/IONiseR.html vignetteTitles: Quality assessment tools for nanopore data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IONiseR/inst/doc/IONiseR.R dependencyCount: 97 Package: iPAC Version: 1.30.0 Depends: R(>= 2.15),gdata, scatterplot3d, Biostrings, multtest License: GPL-2 MD5sum: 834afd7b8646598ea7d8f43604705ee3 NeedsCompilation: no Title: Identification of Protein Amino acid Clustering Description: iPAC is a novel tool to identify somatic amino acid mutation clustering within proteins while taking into account protein structure. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/iPAC git_branch: RELEASE_3_10 git_last_commit: d146d4b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/iPAC_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/iPAC_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/iPAC_1.30.0.tgz vignettes: vignettes/iPAC/inst/doc/iPAC.pdf vignetteTitles: iPAC: identification of Protein Amino acid Mutations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iPAC/inst/doc/iPAC.R dependsOnMe: QuartPAC dependencyCount: 24 Package: ipdDb Version: 1.4.0 Depends: R (>= 3.5.0), methods, AnnotationDbi (>= 1.43.1), AnnotationHub Imports: Biostrings, GenomicRanges, RSQLite, DBI, IRanges, stats, assertthat Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 8ebae969dd8415215da827f4313432eb NeedsCompilation: no Title: IPD IMGT/HLA and IPD KIR database for Homo sapiens Description: All alleles from the IPD IMGT/HLA and IPD KIR database for Homo sapiens. Reference: Robinson J, Maccari G, Marsh SGE, Walter L, Blokhuis J, Bimber B, Parham P, De Groot NG, Bontrop RE, Guethlein LA, and Hammond JA KIR Nomenclature in non-human species Immunogenetics (2018), in preparation. biocViews: GenomicVariation, SequenceMatching, VariantAnnotation, DataRepresentation Author: Steffen Klasberg Maintainer: Steffen Klasberg URL: https://github.com/DKMS-LSL/ipdDb organism: Homo sapiens VignetteBuilder: knitr BugReports: https://github.com/DKMS-LSL/ipdDb/issues/new git_url: https://git.bioconductor.org/packages/ipdDb git_branch: RELEASE_3_10 git_last_commit: 5e756f0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ipdDb_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ipdDb_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ipdDb_1.4.0.tgz vignettes: vignettes/ipdDb/inst/doc/Readme.html vignetteTitles: ipdDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ipdDb/inst/doc/Readme.R dependencyCount: 72 Package: IPO Version: 1.12.0 Depends: xcms (>= 1.50.0), rsm, CAMERA, grDevices, graphics, stats, utils Imports: BiocParallel Suggests: RUnit, BiocGenerics, msdata, mtbls2, faahKO, knitr Enhances: parallel License: GPL (>= 2) + file LICENSE MD5sum: e686cbd590a4447b50b81180c5a31625 NeedsCompilation: no Title: Automated Optimization of XCMS Data Processing parameters Description: The outcome of XCMS data processing strongly depends on the parameter settings. IPO (`Isotopologue Parameter Optimization`) is a parameter optimization tool that is applicable for different kinds of samples and liquid chromatography coupled to high resolution mass spectrometry devices, fast and free of labeling steps. IPO uses natural, stable 13C isotopes to calculate a peak picking score. Retention time correction is optimized by minimizing the relative retention time differences within features and grouping parameters are optimized by maximizing the number of features showing exactly one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiment. The resulting scores are evaluated using response surface models. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry Author: Gunnar Libiseller , Christoph Magnes , Thomas Riebenbauer Maintainer: Thomas Riebenbauer URL: https://github.com/rietho/IPO VignetteBuilder: knitr BugReports: https://github.com/rietho/IPO/issues/new git_url: https://git.bioconductor.org/packages/IPO git_branch: RELEASE_3_10 git_last_commit: 8a15918 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/IPO_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/IPO_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IPO_1.12.0.tgz vignettes: vignettes/IPO/inst/doc/IPO.html vignetteTitles: XCMS Parameter Optimization with IPO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IPO/inst/doc/IPO.R dependencyCount: 130 Package: IPPD Version: 1.34.0 Depends: R (>= 2.12.0), MASS, Matrix, XML, digest, bitops Imports: methods, stats, graphics License: GPL (version 2 or later) MD5sum: 797a37177e22f8f03079d86f0d31ca48 NeedsCompilation: yes Title: Isotopic peak pattern deconvolution for Protein Mass Spectrometry by template matching Description: The package provides functionality to extract isotopic peak patterns from raw mass spectra. This is done by fitting a large set of template basis functions to the raw spectrum using either nonnegative least squares or least absolute deviation fittting. The package offers a flexible function which tries to estimate model parameters in a way tailored to the peak shapes in the data. The package also provides functionality to process LCMS runs. biocViews: Proteomics Author: Martin Slawski , Rene Hussong , Andreas Hildebrandt , Matthias Hein Maintainer: Martin Slawski git_url: https://git.bioconductor.org/packages/IPPD git_branch: RELEASE_3_10 git_last_commit: d463689 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/IPPD_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/IPPD_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IPPD_1.34.0.tgz vignettes: vignettes/IPPD/inst/doc/IPPD.pdf vignetteTitles: IPPD Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IPPD/inst/doc/IPPD.R dependencyCount: 12 Package: IRanges Version: 2.20.2 Depends: R (>= 3.1.0), methods, utils, stats, BiocGenerics (>= 0.25.3), S4Vectors (>= 0.23.22) Imports: stats4 LinkingTo: S4Vectors Suggests: XVector, GenomicRanges, Rsamtools, GenomicAlignments, GenomicFeatures, BSgenome.Celegans.UCSC.ce2, pasillaBamSubset, RUnit, BiocStyle License: Artistic-2.0 MD5sum: bad869d07414625ecd54fe04d294c68d NeedsCompilation: yes Title: Foundation of integer range manipulation in Bioconductor Description: Provides efficient low-level and highly reusable S4 classes for storing, manipulating and aggregating over annotated ranges of integers. Implements an algebra of range operations, including efficient algorithms for finding overlaps and nearest neighbors. Defines efficient list-like classes for storing, transforming and aggregating large grouped data, i.e., collections of atomic vectors and DataFrames. biocViews: Infrastructure, DataRepresentation Author: H. Pagès, P. Aboyoun and M. Lawrence Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/IRanges git_branch: RELEASE_3_10 git_last_commit: 41ed967 git_last_commit_date: 2020-01-12 Date/Publication: 2020-01-13 source.ver: src/contrib/IRanges_2.20.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/IRanges_2.20.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IRanges_2.20.2.tgz vignettes: vignettes/IRanges/inst/doc/IRangesOverview.pdf vignetteTitles: An Overview of the IRanges package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IRanges/inst/doc/IRangesOverview.R dependsOnMe: AnnotationDbi, AnnotationHubData, BaalChIP, BayesPeak, biomvRCNS, Biostrings, BiSeq, BSgenome, BubbleTree, bumphunter, CAFE, casper, CexoR, chimeraviz, ChIPpeakAnno, chipseq, chroGPS, CODEX, consensusSeekeR, CSAR, customProDB, deepSNV, DelayedArray, DESeq2, DEXSeq, DirichletMultinomial, DMCFB, DMCHMM, DMRcaller, epigenomix, epihet, ExCluster, exomeCopy, fCCAC, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, Genominator, groHMM, gtrellis, Gviz, HelloRanges, HiTC, IdeoViz, InTAD, methyAnalysis, MotifDb, motifRG, NADfinder, ORFik, OTUbase, pepStat, PGA, PING, plyranges, proBAMr, PSICQUIC, RefNet, RepViz, rfPred, rGADEM, rGREAT, RIPSeeker, RJMCMCNucleosomes, RNAmodR, Scale4C, scsR, SGSeq, SICtools, Structstrings, TEQC, triform, triplex, VariantTools, XVector importsMe: ALDEx2, AllelicImbalance, alpine, amplican, AneuFinder, annmap, annotatr, appreci8R, ArrayExpressHTS, ArrayTV, ASpediaFI, ASpli, AssessORF, ATACseqQC, ballgown, bamsignals, BayesPeak, BBCAnalyzer, beadarray, BiocOncoTK, biovizBase, BiSeq, BitSeq, bnbc, BPRMeth, branchpointer, breakpointR, BSgenome, bsseq, BUMHMM, CAGEfightR, CAGEr, CHARGE, ChIC, ChIPanalyser, chipenrich, ChIPexoQual, ChIPQC, ChIPseeker, chipseq, ChIPseqR, ChIPSeqSpike, ChIPsim, ChromHeatMap, chromstaR, chromswitch, chromVAR, cicero, CINdex, circRNAprofiler, cleanUpdTSeq, cleaver, cn.mops, CNEr, CNVfilteR, CNVPanelizer, CNVRanger, CNVrd2, cobindR, COCOA, coMET, compEpiTools, contiBAIT, conumee, copynumber, CopyNumberPlots, CopywriteR, CoverageView, CRISPRseek, CrispRVariants, csaw, dada2, debrowser, DECIPHER, DelayedMatrixStats, deltaCaptureC, derfinder, derfinderHelper, derfinderPlot, DEScan2, DiffBind, diffHic, diffloop, DMRcate, DMRScan, dmrseq, DominoEffect, DRIMSeq, easyRNASeq, EDASeq, ELMER, EnrichedHeatmap, enrichTF, ensembldb, epivizr, epivizrData, erma, esATAC, EventPointer, FastqCleaner, fastseg, fcScan, FindMyFriends, FunciSNP, GA4GHclient, gcapc, genbankr, geneAttribution, GeneGeneInteR, GENESIS, GenoGAM, genomation, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicFiles, GenomicInteractions, GenomicOZone, GenomicScores, GenomicTuples, genoset, genotypeeval, GenVisR, GGBase, ggbio, GGtools, girafe, gmapR, GOfuncR, GOpro, GOTHiC, gpart, gQTLstats, GUIDEseq, gwascat, h5vc, HDF5Array, heatmaps, HiCBricks, HiCcompare, HilbertCurve, HTSeqGenie, icetea, ideal, idr2d, IMAS, InPAS, INSPEcT, intansv, InteractionSet, IntEREst, InterMineR, ipdDb, IsoformSwitchAnalyzeR, isomiRs, IVAS, JunctionSeq, karyoploteR, LOLA, M3D, MACPET, MADSEQ, maser, MatrixRider, mCSEA, MDTS, MEAL, MEDIPS, metagene, metagene2, MethCP, methimpute, methInheritSim, methVisual, methyAnalysis, methylCC, methylInheritance, methylKit, methylPipe, MethylSeekR, methylumi, methyvim, minfi, MinimumDistance, MIRA, MMAPPR2, Modstrings, mosaics, MOSim, motifbreakR, motifmatchr, MotIV, msa, msgbsR, MSnbase, MTseeker, MultiAssayExperiment, MultiDataSet, MutationalPatterns, NarrowPeaks, normr, nucleoSim, nucleR, oligoClasses, OmaDB, OMICsPCA, openPrimeR, Organism.dplyr, OrganismDbi, OUTRIDER, panelcn.mops, Pbase, pcaExplorer, pdInfoBuilder, PICS, PING, plethy, podkat, polyester, pqsfinder, pram, prebs, PrecisionTrialDrawer, primirTSS, profileplyr, PureCN, Pviz, QDNAseq, qpgraph, qPLEXanalyzer, qsea, QuasR, R3CPET, r3Cseq, R453Plus1Toolbox, RaggedExperiment, RareVariantVis, Rariant, Rcade, recount, REDseq, regioneR, REMP, Repitools, ReportingTools, RiboProfiling, riboSeqR, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RNAprobR, rnaSeqMap, RnBeads, roar, Rqc, Rsamtools, RSVSim, RTCGAToolbox, RTN, rtracklayer, SCAN.UPC, segmentSeq, SeqArray, seqCAT, seqPattern, seqplots, seqsetvis, SeqSQC, SeqVarTools, sesame, sevenC, ShortRead, signeR, SMITE, SNPchip, SNPhood, soGGi, SomaticSignatures, SparseSignatures, SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR, SummarizedExperiment, target, TarSeqQC, TCGAbiolinks, TCGAutils, TCseq, TFBSTools, TFEA.ChIP, TFHAZ, TitanCNA, TnT, tracktables, trackViewer, transcriptR, TransView, triform, tRNA, tRNAdbImport, tRNAscanImport, TSRchitect, TVTB, TxRegInfra, Uniquorn, VanillaICE, VariantAnnotation, VariantExperiment, VariantFiltering, wavClusteR, waveTiling, wiggleplotr, XCIR, xcms, XVector, yamss, flipflop suggestsMe: annotate, AnnotationHub, BaseSpaceR, BiocGenerics, Chicago, ClassifyR, epivizrChart, Glimma, gQTLBase, GWASTools, HilbertVis, HilbertVisGUI, martini, methrix, MiRaGE, regionReport, RTCGA, S4Vectors, SigsPack, StructuralVariantAnnotation, TFutils linksToMe: Biostrings, CNEr, DECIPHER, GenomicAlignments, GenomicRanges, kebabs, MatrixRider, Rsamtools, rtracklayer, ShortRead, Structstrings, triplex, VariantAnnotation, VariantFiltering, XVector dependencyCount: 8 Package: iSEE Version: 1.6.1 Depends: SummarizedExperiment, SingleCellExperiment Imports: methods, BiocGenerics, S4Vectors, utils, stats, shiny, shinydashboard, shinyAce, shinyjs, DT, rintrojs, ggplot2, colourpicker, igraph, vipor, mgcv, reshape2, rentrez, AnnotationDbi, graphics, grDevices, viridisLite, cowplot, scales, dplyr Suggests: testthat, BiocStyle, knitr, rmarkdown, scRNAseq, scater, DelayedArray (>= 0.7.44), Rtsne, irlba, RColorBrewer, viridis, org.Mm.eg.db, htmltools License: MIT + file LICENSE MD5sum: c239f2b33b9e42385c4eb91cb11610a2 NeedsCompilation: no Title: Interactive SummarizedExperiment Explorer Description: Provides functions for creating an interactive Shiny-based graphical user interface for exploring data stored in SummarizedExperiment objects, including row- and column-level metadata. Particular attention is given to single-cell data in a SingleCellExperiment object with visualization of dimensionality reduction results. biocViews: ImmunoOncology, Visualization, GUI, DimensionReduction, FeatureExtraction, Clustering, Transcription, GeneExpression, Transcriptomics, SingleCell, CellBasedAssays Author: Kevin Rue-Albrecht [aut] (), Federico Marini [aut] (), Charlotte Soneson [aut, cre] (), Aaron Lun [aut] () Maintainer: Charlotte Soneson URL: https://github.com/csoneson/iSEE VignetteBuilder: knitr BugReports: https://github.com/csoneson/iSEE/issues git_url: https://git.bioconductor.org/packages/iSEE git_branch: RELEASE_3_10 git_last_commit: 7208039 git_last_commit_date: 2020-01-30 Date/Publication: 2020-01-30 source.ver: src/contrib/iSEE_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/iSEE_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/iSEE_1.6.1.tgz vignettes: vignettes/iSEE/inst/doc/basic.html, vignettes/iSEE/inst/doc/configure.html, vignettes/iSEE/inst/doc/custom.html, vignettes/iSEE/inst/doc/ecm.html, vignettes/iSEE/inst/doc/voice.html vignetteTitles: 1. The iSEE User's Guide, 3. Configuring iSEE apps, 4. Deploying custom panels, 2. The ExperimentColorMap Class, 5. Speech recognition hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iSEE/inst/doc/basic.R, vignettes/iSEE/inst/doc/configure.R, vignettes/iSEE/inst/doc/custom.R, vignettes/iSEE/inst/doc/ecm.R, vignettes/iSEE/inst/doc/voice.R suggestsMe: schex dependencyCount: 124 Package: iSeq Version: 1.38.0 Depends: R (>= 2.10.0) License: GPL (>= 2) Archs: i386, x64 MD5sum: 8fd2c41731acdce93eae444d4679ee8f NeedsCompilation: yes Title: Bayesian Hierarchical Modeling of ChIP-seq Data Through Hidden Ising Models Description: Bayesian hidden Ising models are implemented to identify IP-enriched genomic regions from ChIP-seq data. They can be used to analyze ChIP-seq data with and without controls and replicates. biocViews: ChIPSeq, Sequencing Author: Qianxing Mo Maintainer: Qianxing Mo git_url: https://git.bioconductor.org/packages/iSeq git_branch: RELEASE_3_10 git_last_commit: 005c891 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/iSeq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/iSeq_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/iSeq_1.38.0.tgz vignettes: vignettes/iSeq/inst/doc/iSeq.pdf vignetteTitles: iSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSeq/inst/doc/iSeq.R dependencyCount: 0 Package: isobar Version: 1.32.0 Depends: R (>= 2.10.0), Biobase, stats, methods Imports: distr, plyr, biomaRt, ggplot2 Suggests: MSnbase, OrgMassSpecR, XML, RJSONIO, Hmisc, gplots, RColorBrewer, gridExtra, limma, boot, DBI, MASS License: LGPL-2 Archs: i386, x64 MD5sum: 8fb59a6cb9f63b7b3c5a272f1e6616df NeedsCompilation: no Title: Analysis and quantitation of isobarically tagged MSMS proteomics data Description: isobar provides methods for preprocessing, normalization, and report generation for the analysis of quantitative mass spectrometry proteomics data labeled with isobaric tags, such as iTRAQ and TMT. Features modules for integrating and validating PTM-centric datasets (isobar-PTM). More information on http://www.ms-isobar.org. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Bioinformatics, MultipleComparisons, QualityControl Author: Florian P Breitwieser and Jacques Colinge , with contributions from Alexey Stukalov , Xavier Robin and Florent Gluck Maintainer: Florian P Breitwieser URL: https://github.com/fbreitwieser/isobar BugReports: https://github.com/fbreitwieser/isobar/issues git_url: https://git.bioconductor.org/packages/isobar git_branch: RELEASE_3_10 git_last_commit: d229f6e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/isobar_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/isobar_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/isobar_1.32.0.tgz vignettes: vignettes/isobar/inst/doc/isobar-devel.pdf, vignettes/isobar/inst/doc/isobar-ptm.pdf, vignettes/isobar/inst/doc/isobar-usecases.pdf, vignettes/isobar/inst/doc/isobar.pdf vignetteTitles: isobar for developers, isobar for quantification of PTM datasets, Usecases for isobar package, isobar package for iTRAQ and TMT protein quantification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/isobar/inst/doc/isobar-devel.R, vignettes/isobar/inst/doc/isobar-ptm.R, vignettes/isobar/inst/doc/isobar-usecases.R, vignettes/isobar/inst/doc/isobar.R dependencyCount: 92 Package: IsoCorrectoR Version: 1.4.1 Depends: R (>= 3.5) Imports: dplyr, magrittr, methods, quadprog, readr, readxl, stringr, tibble, tools, utils, pracma, WriteXLS Suggests: IsoCorrectoRGUI, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 8a14f19cdbe73333bfe8801335301fdb NeedsCompilation: no Title: Correction for natural isotope abundance and tracer purity in MS and MS/MS data from stable isotope labeling experiments Description: IsoCorrectoR performs the correction of mass spectrometry data from stable isotope labeling/tracing metabolomics experiments with regard to natural isotope abundance and tracer impurity. Data from both MS and MS/MS measurements can be corrected (with any tracer isotope: 13C, 15N, 18O...), as well as high resolution MS data from multiple-tracer experiments (e.g. 13C and 15N used simultaneously). See the Bioconductor package IsoCorrectoRGUI for a graphical user interface to IsoCorrectoR. biocViews: Software, Metabolomics, MassSpectrometry, Preprocessing, ImmunoOncology Author: Christian Kohler [cre, aut], Paul Heinrich [aut] Maintainer: Christian Kohler URL: https://genomics.ur.de/files/IsoCorrectoR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IsoCorrectoR git_branch: RELEASE_3_10 git_last_commit: 3acf88e git_last_commit_date: 2020-01-07 Date/Publication: 2020-01-07 source.ver: src/contrib/IsoCorrectoR_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/IsoCorrectoR_1.4.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IsoCorrectoR_1.4.1.tgz vignettes: vignettes/IsoCorrectoR/inst/doc/IsoCorrectoR.html vignetteTitles: IsoCorrectoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoCorrectoR/inst/doc/IsoCorrectoR.R importsMe: IsoCorrectoRGUI dependencyCount: 41 Package: IsoCorrectoRGUI Version: 1.2.0 Depends: R (>= 3.6) Imports: IsoCorrectoR, readxl, tcltk2, tcltk, utils Suggests: knitr, rmarkdown, testthat License: GPL-3 Archs: i386, x64 MD5sum: b9996a974ec6238b94a07771d11d9c63 NeedsCompilation: no Title: Graphical User Interface for IsoCorrectoR Description: IsoCorrectoRGUI is a Graphical User Interface for the IsoCorrectoR package. IsoCorrectoR performs the correction of mass spectrometry data from stable isotope labeling/tracing metabolomics experiments with regard to natural isotope abundance and tracer impurity. Data from both MS and MS/MS measurements can be corrected (with any tracer isotope: 13C, 15N, 18O...), as well as high resolution MS data from multiple-tracer experiments (e.g. 13C and 15N used simultaneously). biocViews: Software, Metabolomics, MassSpectrometry, Preprocessing, GUI, ImmunoOncology Author: Christian Kohler [cre, aut], Paul Kuerner [aut], Paul Heinrich [aut] Maintainer: Christian Kohler URL: https://genomics.ur.de/files/IsoCorrectoRGUI VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IsoCorrectoRGUI git_branch: RELEASE_3_10 git_last_commit: 047e184 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/IsoCorrectoRGUI_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/IsoCorrectoRGUI_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IsoCorrectoRGUI_1.2.0.tgz vignettes: vignettes/IsoCorrectoRGUI/inst/doc/IsoCorrectoRGUI.html vignetteTitles: IsoCorrectoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoCorrectoRGUI/inst/doc/IsoCorrectoRGUI.R suggestsMe: IsoCorrectoR dependencyCount: 44 Package: IsoformSwitchAnalyzeR Version: 1.8.0 Depends: R (>= 3.5), limma, DEXSeq, ggplot2 Imports: methods, BSgenome, plyr, reshape2, gridExtra, Biostrings (>= 2.50.0), IRanges, GenomicRanges, DRIMSeq, RColorBrewer, rtracklayer, VennDiagram, DBI, grDevices, graphics, stats, utils, GenomeInfoDb, grid, tximport (>= 1.7.1), edgeR, futile.logger, stringr, dplyr, magrittr, readr, XVector Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, cummeRbund License: GPL (>= 2) MD5sum: e428cc2144badb00bf55138f20250f4a NeedsCompilation: yes Title: Identify, Annotate and Visualize Alternative Splicing and Isoform Switches with Functional Consequences from both short- and long-read RNA-seq data. Description: IsoformSwitchAnalyzeR enables identification and analysis of alternative splicing and isoform switches with predicted functional consequences (e.g. gain/loss of protein domains etc.) from quantification of all types of RNASeq by tools such as Kallisto, Salmon, Cufflinks/Cuffdiff, RSEM etc. biocViews: GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Visualization, StatisticalMethod, TranscriptomeVariant, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, Transcriptomics, RNASeq, Annotation, FunctionalPrediction, GenePrediction, DataImport, MultipleComparison, BatchEffect, ImmunoOncology Author: Kristoffer Vitting-Seerup Maintainer: Kristoffer Vitting-Seerup URL: http://bioconductor.org/packages/IsoformSwitchAnalyzeR/ VignetteBuilder: knitr BugReports: https://github.com/kvittingseerup/IsoformSwitchAnalyzeR/issues git_url: https://git.bioconductor.org/packages/IsoformSwitchAnalyzeR git_branch: RELEASE_3_10 git_last_commit: 2ad64ab git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/IsoformSwitchAnalyzeR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/IsoformSwitchAnalyzeR_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IsoformSwitchAnalyzeR_1.8.0.tgz vignettes: vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.html vignetteTitles: IsoformSwitchAnalyzeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.R dependencyCount: 153 Package: IsoGeneGUI Version: 2.22.0 Depends: tcltk, xlsx Imports: Rcpp, tkrplot, multtest, relimp, geneplotter, RColorBrewer, Iso, IsoGene, ORCME, ORIClust, orQA, goric, ff, Biobase, jpeg Suggests: RUnit License: GPL-2 MD5sum: 0b1ac1d5a584472bb92cd645ba4d490b NeedsCompilation: no Title: A graphical user interface to conduct a dose-response analysis of microarray data Description: The IsoGene Graphical User Interface (IsoGene-GUI) is a user friendly interface of the IsoGene package which is aimed to identify for genes with a monotonic trend in the expression levels with respect to the increasing doses. Additionally, GUI extension of original package contains various tools to perform clustering of dose-response profiles. Testing is addressed through several test statistics: global likelihood ratio test (E2), Bartholomew 1961, Barlow et al. 1972 and Robertson et al. 1988), Williams (1971, 1972), Marcus (1976), the M (Hu et al. 2005) and the modified M (Lin et al. 2007). The p-values of the global likelihood ratio test (E2) are obtained using the exact distribution and permutations. The other four test statistics are obtained using permutations. Several p-values adjustment are provided: Bonferroni, Holm (1979), Hochberg (1988), and Sidak procedures for controlling the family-wise Type I error rate (FWER), and BH (Benjamini and Hochberg 1995) and BY (Benjamini and Yekutieli 2001) procedures are used for controlling the FDR. The inference is based on resampling methods, which control the False Discovery Rate (FDR), for both permutations (Ge et al., 2003) and the Significance Analysis of Microarrays (SAM, Tusher et al., 2001). Clustering methods are outsourced from CRAN packages ORCME, ORIClust. The package ORCME is based on delta-clustering method (Cheng and Church, 2000) and ORIClust on Order Restricted Information Criterion (Liu et al., 2009), both perform same task but from different perspective and their outputs are clusters of genes. Additionally, profile selection for given gene based on Generalized ORIC (Kuiper et al., 2014) from package goric and permutation test for E2 based on package orQA are included in IsoGene-GUI. None of these four packages has GUI. biocViews: Microarray, DifferentialExpression, GUI Author: Setia Pramana, Dan Lin, Philippe Haldermans, Tobias Verbeke, Martin Otava Maintainer: Setia Pramana URL: http://ibiostat.be/online-resources/online-resources/isogenegui/isogenegui-package git_url: https://git.bioconductor.org/packages/IsoGeneGUI git_branch: RELEASE_3_10 git_last_commit: d284958 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/IsoGeneGUI_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/IsoGeneGUI_2.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IsoGeneGUI_2.22.0.tgz vignettes: vignettes/IsoGeneGUI/inst/doc/IsoGeneGUI.pdf vignetteTitles: IsoGeneGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoGeneGUI/inst/doc/IsoGeneGUI.R dependencyCount: 65 Package: ISoLDE Version: 1.14.0 Depends: R (>= 3.3.0),graphics,grDevices,stats,utils License: GPL (>= 2.0) MD5sum: 1400f2b5966982e09329bf9be503e487 NeedsCompilation: yes Title: Integrative Statistics of alleLe Dependent Expression Description: This package provides ISoLDE a new method for identifying imprinted genes. This method is dedicated to data arising from RNA sequencing technologies. The ISoLDE package implements original statistical methodology described in the publication below. biocViews: ImmunoOncology, GeneExpression, Transcription, GeneSetEnrichment, Genetics, Sequencing, RNASeq, MultipleComparison, SNP, GeneticVariability, Epigenetics, MathematicalBiology, GeneRegulation Author: Christelle Reynès [aut, cre], Marine Rohmer [aut], Guilhem Kister [aut] Maintainer: Christelle Reynès URL: www.r-project.org git_url: https://git.bioconductor.org/packages/ISoLDE git_branch: RELEASE_3_10 git_last_commit: 89daa5a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ISoLDE_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ISoLDE_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ISoLDE_1.14.0.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: isomiRs Version: 1.14.0 Depends: R (>= 3.5), DiscriMiner, SummarizedExperiment Imports: AnnotationDbi, assertive.sets, BiocGenerics, Biobase, broom, cluster, cowplot, DEGreport, DESeq2, IRanges, dplyr, GenomicRanges, gplots, ggplot2, gtools, gridExtra, grid, grDevices, graphics, GGally, limma, methods, RColorBrewer, readr, reshape, rlang, stats, stringr, S4Vectors, tidyr, tibble Suggests: knitr, org.Mm.eg.db, targetscan.Hs.eg.db, pheatmap, BiocStyle, testthat License: MIT + file LICENSE Archs: i386, x64 MD5sum: bb295e699fea169ea7b12b2a3c6859c3 NeedsCompilation: no Title: Analyze isomiRs and miRNAs from small RNA-seq Description: Characterization of miRNAs and isomiRs, clustering and differential expression. biocViews: miRNA, RNASeq, DifferentialExpression, Clustering, ImmunoOncology Author: Lorena Pantano [aut, cre], Georgia Escaramis [aut] Maintainer: Lorena Pantano VignetteBuilder: knitr BugReports: https://github.com/lpantano/isomiRs/issues git_url: https://git.bioconductor.org/packages/isomiRs git_branch: RELEASE_3_10 git_last_commit: 1e5d3d5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/isomiRs_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/isomiRs_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/isomiRs_1.14.0.tgz vignettes: vignettes/isomiRs/inst/doc/isomiRs.html vignetteTitles: miRNA and isomiR analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/isomiRs/inst/doc/isomiRs.R dependencyCount: 163 Package: ITALICS Version: 2.46.0 Depends: R (>= 2.0.0), GLAD, ITALICSData, oligo, affxparser, pd.mapping50k.xba240 Imports: affxparser, DBI, GLAD, oligo, oligoClasses, stats Suggests: pd.mapping50k.hind240, pd.mapping250k.sty, pd.mapping250k.nsp License: GPL-2 MD5sum: 1e8098308dc1196b894d23ca1a4f870b NeedsCompilation: no Title: ITALICS Description: A Method to normalize of Affymetrix GeneChip Human Mapping 100K and 500K set biocViews: Microarray, CopyNumberVariation Author: Guillem Rigaill, Philippe Hupe Maintainer: Guillem Rigaill URL: http://bioinfo.curie.fr git_url: https://git.bioconductor.org/packages/ITALICS git_branch: RELEASE_3_10 git_last_commit: 1ccbc22 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ITALICS_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ITALICS_2.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ITALICS_2.46.0.tgz vignettes: vignettes/ITALICS/inst/doc/ITALICS.pdf vignetteTitles: ITALICS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ITALICS/inst/doc/ITALICS.R dependencyCount: 64 Package: iterativeBMA Version: 1.44.0 Depends: BMA, leaps, Biobase (>= 2.5.5) License: GPL (>= 2) Archs: i386, x64 MD5sum: 2944a108a3b4df99adf1fd311ba1aecb NeedsCompilation: no Title: The Iterative Bayesian Model Averaging (BMA) algorithm Description: The iterative Bayesian Model Averaging (BMA) algorithm is a variable selection and classification algorithm with an application of classifying 2-class microarray samples, as described in Yeung, Bumgarner and Raftery (Bioinformatics 2005, 21: 2394-2402). biocViews: Microarray, Classification Author: Ka Yee Yeung, University of Washington, Seattle, WA, with contributions from Adrian Raftery and Ian Painter Maintainer: Ka Yee Yeung URL: http://faculty.washington.edu/kayee/research.html git_url: https://git.bioconductor.org/packages/iterativeBMA git_branch: RELEASE_3_10 git_last_commit: 1d8fa12 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/iterativeBMA_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/iterativeBMA_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/iterativeBMA_1.44.0.tgz vignettes: vignettes/iterativeBMA/inst/doc/iterativeBMA.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iterativeBMA/inst/doc/iterativeBMA.R dependencyCount: 22 Package: iterativeBMAsurv Version: 1.44.0 Depends: BMA, leaps, survival, splines Imports: graphics, grDevices, stats, survival, utils License: GPL (>= 2) MD5sum: a9fcf10e02318850c5bef95ecd64e3dc NeedsCompilation: no Title: The Iterative Bayesian Model Averaging (BMA) Algorithm For Survival Analysis Description: The iterative Bayesian Model Averaging (BMA) algorithm for survival analysis is a variable selection method for applying survival analysis to microarray data. biocViews: Microarray Author: Amalia Annest, University of Washington, Tacoma, WA Ka Yee Yeung, University of Washington, Seattle, WA Maintainer: Ka Yee Yeung URL: http://expression.washington.edu/ibmasurv/protected git_url: https://git.bioconductor.org/packages/iterativeBMAsurv git_branch: RELEASE_3_10 git_last_commit: 3889a4c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/iterativeBMAsurv_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/iterativeBMAsurv_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/iterativeBMAsurv_1.44.0.tgz vignettes: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm For Survival Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.R dependencyCount: 19 Package: iterClust Version: 1.8.0 Depends: R (>= 3.4.1) Imports: Biobase, cluster, stats, methods Suggests: tsne, bcellViper License: file LICENSE MD5sum: 356e24a4a2d9cd622bc2f704fca52b25 NeedsCompilation: no Title: Iterative Clustering Description: A framework for performing clustering analysis iteratively. biocViews: StatisticalMethod, Clustering Author: Hongxu Ding and Andrea Califano Maintainer: Hongxu Ding URL: https://github.com/hd2326/iterClust BugReports: https://github.com/hd2326/iterClust/issues git_url: https://git.bioconductor.org/packages/iterClust git_branch: RELEASE_3_10 git_last_commit: aedc228 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/iterClust_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/iterClust_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/iterClust_1.8.0.tgz vignettes: vignettes/iterClust/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iterClust/inst/doc/introduction.R dependencyCount: 9 Package: iteremoval Version: 1.6.0 Depends: R (>= 3.5.0), ggplot2 (>= 2.2.1) Imports: magrittr, graphics, utils, GenomicRanges, SummarizedExperiment Suggests: testthat, knitr License: GPL-2 MD5sum: d5f3991ce18b0d70bafe4e3004cb1549 NeedsCompilation: no Title: Iteration removal method for feature selection Description: The package provides a flexible algorithm to screen features of two distinct groups in consideration of overfitting and overall performance. It was originally tailored for methylation locus screening of NGS data, and it can also be used as a generic method for feature selection. Each step of the algorithm provides a default method for simple implemention, and the method can be replaced by a user defined function. biocViews: StatisticalMethod Author: Jiacheng Chuan [aut, cre] Maintainer: Jiacheng Chuan URL: https://github.com/cihga39871/iteremoval VignetteBuilder: knitr BugReports: https://github.com/cihga39871/iteremoval/issues git_url: https://git.bioconductor.org/packages/iteremoval git_branch: RELEASE_3_10 git_last_commit: a8f442d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/iteremoval_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/iteremoval_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/iteremoval_1.6.0.tgz vignettes: vignettes/iteremoval/inst/doc/iteremoval.html vignetteTitles: An introduction to iteremoval hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iteremoval/inst/doc/iteremoval.R dependencyCount: 77 Package: IVAS Version: 2.6.0 Depends: R (> 3.0.0),GenomicFeatures, ggplot2, Biobase Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, GenomeInfoDb, ggfortify, grDevices, methods, Matrix, BiocParallel,utils, stats Suggests: BiocStyle License: GPL-2 MD5sum: 91b9b94d3cda7c4a7819a61d4e2fadba NeedsCompilation: no Title: Identification of genetic Variants affecting Alternative Splicing Description: Identification of genetic variants affecting alternative splicing. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, GeneRegulation, Regression, RNASeq, Sequencing, SNP, Software, Transcription Author: Seonggyun Han, Sangsoo Kim Maintainer: Seonggyun Han git_url: https://git.bioconductor.org/packages/IVAS git_branch: RELEASE_3_10 git_last_commit: 52a1844 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/IVAS_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/IVAS_2.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IVAS_2.6.0.tgz vignettes: vignettes/IVAS/inst/doc/IVAS.pdf vignetteTitles: IVAS : Identification of genetic Variants affecting Alternative Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IVAS/inst/doc/IVAS.R dependsOnMe: IMAS importsMe: ASpediaFI dependencyCount: 123 Package: ivygapSE Version: 1.8.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: shiny, survival, survminer, hwriter, plotly, ggplot2, S4Vectors, graphics, stats, utils, UpSetR Suggests: knitr, png, limma, grid, DT, randomForest, digest, testthat License: Artistic-2.0 MD5sum: 363ba5c2d2b2a36a637899d62d8de71f NeedsCompilation: no Title: A SummarizedExperiment for Ivy-GAP data Description: Define a SummarizedExperiment and exploratory app for Ivy-GAP glioblastoma image, expression, and clinical data. biocViews: Transcription, Software, Visualization, Survival, GeneExpression, Sequencing Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ivygapSE git_branch: RELEASE_3_10 git_last_commit: 48c445e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ivygapSE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ivygapSE_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ivygapSE_1.8.0.tgz vignettes: vignettes/ivygapSE/inst/doc/ivygapSE.html vignetteTitles: ivygapSE -- SummarizedExperiment for Ivy-GAP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ivygapSE/inst/doc/ivygapSE.R dependencyCount: 133 Package: IWTomics Version: 1.10.0 Depends: GenomicRanges Imports: parallel,gtable,grid,graphics,methods,IRanges,KernSmooth,fda,S4Vectors,grDevices,stats,utils,tools Suggests: knitr License: GPL (>=2) MD5sum: 4244507a81fbf6381e6d78d082df79de NeedsCompilation: no Title: Interval-Wise Testing for Omics Data Description: Implementation of the Interval-Wise Testing (IWT) for omics data. This inferential procedure tests for differences in "Omics" data between two groups of genomic regions (or between a group of genomic regions and a reference center of symmetry), and does not require fixing location and scale at the outset. biocViews: StatisticalMethod, MultipleComparison, DifferentialExpression, DifferentialMethylation, DifferentialPeakCalling, GenomeAnnotation, DataImport Author: Marzia A Cremona, Alessia Pini, Francesca Chiaromonte, Simone Vantini Maintainer: Marzia A Cremona VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IWTomics git_branch: RELEASE_3_10 git_last_commit: 5514836 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/IWTomics_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/IWTomics_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/IWTomics_1.10.0.tgz vignettes: vignettes/IWTomics/inst/doc/IWTomics.pdf vignetteTitles: Introduction to IWTomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IWTomics/inst/doc/IWTomics.R dependencyCount: 24 Package: joda Version: 1.34.0 Depends: R (>= 2.0), bgmm, RBGL License: GPL (>= 2) MD5sum: 2ce62606b7b23f98f496a7f281796c09 NeedsCompilation: no Title: JODA algorithm for quantifying gene deregulation using knowledge Description: Package 'joda' implements three steps of an algorithm called JODA. The algorithm computes gene deregulation scores. For each gene, its deregulation score reflects how strongly an effect of a certain regulator's perturbation on this gene differs between two different cell populations. The algorithm utilizes regulator knockdown expression data as well as knowledge about signaling pathways in which the regulators are involved (formalized in a simple matrix model). biocViews: Microarray, Pathways, GraphAndNetwork, StatisticalMethod, NetworkInference Author: Ewa Szczurek Maintainer: Ewa Szczurek URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/joda git_branch: RELEASE_3_10 git_last_commit: 03d25f6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/joda_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/joda_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/joda_1.34.0.tgz vignettes: vignettes/joda/inst/doc/JodaVignette.pdf vignetteTitles: Introduction to joda hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/joda/inst/doc/JodaVignette.R dependencyCount: 74 Package: JunctionSeq Version: 1.16.0 Depends: R (>= 3.2.2), methods, SummarizedExperiment (>= 0.2.0), Rcpp (>= 0.11.0), RcppArmadillo (>= 0.3.4.4) Imports: DESeq2 (>= 1.10.0), statmod, Hmisc, plotrix, stringr, Biobase (>= 2.30.0), locfit, BiocGenerics (>= 0.7.5), BiocParallel, genefilter, geneplotter, S4Vectors, IRanges, GenomicRanges, LinkingTo: Rcpp, RcppArmadillo Suggests: MASS, knitr, JctSeqData, BiocStyle Enhances: Cairo, pryr License: file LICENSE MD5sum: 442a4a3521606cf5af58dbc6385e7c12 NeedsCompilation: yes Title: JunctionSeq: A Utility for Detection of Differential Exon and Splice-Junction Usage in RNA-Seq data Description: A Utility for Detection and Visualization of Differential Exon or Splice-Junction Usage in RNA-Seq data. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression Author: Stephen Hartley [aut, cre] (PhD), Simon Anders [cph], Alejandro Reyes [cph] Maintainer: Stephen Hartley URL: http://hartleys.github.io/JunctionSeq/index.html VignetteBuilder: knitr BugReports: https://github.com/hartleys/JunctionSeq/issues git_url: https://git.bioconductor.org/packages/JunctionSeq git_branch: RELEASE_3_10 git_last_commit: 6792868 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/JunctionSeq_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/JunctionSeq_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/JunctionSeq_1.16.0.tgz vignettes: vignettes/JunctionSeq/inst/doc/JunctionSeq.pdf vignetteTitles: JunctionSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: PathwaySplice dependencyCount: 123 Package: karyoploteR Version: 1.12.4 Depends: R (>= 3.4), regioneR, GenomicRanges, methods Imports: regioneR, GenomicRanges, IRanges, Rsamtools, stats, graphics, memoise, rtracklayer, GenomeInfoDb, S4Vectors, biovizBase, digest, bezier, GenomicFeatures, bamsignals, AnnotationDbi, grDevices, VariantAnnotation Suggests: BiocStyle, knitr, testthat, magrittr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Hs.eg.db, org.Mm.eg.db, pasillaBamSubset License: Artistic-2.0 MD5sum: b31c20bcb30bc4692d450abf1654fdc8 NeedsCompilation: no Title: Plot customizable linear genomes displaying arbitrary data Description: karyoploteR creates karyotype plots of arbitrary genomes and offers a complete set of functions to plot arbitrary data on them. It mimicks many R base graphics functions coupling them with a coordinate change function automatically mapping the chromosome and data coordinates into the plot coordinates. In addition to the provided data plotting functions, it is easy to add new ones. biocViews: Visualization, CopyNumberVariation, Sequencing, Coverage, DNASeq, ChIPSeq, MethylSeq, DataImport, OneChannel Author: Bernat Gel Maintainer: Bernat Gel URL: https://github.com/bernatgel/karyoploteR VignetteBuilder: knitr BugReports: https://github.com/bernatgel/karyoploteR/issues git_url: https://git.bioconductor.org/packages/karyoploteR git_branch: RELEASE_3_10 git_last_commit: 15364ab git_last_commit_date: 2020-01-15 Date/Publication: 2020-01-15 source.ver: src/contrib/karyoploteR_1.12.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/karyoploteR_1.12.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/karyoploteR_1.12.4.tgz vignettes: vignettes/karyoploteR/inst/doc/karyoploteR.html vignetteTitles: karyoploteR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/karyoploteR/inst/doc/karyoploteR.R dependsOnMe: CopyNumberPlots importsMe: CNVfilteR suggestsMe: Category dependencyCount: 145 Package: KCsmart Version: 2.44.0 Depends: siggenes, multtest, KernSmooth Imports: methods, BiocGenerics Enhances: Biobase, CGHbase License: GPL-3 MD5sum: 9eb1672f1d888f70d5452e4182175fae NeedsCompilation: no Title: Multi sample aCGH analysis package using kernel convolution Description: Multi sample aCGH analysis package using kernel convolution biocViews: CopyNumberVariation, Visualization, aCGH, Microarray Author: Jorma de Ronde, Christiaan Klijn, Arno Velds Maintainer: Jorma de Ronde git_url: https://git.bioconductor.org/packages/KCsmart git_branch: RELEASE_3_10 git_last_commit: 2bb3e31 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/KCsmart_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/KCsmart_2.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/KCsmart_2.44.0.tgz vignettes: vignettes/KCsmart/inst/doc/KCS.pdf vignetteTitles: KCsmart example session hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KCsmart/inst/doc/KCS.R dependencyCount: 19 Package: kebabs Version: 1.20.0 Depends: R (>= 3.2.0), Biostrings (>= 2.35.5), kernlab Imports: methods, stats, Rcpp (>= 0.11.2), Matrix, XVector (>= 0.7.3), S4Vectors (>= 0.5.11), e1071, LiblineaR, graphics, grDevices, utils, apcluster LinkingTo: IRanges, XVector, Biostrings, Rcpp, S4Vectors Suggests: SparseM, Biobase, BiocGenerics, knitr License: GPL (>= 2.1) Archs: i386, x64 MD5sum: 8ead57beb3aebb2c7ec09988b4b63a12 NeedsCompilation: yes Title: Kernel-Based Analysis Of Biological Sequences Description: The package provides functionality for kernel-based analysis of DNA, RNA, and amino acid sequences via SVM-based methods. As core functionality, kebabs implements following sequence kernels: spectrum kernel, mismatch kernel, gappy pair kernel, and motif kernel. Apart from an efficient implementation of standard position-independent functionality, the kernels are extended in a novel way to take the position of patterns into account for the similarity measure. Because of the flexibility of the kernel formulation, other kernels like the weighted degree kernel or the shifted weighted degree kernel with constant weighting of positions are included as special cases. An annotation-specific variant of the kernels uses annotation information placed along the sequence together with the patterns in the sequence. The package allows for the generation of a kernel matrix or an explicit feature representation in dense or sparse format for all available kernels which can be used with methods implemented in other R packages. With focus on SVM-based methods, kebabs provides a framework which simplifies the usage of existing SVM implementations in kernlab, e1071, and LiblineaR. Binary and multi-class classification as well as regression tasks can be used in a unified way without having to deal with the different functions, parameters, and formats of the selected SVM. As support for choosing hyperparameters, the package provides cross validation - including grouped cross validation, grid search and model selection functions. For easier biological interpretation of the results, the package computes feature weights for all SVMs and prediction profiles which show the contribution of individual sequence positions to the prediction result and indicate the relevance of sequence sections for the learning result and the underlying biological functions. biocViews: SupportVectorMachine, Classification, Clustering, Regression Author: Johannes Palme Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/kebabs/ https://github.com/UBod/kebabs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/kebabs git_branch: RELEASE_3_10 git_last_commit: fe29819 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/kebabs_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/kebabs_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/kebabs_1.20.0.tgz vignettes: vignettes/kebabs/inst/doc/kebabs.pdf vignetteTitles: KeBABS - An R Package for Kernel Based Analysis of Biological Sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kebabs/inst/doc/kebabs.R dependsOnMe: procoil importsMe: FindMyFriends, odseq dependencyCount: 23 Package: KEGGgraph Version: 1.46.0 Depends: R (>= 2.10.0) Imports: methods, XML (>= 2.3-0), graph, utils, RCurl Suggests: Rgraphviz, RBGL, testthat, RColorBrewer, KEGG.db, org.Hs.eg.db, hgu133plus2.db, SPIA License: GPL (>= 2) MD5sum: 7a13f83e3d434d9245089897f3c28d1a NeedsCompilation: no Title: KEGGgraph: A graph approach to KEGG PATHWAY in R and Bioconductor Description: KEGGGraph is an interface between KEGG pathway and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated KGML (KEGG XML) files into graph models maintaining all essential pathway attributes. The package offers functionalities including parsing, graph operation, visualization and etc. biocViews: Pathways, GraphAndNetwork, Visualization, KEGG Author: Jitao David Zhang, with inputs from Paul Shannon Maintainer: Jitao David Zhang URL: http://www.nextbiomotif.com git_url: https://git.bioconductor.org/packages/KEGGgraph git_branch: RELEASE_3_10 git_last_commit: 83fad70 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/KEGGgraph_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/KEGGgraph_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/KEGGgraph_1.46.0.tgz vignettes: vignettes/KEGGgraph/inst/doc/KEGGgraph.pdf, vignettes/KEGGgraph/inst/doc/KEGGgraphApp.pdf vignetteTitles: KEGGgraph: graph approach to KEGG PATHWAY, KEGGgraph: Application Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGgraph/inst/doc/KEGGgraph.R, vignettes/KEGGgraph/inst/doc/KEGGgraphApp.R dependsOnMe: ROntoTools, SPIA importsMe: clipper, DEGraph, EnrichmentBrowser, MAGeCKFlute, MetaboSignal, MWASTools, NCIgraph, pathview suggestsMe: DEGraph, GenomicRanges dependencyCount: 11 Package: KEGGlincs Version: 1.12.0 Depends: R (>= 3.3), KOdata, hgu133a.db, org.Hs.eg.db (>= 3.3.0) Imports: AnnotationDbi,KEGGgraph,igraph,plyr,gtools,httr,RJSONIO,KEGGREST, methods,graphics,stats,utils, XML, grDevices Suggests: BiocManager (>= 1.20.3), knitr, graph License: GPL-3 MD5sum: 532e3ecae9c238cbc6b119e0bb936ef3 NeedsCompilation: no Title: Visualize all edges within a KEGG pathway and overlay LINCS data Description: See what is going on 'under the hood' of KEGG pathways by explicitly re-creating the pathway maps from information obtained from KGML files. biocViews: NetworkInference, GeneExpression, DataRepresentation, ThirdPartyClient,CellBiology,GraphAndNetwork,Pathways,KEGG,Network Author: Shana White Maintainer: Shana White , Mario Medvedovic SystemRequirements: Cytoscape (>= 3.3.0), Java (>= 8) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KEGGlincs git_branch: RELEASE_3_10 git_last_commit: b292ad7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/KEGGlincs_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/KEGGlincs_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/KEGGlincs_1.12.0.tgz vignettes: vignettes/KEGGlincs/inst/doc/Example-workflow.html vignetteTitles: KEGGlincs Workflows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGlincs/inst/doc/Example-workflow.R dependencyCount: 57 Package: keggorthology Version: 2.38.0 Depends: R (>= 2.5.0),stats,graph,hgu95av2.db Imports: AnnotationDbi,graph,DBI, graph, grDevices, methods, stats, tools, utils Suggests: RBGL,ALL License: Artistic-2.0 Archs: i386, x64 MD5sum: 18fb3465da7dec3d071a3a3f0f5bdfb7 NeedsCompilation: no Title: graph support for KO, KEGG Orthology Description: graphical representation of the Feb 2010 KEGG Orthology. The KEGG orthology is a set of pathway IDs that are not to be confused with the KEGG ortholog IDs. biocViews: Pathways, GraphAndNetwork, Visualization, KEGG Author: VJ Carey Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/keggorthology git_branch: RELEASE_3_10 git_last_commit: 3a59cf9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/keggorthology_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/keggorthology_2.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/keggorthology_2.38.0.tgz vignettes: vignettes/keggorthology/inst/doc/keggorth.pdf vignetteTitles: keggorthology overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/keggorthology/inst/doc/keggorth.R suggestsMe: MLInterfaces dependencyCount: 31 Package: KEGGprofile Version: 1.28.0 Imports: AnnotationDbi,png,TeachingDemos,XML,KEGG.db,KEGGREST,biomaRt,RCurl,ggplot2,reshape2 License: GPL (>= 2) MD5sum: fd4d9d9d1594e93ae070c2bd253ab9e3 NeedsCompilation: no Title: An annotation and visualization package for multi-types and multi-groups expression data in KEGG pathway Description: KEGGprofile is an annotation and visualization tool which integrated the expression profiles and the function annotation in KEGG pathway maps. The multi-types and multi-groups expression data can be visualized in one pathway map. KEGGprofile facilitated more detailed analysis about the specific function changes inner pathway or temporal correlations in different genes and samples. biocViews: Pathways, KEGG Author: Shilin Zhao, Yan Guo, Yu Shyr Maintainer: Shilin Zhao git_url: https://git.bioconductor.org/packages/KEGGprofile git_branch: RELEASE_3_10 git_last_commit: 1a1a267 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/KEGGprofile_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/KEGGprofile_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/KEGGprofile_1.28.0.tgz vignettes: vignettes/KEGGprofile/inst/doc/KEGGprofile.pdf vignetteTitles: KEGGprofile: Application Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGprofile/inst/doc/KEGGprofile.R suggestsMe: IntramiRExploreR dependencyCount: 99 Package: KEGGREST Version: 1.26.1 Imports: methods, httr, png, Biostrings Suggests: RUnit, BiocGenerics, knitr License: Artistic-2.0 MD5sum: d0b7ce69841b7ea1ead2b180c694be43 NeedsCompilation: no Title: Client-side REST access to KEGG Description: A package that provides a client interface to the KEGG REST server. Based on KEGGSOAP by J. Zhang, R. Gentleman, and Marc Carlson, and KEGG (python package) by Aurelien Mazurie. biocViews: Annotation, Pathways, ThirdPartyClient, KEGG Author: Dan Tenenbaum Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KEGGREST git_branch: RELEASE_3_10 git_last_commit: 56d6223 git_last_commit_date: 2019-11-06 Date/Publication: 2019-11-06 source.ver: src/contrib/KEGGREST_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/KEGGREST_1.26.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/KEGGREST_1.26.1.tgz vignettes: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.html vignetteTitles: Accessing the KEGG REST API hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.R dependsOnMe: PAPi, ROntoTools importsMe: ADAM, attract, BiocSet, CNEr, EnrichmentBrowser, FELLA, gage, MAGeCKFlute, MetaboSignal, MWASTools, pathview, PathwaySplice, YAPSA dependencyCount: 22 Package: kimod Version: 1.14.0 Depends: R(>= 3.3),methods Imports: cluster, graphics, Biobase License: GPL (>=2) MD5sum: f1c3bf658e6ab71fe608b102a6074e6f NeedsCompilation: no Title: A k-tables approach to integrate multiple Omics-Data Description: This package allows to work with mixed omics data (transcriptomics, proteomics, microarray-chips, rna-seq data), introducing the following improvements: distance options (for numeric and/or categorical variables) for each of the tables, bootstrap resampling techniques on the residuals matrices for all methods, that enable perform confidence ellipses for the projection of individuals, variables and biplot methodology to project variables (gene expression) on the compromise. Since the main purpose of the package is to use these techniques to omic data analysis, it includes an example data from four different microarray platforms (i.e.,Agilent, Affymetrix HGU 95, Affymetrix HGU 133 and Affymetrix HGU 133plus 2.0) on the NCI-60 cell lines.NCI60_4arrays is a list containing the NCI-60 microarray data with only few hundreds of genes randomly selected in each platform to keep the size of the package small. The data are the same that the package omicade4 used to implement the co-inertia analysis. The references in packages follow the style of the APA-6th norm. biocViews: Microarray, Visualization, GeneExpression, ExperimentData, Proteomics Author: Maria Laura Zingaretti, Johanna Altair Demey-Zambrano, Jose Luis Vicente-Villardon, Jhonny Rafael Demey Maintainer: M L Zingaretti git_url: https://git.bioconductor.org/packages/kimod git_branch: RELEASE_3_10 git_last_commit: e5336b0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/kimod_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/kimod_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/kimod_1.14.0.tgz vignettes: vignettes/kimod/inst/doc/kimod-vignette.pdf vignetteTitles: kimod A K-tables approach to integrate multiple Omics-Data in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kimod/inst/doc/kimod-vignette.R dependencyCount: 9 Package: KinSwingR Version: 1.4.0 Depends: R (>= 3.5) Imports: data.table, BiocParallel, sqldf, stats, grid, grDevices Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 5e17b845fb926f7cd390f96f59829434 NeedsCompilation: no Title: KinSwingR: network-based kinase activity prediction Description: KinSwingR integrates phosphosite data derived from mass-spectrometry data and kinase-substrate predictions to predict kinase activity. Several functions allow the user to build PWM models of kinase-subtrates, statistically infer PWM:substrate matches, and integrate these data to infer kinase activity. biocViews: Proteomics, SequenceMatching, Network Author: Ashley J. Waardenberg [aut, cre] Maintainer: Ashley J. Waardenberg VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KinSwingR git_branch: RELEASE_3_10 git_last_commit: ef03f35 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/KinSwingR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/KinSwingR_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/KinSwingR_1.4.0.tgz vignettes: vignettes/KinSwingR/inst/doc/KinSwingR.html vignetteTitles: KinSwingR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KinSwingR/inst/doc/KinSwingR.R dependencyCount: 33 Package: kissDE Version: 1.6.0 Imports: aod, Biobase, DESeq2, DSS, ggplot2, gplots, graphics, grDevices, matrixStats, stats, utils, foreach, doParallel, parallel Suggests: BiocStyle, testthat License: GPL (>= 2) MD5sum: 25d2f2ff10cdb49d3cd0da5c73697ba9 NeedsCompilation: no Title: Retrieves Condition-Specific Variants in RNA-Seq Data Description: Retrieves condition-specific variants in RNA-seq data (SNVs, alternative-splicings, indels). It has been developed as a post-treatment of 'KisSplice' but can also be used with user's own data. biocViews: AlternativeSplicing, DifferentialSplicing, ExperimentalDesign, GenomicVariation, RNASeq, Transcriptomics Author: Clara Benoit-Pilven [aut], Camille Marchet [aut], Janice Kielbassa [aut], Lilia Brinza [aut], Audric Cologne [aut], Aurélie Siberchicot [aut, cre], Vincent Lacroix [aut], Frank Picard [ctb], Laurent Jacob [ctb], Vincent Miele [ctb] Maintainer: Aurélie Siberchicot git_url: https://git.bioconductor.org/packages/kissDE git_branch: RELEASE_3_10 git_last_commit: c9e20e6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/kissDE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/kissDE_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/kissDE_1.6.0.tgz vignettes: vignettes/kissDE/inst/doc/kissDE.pdf vignetteTitles: kissDE.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kissDE/inst/doc/kissDE.R dependencyCount: 149 Package: KnowSeq Version: 1.0.0 Depends: R (>= 3.6.0), quantreg, mclust, topGO (>= 2.34.0) Imports: stringr, factoextra, kernlab, ggplot2, reshape2, gplots, caret, RCurl, XML, class, praznik, R.utils, e1071, randomForest, httr, jsonlite, sva (>= 3.30.1), cqn (>= 1.28.1), edgeR (>= 3.24.3), biomaRt (>= 2.38.0), limma (>= 3.38.3), arrayQualityMetrics (>= 3.38.0), tximport (>= 1.10.1), tximportData (>= 1.10.0), rhdf5 (>= 2.26.2), Biobase, multtest, pathview (>= 1.22.3), grDevices, graphics, stats, utils Suggests: knitr License: GPL (>=2) MD5sum: 5bfab3c420fa44aee7a9dc5208f0a297 NeedsCompilation: no Title: A R package to extract knowledge by using RNA-seq raw files Description: KnowSeq proposes a whole pipeline that comprises the most relevant steps in the RNA-seq gene expression analysis, with the main goal of extracting biological knowledge from raw data (Differential Expressed Genes, Gene Ontology enrichment, pathway visualization and related diseases). In this sense, KnowSeq allows aligning raw data from the original fastq or sra files, by using the most renowned aligners such as tophat2, hisat2, salmon and kallisto. Nowadays, there is no package that only from the information of the samples to align -included in a text file-, automatically performs the download and alignment of all of the samples. Furthermore, the package includes functions to: calculate the gene expression values; remove batch effect; calculate the Differentially Expressed Genes (DEGs); plot different graphs; and perform the DEGs enrichment with the GO information, pathways visualization and related diseases information retrieval. Moreover, KnowSeq is the only package that allows applying both a machine learning and DEGs enrichment processes just after the DEGs extraction. This idea emerged with the aim of proposing a complete tool to the research community containing all the necessary steps to carry out complete studies in a simple and fast way. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DataImport, Classification, FeatureExtraction, Sequencing, RNASeq, BatchEffect, Normalization, Preprocessing, QualityControl, Genetics, Transcriptomics, Microarray, Metabolomics, Proteomics, Alignment, Pathways, SystemsBiology, MultipleComparison, GO, GraphAndNetwork Author: Daniel Castillo-Secilla, Juan Manuel Galvez, Francisco Manuel Ortuno, Luis Javier Herrera and Ignacio Rojas. Maintainer: Daniel Castillo Secilla VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KnowSeq git_branch: RELEASE_3_10 git_last_commit: 66d1b81 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/KnowSeq_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/KnowSeq_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/KnowSeq_1.0.0.tgz vignettes: vignettes/KnowSeq/inst/doc/KnowSeq.pdf vignetteTitles: The KnowSeq users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KnowSeq/inst/doc/KnowSeq.R dependencyCount: 240 Package: lapmix Version: 1.52.0 Depends: R (>= 2.6.0),stats Imports: Biobase, graphics, grDevices, methods, stats, tools, utils License: GPL (>= 2) MD5sum: 353fc589c4d349a7e186847006663200 NeedsCompilation: no Title: Laplace Mixture Model in Microarray Experiments Description: Laplace mixture modelling of microarray experiments. A hierarchical Bayesian approach is used, and the hyperparameters are estimated using empirical Bayes. The main purpose is to identify differentially expressed genes. biocViews: Microarray, OneChannel, DifferentialExpression Author: Yann Ruffieux, contributions from Debjani Bhowmick, Anthony C. Davison, and Darlene R. Goldstein Maintainer: Yann Ruffieux URL: http://www.r-project.org, http://www.bioconductor.org, http://stat.epfl.ch git_url: https://git.bioconductor.org/packages/lapmix git_branch: RELEASE_3_10 git_last_commit: 12b67ef git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/lapmix_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/lapmix_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/lapmix_1.52.0.tgz vignettes: vignettes/lapmix/inst/doc/lapmix-example.pdf vignetteTitles: lapmix example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lapmix/inst/doc/lapmix-example.R dependencyCount: 9 Package: LBE Version: 1.54.0 Depends: stats Imports: graphics, grDevices, methods, stats, utils Suggests: qvalue License: GPL-2 MD5sum: b747e147f93f0b15436ce61923573331 NeedsCompilation: no Title: Estimation of the false discovery rate. Description: LBE is an efficient procedure for estimating the proportion of true null hypotheses, the false discovery rate (and so the q-values) in the framework of estimating procedures based on the marginal distribution of the p-values without assumption for the alternative hypothesis. biocViews: MultipleComparison Author: Cyril Dalmasso Maintainer: Cyril Dalmasso git_url: https://git.bioconductor.org/packages/LBE git_branch: RELEASE_3_10 git_last_commit: b6a84b7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/LBE_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/LBE_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/LBE_1.54.0.tgz vignettes: vignettes/LBE/inst/doc/LBE.pdf vignetteTitles: LBE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LBE/inst/doc/LBE.R dependencyCount: 5 Package: ldblock Version: 1.16.0 Depends: R (>= 3.5), methods Imports: Matrix, snpStats, VariantAnnotation, GenomeInfoDb, httr, BiocGenerics, ensembldb, EnsDb.Hsapiens.v75, Rsamtools, GenomicFiles (>= 1.13.6), BiocGenerics (>= 0.25.1) Suggests: RUnit, knitr, BiocStyle, gwascat License: Artistic-2.0 MD5sum: 6ea5969602c2e35ab88977c0da1c868b NeedsCompilation: no Title: data structures for linkage disequilibrium measures in populations Description: Define data structures for linkage disequilibrium measures in populations. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ldblock git_branch: RELEASE_3_10 git_last_commit: 1a9e941 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ldblock_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ldblock_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ldblock_1.16.0.tgz vignettes: vignettes/ldblock/inst/doc/ldblock.html vignetteTitles: ldblock package: linkage disequilibrium data structures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ldblock/inst/doc/ldblock.R suggestsMe: gQTLstats dependencyCount: 94 Package: LEA Version: 2.8.0 Depends: R (>= 3.3.0), methods, stats, utils, graphics Suggests: knitr License: GPL-3 MD5sum: 7dead19e47bbe452aabe585415724752 NeedsCompilation: yes Title: LEA: an R package for Landscape and Ecological Association Studies Description: LEA is an R package dedicated to landscape genomics and ecological association tests. LEA can run analyses of population structure and genomewide tests for local adaptation. The package includes statistical methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf, pca). It performs statistical tests using latent factor mixed models for identifying genetic polymorphisms that exhibit association with environmental gradients or phenotypic traits (lfmm). LEA is mainly based on optimized C programs that can scale with the dimension of large data sets. biocViews: Software, StatisticalMethod, Clustering, Regression Author: Eric Frichot , Olivier Francois Maintainer: Eric Frichot , Olivier Francois URL: http://membres-timc.imag.fr/Olivier.Francois/lea.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LEA git_branch: RELEASE_3_10 git_last_commit: 8f6871c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/LEA_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/LEA_2.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/LEA_2.8.0.tgz vignettes: vignettes/LEA/inst/doc/LEA.pdf vignetteTitles: LEA: An R Package for Landscape and Ecological Association Studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LEA/inst/doc/LEA.R dependencyCount: 4 Package: LedPred Version: 1.20.0 Depends: R (>= 3.2.0), e1071 (>= 1.6) Imports: akima, ggplot2, irr, jsonlite, parallel, plot3D, plyr, RCurl, ROCR, testthat License: MIT | file LICENSE MD5sum: 6f716fea26ae26e288b62b84bd69950b NeedsCompilation: no Title: Learning from DNA to Predict Enhancers Description: This package aims at creating a predictive model of regulatory sequences used to score unknown sequences based on the content of DNA motifs, next-generation sequencing (NGS) peaks and signals and other numerical scores of the sequences using supervised classification. The package contains a workflow based on the support vector machine (SVM) algorithm that maps features to sequences, optimize SVM parameters and feature number and creates a model that can be stored and used to score the regulatory potential of unknown sequences. biocViews: SupportVectorMachine, Software, MotifAnnotation, ChIPSeq, Sequencing, Classification Author: Elodie Darbo, Denis Seyres, Aitor Gonzalez Maintainer: Aitor Gonzalez BugReports: https://github.com/aitgon/LedPred/issues git_url: https://git.bioconductor.org/packages/LedPred git_branch: RELEASE_3_10 git_last_commit: 7cda442 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/LedPred_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/LedPred_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/LedPred_1.20.0.tgz vignettes: vignettes/LedPred/inst/doc/LedPred.pdf vignetteTitles: LedPred Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LedPred/inst/doc/LedPred.R dependencyCount: 73 Package: les Version: 1.36.0 Depends: R (>= 2.13.2), methods, graphics, fdrtool Imports: boot, gplots, RColorBrewer Suggests: Biobase, limma Enhances: parallel License: GPL-3 MD5sum: 384b373ce54e58ff792e9552209951bd NeedsCompilation: no Title: Identifying Differential Effects in Tiling Microarray Data Description: The 'les' package estimates Loci of Enhanced Significance (LES) in tiling microarray data. These are regions of regulation such as found in differential transcription, CHiP-chip, or DNA modification analysis. The package provides a universal framework suitable for identifying differential effects in tiling microarray data sets, and is independent of the underlying statistics at the level of single probes. biocViews: Microarray, DifferentialExpression, ChIPchip, DNAMethylation, Transcription Author: Julian Gehring, Clemens Kreutz, Jens Timmer Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/les git_branch: RELEASE_3_10 git_last_commit: 9d04cd2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/les_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/les_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/les_1.36.0.tgz vignettes: vignettes/les/inst/doc/les.pdf vignetteTitles: Introduction to the les package: Identifying Differential Effects in Tiling Microarray Data with the Loci of Enhanced Significance Framework hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/les/inst/doc/les.R importsMe: GSRI dependencyCount: 14 Package: levi Version: 1.4.2 Imports: DT(>= 0.4), RColorBrewer(>= 1.1-2), colorspace(>= 1.3-2), dplyr(>= 0.7.4), ggplot2(>= 2.2.1), httr(>= 1.3.1), igraph(>= 1.2.1), reshape2(>= 1.4.3), shiny(>= 1.0.5), shinydashboard(>= 0.7.0), shinyjs(>= 1.0), xml2(>= 1.2.0), knitr, Rcpp (>= 0.12.18), grid, grDevices, stats, utils, testthat, methods LinkingTo: Rcpp License: GPL (>= 2) Archs: i386, x64 MD5sum: 3243a11b380d80a423ca9722905317f6 NeedsCompilation: yes Title: Landscape Expression Visualization Interface Description: The tool integrates data from biological networks with transcriptomes, displaying a heatmap with surface curves to evidence the altered regions. biocViews: GeneExpression, Sequencing, Network, Software Author: Jose Rafael Pilan , Isabelle Mira da Silva , Agnes Alessandra Sekijima Takeda , Jose Luiz Rybarczyk Filho Maintainer: Jose Luiz Rybarczyk Filho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/levi git_branch: RELEASE_3_10 git_last_commit: ac16517 git_last_commit_date: 2020-04-08 Date/Publication: 2020-04-09 source.ver: src/contrib/levi_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/levi_1.4.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/levi_1.4.2.tgz vignettes: vignettes/levi/inst/doc/levi.html vignetteTitles: "Using levi" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/levi/inst/doc/levi.R dependencyCount: 91 Package: lfa Version: 1.16.0 Depends: R (>= 3.2) Imports: corpcor Suggests: knitr, ggplot2 License: GPL-3 MD5sum: f5555fc000520963087eee2aa15dabe4 NeedsCompilation: yes Title: Logistic Factor Analysis for Categorical Data Description: LFA is a method for a PCA analogue on Binomial data via estimation of latent structure in the natural parameter. biocViews: SNP, DimensionReduction, PrincipalComponent Author: Wei Hao, Minsun Song, John D. Storey Maintainer: Wei Hao , John D. Storey URL: https://github.com/StoreyLab/lfa VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/lfa/issues git_url: https://git.bioconductor.org/packages/lfa git_branch: RELEASE_3_10 git_last_commit: 4e55fec git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/lfa_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/lfa_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/lfa_1.16.0.tgz vignettes: vignettes/lfa/inst/doc/lfa.pdf vignetteTitles: lfa Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lfa/inst/doc/lfa.R importsMe: gcatest dependencyCount: 2 Package: limma Version: 3.42.2 Depends: R (>= 3.6.0) Imports: grDevices, graphics, stats, utils, methods Suggests: affy, AnnotationDbi, BiasedUrn, Biobase, ellipse, GO.db, gplots, illuminaio, locfit, MASS, org.Hs.eg.db, splines, statmod (>= 1.2.2), vsn License: GPL (>=2) MD5sum: fc3c669cc4ffc4fd7af6b42b6391728b NeedsCompilation: yes Title: Linear Models for Microarray Data Description: Data analysis, linear models and differential expression for microarray data. biocViews: ExonArray, GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneSetEnrichment, DataImport, Bayesian, Clustering, Regression, TimeCourse, Microarray, MicroRNAArray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, TwoChannel, Sequencing, RNASeq, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl, BiomedicalInformatics, CellBiology, Cheminformatics, Epigenetics, FunctionalGenomics, Genetics, ImmunoOncology, Metabolomics, Proteomics, SystemsBiology, Transcriptomics Author: Gordon Smyth [cre,aut], Yifang Hu [ctb], Matthew Ritchie [ctb], Jeremy Silver [ctb], James Wettenhall [ctb], Davis McCarthy [ctb], Di Wu [ctb], Wei Shi [ctb], Belinda Phipson [ctb], Aaron Lun [ctb], Natalie Thorne [ctb], Alicia Oshlack [ctb], Carolyn de Graaf [ctb], Yunshun Chen [ctb], Mette Langaas [ctb], Egil Ferkingstad [ctb], Marcus Davy [ctb], Francois Pepin [ctb], Dongseok Choi [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/limma git_url: https://git.bioconductor.org/packages/limma git_branch: RELEASE_3_10 git_last_commit: 4b763ee git_last_commit_date: 2020-02-02 Date/Publication: 2020-02-03 source.ver: src/contrib/limma_3.42.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/limma_3.42.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/limma_3.42.2.tgz vignettes: vignettes/limma/inst/doc/intro.pdf, vignettes/limma/inst/doc/usersguide.pdf vignetteTitles: Limma One Page Introduction, usersguide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4Base, AffyExpress, birta, BLMA, CALIB, cghMCR, codelink, convert, Cormotif, deco, DrugVsDisease, edgeR, ExiMiR, ExpressionAtlas, gCMAP, genefu, HTqPCR, IsoformSwitchAnalyzeR, maigesPack, marray, metagenomeSeq, metaseqR, MmPalateMiRNA, mpra, qpcrNorm, qusage, RBM, Ringo, RnBeads, Rnits, splineTimeR, SRGnet, SSPA, tRanslatome, TurboNorm, variancePartition, wateRmelon importsMe: ABSSeq, affycoretools, affylmGUI, AMARETTO, anamiR, animalcules, ArrayExpress, arrayQuality, arrayQualityMetrics, ArrayTools, artMS, ASpediaFI, ATACseqQC, attract, AWFisher, ballgown, BatchQC, beadarray, biotmle, bsseq, BubbleTree, bumphunter, CALIB, CancerMutationAnalysis, CancerSubtypes, casper, CATALYST, ChIPpeakAnno, clusterExperiment, CNVRanger, coexnet, compcodeR, consensusDE, consensusOV, CountClust, crlmm, CrossICC, crossmeta, csaw, cTRAP, ctsGE, DaMiRseq, debrowser, DEP, derfinderPlot, DEsubs, DiffBind, diffcyt, diffHic, diffloop, DMRcate, Doscheda, DRIMSeq, EBSEA, eegc, EGAD, EGSEA, EnrichmentBrowser, erccdashboard, EventPointer, explorase, flowBin, flowSpy, gCrisprTools, GDCRNATools, GeneSelectMMD, GEOquery, GGBase, GOsummaries, gQTLstats, GUIDEseq, hipathia, HTqPCR, icetea, iCheck, iChip, iCOBRA, ideal, InPAS, isomiRs, KnowSeq, limmaGUI, Linnorm, lipidr, lmdme, LVSmiRNA, mAPKL, MBQN, mCSEA, MEAL, methylKit, MethylMix, methyvim, MIGSA, minfi, miRLAB, missMethyl, MLSeq, MmPalateMiRNA, monocle, MoonlightR, MSstats, MSstatsTMT, MultiDataSet, muscat, NADfinder, nem, nethet, nondetects, NormalyzerDE, OGSA, OLIN, omicRexposome, oppti, OVESEG, PAA, PADOG, PathoStat, pcaExplorer, PECA, pepStat, phantasus, phenoTest, polyester, projectR, psichomics, pwrEWAS, qPLEXanalyzer, qsea, regsplice, Ringo, RNAinteract, RNAither, RTCGAToolbox, RTN, RTopper, scone, scran, SEPIRA, seqsetvis, sigaR, SimBindProfiles, singleCellTK, snapCGH, STATegRa, SVAPLSseq, systemPipeR, TCGAbiolinks, timecourse, TimeSeriesExperiment, ToPASeq, TPP, transcriptogramer, TVTB, tweeDEseq, vsn, Wrench, yamss, yarn, birte suggestsMe: ABarray, ADaCGH2, beadarraySNP, biobroom, BiocCaseStudies, BiocSet, BioNet, Category, categoryCompare, celaref, CellBench, CellMixS, ClassifyR, CMA, coGPS, cydar, DEGreport, derfinder, DEScan2, dyebias, ELBOW, fgsea, gage, Glimma, GSRI, GSVA, Harman, Heatplus, isobar, ivygapSE, les, lumi, MAST, mdgsa, methylumi, MLP, npGSEA, oligo, oppar, paxtoolsr, PGSEA, piano, plw, PREDA, proDA, puma, Rcade, RTopper, rtracklayer, scater, stageR, subSeq, SummarizedBenchmark, topconfects, tximeta, tximport, ViSEAGO, zFPKM dependencyCount: 5 Package: limmaGUI Version: 1.62.0 Imports: methods, grDevices, graphics, limma, R2HTML, tcltk, tkrplot, xtable, utils License: GPL (>=2) Archs: i386, x64 MD5sum: b285d1350e0b35a29a5222c880656940 NeedsCompilation: no Title: GUI for limma Package With Two Color Microarrays Description: A Graphical User Interface for differential expression analysis of two-color microarray data using the limma package. biocViews: GUI, GeneExpression, DifferentialExpression, DataImport, Bayesian, Regression, TimeCourse, Microarray, mRNAMicroarray, TwoChannel, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: James Wettenhall [aut], Gordon Smyth [aut], Keith Satterley [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/limmaGUI/ git_url: https://git.bioconductor.org/packages/limmaGUI git_branch: RELEASE_3_10 git_last_commit: 72bdbd2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/limmaGUI_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/limmaGUI_1.62.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/limmaGUI_1.62.0.tgz vignettes: vignettes/limmaGUI/inst/doc/extract.pdf, vignettes/limmaGUI/inst/doc/limmaGUI.pdf, vignettes/limmaGUI/inst/doc/LinModIntro.pdf, vignettes/limmaGUI/inst/doc/about.html, vignettes/limmaGUI/inst/doc/CustMenu.html, vignettes/limmaGUI/inst/doc/import.html, vignettes/limmaGUI/inst/doc/index.html, vignettes/limmaGUI/inst/doc/InputFiles.html, vignettes/limmaGUI/inst/doc/lgDevel.html, vignettes/limmaGUI/inst/doc/windowsFocus.html vignetteTitles: Extracting limma objects from limmaGUI files, limmaGUI Vignette, LinModIntro.pdf, about.html, CustMenu.html, import.html, index.html, InputFiles.html, lgDevel.html, windowsFocus.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limmaGUI/inst/doc/limmaGUI.R dependencyCount: 10 Package: LINC Version: 1.14.0 Depends: R (>= 3.3.1), methods, stats Imports: Rcpp (>= 0.11.0), DOSE, ggtree, gridExtra, ape, grid, png, Biobase, sva, reshape2, utils, grDevices, org.Hs.eg.db, clusterProfiler, ggplot2, ReactomePA LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, knitr, biomaRt License: Artistic-2.0 Archs: i386, x64 MD5sum: b85022f854cbbfd56d4b37db88d5a938 NeedsCompilation: yes Title: co-expression of lincRNAs and protein-coding genes Description: This package provides methods to compute co-expression networks of lincRNAs and protein-coding genes. Biological terms associated with the sets of protein-coding genes predict the biological contexts of lincRNAs according to the 'Guilty by Association' approach. biocViews: Software, BiologicalQuestion, GeneRegulation, GeneExpression Author: Manuel Goepferich, Carl Herrmann Maintainer: Manuel Goepferich VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LINC git_branch: RELEASE_3_10 git_last_commit: 5c7701c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/LINC_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/LINC_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/LINC_1.14.0.tgz vignettes: vignettes/LINC/inst/doc/LINC.html vignetteTitles: "LINC - Co-Expression Analysis of lincRNAs" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LINC/inst/doc/LINC.R dependencyCount: 147 Package: LineagePulse Version: 1.6.0 Imports: BiocParallel, circlize, compiler, ComplexHeatmap, ggplot2, gplots, grDevices, grid, knitr, Matrix, methods, RColorBrewer, SingleCellExperiment, splines, stats, SummarizedExperiment, utils License: Artistic-2.0 Archs: i386, x64 MD5sum: ef5485a5964328a0435538c60ee3222b NeedsCompilation: no Title: Differential expression analysis and model fitting for single-cell RNA-seq data Description: LineagePulse is a differential expression and expression model fitting package tailored to single-cell RNA-seq data (scRNA-seq). LineagePulse accounts for batch effects, drop-out and variable sequencing depth. One can use LineagePulse to perform longitudinal differential expression analysis across pseudotime as a continuous coordinate or between discrete groups of cells (e.g. pre-defined clusters or experimental conditions). Expression model fits can be directly extracted from LineagePulse. biocViews: ImmunoOncology, Software, StatisticalMethod, TimeCourse, Sequencing, DifferentialExpression, GeneExpression, CellBiology, CellBasedAssays, SingleCell Author: David S Fischer [aut, cre], Fabian Theis [ctb], Nir Yosef [ctb] Maintainer: David S Fischer VignetteBuilder: knitr BugReports: https://github.com/YosefLab/LineagePulse/issues git_url: https://git.bioconductor.org/packages/LineagePulse git_branch: RELEASE_3_10 git_last_commit: aab186f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/LineagePulse_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/LineagePulse_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/LineagePulse_1.6.0.tgz vignettes: vignettes/LineagePulse/inst/doc/LineagePulse_Tutorial.html vignetteTitles: LineagePulse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LineagePulse/inst/doc/LineagePulse_Tutorial.R dependencyCount: 101 Package: LinkHD Version: 1.0.0 Depends: R(>= 3.6.0), methods, ggplot2, stats Imports: scales, cluster, graphics, ggpubr, gridExtra, vegan, rio, MultiAssayExperiment, emmeans, reshape2, data.table Suggests: MASS (>= 7.3.0), knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 9ed1e950899a102be797984deaf3e5c5 NeedsCompilation: no Title: LinkHD: a versatile framework to explore and integrate heterogeneous data Description: Here we present Link-HD, an approach to integrate heterogeneous datasets, as a generalization of STATIS-ACT (“Structuration des Tableaux A Trois Indices de la Statistique–Analyse Conjointe de Tableaux”), a family of methods to join and compare information from multiple subspaces. However, STATIS-ACT has some drawbacks since it only allows continuous data and it is unable to establish relationships between samples and features. In order to tackle these constraints, we incorporate multiple distance options and a linear regression based Biplot model in order to stablish relationships between observations and variable and perform variable selection. biocViews: Classification,MultipleComparison,Regression,Software Author: Laura M. Zingaretti [aut, cre] Maintainer: "Laura M Zingaretti" VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LinkHD git_branch: RELEASE_3_10 git_last_commit: a49d1a2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/LinkHD_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/LinkHD_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/LinkHD_1.0.0.tgz vignettes: vignettes/LinkHD/inst/doc/LinkHD.html vignetteTitles: Annotating Genomic Variants hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LinkHD/inst/doc/LinkHD.R dependencyCount: 117 Package: Linnorm Version: 2.10.0 Depends: R(>= 3.4) Imports: Rcpp (>= 0.12.2), RcppArmadillo (>= 0.8.100.1.0), fpc, vegan, mclust, apcluster, ggplot2, ellipse, limma, utils, statmod, MASS, igraph, grDevices, graphics, fastcluster, ggdendro, zoo, stats, amap, Rtsne, gmodels LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, gplots, RColorBrewer, moments, testthat License: MIT + file LICENSE Archs: i386, x64 MD5sum: 412b454563241af3f57be806a8db8fcd NeedsCompilation: yes Title: Linear model and normality based normalization and transformation method (Linnorm) Description: Linnorm is an algorithm for normalizing and transforming RNA-seq, single cell RNA-seq, ChIP-seq count data or any large scale count data. It has been independently reviewed by Tian et al. on Nature Methods (https://doi.org/10.1038/s41592-019-0425-8). Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. biocViews: ImmunoOncology, Sequencing, ChIPSeq, RNASeq, DifferentialExpression, GeneExpression, Genetics, Normalization, Software, Transcription, BatchEffect, PeakDetection, Clustering, Network, SingleCell Author: Shun Hang Yip , Panwen Wang , Jean-Pierre Kocher , Pak Chung Sham , Junwen Wang Maintainer: Ken Shun Hang Yip URL: https://doi.org/10.1093/nar/gkx828 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Linnorm git_branch: RELEASE_3_10 git_last_commit: f90b334 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Linnorm_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Linnorm_2.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Linnorm_2.10.0.tgz vignettes: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.pdf vignetteTitles: Linnorm User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.R importsMe: mnem dependencyCount: 84 Package: lionessR Version: 1.0.0 Depends: R (>= 3.6.0) Imports: stats, SummarizedExperiment, S4Vectors Suggests: knitr, rmarkdown, igraph, reshape2, limma, License: MIT + file LICENSE MD5sum: eedb490270482de912a9fc065e874b3e NeedsCompilation: no Title: Modeling networks for individual samples using LIONESS Description: LIONESS, or Linear Interpolation to Obtain Network Estimates for Single Samples, can be used to reconstruct single-sample networks (https://arxiv.org/abs/1505.06440). This code implements the LIONESS equation in the lioness function in R to reconstruct single-sample networks. The default network reconstruction method we use is based on Pearson correlation. However, lionessR can run on any network reconstruction algorithms that returns a complete, weighted adjacency matrix. lionessR works for both unipartite and bipartite networks. biocViews: Network, NetworkInference, GeneExpression Author: Marieke Lydia Kuijjer [aut] (), Ping-Han Hsieh [cre] () Maintainer: Ping-Han Hsieh URL: https://github.com/mararie/lionessR VignetteBuilder: knitr BugReports: https://github.com/mararie/lionessR/issues git_url: https://git.bioconductor.org/packages/lionessR git_branch: RELEASE_3_10 git_last_commit: 69e39f3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/lionessR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/lionessR_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/lionessR_1.0.0.tgz vignettes: vignettes/lionessR/inst/doc/lionessR.html vignetteTitles: lionessR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lionessR/inst/doc/lionessR.R dependencyCount: 32 Package: lipidr Version: 2.0.0 Depends: R (>= 3.6.0), SummarizedExperiment Imports: methods, stats, utils, data.table, S4Vectors, rlang, dplyr, tidyr, forcats, ggplot2, limma, fgsea, ropls, magrittr Suggests: knitr, rmarkdown, BiocStyle, ggrepel, plotly, iheatmapr, spelling, testthat License: MIT + file LICENSE MD5sum: 3f87242604ebe3545319b95290823a08 NeedsCompilation: no Title: Data Mining and Analysis of Lipidomics Datasets Description: lipidr an easy-to-use R package implementing a complete workflow for downstream analysis of targeted and untargeted lipidomics data. lipidomics results can be imported into lipidr as a numerical matrix or a Skyline export, allowing integration into current analysis frameworks. Data mining of lipidomics datasets is enabled through integration with Metabolomics Workbench API. lipidr allows data inspection, normalization, univariate and multivariate analysis, displaying informative visualizations. lipidr also implements a novel Lipid Set Enrichment Analysis (LSEA), harnessing molecular information such as lipid class, chain length and unsaturation. biocViews: Lipidomics, MassSpectrometry, Normalization, QualityControl, Visualization Author: Ahmed Mohamed [cre] (), Ahmed Mohamed [aut], Jeffrey Molendijk [aut] Maintainer: Ahmed Mohamed URL: https://github.com/ahmohamed/lipidr VignetteBuilder: knitr BugReports: https://github.com/ahmohamed/lipidr/issues/ git_url: https://git.bioconductor.org/packages/lipidr git_branch: RELEASE_3_10 git_last_commit: 36bb416 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/lipidr_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/lipidr_2.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/lipidr_2.0.0.tgz vignettes: vignettes/lipidr/inst/doc/workflow.html vignetteTitles: lipidr_workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lipidr/inst/doc/workflow.R dependencyCount: 94 Package: LiquidAssociation Version: 1.40.0 Depends: geepack, methods, yeastCC, org.Sc.sgd.db Imports: Biobase, graphics, grDevices, methods, stats License: GPL (>=3) Archs: i386, x64 MD5sum: 05f8615be80b1f6592f29f34e03a9c32 NeedsCompilation: no Title: LiquidAssociation Description: The package contains functions for calculate direct and model-based estimators for liquid association. It also provides functions for testing the existence of liquid association given a gene triplet data. biocViews: Pathways, GeneExpression, CellBiology, Genetics, Network, TimeCourse Author: Yen-Yi Ho Maintainer: Yen-Yi Ho git_url: https://git.bioconductor.org/packages/LiquidAssociation git_branch: RELEASE_3_10 git_last_commit: 8c6768f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/LiquidAssociation_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/LiquidAssociation_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/LiquidAssociation_1.40.0.tgz vignettes: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.pdf vignetteTitles: LiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.R dependsOnMe: fastLiquidAssociation dependencyCount: 56 Package: lmdme Version: 1.28.0 Depends: R (>= 2.14.1), pls, stemHypoxia Imports: stats, methods, limma Enhances: parallel License: GPL (>=2) MD5sum: 97192fea6524631a5f7e4f507d59df90 NeedsCompilation: no Title: Linear Model decomposition for Designed Multivariate Experiments Description: linear ANOVA decomposition of Multivariate Designed Experiments implementation based on limma lmFit. Features: i)Flexible formula type interface, ii) Fast limma based implementation, iii) p-values for each estimated coefficient levels in each factor, iv) F values for factor effects and v) plotting functions for PCA and PLS. biocViews: Microarray, OneChannel, TwoChannel, Visualization, DifferentialExpression, ExperimentData, Cancer Author: Cristobal Fresno and Elmer A. Fernandez Maintainer: Cristobal Fresno URL: http://www.bdmg.com.ar/?page_id=38 git_url: https://git.bioconductor.org/packages/lmdme git_branch: RELEASE_3_10 git_last_commit: 6b4ac61 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/lmdme_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/lmdme_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/lmdme_1.28.0.tgz vignettes: vignettes/lmdme/inst/doc/lmdme-vignette.pdf vignetteTitles: lmdme: linear model framework for PCA/PLS analysis of ANOVA decomposition on Designed Multivariate Experiments in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lmdme/inst/doc/lmdme-vignette.R dependencyCount: 8 Package: LMGene Version: 2.42.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5), multtest, survival, affy Suggests: affydata License: LGPL MD5sum: 95c8dda4895a6f0e0c000cb2e2b005f8 NeedsCompilation: no Title: LMGene Software for Data Transformation and Identification of Differentially Expressed Genes in Gene Expression Arrays Description: LMGene package for analysis of microarray data using a linear model and glog data transformation biocViews: Microarray, DifferentialExpression, Preprocessing Author: David Rocke, Geun Cheol Lee, John Tillinghast, Blythe Durbin-Johnson, and Shiquan Wu Maintainer: Blythe Durbin-Johnson URL: http://dmrocke.ucdavis.edu/software.html git_url: https://git.bioconductor.org/packages/LMGene git_branch: RELEASE_3_10 git_last_commit: 1058eab git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/LMGene_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/LMGene_2.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/LMGene_2.42.0.tgz vignettes: vignettes/LMGene/inst/doc/LMGene.pdf vignetteTitles: LMGene User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LMGene/inst/doc/LMGene.R dependencyCount: 21 Package: LOBSTAHS Version: 1.12.0 Depends: R (>= 3.4), xcms, CAMERA, methods Imports: utils Suggests: PtH2O2lipids, knitr, rmarkdown License: GPL (>= 3) + file LICENSE MD5sum: 3cb1b18d9aacfe1c56444c4040ee3246 NeedsCompilation: no Title: Lipid and Oxylipin Biomarker Screening through Adduct Hierarchy Sequences Description: LOBSTAHS is a multifunction package for screening, annotation, and putative identification of mass spectral features in large, HPLC-MS lipid datasets. In silico data for a wide range of lipids, oxidized lipids, and oxylipins can be generated from user-supplied structural criteria with a database generation function. LOBSTAHS then applies these databases to assign putative compound identities to features in any high-mass accuracy dataset that has been processed using xcms and CAMERA. Users can then apply a series of orthogonal screening criteria based on adduct ion formation patterns, chromatographic retention time, and other properties, to evaluate and assign confidence scores to this list of preliminary assignments. During the screening routine, LOBSTAHS rejects assignments that do not meet the specified criteria, identifies potential isomers and isobars, and assigns a variety of annotation codes to assist the user in evaluating the accuracy of each assignment. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics, Lipidomics, DataImport Author: James Collins [aut, cre], Helen Fredricks [aut], Bethanie Edwards [aut], Benjamin Van Mooy [aut] Maintainer: James Collins URL: http://bioconductor.org/packages/LOBSTAHS VignetteBuilder: knitr BugReports: https://github.com/vanmooylipidomics/LOBSTAHS/issues/new git_url: https://git.bioconductor.org/packages/LOBSTAHS git_branch: RELEASE_3_10 git_last_commit: e6adc05 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/LOBSTAHS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/LOBSTAHS_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/LOBSTAHS_1.12.0.tgz vignettes: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.html vignetteTitles: Discovery,, Identification,, and Screening of Lipids and Oxylipins in HPLC-MS Datasets Using LOBSTAHS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.R dependencyCount: 128 Package: loci2path Version: 1.6.0 Depends: R (>= 3.4) Imports: pheatmap, wordcloud, RColorBrewer, data.table, methods, grDevices, stats, graphics, GenomicRanges, BiocParallel, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 4b1d8a84587e94db3ecc9de13e116a2d NeedsCompilation: no Title: Loci2path: regulatory annotation of genomic intervals based on tissue-specific expression QTLs Description: loci2path performs statistics-rigorous enrichment analysis of eQTLs in genomic regions of interest. Using eQTL collections provided by the Genotype-Tissue Expression (GTEx) project and pathway collections from MSigDB. biocViews: FunctionalGenomics, Genetics, GeneSetEnrichment, Software, GeneExpression, Sequencing, Coverage, BioCarta Author: Tianlei Xu Maintainer: Tianlei Xu URL: https://github.com/StanleyXu/loci2path VignetteBuilder: knitr BugReports: https://github.com/StanleyXu/loci2path/issues git_url: https://git.bioconductor.org/packages/loci2path git_branch: RELEASE_3_10 git_last_commit: 4556555 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/loci2path_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/loci2path_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/loci2path_1.6.0.tgz vignettes: vignettes/loci2path/inst/doc/loci2path-vignette.html vignetteTitles: loci2path hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/loci2path/inst/doc/loci2path-vignette.R dependencyCount: 41 Package: logicFS Version: 2.6.2 Depends: LogicReg, mcbiopi, survival Imports: graphics, methods, stats Suggests: genefilter, siggenes License: LGPL (>= 2) MD5sum: 744fe31e1254b08d39b3628254fc4ee7 NeedsCompilation: no Title: Identification of SNP Interactions Description: Identification of interactions between binary variables using Logic Regression. Can, e.g., be used to find interesting SNP interactions. Contains also a bagging version of logic regression for classification. biocViews: SNP, Classification, Genetics Author: Holger Schwender, Tobias Tietz Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/logicFS git_branch: RELEASE_3_10 git_last_commit: 3a0aece git_last_commit_date: 2020-04-12 Date/Publication: 2020-04-13 source.ver: src/contrib/logicFS_2.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/logicFS_2.6.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/logicFS_2.6.2.tgz vignettes: vignettes/logicFS/inst/doc/logicFS.pdf vignetteTitles: logicFS Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/logicFS/inst/doc/logicFS.R suggestsMe: trio dependencyCount: 12 Package: logitT Version: 1.44.0 Depends: affy Suggests: SpikeInSubset License: GPL (>= 2) MD5sum: 2e99a1b26f753182f9b9aa96d1bc99e6 NeedsCompilation: yes Title: logit-t Package Description: The logitT library implements the Logit-t algorithm introduced in --A high performance test of differential gene expression for oligonucleotide arrays-- by William J Lemon, Sandya Liyanarachchi and Ming You for use with Affymetrix data stored in an AffyBatch object in R. biocViews: Microarray, DifferentialExpression Author: Tobias Guennel Maintainer: Tobias Guennel URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/logitT git_branch: RELEASE_3_10 git_last_commit: 128ef18 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/logitT_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/logitT_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/logitT_1.44.0.tgz vignettes: vignettes/logitT/inst/doc/logitT.pdf vignetteTitles: logitT primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/logitT/inst/doc/logitT.R dependencyCount: 13 Package: Logolas Version: 1.10.0 Depends: R (>= 3.4) Imports: grid, SQUAREM, LaplacesDemon, stats, graphics, utils, ggplot2, gridBase, Biostrings Suggests: knitr, rmarkdown, BiocStyle, Biobase, devtools, xtable, gridExtra, RColorBrewer, seqLogo, ggseqlogo License: GPL (>= 2) MD5sum: a01def64ed669939dbf8a477da6b3e7a NeedsCompilation: no Title: EDLogo Plots Featuring String Logos and Adaptive Scaling of Position-Weight Matrices Description: Produces logo plots highlighting both enrichment and depletion of characters, allows for plotting of string symbols, and performs scaling of position-weights adaptively, along with several fun stylizations. biocViews: SequenceMatching, Alignment, Software, Visualization, Bayesian Author: Kushal Dey [aut, cre], Dongyue Xie [aut], Peter Carbonetto [ctb], Matthew Stephens [aut] Maintainer: Kushal Dey URL: https://github.com/kkdey/Logolas VignetteBuilder: knitr BugReports: http://github.com/kkdey/Logolas/issues git_url: https://git.bioconductor.org/packages/Logolas git_branch: RELEASE_3_10 git_last_commit: 447190b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Logolas_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Logolas_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Logolas_1.10.0.tgz vignettes: vignettes/Logolas/inst/doc/Logolas.html vignetteTitles: Guided Logolas Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Logolas/inst/doc/Logolas.R suggestsMe: universalmotif dependencyCount: 65 Package: lol Version: 1.34.0 Depends: penalized, Matrix Imports: Matrix, penalized, graphics, grDevices, stats License: GPL-2 MD5sum: 13283890a3a335eca0a07153dd6d8c5b NeedsCompilation: no Title: Lots Of Lasso Description: Various optimization methods for Lasso inference with matrix warpper biocViews: StatisticalMethod Author: Yinyin Yuan Maintainer: Yinyin Yuan git_url: https://git.bioconductor.org/packages/lol git_branch: RELEASE_3_10 git_last_commit: 5cd0f00 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/lol_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/lol_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/lol_1.34.0.tgz vignettes: vignettes/lol/inst/doc/lol.pdf vignetteTitles: An introduction to the lol package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lol/inst/doc/lol.R dependencyCount: 13 Package: LOLA Version: 1.16.0 Depends: R (>= 2.10) Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table, reshape2, utils, stats, methods Suggests: parallel, testthat, knitr, BiocStyle, rmarkdown Enhances: simpleCache, qvalue, ggplot2 License: GPL-3 Archs: i386, x64 MD5sum: 30685016f824175c41816e2e352e37ba NeedsCompilation: no Title: Locus overlap analysis for enrichment of genomic ranges Description: Provides functions for testing overlap of sets of genomic regions with public and custom region set (genomic ranges) databases. This makes it possible to do automated enrichment analysis for genomic region sets, thus facilitating interpretation of functional genomics and epigenomics data. biocViews: GeneSetEnrichment, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, MethylSeq, Sequencing Author: Nathan Sheffield [aut, cre], Christoph Bock [ctb] Maintainer: Nathan Sheffield URL: http://code.databio.org/LOLA VignetteBuilder: knitr BugReports: http://github.com/nsheff/LOLA git_url: https://git.bioconductor.org/packages/LOLA git_branch: RELEASE_3_10 git_last_commit: 4373d2b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/LOLA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/LOLA_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/LOLA_1.16.0.tgz vignettes: vignettes/LOLA/inst/doc/choosingUniverse.html, vignettes/LOLA/inst/doc/gettingStarted.html, vignettes/LOLA/inst/doc/usingLOLACore.html vignetteTitles: 3. Choosing a Universe, 1. Getting Started with LOLA, 2. Using LOLA Core hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LOLA/inst/doc/choosingUniverse.R, vignettes/LOLA/inst/doc/gettingStarted.R, vignettes/LOLA/inst/doc/usingLOLACore.R suggestsMe: COCOA, DeepBlueR, MIRA dependencyCount: 25 Package: LoomExperiment Version: 1.4.0 Depends: R (>= 3.5.0), S4Vectors, SingleCellExperiment, SummarizedExperiment, methods, rhdf5, rtracklayer Imports: DelayedArray, GenomicRanges, HDF5Array, Matrix, stats, stringr, utils Suggests: testthat, BiocStyle, knitr License: Artistic-2.0 MD5sum: b41b74b8753a263ed970a663f6654eca NeedsCompilation: no Title: LoomExperiment container Description: The LoomExperiment class provide a means to easily convert Bioconductor's "Experiment" classes to loom files and vice versa. biocViews: ImmunoOncology, DataRepresentation, DataImport, Infrastructure, SingleCell Author: Martin Morgan, Daniel Van Twisk Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LoomExperiment git_branch: RELEASE_3_10 git_last_commit: 0793ef0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/LoomExperiment_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/LoomExperiment_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/LoomExperiment_1.4.0.tgz vignettes: vignettes/LoomExperiment/inst/doc/LoomExperiment.html vignetteTitles: An introduction to the LoomExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LoomExperiment/inst/doc/LoomExperiment.R dependencyCount: 46 Package: LowMACA Version: 1.16.0 Depends: R (>= 2.10) Imports: cgdsr, parallel, stringr, reshape2, data.table, RColorBrewer, methods, LowMACAAnnotation, BiocParallel, motifStack, Biostrings, httr, grid, gridBase Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 0c62924f10ef16cafb0510285ee9f1a4 NeedsCompilation: no Title: LowMACA - Low frequency Mutation Analysis via Consensus Alignment Description: The LowMACA package is a simple suite of tools to investigate and analyze the mutation profile of several proteins or pfam domains via consensus alignment. You can conduct an hypothesis driven exploratory analysis using our package simply providing a set of genes or pfam domains of your interest. biocViews: SomaticMutation, SequenceMatching, WholeGenome, Sequencing, Alignment, DataImport, MultipleSequenceAlignment Author: Stefano de Pretis , Giorgio Melloni Maintainer: Stefano de Pretis , Giorgio Melloni SystemRequirements: clustalo, gs, perl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LowMACA git_branch: RELEASE_3_10 git_last_commit: cd38d0a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/LowMACA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/LowMACA_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/LowMACA_1.16.0.tgz vignettes: vignettes/LowMACA/inst/doc/LowMACA.html vignetteTitles: Bioconductor style for HTML documents hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LowMACA/inst/doc/LowMACA.R dependencyCount: 115 Package: LPE Version: 1.60.0 Depends: R (>= 2.10) Imports: stats License: LGPL MD5sum: f9f443210809e466e454e9e72e3e5036 NeedsCompilation: no Title: Methods for analyzing microarray data using Local Pooled Error (LPE) method Description: This LPE library is used to do significance analysis of microarray data with small number of replicates. It uses resampling based FDR adjustment, and gives less conservative results than traditional 'BH' or 'BY' procedures. Data accepted is raw data in txt format from MAS4, MAS5 or dChip. Data can also be supplied after normalization. LPE library is primarily used for analyzing data between two conditions. To use it for paired data, see LPEP library. For using LPE in multiple conditions, use HEM library. biocViews: Microarray, DifferentialExpression Author: Nitin Jain , Michael O'Connell , Jae K. Lee . Includes R source code contributed by HyungJun Cho Maintainer: Nitin Jain URL: http://www.r-project.org, http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/, http://sourceforge.net/projects/r-lpe/ git_url: https://git.bioconductor.org/packages/LPE git_branch: RELEASE_3_10 git_last_commit: 14eee86 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/LPE_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/LPE_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/LPE_1.60.0.tgz vignettes: vignettes/LPE/inst/doc/LPE.pdf vignetteTitles: LPE test for microarray data with small number of replicates hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LPE/inst/doc/LPE.R dependsOnMe: LPEadj, PLPE importsMe: LPEadj suggestsMe: ABarray dependencyCount: 1 Package: LPEadj Version: 1.46.0 Depends: LPE Imports: LPE, stats License: LGPL MD5sum: 86b8e6aa833fbfe627eb47c24b8ce908 NeedsCompilation: no Title: A correction of the local pooled error (LPE) method to replace the asymptotic variance adjustment with an unbiased adjustment based on sample size. Description: Two options are added to the LPE algorithm. The original LPE method sets all variances below the max variance in the ordered distribution of variances to the maximum variance. in LPEadj this option is turned off by default. The second option is to use a variance adjustment based on sample size rather than pi/2. By default the LPEadj uses the sample size based variance adjustment. biocViews: Microarray, Proteomics Author: Carl Murie , Robert Nadon Maintainer: Carl Murie git_url: https://git.bioconductor.org/packages/LPEadj git_branch: RELEASE_3_10 git_last_commit: 6f16e0a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/LPEadj_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/LPEadj_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/LPEadj_1.46.0.tgz vignettes: vignettes/LPEadj/inst/doc/LPEadj.pdf vignetteTitles: LPEadj test for microarray data with small number of replicates hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LPEadj/inst/doc/LPEadj.R dependencyCount: 2 Package: lpNet Version: 2.18.0 Depends: lpSolve, nem License: Artistic License 2.0 MD5sum: 276602cf9dc8e5a89afc14c80b524125 NeedsCompilation: no Title: Linear Programming Model for Network Inference Description: lpNet aims at infering biological networks, in particular signaling and gene networks. For that it takes perturbation data, either steady-state or time-series, as input and generates an LP model which allows the inference of signaling networks. For parameter identification either leave-one-out cross-validation or stratified n-fold cross-validation can be used. biocViews: NetworkInference Author: Bettina Knapp, Marta R. A. Matos, Johanna Mazur, Lars Kaderali Maintainer: Lars Kaderali git_url: https://git.bioconductor.org/packages/lpNet git_branch: RELEASE_3_10 git_last_commit: d35b7e5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/lpNet_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/lpNet_2.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/lpNet_2.18.0.tgz vignettes: vignettes/lpNet/inst/doc/vignette_lpNet.pdf vignetteTitles: lpNet,, network inference with a linear optimization program. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lpNet/inst/doc/vignette_lpNet.R dependencyCount: 23 Package: lpsymphony Version: 1.14.0 Depends: R (>= 3.0.0) Suggests: BiocStyle, knitr Enhances: slam License: EPL MD5sum: c885acf3c0fc22e4ceaa7e71dbe17438 NeedsCompilation: yes Title: Symphony integer linear programming solver in R Description: This package was derived from Rsymphony_0.1-17 from CRAN. These packages provide an R interface to SYMPHONY, an open-source linear programming solver written in C++. The main difference between this package and Rsymphony is that it includes the solver source code (SYMPHONY version 5.6), while Rsymphony expects to find header and library files on the users' system. Thus the intention of lpsymphony is to provide an easy to install interface to SYMPHONY. For Windows, precompiled DLLs are included in this package. biocViews: Infrastructure, ThirdPartyClient Author: Vladislav Kim [aut, cre], Ted Ralphs [ctb], Menal Guzelsoy [ctb], Ashutosh Mahajan [ctb], Reinhard Harter [ctb], Kurt Hornik [ctb], Cyrille Szymanski [ctb], Stefan Theussl [ctb] Maintainer: Vladislav Kim URL: http://R-Forge.R-project.org/projects/rsymphony, https://projects.coin-or.org/SYMPHONY, http://www.coin-or.org/download/source/SYMPHONY/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/lpsymphony git_branch: RELEASE_3_10 git_last_commit: f4fe270 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/lpsymphony_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/lpsymphony_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/lpsymphony_1.14.0.tgz vignettes: vignettes/lpsymphony/inst/doc/lpsymphony.pdf vignetteTitles: Introduction to lpsymphony hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lpsymphony/inst/doc/lpsymphony.R importsMe: IHW, Maaslin2 dependencyCount: 0 Package: LRBaseDbi Version: 1.4.0 Depends: R (>= 3.5.0) Imports: methods, stats, utils, AnnotationDbi, RSQLite, DBI, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 MD5sum: 0c4f2c10f803a26f609f5dd3a8c634b0 NeedsCompilation: no Title: DBI to construct LRBase-related package Description: Interface to construct LRBase package (LRBase.XXX.eg.db). biocViews: Infrastructure Author: Koki Tsuyuzaki Maintainer: Koki Tsuyuzaki VignetteBuilder: utils git_url: https://git.bioconductor.org/packages/LRBaseDbi git_branch: RELEASE_3_10 git_last_commit: cd19d55 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/LRBaseDbi_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/LRBaseDbi_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/LRBaseDbi_1.4.0.tgz vignettes: vignettes/LRBaseDbi/inst/doc/LRBaseDbi.pdf vignetteTitles: LRBaseDbi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LRBaseDbi/inst/doc/LRBaseDbi.R suggestsMe: scTensor dependencyCount: 26 Package: lumi Version: 2.38.0 Depends: R (>= 2.10), Biobase (>= 2.5.5) Imports: affy (>= 1.23.4), methylumi (>= 2.3.2), GenomicFeatures, GenomicRanges, annotate, lattice, mgcv (>= 1.4-0), nleqslv, KernSmooth, preprocessCore, RSQLite, DBI, AnnotationDbi, MASS, graphics, stats, stats4, methods Suggests: beadarray, limma, vsn, lumiBarnes, lumiHumanAll.db, lumiHumanIDMapping, genefilter, RColorBrewer License: LGPL (>= 2) MD5sum: 5aca1716f3d053360019e560e892bc1c NeedsCompilation: no Title: BeadArray Specific Methods for Illumina Methylation and Expression Microarrays Description: The lumi package provides an integrated solution for the Illumina microarray data analysis. It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays. biocViews: Microarray, OneChannel, Preprocessing, DNAMethylation, QualityControl, TwoChannel Author: Pan Du, Richard Bourgon, Gang Feng, Simon Lin Maintainer: Pan Du , Lei Huang , Gang Feng git_url: https://git.bioconductor.org/packages/lumi git_branch: RELEASE_3_10 git_last_commit: 321d480 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/lumi_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/lumi_2.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/lumi_2.38.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: arrayMvout, iCheck, wateRmelon importsMe: anamiR, ffpe, methyAnalysis, MineICA suggestsMe: beadarray, blima, Harman, methylumi, tigre dependencyCount: 155 Package: LVSmiRNA Version: 1.36.0 Depends: R (>= 3.1.0), methods, splines Imports: BiocGenerics, stats4, graphics, stats, utils, MASS, Biobase, quantreg, limma, affy, SparseM, vsn, zlibbioc Enhances: parallel,snow, Rmpi License: GPL-2 Archs: i386, x64 MD5sum: b1f7f72f8b5a8a19018b0c9c91a4db68 NeedsCompilation: yes Title: LVS normalization for Agilent miRNA data Description: Normalization of Agilent miRNA arrays. biocViews: Microarray,AgilentChip,OneChannel,Preprocessing Author: Stefano Calza, Suo Chen, Yudi Pawitan Maintainer: Stefano Calza git_url: https://git.bioconductor.org/packages/LVSmiRNA git_branch: RELEASE_3_10 git_last_commit: 451ff07 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/LVSmiRNA_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/LVSmiRNA_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/LVSmiRNA_1.36.0.tgz vignettes: vignettes/LVSmiRNA/inst/doc/LVSmiRNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LVSmiRNA/inst/doc/LVSmiRNA.R dependencyCount: 68 Package: LymphoSeq Version: 1.14.0 Depends: R (>= 3.3), LymphoSeqDB Imports: data.table, plyr, dplyr, reshape, VennDiagram, ggplot2, ineq, RColorBrewer, circlize, grid, utils, stats, ggtree, msa, Biostrings, phangorn, stringdist, UpSetR Suggests: knitr, pheatmap, wordcloud, rmarkdown License: Artistic-2.0 MD5sum: bfb3e415bbef9186b2463d2660760bf8 NeedsCompilation: no Title: Analyze high-throughput sequencing of T and B cell receptors Description: This R package analyzes high-throughput sequencing of T and B cell receptor complementarity determining region 3 (CDR3) sequences generated by Adaptive Biotechnologies' ImmunoSEQ assay. Its input comes from tab-separated value (.tsv) files exported from the ImmunoSEQ analyzer. biocViews: Software, Technology, Sequencing, TargetedResequencing, Alignment, MultipleSequenceAlignment Author: David Coffey Maintainer: David Coffey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LymphoSeq git_branch: RELEASE_3_10 git_last_commit: 4d8ac03 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/LymphoSeq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/LymphoSeq_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/LymphoSeq_1.14.0.tgz vignettes: vignettes/LymphoSeq/inst/doc/LymphoSeq.html vignetteTitles: Analysis of high-throughput sequencing of T and B cell receptors with LymphoSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LymphoSeq/inst/doc/LymphoSeq.R dependencyCount: 98 Package: M3C Version: 1.8.0 Depends: R (>= 3.5.0) Imports: ggplot2, Matrix, doSNOW, NMF, RColorBrewer, cluster, parallel, foreach, doParallel, matrixcalc, dendextend, sigclust, Rtsne, survival, corpcor, umap Suggests: knitr, rmarkdown License: AGPL-3 MD5sum: d55ffb041782cb0750e1c836b06f450f NeedsCompilation: no Title: Monte Carlo Reference-based Consensus Clustering Description: M3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overestimation of K and can reject the null hypothesis K=1. biocViews: Clustering, GeneExpression, Transcription, RNASeq, Sequencing, ImmunoOncology Author: Christopher John, David Watson Maintainer: Christopher John VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/M3C git_branch: RELEASE_3_10 git_last_commit: 8fb541e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/M3C_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/M3C_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/M3C_1.8.0.tgz vignettes: vignettes/M3C/inst/doc/M3Cvignette.pdf vignetteTitles: M3C hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3C/inst/doc/M3Cvignette.R dependencyCount: 94 Package: M3D Version: 1.20.0 Depends: R (>= 3.3.0) Imports: parallel, Rcpp, BiocGenerics, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, BiSeq LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat License: Artistic License 2.0 Archs: i386, x64 MD5sum: e992ce5de18dc1212e35c360f2bf5a9c NeedsCompilation: yes Title: Identifies differentially methylated regions across testing groups Description: This package identifies statistically significantly differentially methylated regions of CpGs. It uses kernel methods (the Maximum Mean Discrepancy) to measure differences in methylation profiles, and relates these to inter-replicate changes, whilst accounting for variation in coverage profiles. biocViews: DNAMethylation, DifferentialMethylation, Coverage, CpGIsland Author: Tom Mayo Maintainer: Tom Mayo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/M3D git_branch: RELEASE_3_10 git_last_commit: 0810f51 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/M3D_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/M3D_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/M3D_1.20.0.tgz vignettes: vignettes/M3D/inst/doc/M3D_vignette.pdf vignetteTitles: An Introduction to the M$^3$D method hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3D/inst/doc/M3D_vignette.R dependencyCount: 69 Package: M3Drop Version: 1.12.0 Depends: R (>= 3.4), numDeriv Imports: RColorBrewer, gplots, bbmle, statmod, grDevices, graphics, stats, matrixStats, Matrix, irlba, reldist, Hmisc, methods Suggests: ROCR, knitr, M3DExampleData, scater, SingleCellExperiment, monocle, Seurat, Biobase License: GPL (>=2) MD5sum: 6390a998eabfc0afc8a3166003858db4 NeedsCompilation: no Title: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq Description: This package fits a Michaelis-Menten model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells. biocViews: RNASeq, Sequencing, Transcriptomics, GeneExpression, Software, DifferentialExpression, DimensionReduction, FeatureExtraction Author: Tallulah Andrews Maintainer: Tallulah Andrews URL: https://github.com/tallulandrews/M3Drop VignetteBuilder: knitr BugReports: https://github.com/tallulandrews/M3Drop/issues git_url: https://git.bioconductor.org/packages/M3Drop git_branch: RELEASE_3_10 git_last_commit: 5552690 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/M3Drop_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/M3Drop_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/M3Drop_1.12.0.tgz vignettes: vignettes/M3Drop/inst/doc/M3Drop_Vignette.pdf vignetteTitles: Introduction to M3Drop hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3Drop/inst/doc/M3Drop_Vignette.R importsMe: scMerge dependencyCount: 97 Package: maanova Version: 1.56.0 Depends: R (>= 2.10) Imports: Biobase, graphics, grDevices, methods, stats, utils Suggests: qvalue, snow Enhances: Rmpi License: GPL (>= 2) MD5sum: 27ef0f4c3e815b9f6bf679b0a1dc3867 NeedsCompilation: yes Title: Tools for analyzing Micro Array experiments Description: Analysis of N-dye Micro Array experiment using mixed model effect. Containing analysis of variance, permutation and bootstrap, cluster and consensus tree. biocViews: Microarray, DifferentialExpression, Clustering Author: Hao Wu, modified by Hyuna Yang and Keith Sheppard with ideas from Gary Churchill, Katie Kerr and Xiangqin Cui. Maintainer: Keith Sheppard URL: http://research.jax.org/faculty/churchill git_url: https://git.bioconductor.org/packages/maanova git_branch: RELEASE_3_10 git_last_commit: 34241b2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/maanova_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/maanova_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/maanova_1.56.0.tgz vignettes: vignettes/maanova/inst/doc/maanova.pdf vignetteTitles: R/maanova HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: Maaslin2 Version: 1.0.0 Depends: R (>= 3.6) Imports: robustbase, biglm, pcaPP, edgeR, metagenomeSeq, lpsymphony, pscl, pbapply, car, dplyr, vegan, chemometrics, ggplot2, pheatmap, cplm, logging, data.table, lmerTest, hash, optparse, MASS, MuMIn, grDevices, stats, utils Suggests: knitr, testthat (>= 2.1.0) License: MIT + file LICENSE Archs: x64 MD5sum: 8e2b8e1aa4648113917962a2b5b7c4a3 NeedsCompilation: no Title: Maaslin2 Description: MaAsLin2 is comprehensive R package for efficiently determining multivariable association between clinical metadata and microbial meta'omic features. MaAsLin2 relies on general linear models to accommodate most modern epidemiological study designs, including cross-sectional and longitudinal, and offers a variety of data exploration, normalization, and transformation methods. MaAsLin2 is the next generation of MaAsLin. biocViews: Metagenomics, Software, Microbiome, Normalization Author: Himel Mallick [aut], Ali Rahnavard [aut], Lauren McIver [aut, cre] Maintainer: Lauren McIver URL: http://huttenhower.sph.harvard.edu/maaslin2 VignetteBuilder: knitr BugReports: https://bitbucket.org/biobakery/maaslin2/issues git_url: https://git.bioconductor.org/packages/Maaslin2 git_branch: RELEASE_3_10 git_last_commit: f70c81e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Maaslin2_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Maaslin2_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Maaslin2_1.0.0.tgz vignettes: vignettes/Maaslin2/inst/doc/maaslin2.html vignetteTitles: MaAsLin2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Maaslin2/inst/doc/maaslin2.R importsMe: MMUPHin dependencyCount: 150 Package: macat Version: 1.60.0 Depends: Biobase, annotate Suggests: hgu95av2.db, stjudem License: Artistic-2.0 MD5sum: 39f85b4d4bed3aaec65674e12c994f9b NeedsCompilation: no Title: MicroArray Chromosome Analysis Tool Description: This library contains functions to investigate links between differential gene expression and the chromosomal localization of the genes. MACAT is motivated by the common observation of phenomena involving large chromosomal regions in tumor cells. MACAT is the implementation of a statistical approach for identifying significantly differentially expressed chromosome regions. The functions have been tested on a publicly available data set about acute lymphoblastic leukemia (Yeoh et al.Cancer Cell 2002), which is provided in the library 'stjudem'. biocViews: Microarray, DifferentialExpression, Visualization Author: Benjamin Georgi, Matthias Heinig, Stefan Roepcke, Sebastian Schmeier, Joern Toedling Maintainer: Joern Toedling git_url: https://git.bioconductor.org/packages/macat git_branch: RELEASE_3_10 git_last_commit: 8aa955e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/macat_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/macat_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/macat_1.60.0.tgz vignettes: vignettes/macat/inst/doc/macat.pdf vignetteTitles: MicroArray Chromosome Analysis Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/macat/inst/doc/macat.R dependencyCount: 31 Package: maCorrPlot Version: 1.56.0 Depends: lattice Imports: graphics, grDevices, lattice, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: d97fff2930e95bd68abbfb4569b19b37 NeedsCompilation: no Title: Visualize artificial correlation in microarray data Description: Graphically displays correlation in microarray data that is due to insufficient normalization biocViews: Microarray, Preprocessing, Visualization Author: Alexander Ploner Maintainer: Alexander Ploner URL: http://www.pubmedcentral.gov/articlerender.fcgi?tool=pubmed&pubmedid=15799785 git_url: https://git.bioconductor.org/packages/maCorrPlot git_branch: RELEASE_3_10 git_last_commit: 8549acb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/maCorrPlot_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/maCorrPlot_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/maCorrPlot_1.56.0.tgz vignettes: vignettes/maCorrPlot/inst/doc/maCorrPlot.pdf vignetteTitles: maCorrPlot Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maCorrPlot/inst/doc/maCorrPlot.R dependencyCount: 6 Package: MACPET Version: 1.6.0 Depends: R (>= 3.6.1), InteractionSet (>= 1.13.0), bigmemory (>= 4.5.33), BH (>= 1.66.0.1), Rcpp (>= 1.0.1) Imports: intervals (>= 0.15.1), plyr (>= 1.8.4), Rsamtools (>= 2.1.3), stats (>= 3.6.1), utils (>= 3.6.1), methods (>= 3.6.1), GenomicRanges (>= 1.37.14), S4Vectors (>= 0.23.17), IRanges (>= 2.19.10), GenomeInfoDb (>= 1.21.1), gtools (>= 3.8.1), GenomicAlignments (>= 1.21.4), knitr (>= 1.23), rtracklayer (>= 1.45.1), BiocParallel (>= 1.19.0), Rbowtie (>= 1.25.0), GEOquery (>= 2.53.0), Biostrings (>= 2.53.2), ShortRead (>= 1.43.0), futile.logger (>= 1.4.3) LinkingTo: Rcpp, bigmemory, BH Suggests: ggplot2 (>= 3.2.0), igraph (>= 1.2.4.1), rmarkdown (>= 1.14), reshape2 (>= 1.4.3), BiocStyle (>= 2.13.2) License: GPL-3 MD5sum: f182cf0d9b706daf9e0ab5d8544fa465 NeedsCompilation: yes Title: Model based analysis for paired-end data Description: The MACPET package can be used for complete interaction analysis for ChIA-PET data. MACPET reads ChIA-PET data in BAM or SAM format and separates the data into Self-ligated, Intra- and Inter-chromosomal PETs. Furthermore, MACPET breaks the genome into regions and applies 2D mixture models for identifying candidate peaks/binding sites using skewed generalized students-t distributions (SGT). It then uses a local poisson model for finding significant binding sites. Finally it runs an additive interaction-analysis model for calling for significant interactions between those peaks. MACPET is mainly written in C++, and it also supports the BiocParallel package. biocViews: Software, DNA3DStructure, PeakDetection, StatisticalMethod, Clustering, Classification, HiC Author: Ioannis Vardaxis Maintainer: Ioannis Vardaxis SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MACPET git_branch: RELEASE_3_10 git_last_commit: 9d500e7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MACPET_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MACPET_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MACPET_1.6.0.tgz vignettes: vignettes/MACPET/inst/doc/MACPET.pdf vignetteTitles: MACPET hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MACPET/inst/doc/MACPET.R Package: MACSQuantifyR Version: 1.0.1 Imports: readxl, graphics, tools, utils, grDevices, ggplot2, ggrepel, methods, stats, latticeExtra, lattice, rmarkdown, png, grid, gridExtra, prettydoc, rvest, xml2 Suggests: knitr, testthat, R.utils, spelling License: Artistic-2.0 MD5sum: 3ad3560b1aff65f960343b7d74fdc5d2 NeedsCompilation: no Title: Fast treatment of MACSQuantify FACS data Description: Automatically process the metadata of MACSQuantify FACS sorter. It runs multiple modules: i) imports of raw file and graphical selection of duplicates in well plate, ii) computes statistics on data and iii) can compute combination index. biocViews: DataImport, Preprocessing, Normalization, FlowCytometry, DataRepresentation, GUI Author: Raphaël Bonnet [aut, cre], Marielle Nebout [dtc],Giulia Biondani [dtc], Jean-François Peyron[aut,ths], Inserm [fnd] Maintainer: Raphaël Bonnet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MACSQuantifyR git_branch: RELEASE_3_10 git_last_commit: 5e8b219 git_last_commit_date: 2020-04-08 Date/Publication: 2020-04-08 source.ver: src/contrib/MACSQuantifyR_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/MACSQuantifyR_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MACSQuantifyR_1.0.1.tgz vignettes: vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_combo.html, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_pipeline.html, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR.html vignetteTitles: MACSQuantifyR_step_by_step_analysis, MACSQuantifyR_simple_pipeline, MACSQuantifyR_quick_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_combo.R, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_pipeline.R, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR.R dependencyCount: 86 Package: made4 Version: 1.60.0 Depends: ade4, RColorBrewer,gplots,scatterplot3d Suggests: affy License: Artistic-2.0 MD5sum: 09542d4e1472f5ca06bf3812ab17a656 NeedsCompilation: no Title: Multivariate analysis of microarray data using ADE4 Description: Multivariate data analysis and graphical display of microarray data. Functions include between group analysis and coinertia analysis. It contains functions that require ADE4. biocViews: Clustering, Classification, MultipleComparison Author: Aedin Culhane Maintainer: Aedin Culhane URL: http://www.hsph.harvard.edu/aedin-culhane/ git_url: https://git.bioconductor.org/packages/made4 git_branch: RELEASE_3_10 git_last_commit: 724036f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/made4_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/made4_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/made4_1.60.0.tgz vignettes: vignettes/made4/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/made4/inst/doc/introduction.R dependsOnMe: bgafun importsMe: deco, omicade4 dependencyCount: 19 Package: MADSEQ Version: 1.12.0 Depends: R(>= 3.4), rjags(>= 4-6), Imports: VGAM, coda, BSgenome, BSgenome.Hsapiens.UCSC.hg19, S4Vectors, methods, preprocessCore, GenomicAlignments, Rsamtools, Biostrings, GenomicRanges, IRanges, VariantAnnotation, SummarizedExperiment, GenomeInfoDb, rtracklayer, graphics, stats, grDevices, utils, zlibbioc, vcfR Suggests: knitr License: GPL(>=2) Archs: i386, x64 MD5sum: d6e17095e51fca079232b77b56102c83 NeedsCompilation: no Title: Mosaic Aneuploidy Detection and Quantification using Massive Parallel Sequencing Data Description: The MADSEQ package provides a group of hierarchical Bayeisan models for the detection of mosaic aneuploidy, the inference of the type of aneuploidy and also for the quantification of the fraction of aneuploid cells in the sample. biocViews: GenomicVariation, SomaticMutation, VariantDetection, Bayesian, CopyNumberVariation, Sequencing, Coverage Author: Yu Kong, Adam Auton, John Murray Greally Maintainer: Yu Kong URL: https://github.com/ykong2/MADSEQ VignetteBuilder: knitr BugReports: https://github.com/ykong2/MADSEQ/issues git_url: https://git.bioconductor.org/packages/MADSEQ git_branch: RELEASE_3_10 git_last_commit: d450e4e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MADSEQ_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MADSEQ_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MADSEQ_1.12.0.tgz vignettes: vignettes/MADSEQ/inst/doc/MADSEQ-vignette.html vignetteTitles: R Package MADSEQ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MADSEQ/inst/doc/MADSEQ-vignette.R dependencyCount: 102 Package: maftools Version: 2.2.10 Depends: R (>= 3.3) Imports: data.table, RColorBrewer, methods, wordcloud, grDevices, survival Suggests: knitr, rmarkdown, BSgenome, Biostrings, NMF, mclust License: MIT + file LICENSE MD5sum: d5bb1e81e96175291349c31a2eb38f78 NeedsCompilation: no Title: Summarize, Analyze and Visualize MAF Files Description: Analyze and visualize Mutation Annotation Format (MAF) files from large scale sequencing studies. This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort. biocViews: DataRepresentation, DNASeq, Visualization, DriverMutation, VariantAnnotation, FeatureExtraction, Classification, SomaticMutation, Sequencing, FunctionalGenomics, Survival Author: Anand Mayakonda [aut, cre] () Maintainer: Anand Mayakonda URL: https://github.com/PoisonAlien/maftools VignetteBuilder: knitr BugReports: https://github.com/PoisonAlien/maftools/issues git_url: https://git.bioconductor.org/packages/maftools git_branch: RELEASE_3_10 git_last_commit: 5f391c5 git_last_commit_date: 2019-12-15 Date/Publication: 2019-12-16 source.ver: src/contrib/maftools_2.2.10.tar.gz win.binary.ver: bin/windows/contrib/3.6/maftools_2.2.10.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/maftools_2.2.10.tgz vignettes: vignettes/maftools/inst/doc/maftools.html, vignettes/maftools/inst/doc/oncoplots.html vignetteTitles: 01: Summarize,, Analyze,, and Visualize MAF Files, 02: Customizing oncoplots hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/maftools/inst/doc/maftools.R, vignettes/maftools/inst/doc/oncoplots.R importsMe: TCGAbiolinksGUI suggestsMe: survtype, TCGAbiolinks dependencyCount: 14 Package: MAGeCKFlute Version: 1.6.5 Depends: R (>= 3.5) Imports: clusterProfiler, DOSE, enrichplot, gridExtra, biomaRt, sva, ggsci, ggplot2, ggrepel, ggpubr, data.table, pheatmap, png, grDevices, grid, stats, utils, dendextend, scales, Biobase, msigdbr, KEGGgraph, KEGGREST, graph, graphics, pathview, XML Suggests: knitr, testthat License: GPL (>=3) MD5sum: b7cddcab26e54a7c76fa1e07fe1a2c86 NeedsCompilation: no Title: Integrative Analysis Pipeline for Pooled CRISPR Functional Genetic Screens Description: CRISPR (clustered regularly interspaced short palindrome repeats) coupled with nuclease Cas9 (CRISPR/Cas9) screens represent a promising technology to systematically evaluate gene functions. Data analysis for CRISPR/Cas9 screens is a critical process that includes identifying screen hits and exploring biological functions for these hits in downstream analysis. We have previously developed two algorithms, MAGeCK and MAGeCK-VISPR, to analyze CRISPR/Cas9 screen data in various scenarios. These two algorithms allow users to perform quality control, read count generation and normalization, and calculate beta score to evaluate gene selection performance. In downstream analysis, the biological functional analysis is required for understanding biological functions of these identified genes with different screening purposes. Here, We developed MAGeCKFlute for supporting downstream analysis. MAGeCKFlute provides several strategies to remove potential biases within sgRNA-level read counts and gene-level beta scores. The downstream analysis with the package includes identifying essential, non-essential, and target-associated genes, and performing biological functional category analysis, pathway enrichment analysis and protein complex enrichment analysis of these genes. The package also visualizes genes in multiple ways to benefit users exploring screening data. Collectively, MAGeCKFlute enables accurate identification of essential, non-essential, and targeted genes, as well as their related biological functions. This vignette explains the use of the package and demonstrates typical workflows. biocViews: FunctionalGenomics, CRISPR, BatchEffect, QualityControl, Normalization, GeneSetEnrichment, Pathways, Visualization, PooledScreens, GeneTarget, KEGG Author: Binbin Wang, Wubing Zhang, Feizhen Wu, Wei Li & X. Shirley Liu Maintainer: Wubing Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MAGeCKFlute git_branch: RELEASE_3_10 git_last_commit: 339c060 git_last_commit_date: 2020-04-09 Date/Publication: 2020-04-09 source.ver: src/contrib/MAGeCKFlute_1.6.5.tar.gz win.binary.ver: bin/windows/contrib/3.6/MAGeCKFlute_1.6.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MAGeCKFlute_1.6.5.tgz vignettes: vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute_enrichment.html, vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute.html vignetteTitles: MAGeCKFlute_enrichment.Rmd, MAGeCKFlute.Rmd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute_enrichment.R, vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute.R dependencyCount: 156 Package: maigesPack Version: 1.50.0 Depends: R (>= 2.10), convert, graph, limma, marray, methods Suggests: amap, annotate, class, e1071, MASS, multtest, OLIN, R2HTML, rgl, som License: GPL (>= 2) MD5sum: 04fd7209ca07fc6f0161f69416aa4bf2 NeedsCompilation: yes Title: Functions to handle cDNA microarray data, including several methods of data analysis Description: This package uses functions of various other packages together with other functions in a coordinated way to handle and analyse cDNA microarray data biocViews: Microarray, TwoChannel, Preprocessing, ThirdPartyClient, DifferentialExpression, Clustering, Classification, GraphAndNetwork Author: Gustavo H. Esteves , with contributions from Roberto Hirata Jr , E. Jordao Neves , Elier B. Cristo , Ana C. Simoes and Lucas Fahham Maintainer: Gustavo H. Esteves URL: http://www.maiges.org/en/software/ git_url: https://git.bioconductor.org/packages/maigesPack git_branch: RELEASE_3_10 git_last_commit: da1068b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/maigesPack_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/maigesPack_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/maigesPack_1.50.0.tgz vignettes: vignettes/maigesPack/inst/doc/maigesPack_tutorial.pdf vignetteTitles: maigesPack Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maigesPack/inst/doc/maigesPack_tutorial.R dependencyCount: 13 Package: MAIT Version: 1.20.0 Depends: R (>= 2.10), CAMERA, Rcpp, pls Imports: gplots,e1071,class,MASS,plsgenomics,agricolae,xcms,methods,caret Suggests: faahKO Enhances: rgl License: GPL-2 MD5sum: e95094deea4fc5d57df660f0d0a99c27 NeedsCompilation: no Title: Statistical Analysis of Metabolomic Data Description: The MAIT package contains functions to perform end-to-end statistical analysis of LC/MS Metabolomic Data. Special emphasis is put on peak annotation and in modular function design of the functions. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics, Software Author: Francesc Fernandez-Albert, Rafael Llorach, Cristina Andres-LaCueva, Alexandre Perera Maintainer: Pol Sola-Santos git_url: https://git.bioconductor.org/packages/MAIT git_branch: RELEASE_3_10 git_last_commit: 59e57b2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MAIT_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MAIT_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MAIT_1.20.0.tgz vignettes: vignettes/MAIT/inst/doc/MAIT_Vignette.pdf vignetteTitles: \maketitleMAIT Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAIT/inst/doc/MAIT_Vignette.R dependencyCount: 183 Package: makecdfenv Version: 1.62.0 Depends: R (>= 2.6.0), affyio Imports: Biobase, affy, methods, stats, utils, zlibbioc License: GPL (>= 2) MD5sum: ed2f457e073db52140528d3a17f26e5d NeedsCompilation: yes Title: CDF Environment Maker Description: This package has two functions. One reads a Affymetrix chip description file (CDF) and creates a hash table environment containing the location/probe set membership mapping. The other creates a package that automatically loads that environment. biocViews: OneChannel, DataImport, Preprocessing Author: Rafael A. Irizarry , Laurent Gautier , Wolfgang Huber , Ben Bolstad Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/makecdfenv git_branch: RELEASE_3_10 git_last_commit: 9981b3c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/makecdfenv_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/makecdfenv_1.62.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/makecdfenv_1.62.0.tgz vignettes: vignettes/makecdfenv/inst/doc/makecdfenv.pdf vignetteTitles: makecdfenv primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/makecdfenv/inst/doc/makecdfenv.R dependsOnMe: altcdfenvs dependencyCount: 13 Package: MANOR Version: 1.58.0 Depends: R (>= 2.10), GLAD Imports: GLAD, graphics, grDevices, stats, utils License: GPL-2 Archs: i386, x64 MD5sum: 0d1b129b1bcc4833d26a90eb7bd780fb NeedsCompilation: yes Title: CGH Micro-Array NORmalization Description: Importation, normalization, visualization, and quality control functions to correct identified sources of variability in array-CGH experiments. biocViews: Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, CopyNumberVariation Author: Pierre Neuvial , Philippe Hupe Maintainer: Pierre Neuvial URL: http://bioinfo.curie.fr/projects/manor/index.html git_url: https://git.bioconductor.org/packages/MANOR git_branch: RELEASE_3_10 git_last_commit: 483c3fa git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MANOR_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MANOR_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MANOR_1.58.0.tgz vignettes: vignettes/MANOR/inst/doc/MANOR.pdf vignetteTitles: MANOR overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MANOR/inst/doc/MANOR.R dependencyCount: 9 Package: manta Version: 1.32.0 Depends: R (>= 1.8.0), methods, edgeR (>= 2.5.13) Imports: Hmisc, caroline(>= 0.6.6) Suggests: RSQLite, plotrix License: Artistic-2.0 MD5sum: 3bc697d4b57f5cc24eee2000bc410c63 NeedsCompilation: no Title: Microbial Assemblage Normalized Transcript Analysis Description: Tools for robust comparative metatranscriptomics. biocViews: ImmunoOncology, DifferentialExpression, RNASeq, Genetics, GeneExpression, Sequencing, QualityControl, DataImport, Visualization Author: Ginger Armbrust, Adrian Marchetti Maintainer: Chris Berthiaume , Adrian Marchetti URL: http://manta.ocean.washington.edu/ git_url: https://git.bioconductor.org/packages/manta git_branch: RELEASE_3_10 git_last_commit: 93c5652 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/manta_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/manta_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/manta_1.32.0.tgz vignettes: vignettes/manta/inst/doc/manta.pdf vignetteTitles: manta hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/manta/inst/doc/manta.R dependencyCount: 86 Package: MantelCorr Version: 1.56.0 Depends: R (>= 2.10) Imports: stats License: GPL (>= 2) Archs: i386, x64 MD5sum: cc52f60d4f71f23c78820e8e20004c0a NeedsCompilation: no Title: Compute Mantel Cluster Correlations Description: Computes Mantel cluster correlations from a (p x n) numeric data matrix (e.g. microarray gene-expression data). biocViews: Clustering Author: Brian Steinmeyer and William Shannon Maintainer: Brian Steinmeyer git_url: https://git.bioconductor.org/packages/MantelCorr git_branch: RELEASE_3_10 git_last_commit: b883cd8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MantelCorr_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MantelCorr_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MantelCorr_1.56.0.tgz vignettes: vignettes/MantelCorr/inst/doc/MantelCorrVignette.pdf vignetteTitles: MantelCorrVignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MantelCorr/inst/doc/MantelCorrVignette.R dependencyCount: 1 Package: mAPKL Version: 1.16.0 Depends: R (>= 3.6.0), Biobase Imports: multtest, clusterSim, apcluster, limma, e1071, AnnotationDbi, methods, parmigene,igraph,reactome.db Suggests: BiocStyle, knitr, mAPKLData, hgu133plus2.db, RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 353d88bf5fc26ffb07bf5d69e81489dc NeedsCompilation: no Title: A Hybrid Feature Selection method for gene expression data Description: We propose a hybrid FS method (mAP-KL), which combines multiple hypothesis testing and affinity propagation (AP)-clustering algorithm along with the Krzanowski & Lai cluster quality index, to select a small yet informative subset of genes. biocViews: FeatureExtraction, DifferentialExpression, Microarray, GeneExpression Author: Argiris Sakellariou Maintainer: Argiris Sakellariou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mAPKL git_branch: RELEASE_3_10 git_last_commit: d6cd4e0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mAPKL_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mAPKL_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mAPKL_1.16.0.tgz vignettes: vignettes/mAPKL/inst/doc/mAPKL.pdf vignetteTitles: mAPKL Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mAPKL/inst/doc/mAPKL.R dependencyCount: 81 Package: maPredictDSC Version: 1.24.0 Depends: R (>= 2.15.0), MASS,affy,limma,gcrma,ROC,class,e1071,caret,hgu133plus2.db,ROCR,AnnotationDbi,LungCancerACvsSCCGEO Suggests: parallel License: GPL-2 MD5sum: e0a89e82acafc1134459e786c544097b NeedsCompilation: no Title: Phenotype prediction using microarray data: approach of the best overall team in the IMPROVER Diagnostic Signature Challenge Description: This package implements the classification pipeline of the best overall team (Team221) in the IMPROVER Diagnostic Signature Challenge. Additional functionality is added to compare 27 combinations of data preprocessing, feature selection and classifier types. biocViews: Microarray, Classification Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/maPredictDSC git_url: https://git.bioconductor.org/packages/maPredictDSC git_branch: RELEASE_3_10 git_last_commit: fec4114 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/maPredictDSC_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/maPredictDSC_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/maPredictDSC_1.24.0.tgz vignettes: vignettes/maPredictDSC/inst/doc/maPredictDSC.pdf vignetteTitles: maPredictDSC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maPredictDSC/inst/doc/maPredictDSC.R dependencyCount: 125 Package: mapscape Version: 1.10.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), base64enc (>= 0.1-3), stringr (>= 1.0.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 77a82073be27a232d11269469f4ffc3b NeedsCompilation: no Title: mapscape Description: MapScape integrates clonal prevalence, clonal hierarchy, anatomic and mutational information to provide interactive visualization of spatial clonal evolution. There are four inputs to MapScape: (i) the clonal phylogeny, (ii) clonal prevalences, (iii) an image reference, which may be a medical image or drawing and (iv) pixel locations for each sample on the referenced image. Optionally, MapScape can accept a data table of mutations for each clone and their variant allele frequencies in each sample. The output of MapScape consists of a cropped anatomical image surrounded by two representations of each tumour sample. The first, a cellular aggregate, visually displays the prevalence of each clone. The second shows a skeleton of the clonal phylogeny while highlighting only those clones present in the sample. Together, these representations enable the analyst to visualize the distribution of clones throughout anatomic space. biocViews: Visualization Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mapscape git_branch: RELEASE_3_10 git_last_commit: 5ac0c91 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mapscape_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mapscape_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mapscape_1.10.0.tgz vignettes: vignettes/mapscape/inst/doc/mapscape_vignette.html vignetteTitles: MapScape vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mapscape/inst/doc/mapscape_vignette.R dependencyCount: 17 Package: marray Version: 1.64.0 Depends: R (>= 2.10.0), limma, methods Suggests: tkWidgets License: LGPL MD5sum: 7acb9fa72c03448142298363d068abe5 NeedsCompilation: no Title: Exploratory analysis for two-color spotted microarray data Description: Class definitions for two-color spotted microarray data. Fuctions for data input, diagnostic plots, normalization and quality checking. biocViews: Microarray, TwoChannel, Preprocessing Author: Yee Hwa (Jean) Yang with contributions from Agnes Paquet and Sandrine Dudoit. Maintainer: Yee Hwa (Jean) Yang URL: http://www.maths.usyd.edu.au/u/jeany/ git_url: https://git.bioconductor.org/packages/marray git_branch: RELEASE_3_10 git_last_commit: aaaf7e4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/marray_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/marray_1.64.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/marray_1.64.0.tgz vignettes: vignettes/marray/inst/doc/marray.pdf, vignettes/marray/inst/doc/marrayClasses.pdf, vignettes/marray/inst/doc/marrayClassesShort.pdf, vignettes/marray/inst/doc/marrayInput.pdf, vignettes/marray/inst/doc/marrayNorm.pdf, vignettes/marray/inst/doc/marrayPlots.pdf vignetteTitles: marray Overview, marrayClasses Overview, marrayClasses Tutorial (short), marrayInput Introduction, marray Normalization, marrayPlots Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/marray/inst/doc/marray.R, vignettes/marray/inst/doc/marrayClasses.R, vignettes/marray/inst/doc/marrayClassesShort.R, vignettes/marray/inst/doc/marrayInput.R, vignettes/marray/inst/doc/marrayNorm.R, vignettes/marray/inst/doc/marrayPlots.R dependsOnMe: CGHbase, convert, dyebias, maigesPack, MineICA, nnNorm, OLIN, RBM, stepNorm, TurboNorm importsMe: arrayQuality, ChAMP, methylPipe, MSstats, nnNorm, OLIN, OLINgui, piano, plrs, sigaR, stepNorm, timecourse suggestsMe: DEGraph, Mfuzz dependencyCount: 6 Package: martini Version: 1.6.0 Depends: R (>= 3.5) Imports: igraph (>= 1.0.1), Matrix, methods (>= 3.3.2), Rcpp (>= 0.12.8), snpStats (>= 1.20.0), S4Vectors (>= 0.12.2), stats, utils LinkingTo: Rgin, Rcpp, RcppEigen (>= 0.3.3.5.0) Suggests: biomaRt (>= 2.34.1), httr (>= 1.2.1), IRanges (>= 2.8.2), knitr, testthat, tidyverse, rmarkdown License: MIT + file LICENSE MD5sum: 4586bc9ff29b15a95bd4957eaf03fcc5 NeedsCompilation: yes Title: GWAS Incorporating Networks Description: martini deals with the low power inherent to GWAS studies by using prior knowledge represented as a network. SNPs are the vertices of the network, and the edges represent biological relationships between them (genomic adjacency, belonging to the same gene, physical interaction between protein products). The network is scanned using SConES, which looks for groups of SNPs maximally associated with the phenotype, that form a close subnetwork. biocViews: Software, GenomeWideAssociation, SNP, GeneticVariability, Genetics, FeatureExtraction, GraphAndNetwork, Network Author: Hector Climente-Gonzalez [aut, cre], Chloe-Agathe Azencott [aut] Maintainer: Hector Climente-Gonzalez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/martini git_branch: RELEASE_3_10 git_last_commit: 4f0f2b9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/martini_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/martini_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/martini_1.6.0.tgz vignettes: vignettes/martini/inst/doc/scones_usage.html, vignettes/martini/inst/doc/simulate_phenotype.html vignetteTitles: Running SConES, Simulating SConES-based phenotypes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/martini/inst/doc/scones_usage.R, vignettes/martini/inst/doc/simulate_phenotype.R dependencyCount: 22 Package: maser Version: 1.4.0 Depends: R (>= 3.5.0), ggplot2, GenomicRanges Imports: dplyr, rtracklayer, reshape2, Gviz, DT, GenomeInfoDb, stats, utils, IRanges, methods, BiocGenerics, parallel, data.table Suggests: testthat, knitr, rmarkdown, BiocStyle, AnnotationHub License: MIT + file LICENSE MD5sum: b908d9a7544188e6ed32655e6fa8590b NeedsCompilation: no Title: Mapping Alternative Splicing Events to pRoteins Description: This package provides functionalities for downstream analysis, annotation and visualizaton of alternative splicing events generated by rMATS. biocViews: AlternativeSplicing, Transcriptomics, Visualization Author: Diogo F.T. Veiga [aut, cre] Maintainer: Diogo F.T. Veiga URL: https://github.com/DiogoVeiga/maser VignetteBuilder: knitr BugReports: https://github.com/DiogoVeiga/maser/issues git_url: https://git.bioconductor.org/packages/maser git_branch: RELEASE_3_10 git_last_commit: 2cdfe57 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/maser_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/maser_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/maser_1.4.0.tgz vignettes: vignettes/maser/inst/doc/Introduction.html, vignettes/maser/inst/doc/Protein_mapping.html vignetteTitles: Introduction, Mapping protein features to splicing events hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/maser/inst/doc/Introduction.R, vignettes/maser/inst/doc/Protein_mapping.R dependencyCount: 149 Package: maSigPro Version: 1.58.0 Depends: R (>= 2.3.1) Imports: Biobase, graphics, grDevices, venn, mclust, stats, MASS License: GPL (>= 2) MD5sum: 9ff4405c92826ddde000e04129b8f3db NeedsCompilation: no Title: Significant Gene Expression Profile Differences in Time Course Gene Expression Data Description: maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments. biocViews: Microarray, RNA-Seq, Differential Expression, TimeCourse Author: Ana Conesa , Maria Jose Nueda Maintainer: Maria Jose Nueda URL: http://bioinfo.cipf.es/ git_url: https://git.bioconductor.org/packages/maSigPro git_branch: RELEASE_3_10 git_last_commit: 7dd0dba git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/maSigPro_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/maSigPro_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/maSigPro_1.58.0.tgz vignettes: vignettes/maSigPro/inst/doc/maSigPro.pdf, vignettes/maSigPro/inst/doc/maSigProUsersGuide.pdf vignetteTitles: maSigPro Vignette, maSigProUsersGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: maskBAD Version: 1.30.0 Depends: R (>= 2.10), gcrma (>= 2.27.1), affy Suggests: hgu95av2probe, hgu95av2cdf License: GPL (>= 2) Archs: i386, x64 MD5sum: c2a59bacf7e368c9eb4ae655b305dc3e NeedsCompilation: no Title: Masking probes with binding affinity differences Description: Package includes functions to analyze and mask microarray expression data. biocViews: Microarray Author: Michael Dannemann Maintainer: Michael Dannemann git_url: https://git.bioconductor.org/packages/maskBAD git_branch: RELEASE_3_10 git_last_commit: 901618a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/maskBAD_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/maskBAD_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/maskBAD_1.30.0.tgz vignettes: vignettes/maskBAD/inst/doc/maskBAD.pdf vignetteTitles: Package maskBAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maskBAD/inst/doc/maskBAD.R dependencyCount: 20 Package: MassArray Version: 1.38.0 Depends: R (>= 2.10.0), methods Imports: graphics, grDevices, stats, utils License: GPL (>=2) MD5sum: 9002b228af9cf67ff76536e40b80f392 NeedsCompilation: no Title: Analytical Tools for MassArray Data Description: This package is designed for the import, quality control, analysis, and visualization of methylation data generated using Sequenom's MassArray platform. The tools herein contain a highly detailed amplicon prediction for optimal assay design. Also included are quality control measures of data, such as primer dimer and bisulfite conversion efficiency estimation. Methylation data are calculated using the same algorithms contained in the EpiTyper software package. Additionally, automatic SNP-detection can be used to flag potentially confounded data from specific CG sites. Visualization includes barplots of methylation data as well as UCSC Genome Browser-compatible BED tracks. Multiple assays can be positionally combined for integrated analysis. biocViews: ImmunoOncology, DNAMethylation, SNP, MassSpectrometry, Genetics, DataImport, Visualization Author: Reid F. Thompson , John M. Greally Maintainer: Reid F. Thompson git_url: https://git.bioconductor.org/packages/MassArray git_branch: RELEASE_3_10 git_last_commit: 396a831 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MassArray_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MassArray_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MassArray_1.38.0.tgz vignettes: vignettes/MassArray/inst/doc/MassArray.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassArray/inst/doc/MassArray.R dependencyCount: 5 Package: massiR Version: 1.22.0 Depends: cluster, gplots, diptest, Biobase, R (>= 3.0.2) Suggests: biomaRt, RUnit, BiocGenerics License: GPL-3 MD5sum: 5ea197f2b870c6c17ed082f1fc2833f6 NeedsCompilation: no Title: massiR: MicroArray Sample Sex Identifier Description: Predicts the sex of samples in gene expression microarray datasets biocViews: Software, Microarray, GeneExpression, Clustering, Classification, QualityControl Author: Sam Buckberry Maintainer: Sam Buckberry git_url: https://git.bioconductor.org/packages/massiR git_branch: RELEASE_3_10 git_last_commit: 08f9f98 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/massiR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/massiR_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/massiR_1.22.0.tgz vignettes: vignettes/massiR/inst/doc/massiR_Vignette.pdf vignetteTitles: massiR_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/massiR/inst/doc/massiR_Vignette.R dependencyCount: 16 Package: MassSpecWavelet Version: 1.52.0 Depends: waveslim Suggests: xcms, caTools License: LGPL (>= 2) MD5sum: 0d8c46f89835f7e6f7ad8990101dc583 NeedsCompilation: yes Title: Mass spectrum processing by wavelet-based algorithms Description: Processing Mass Spectrometry spectrum by using wavelet based algorithm biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Pan Du, Warren Kibbe, Simon Lin Maintainer: Pan Du git_url: https://git.bioconductor.org/packages/MassSpecWavelet git_branch: RELEASE_3_10 git_last_commit: 11469a7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MassSpecWavelet_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MassSpecWavelet_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MassSpecWavelet_1.52.0.tgz vignettes: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.pdf vignetteTitles: MassSpecWavelet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.R importsMe: cosmiq, xcms dependencyCount: 5 Package: MAST Version: 1.12.0 Depends: SingleCellExperiment (>= 1.2.0), R(>= 3.5) Imports: Biobase, BiocGenerics, S4Vectors, data.table, ggplot2, plyr, stringr, abind, methods, parallel, reshape2, stats, stats4, graphics, utils, SummarizedExperiment(>= 1.5.3), progress Suggests: knitr, rmarkdown, testthat, lme4(>= 1.0), blme, roxygen2(> 6.0.0), numDeriv, car, gdata, lattice, GGally, GSEABase, NMF, TxDb.Hsapiens.UCSC.hg19.knownGene, rsvd, limma, RColorBrewer, BiocStyle, scater, DelayedArray, Matrix, HDF5Array License: GPL(>= 2) MD5sum: f0f298c36d8058291e5b00e898835c20 NeedsCompilation: no Title: Model-based Analysis of Single Cell Transcriptomics Description: Methods and models for handling zero-inflated single cell assay data. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, RNASeq, Transcriptomics, SingleCell Author: Andrew McDavid [aut, cre], Greg Finak [aut], Masanao Yajima [aut] Maintainer: Andrew McDavid URL: https://github.com/RGLab/MAST/ VignetteBuilder: knitr BugReports: https://github.com/RGLab/MAST/issues git_url: https://git.bioconductor.org/packages/MAST git_branch: RELEASE_3_10 git_last_commit: cd3b54a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MAST_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MAST_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MAST_1.12.0.tgz vignettes: vignettes/MAST/inst/doc/MAITAnalysis.html, vignettes/MAST/inst/doc/MAST-interoperability.html, vignettes/MAST/inst/doc/MAST-Intro.html vignetteTitles: Using MAST for filtering,, differential expression and gene set enrichment in MAIT cells, Interoptability between MAST and SingleCellExperiment-derived packages, An Introduction to MAST hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAST/inst/doc/MAITAnalysis.R, vignettes/MAST/inst/doc/MAST-interoperability.R, vignettes/MAST/inst/doc/MAST-Intro.R importsMe: celaref, celda, singleCellTK suggestsMe: clusterExperiment dependencyCount: 86 Package: matchBox Version: 1.28.0 Depends: R (>= 2.8.0) License: Artistic-2.0 MD5sum: 9bb9fa09f155d59e8f7a87bfe9a04ea5 NeedsCompilation: no Title: Utilities to compute, compare, and plot the agreement between ordered vectors of features (ie. distinct genomic experiments). The package includes Correspondence-At-the-TOP (CAT) analysis. Description: The matchBox package enables comparing ranked vectors of features, merging multiple datasets, removing redundant features, using CAT-plots and Venn diagrams, and computing statistical significance. biocViews: Software, Annotation, Microarray, MultipleComparison, Visualization Author: Luigi Marchionni , Anuj Gupta Maintainer: Luigi Marchionni , Anuj Gupta git_url: https://git.bioconductor.org/packages/matchBox git_branch: RELEASE_3_10 git_last_commit: 78691ef git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/matchBox_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/matchBox_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/matchBox_1.28.0.tgz vignettes: vignettes/matchBox/inst/doc/matchBox.pdf vignetteTitles: Working with the matchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/matchBox/inst/doc/matchBox.R dependencyCount: 0 Package: MatrixRider Version: 1.18.0 Depends: R (>= 3.1.2) Imports: methods, TFBSTools, IRanges, XVector, Biostrings LinkingTo: IRanges, XVector, Biostrings, S4Vectors Suggests: RUnit, BiocGenerics, BiocStyle, JASPAR2014 License: GPL-3 Archs: i386, x64 MD5sum: 20443cc53f2ade96652e5b32d3887cbf NeedsCompilation: yes Title: Obtain total affinity and occupancies for binding site matrices on a given sequence Description: Calculates a single number for a whole sequence that reflects the propensity of a DNA binding protein to interact with it. The DNA binding protein has to be described with a PFM matrix, for example gotten from Jaspar. biocViews: GeneRegulation, Genetics, MotifAnnotation Author: Elena Grassi Maintainer: Elena Grassi git_url: https://git.bioconductor.org/packages/MatrixRider git_branch: RELEASE_3_10 git_last_commit: 5a13d6f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MatrixRider_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MatrixRider_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MatrixRider_1.18.0.tgz vignettes: vignettes/MatrixRider/inst/doc/MatrixRider.pdf vignetteTitles: Total affinity and occupancies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MatrixRider/inst/doc/MatrixRider.R dependencyCount: 123 Package: matter Version: 1.12.0 Depends: R (>= 3.5), BiocParallel, methods, stats, biglm Imports: BiocGenerics, ProtGenerics, digest, irlba, utils Suggests: BiocStyle, testthat License: Artistic-2.0 MD5sum: 374328cb7f39b07f26e3781908530cda NeedsCompilation: yes Title: A framework for rapid prototyping with file-based data structures Description: Memory-efficient reading, writing, and manipulation of structured binary data as file-based vectors, matrices, arrays, lists, and data frames. biocViews: Infrastructure, DataRepresentation Author: Kylie A. Bemis Maintainer: Kylie A. Bemis URL: https://github.com/kuwisdelu/matter git_url: https://git.bioconductor.org/packages/matter git_branch: RELEASE_3_10 git_last_commit: dc00133 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/matter_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/matter_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/matter_1.12.0.tgz vignettes: vignettes/matter/inst/doc/matter-supp1.pdf, vignettes/matter/inst/doc/matter-supp2.pdf, vignettes/matter/inst/doc/matter.pdf vignetteTitles: matter: Supplementary 1 - Simulations and comparative benchmarks, matter: Supplementary 2 - 3D mass spectrometry imaging case study, matter: Rapid prototyping with data on disk hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/matter/inst/doc/matter-supp1.R, vignettes/matter/inst/doc/matter-supp2.R, vignettes/matter/inst/doc/matter.R importsMe: Cardinal dependencyCount: 22 Package: MaxContrastProjection Version: 1.10.0 Depends: R (>= 3.4) Imports: EBImage, stats, methods Suggests: knitr, BiocStyle, testthat License: Artistic-2.0 MD5sum: 756cc41c900531efcff51ee1d0a33516 NeedsCompilation: no Title: Perform a maximum contrast projection of 3D images along the z-dimension into 2D Description: A problem when recording 3D fluorescent microscopy images is how to properly present these results in 2D. Maximum intensity projections are a popular method to determine the focal plane of each pixel in the image. The problem with this approach, however, is that out-of-focus elements will still be visible, making edges and fine structures difficult to detect. This package aims to resolve this problem by using the contrast around a given pixel to determine the focal plane, allowing for a much cleaner structure detection than would be otherwise possible. For convenience, this package also contains functions to perform various other types of projections, including a maximum intensity projection. biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, Software, Visualization Author: Jan Sauer, Bernd Fischer Maintainer: Jan Sauer SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MaxContrastProjection git_branch: RELEASE_3_10 git_last_commit: 55e7351 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MaxContrastProjection_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MaxContrastProjection_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MaxContrastProjection_1.10.0.tgz vignettes: vignettes/MaxContrastProjection/inst/doc/MaxContrastProjection.pdf vignetteTitles: MaxContrastProjection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MaxContrastProjection/inst/doc/MaxContrastProjection.R dependencyCount: 25 Package: MBAmethyl Version: 1.20.0 Depends: R (>= 2.15) License: Artistic-2.0 Archs: i386, x64 MD5sum: 5a76e07495ffc23624da1c42f372ef50 NeedsCompilation: no Title: Model-based analysis of DNA methylation data Description: This package provides a function for reconstructing DNA methylation values from raw measurements. It iteratively implements the group fused lars to smooth related-by-location methylation values and the constrained least squares to remove probe affinity effect across multiple sequences. biocViews: DNAMethylation, MethylationArray Author: Tao Wang, Mengjie Chen Maintainer: Tao Wang git_url: https://git.bioconductor.org/packages/MBAmethyl git_branch: RELEASE_3_10 git_last_commit: 21c2cb4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MBAmethyl_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MBAmethyl_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MBAmethyl_1.20.0.tgz vignettes: vignettes/MBAmethyl/inst/doc/MBAmethyl.pdf vignetteTitles: MBAmethyl Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBAmethyl/inst/doc/MBAmethyl.R dependencyCount: 0 Package: MBASED Version: 1.20.0 Depends: RUnit, BiocGenerics, BiocParallel, GenomicRanges, SummarizedExperiment Suggests: BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: d4666a43c13d4c9608f8cac5ad74f5da NeedsCompilation: no Title: Package containing functions for ASE analysis using Meta-analysis Based Allele-Specific Expression Detection Description: The package implements MBASED algorithm for detecting allele-specific gene expression from RNA count data, where allele counts at individual loci (SNVs) are integrated into a gene-specific measure of ASE, and utilizes simulations to appropriately assess the statistical significance of observed ASE. biocViews: Sequencing, GeneExpression, Transcription Author: Oleg Mayba, Houston Gilbert Maintainer: Oleg Mayba git_url: https://git.bioconductor.org/packages/MBASED git_branch: RELEASE_3_10 git_last_commit: 9861f6d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MBASED_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MBASED_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MBASED_1.20.0.tgz vignettes: vignettes/MBASED/inst/doc/MBASED.pdf vignetteTitles: MBASED hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBASED/inst/doc/MBASED.R dependencyCount: 33 Package: MBCB Version: 1.40.0 Depends: R (>= 2.9.0), tcltk, tcltk2 Imports: preprocessCore, stats, utils License: GPL (>= 2) MD5sum: 814fdc343c0ec00241c1ccef8f1e43bc NeedsCompilation: no Title: MBCB (Model-based Background Correction for Beadarray) Description: This package provides a model-based background correction method, which incorporates the negative control beads to pre-process Illumina BeadArray data. biocViews: Microarray, Preprocessing Author: Yang Xie Maintainer: Jeff Allen URL: http://www.utsouthwestern.edu git_url: https://git.bioconductor.org/packages/MBCB git_branch: RELEASE_3_10 git_last_commit: 0117a67 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MBCB_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MBCB_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MBCB_1.40.0.tgz vignettes: vignettes/MBCB/inst/doc/MBCB.pdf vignetteTitles: MBCB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBCB/inst/doc/MBCB.R dependencyCount: 5 Package: mbkmeans Version: 1.2.0 Depends: R (>= 3.6) Imports: methods, DelayedArray, Rcpp, SingleCellExperiment, SummarizedExperiment, ClusterR, benchmarkme, Matrix LinkingTo: Rcpp, RcppArmadillo (>= 0.7.2), Rhdf5lib, beachmat, ClusterR Suggests: beachmat, HDF5Array, Rhdf5lib, BiocStyle, TENxPBMCData, scater, DelayedMatrixStats, knitr, testthat License: MIT + file LICENSE MD5sum: 34351bbd92d55eb2b6bd8b77d59a66d5 NeedsCompilation: yes Title: Mini-batch K-means Clustering for Single-Cell RNA-seq Description: Implements the mini-batch k-means algorithm for large datasets, including support for on-disk data representation. biocViews: Clustering, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Yuwei Ni [aut, cph], Davide Risso [aut, cre, cph], Stephanie Hicks [aut, cph], Elizabeth Purdom [aut, cph] Maintainer: Davide Risso SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/drisso/mbkmeans/issues git_url: https://git.bioconductor.org/packages/mbkmeans git_branch: RELEASE_3_10 git_last_commit: 6ea1350 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mbkmeans_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mbkmeans_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mbkmeans_1.2.0.tgz vignettes: vignettes/mbkmeans/inst/doc/Vignette.html vignetteTitles: mbkmeans vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mbkmeans/inst/doc/Vignette.R dependencyCount: 102 Package: mBPCR Version: 1.40.0 Depends: oligoClasses, SNPchip Imports: Biobase Suggests: xtable License: GPL (>= 2) MD5sum: 2a3ae593ca72a576fabe77daff507321 NeedsCompilation: no Title: Bayesian Piecewise Constant Regression for DNA copy number estimation Description: Estimates the DNA copy number profile using mBPCR to detect regions with copy number changes biocViews: aCGH, SNP, Microarray, CopyNumberVariation Author: P.M.V. Rancoita , with contributions from M. Hutter Maintainer: P.M.V. Rancoita URL: http://www.idsia.ch/~paola/mBPCR git_url: https://git.bioconductor.org/packages/mBPCR git_branch: RELEASE_3_10 git_last_commit: fcd072c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mBPCR_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mBPCR_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mBPCR_1.40.0.tgz vignettes: vignettes/mBPCR/inst/doc/mBPCR.pdf vignetteTitles: mBPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mBPCR/inst/doc/mBPCR.R dependencyCount: 55 Package: MBQN Version: 1.0.1 Depends: R (>= 3.6) Imports: stats, graphics, utils, limma (>= 3.30.13), SummarizedExperiment (>= 1.10.0), preprocessCore (>= 1.36.0), BiocFileCache, rappdirs, rpx, xml2, RCurl, ggplot2, reshape2, PairedData Suggests: knitr License: GPL-3 + file LICENSE MD5sum: 37b432758e5216ceb42e3e8acd556032 NeedsCompilation: no Title: Mean/Median-balanced quantile normalization Description: Modified quantile normalization for omics or other matrix-like data distorted in location and scale. biocViews: Normalization, Preprocessing, Proteomics, Software Author: Ariane Schad [aut, cre] (), Clemens Kreutz [aut, ctb] (), Eva Brombacher [aut, ctb] () Maintainer: Ariane Schad URL: https://github.com/arianeschad/mbqn VignetteBuilder: knitr BugReports: https://github.com/arianeschad/MBQN/issues git_url: https://git.bioconductor.org/packages/MBQN git_branch: RELEASE_3_10 git_last_commit: 68413ed git_last_commit_date: 2020-03-26 Date/Publication: 2020-03-26 source.ver: src/contrib/MBQN_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/MBQN_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MBQN_1.0.1.tgz vignettes: vignettes/MBQN/inst/doc/MBQNpackage.html vignetteTitles: MBQN Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MBQN/inst/doc/MBQNpackage.R dependencyCount: 111 Package: MBttest Version: 1.14.0 Depends: R (>= 3.3.0), stats, gplots, gtools,graphics,base, utils,grDevices Suggests: BiocStyle, BiocGenerics License: GPL-3 Archs: i386, x64 MD5sum: 77f1dfb16c252b8619b3e0f90abe59d8 NeedsCompilation: no Title: Multiple Beta t-Tests Description: MBttest method was developed from beta t-test method of Baggerly et al(2003). Compared to baySeq (Hard castle and Kelly 2010), DESeq (Anders and Huber 2010) and exact test (Robinson and Smyth 2007, 2008) and the GLM of McCarthy et al(2012), MBttest is of high work efficiency,that is, it has high power, high conservativeness of FDR estimation and high stability. MBttest is suit- able to transcriptomic data, tag data, SAGE data (count data) from small samples or a few replicate libraries. It can be used to identify genes, mRNA isoforms or tags differentially expressed between two conditions. biocViews: Sequencing, DifferentialExpression, MultipleComparison, SAGE, GeneExpression, Transcription, AlternativeSplicing,Coverage, DifferentialSplicing Author: Yuan-De Tan Maintainer: Yuan-De Tan git_url: https://git.bioconductor.org/packages/MBttest git_branch: RELEASE_3_10 git_last_commit: 60f39cf git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MBttest_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MBttest_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MBttest_1.14.0.tgz vignettes: vignettes/MBttest/inst/doc/MBttest-manual.pdf, vignettes/MBttest/inst/doc/MBttest.pdf vignetteTitles: MBttest-manual.pdf, Analysing RNA-Seq count data with the "MBttest" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBttest/inst/doc/MBttest.R dependencyCount: 12 Package: mcaGUI Version: 1.34.0 Depends: lattice, MASS, proto, foreign, gWidgets(>= 0.0-36), gWidgetsRGtk2(>= 0.0-53), OTUbase, vegan, bpca Enhances: iplots, reshape, ggplot2, cairoDevice, OTUbase License: GPL (>= 2) MD5sum: 733855b4a6eff2535b3c18e211256b6c NeedsCompilation: no Title: Microbial Community Analysis GUI Description: Microbial community analysis GUI for R using gWidgets. biocViews: GUI, Visualization, Clustering, Sequencing Author: Wade K. Copeland, Vandhana Krishnan, Daniel Beck, Matt Settles, James Foster, Kyu-Chul Cho, Mitch Day, Roxana Hickey, Ursel M.E. Schutte, Xia Zhou, Chris Williams, Larry J. Forney, Zaid Abdo, Poor Man's GUI (PMG) base code by John Verzani with contributions by Yvonnick Noel Maintainer: Wade K. Copeland URL: http://www.ibest.uidaho.edu/ibest/index.php git_url: https://git.bioconductor.org/packages/mcaGUI git_branch: RELEASE_3_10 git_last_commit: ae52c34 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mcaGUI_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mcaGUI_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mcaGUI_1.34.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 95 Package: MCbiclust Version: 1.10.0 Depends: R (>= 3.4) Imports: BiocParallel, graphics, utils, stats, AnnotationDbi, GO.db, org.Hs.eg.db, GGally, ggplot2, scales, cluster, WGCNA Suggests: gplots, knitr, rmarkdown, BiocStyle, gProfileR, MASS, dplyr, pander, devtools, testthat, GSVA License: GPL-2 MD5sum: 37448257ce6111ab9335fcdf84a02c39 NeedsCompilation: no Title: Massive correlating biclusters for gene expression data and associated methods Description: Custom made algorithm and associated methods for finding, visualising and analysing biclusters in large gene expression data sets. Algorithm is based on with a supplied gene set of size n, finding the maximum strength correlation matrix containing m samples from the data set. biocViews: ImmunoOncology, Clustering, Microarray, StatisticalMethod, Software, RNASeq, GeneExpression Author: Robert Bentham Maintainer: Robert Bentham VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MCbiclust git_branch: RELEASE_3_10 git_last_commit: e63d5e2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MCbiclust_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MCbiclust_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MCbiclust_1.10.0.tgz vignettes: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.html vignetteTitles: Introduction to MCbiclust hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.R dependencyCount: 120 Package: MCRestimate Version: 2.42.0 Depends: R (>= 2.7.2), golubEsets (>= 1.4.6) Imports: e1071 (>= 1.5-12), pamr (>= 1.22), randomForest (>= 3.9-6), RColorBrewer (>= 0.1-3), Biobase (>= 2.5.5), graphics, grDevices, stats, utils Suggests: xtable (>= 1.2-1), ROC (>= 1.8.0), genefilter (>= 1.12.0), gpls (>= 1.6.0) License: GPL (>= 2) MD5sum: ee544cb05deb0550c835e6740d4e336e NeedsCompilation: no Title: Misclassification error estimation with cross-validation Description: This package includes a function for combining preprocessing and classification methods to calculate misclassification errors biocViews: Classification Author: Marc Johannes, Markus Ruschhaupt, Holger Froehlich, Ulrich Mansmann, Andreas Buness, Patrick Warnat, Wolfgang Huber, Axel Benner, Tim Beissbarth Maintainer: Marc Johannes git_url: https://git.bioconductor.org/packages/MCRestimate git_branch: RELEASE_3_10 git_last_commit: 2a7b08b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MCRestimate_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MCRestimate_2.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MCRestimate_2.42.0.tgz vignettes: vignettes/MCRestimate/inst/doc/UsingMCRestimate.pdf vignetteTitles: HOW TO use MCRestimate hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MCRestimate/inst/doc/UsingMCRestimate.R dependencyCount: 21 Package: mCSEA Version: 1.6.0 Depends: R (>= 3.5), mCSEAdata, Homo.sapiens Imports: biomaRt, fgsea, GenomicFeatures, GenomicRanges, ggplot2, graphics, grDevices, Gviz, IRanges, limma, parallel, S4Vectors, stats, SummarizedExperiment, utils Suggests: Biobase, BiocGenerics, BiocStyle, FlowSorted.Blood.450k, knitr, leukemiasEset, minfi, minfiData, rmarkdown, RUnit License: GPL-2 MD5sum: b9b39e1b413eb9d6003976e1cc568750 NeedsCompilation: no Title: Methylated CpGs Set Enrichment Analysis Description: Identification of diferentially methylated regions (DMRs) in predefined regions (promoters, CpG islands...) from the human genome using Illumina's 450K or EPIC microarray data. Provides methods to rank CpG probes based on linear models and includes plotting functions. biocViews: ImmunoOncology, DifferentialMethylation, DNAMethylation, Epigenetics, Genetics, GenomeAnnotation, MethylationArray, Microarray, MultipleComparison, TwoChannel Author: Jordi Martorell-Marugán and Pedro Carmona-Sáez Maintainer: Jordi Martorell-Marugán VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mCSEA git_branch: RELEASE_3_10 git_last_commit: a12fee2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mCSEA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mCSEA_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mCSEA_1.6.0.tgz vignettes: vignettes/mCSEA/inst/doc/mCSEA.pdf vignetteTitles: Predefined DMRs identification with mCSEA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mCSEA/inst/doc/mCSEA.R dependencyCount: 155 Package: mdgsa Version: 1.18.0 Depends: R (>= 2.14) Imports: AnnotationDbi, DBI, GO.db, KEGG.db, cluster, Matrix Suggests: BiocStyle, knitr, rmarkdown, limma, ALL, hgu95av2.db, RUnit, BiocGenerics License: GPL MD5sum: 3a4c8632d637a955d1eaf90472b00e79 NeedsCompilation: no Title: Multi Dimensional Gene Set Analysis. Description: Functions to preform a Gene Set Analysis in several genomic dimensions. Including methods for miRNAs. biocViews: GeneSetEnrichment, Annotation, Pathways, GO Author: David Montaner Maintainer: David Montaner URL: https://github.com/dmontaner/mdgsa, http://www.dmontaner.com VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mdgsa git_branch: RELEASE_3_10 git_last_commit: 2a0d1d2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mdgsa_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mdgsa_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mdgsa_1.18.0.tgz vignettes: vignettes/mdgsa/inst/doc/mdgsa_vignette.pdf vignetteTitles: mdgsa_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdgsa/inst/doc/mdgsa_vignette.R dependencyCount: 33 Package: mdp Version: 1.6.0 Depends: R (>= 3.5) Imports: ggplot2, gridExtra, grid, stats, utils Suggests: testthat, knitr, rmarkdown, fgsea License: GPL-3 MD5sum: 86a9c52cc81d7eaad71f05c9fc9d84a2 NeedsCompilation: no Title: Molecular Degree of Perturbation calculates scores for transcriptome data samples based on their perturbation from controls Description: The Molecular Degree of Perturbation webtool quantifies the heterogeneity of samples. It takes a data.frame of omic data that contains at least two classes (control and test) and assigns a score to all samples based on how perturbed they are compared to the controls. It is based on the Molecular Distance to Health (Pankla et al. 2009), and expands on this algorithm by adding the options to calculate the z-score using the modified z-score (using median absolute deviation), change the z-score zeroing threshold, and look at genes that are most perturbed in the test versus control classes. biocViews: BiomedicalInformatics, QualityControl, Transcriptomics, SystemsBiology, Microarray, QualityControl Author: Melissa Lever [aut], Pedro Russo [aut], Helder Nakaya [aut, cre] Maintainer: Helder Nakaya URL: https://mdp.sysbio.tools/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mdp git_branch: RELEASE_3_10 git_last_commit: 6228bd2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mdp_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mdp_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mdp_1.6.0.tgz vignettes: vignettes/mdp/inst/doc/my-vignette.html vignetteTitles: Running the mdp package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdp/inst/doc/my-vignette.R dependencyCount: 55 Package: mdqc Version: 1.48.0 Depends: R (>= 2.2.1), cluster, MASS License: LGPL (>= 2) MD5sum: c69d227335e1e19df32fd63666f2683b NeedsCompilation: no Title: Mahalanobis Distance Quality Control for microarrays Description: MDQC is a multivariate quality assessment method for microarrays based on quality control (QC) reports. The Mahalanobis distance of an array's quality attributes is used to measure the similarity of the quality of that array against the quality of the other arrays. Then, arrays with unusually high distances can be flagged as potentially low-quality. biocViews: Microarray, QualityControl Author: Justin Harrington Maintainer: Gabriela Cohen-Freue git_url: https://git.bioconductor.org/packages/mdqc git_branch: RELEASE_3_10 git_last_commit: 0cb27a8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mdqc_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mdqc_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mdqc_1.48.0.tgz vignettes: vignettes/mdqc/inst/doc/mdqcvignette.pdf vignetteTitles: Introduction to MDQC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdqc/inst/doc/mdqcvignette.R importsMe: arrayMvout dependencyCount: 7 Package: MDTS Version: 1.6.0 Depends: R (>= 3.5.0) Imports: GenomicAlignments, GenomicRanges, IRanges, Biostrings, DNAcopy, Rsamtools, parallel, stringr Suggests: testthat, knitr License: Artistic-2.0 MD5sum: 283ab06c8ec658ca1c86db104a767dfe NeedsCompilation: no Title: Detection of de novo deletion in targeted sequencing trios Description: A package for the detection of de novo copy number deletions in targeted sequencing of trios with high sensitivity and positive predictive value. biocViews: StatisticalMethod, Technology, Sequencing, TargetedResequencing, Coverage, DataImport Author: Jack M.. Fu [aut, cre] Maintainer: Jack M.. Fu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MDTS git_branch: RELEASE_3_10 git_last_commit: e0f68f3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MDTS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MDTS_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MDTS_1.6.0.tgz vignettes: vignettes/MDTS/inst/doc/mdts.html vignetteTitles: Title of your vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MDTS/inst/doc/mdts.R dependencyCount: 41 Package: MEAL Version: 1.16.0 Depends: R (>= 3.2.0), Biobase, MultiDataSet Imports: GenomicRanges, limma, DMRcate, vegan, BiocGenerics, minfi, IRanges, S4Vectors, methods, parallel, ggplot2 (>= 2.0.0), permute, Gviz, missMethyl, isva, SummarizedExperiment, SmartSVA, graphics, stats, utils, matrixStats Suggests: testthat, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylation450kanno.ilmn12.hg19, knitr, minfiData, BiocStyle, rmarkdown, brgedata License: Artistic-2.0 MD5sum: 3d912dcb091530302cd4bb09160d0c4c NeedsCompilation: no Title: Perform methylation analysis Description: Package to integrate methylation and expression data. It can also perform methylation or expression analysis alone. Several plotting functionalities are included as well as a new region analysis based on redundancy analysis. Effect of SNPs on a region can also be estimated. biocViews: DNAMethylation, Microarray, Software, WholeGenome Author: Carlos Ruiz-Arenas [aut, cre], Juan R. Gonzalez [aut] Maintainer: Carlos Ruiz-Arenas VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEAL git_branch: RELEASE_3_10 git_last_commit: 8ca2f71 git_last_commit_date: 2019-10-29 Date/Publication: 2019-11-07 source.ver: src/contrib/MEAL_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MEAL_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MEAL_1.16.0.tgz vignettes: vignettes/MEAL/inst/doc/caseExample.html, vignettes/MEAL/inst/doc/MEAL.html vignetteTitles: Expression and Methylation Analysis with MEAL, Methylation Analysis with MEAL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MEAL/inst/doc/caseExample.R, vignettes/MEAL/inst/doc/MEAL.R dependencyCount: 225 Package: MeasurementError.cor Version: 1.58.0 License: LGPL MD5sum: f6a631a491466a4aa0085a6d67216168 NeedsCompilation: no Title: Measurement Error model estimate for correlation coefficient Description: Two-stage measurement error model for correlation estimation with smaller bias than the usual sample correlation biocViews: StatisticalMethod Author: Beiying Ding Maintainer: Beiying Ding git_url: https://git.bioconductor.org/packages/MeasurementError.cor git_branch: RELEASE_3_10 git_last_commit: 26bdb22 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MeasurementError.cor_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MeasurementError.cor_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MeasurementError.cor_1.58.0.tgz vignettes: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.pdf vignetteTitles: MeasurementError.cor Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.R dependencyCount: 0 Package: MEB Version: 1.0.0 Depends: R (>= 3.6.0) Imports: e1071, SummarizedExperiment Suggests: knitr,rmarkdown License: GPL-2 MD5sum: 014786691eabf7ab767d14c351c2c69c NeedsCompilation: no Title: A Minimum Enclosing Ball (MEB) method to detect differential expression genes for RNA-seq data Description: Identifying differential expression genes between the same or different species is an urgent demand for biological research. In most of cases, normalization is the first step to solve this problem, then by employing the hypothesis testing, we could detect statistically significant genes. With the development of machine learning, it gives us a new perspective on discrimination between differential expression (DE) and non-differential expression (non-DE) genes. Provided a set of training data, the procedure of distinguishing genes could be simplified as a classification problem. However, in reality, it is hard for us to get the information from both DE and non-DE genes. To solve this problem, we try to identify differential cases only in the domain of non-DE genes, and transform the problem to an outlier detection in machine learning. Given that non-DE genes have some similarities in features, we build a Minimum Enclosing Ball (MEB) to cover those non-DE genes in feature space, then those DE genes, which are enormously different from non-DE genes, being regarded as outliers and rejected outside the ball. Compared with existing methods, it is no need for the MEB method to normalize data in advance. Besides, the MEB method could be easily applied to the same or different species data and without changing too much. biocViews: DifferentialExpression, GeneExpression, Normalization, Classification Author: Yan Zhou, Jiadi Zhu Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn>, Yan Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEB git_branch: RELEASE_3_10 git_last_commit: 8d8a47b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MEB_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MEB_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MEB_1.0.0.tgz vignettes: vignettes/MEB/inst/doc/MEB.html vignetteTitles: MEB Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEB/inst/doc/MEB.R dependencyCount: 35 Package: MEDIPS Version: 1.38.0 Depends: R (>= 3.0), BSgenome, Rsamtools Imports: GenomicRanges, Biostrings, graphics, gtools, IRanges, methods, stats, utils, edgeR, DNAcopy, biomaRt, rtracklayer, preprocessCore Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, BiocStyle License: GPL (>=2) MD5sum: 91e19682c3f92da58fdedec58d59edd0 NeedsCompilation: no Title: DNA IP-seq data analysis Description: MEDIPS was developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, MEDIPS provides functionalities for the analysis of any kind of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential coverage between groups of samples and saturation and correlation analysis. biocViews: DNAMethylation, CpGIsland, DifferentialExpression, Sequencing, ChIPSeq, Preprocessing, QualityControl, Visualization, Microarray, Genetics, Coverage, GenomeAnnotation, CopyNumberVariation, SequenceMatching Author: Lukas Chavez, Matthias Lienhard, Joern Dietrich, Isaac Lopez Moyado Maintainer: Lukas Chavez git_url: https://git.bioconductor.org/packages/MEDIPS git_branch: RELEASE_3_10 git_last_commit: 0427bdb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MEDIPS_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MEDIPS_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MEDIPS_1.38.0.tgz vignettes: vignettes/MEDIPS/inst/doc/MEDIPS.pdf vignetteTitles: MEDIPS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEDIPS/inst/doc/MEDIPS.R dependencyCount: 89 Package: MEDME Version: 1.46.0 Depends: R (>= 2.15), grDevices, graphics, methods, stats, utils Imports: Biostrings, MASS, drc Suggests: BSgenome.Hsapiens.UCSC.hg18, BSgenome.Mmusculus.UCSC.mm9 License: GPL (>= 2) MD5sum: a99d5f413a7b7bbd0f9e2bbb365e7a12 NeedsCompilation: yes Title: Modelling Experimental Data from MeDIP Enrichment Description: Description: MEDME allows the prediction of absolute and relative methylation levels based on measures obtained by MeDIP-microarray experiments biocViews: Microarray, CpGIsland, DNAMethylation Author: Mattia Pelizzola and Annette Molinaro Maintainer: Mattia Pelizzola git_url: https://git.bioconductor.org/packages/MEDME git_branch: RELEASE_3_10 git_last_commit: 51d3e9c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MEDME_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MEDME_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MEDME_1.46.0.tgz vignettes: vignettes/MEDME/inst/doc/MEDME.pdf vignetteTitles: MEDME.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEDME/inst/doc/MEDME.R dependencyCount: 91 Package: MEIGOR Version: 1.20.0 Depends: Rsolnp, snowfall, CNORode, deSolve Suggests: CellNOptR License: GPL-3 MD5sum: 74e305f0488b41d3a3d60f9a10b2d399 NeedsCompilation: no Title: MEIGO - MEtaheuristics for bIoinformatics Global Optimization Description: Global Optimization biocViews: SystemsBiology Author: Jose Egea, David Henriques, Alexandre Fdez. Villaverde, Thomas Cokelaer Maintainer: Jose Egea git_url: https://git.bioconductor.org/packages/MEIGOR git_branch: RELEASE_3_10 git_last_commit: a4af632 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MEIGOR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MEIGOR_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MEIGOR_1.20.0.tgz vignettes: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.pdf vignetteTitles: Main vignette:Global Optimization for Bioinformatics and Systems Biology hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.R dependencyCount: 76 Package: Melissa Version: 1.2.0 Depends: R (>= 3.5.0), BPRMeth, GenomicRanges Imports: data.table, parallel, ROCR, matrixcalc, clues, ggplot2, doParallel, foreach, MCMCpack, cowplot, magrittr, mvtnorm, truncnorm, assertthat, BiocStyle, stats, utils Suggests: testthat, knitr, rmarkdown License: GPL-3 | file LICENSE MD5sum: 2128361f689f744acef7be62556dc079 NeedsCompilation: no Title: Bayesian clustering and imputationa of single cell methylomes Description: Melissa is a Baysian probabilistic model for jointly clustering and imputing single cell methylomes. This is done by taking into account local correlations via a Generalised Linear Model approach and global similarities using a mixture modelling approach. biocViews: ImmunoOncology, DNAMethylation, GeneExpression, GeneRegulation, Epigenetics, Genetics, Clustering, FeatureExtraction, Regression, RNASeq, Bayesian, KEGG, Sequencing, Coverage, SingleCell Author: C. A. Kapourani [aut, cre] Maintainer: C. A. Kapourani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Melissa git_branch: RELEASE_3_10 git_last_commit: cd408c1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Melissa_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Melissa_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Melissa_1.2.0.tgz vignettes: vignettes/Melissa/inst/doc/process_files.html, vignettes/Melissa/inst/doc/run_melissa.html vignetteTitles: 1: Process and filter scBS-seq data, 2: Cluster and impute scBS-seq data using Melissa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Melissa/inst/doc/process_files.R, vignettes/Melissa/inst/doc/run_melissa.R dependencyCount: 115 Package: MergeMaid Version: 2.58.0 Depends: R (>= 2.10.0), survival, Biobase, MASS, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 720cc79bfcd3e20247b3afc07fb0dc39 NeedsCompilation: no Title: Merge Maid Description: The functions in this R extension are intended for cross-study comparison of gene expression array data. Required from the user is gene expression matrices, their corresponding gene-id vectors and other useful information, and they could be 'list','matrix', or 'ExpressionSet'. The main function is 'mergeExprs' which transforms the input objects into data in the merged format, such that common genes in different datasets can be easily found. And the function 'intcor' calculate the correlation coefficients. Other functions use the output from 'modelOutcome' to graphically display the results and cross-validate associations of gene expression data with survival. biocViews: Microarray, DifferentialExpression, Visualization Author: Xiaogang Zhong Leslie Cope Elizabeth Garrett Giovanni Parmigiani Maintainer: Xiaogang Zhong URL: http://astor.som.jhmi.edu/MergeMaid git_url: https://git.bioconductor.org/packages/MergeMaid git_branch: RELEASE_3_10 git_last_commit: ea24097 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MergeMaid_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MergeMaid_2.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MergeMaid_2.58.0.tgz vignettes: vignettes/MergeMaid/inst/doc/MergeMaid.pdf vignetteTitles: MergeMaid primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: CrossICC, metaArray, XDE dependencyCount: 14 Package: Mergeomics Version: 1.14.0 Depends: R (>= 3.0.1) Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 7b6aa99bb64f0701687d66454f62da0e NeedsCompilation: no Title: Integrative network analysis of omics data Description: The Mergeomics pipeline serves as a flexible framework for integrating multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It includes two main parts, 1) Marker set enrichment analysis (MSEA); 2) Weighted Key Driver Analysis (wKDA). biocViews: Software Author: Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin Zhang, Xia Yang Maintainer: Zeyneb Kurt git_url: https://git.bioconductor.org/packages/Mergeomics git_branch: RELEASE_3_10 git_last_commit: 70e92ab git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Mergeomics_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Mergeomics_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Mergeomics_1.14.0.tgz vignettes: vignettes/Mergeomics/inst/doc/Mergeomics.pdf vignetteTitles: Mergeomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mergeomics/inst/doc/Mergeomics.R dependencyCount: 0 Package: MeSHDbi Version: 1.22.0 Depends: R (>= 3.0.1), BiocGenerics (>= 0.15.10) Imports: methods, AnnotationDbi (>= 1.31.19), RSQLite, Biobase Suggests: RUnit License: Artistic-2.0 MD5sum: f588d0ef59943bb98a3ba30fa07d2ad3 NeedsCompilation: no Title: DBI to construct MeSH-related package from sqlite file Description: The package is unified implementation of MeSH.db, MeSH.AOR.db, and MeSH.PCR.db and also is interface to construct Gene-MeSH package (MeSH.XXX.eg.db). loadMeSHDbiPkg import sqlite file and generate MeSH.XXX.eg.db. biocViews: Annotation, AnnotationData, Infrastructure Author: Koki Tsuyuzaki Maintainer: Koki Tsuyuzaki git_url: https://git.bioconductor.org/packages/MeSHDbi git_branch: RELEASE_3_10 git_last_commit: c2b23ee git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MeSHDbi_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MeSHDbi_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MeSHDbi_1.22.0.tgz vignettes: vignettes/MeSHDbi/inst/doc/MeSHDbi.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: meshr, scTensor dependencyCount: 26 Package: meshes Version: 1.12.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, DOSE (>= 3.5.1), enrichplot, GOSemSim (>= 1.99.3), MeSH.db, methods, rvcheck, utils Suggests: knitr, MeSH.Cel.eg.db, MeSH.Hsa.eg.db, prettydoc License: Artistic-2.0 MD5sum: 3cbb31ab98103f62b98cf881860d2578 NeedsCompilation: no Title: MeSH Enrichment and Semantic analyses Description: MeSH (Medical Subject Headings) is the NLM controlled vocabulary used to manually index articles for MEDLINE/PubMed. MeSH terms were associated by Entrez Gene ID by three methods, gendoo, gene2pubmed and RBBH. This association is fundamental for enrichment and semantic analyses. meshes supports enrichment analysis (over-representation and gene set enrichment analysis) of gene list or whole expression profile. The semantic comparisons of MeSH terms provide quantitative ways to compute similarities between genes and gene groups. meshes implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively and supports more than 70 species. biocViews: Annotation, Clustering, MultipleComparison, Software Author: Guangchuang Yu [aut, cre] Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/software/meshes VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/meshes/issues git_url: https://git.bioconductor.org/packages/meshes git_branch: RELEASE_3_10 git_last_commit: 8ed375c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/meshes_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/meshes_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/meshes_1.12.0.tgz vignettes: vignettes/meshes/inst/doc/meshes.html vignetteTitles: An introduction to meshes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshes/inst/doc/meshes.R dependencyCount: 125 Package: meshr Version: 1.22.0 Depends: R (>= 3.0.1) Imports: methods, stats, utils, fdrtool, MeSH.db, MeSH.AOR.db, MeSH.PCR.db, MeSHDbi, MeSH.Hsa.eg.db, MeSH.Aca.eg.db, MeSH.Bsu.168.eg.db, MeSH.Syn.eg.db, cummeRbund, org.Hs.eg.db, Category, S4Vectors, BiocGenerics, RSQLite License: Artistic-2.0 MD5sum: 0128a7bbc7ae3bf71116bcfa869caa0b NeedsCompilation: no Title: Tools for conducting enrichment analysis of MeSH Description: A set of annotation maps describing the entire MeSH assembled using data from MeSH. biocViews: AnnotationData, FunctionalAnnotation, Bioinformatics, Statistics, Annotation, MultipleComparisons, MeSHDb Author: Koki Tsuyuzaki, Itoshi Nikaido, Gota Morota Maintainer: Koki Tsuyuzaki git_url: https://git.bioconductor.org/packages/meshr git_branch: RELEASE_3_10 git_last_commit: 5a7f351 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/meshr_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/meshr_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/meshr_1.22.0.tgz vignettes: vignettes/meshr/inst/doc/MeSH.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshr/inst/doc/MeSH.R importsMe: scTensor dependencyCount: 164 Package: messina Version: 1.22.0 Depends: R (>= 3.1.0), survival (>= 2.37-4), methods Imports: Rcpp (>= 0.11.1), plyr (>= 1.8), ggplot2 (>= 0.9.3.1), grid (>= 3.1.0), foreach (>= 1.4.1), graphics LinkingTo: Rcpp Suggests: knitr (>= 1.5), antiProfilesData (>= 0.99.2), Biobase (>= 2.22.0), BiocStyle Enhances: doMC (>= 1.3.3) License: EPL (>= 1.0) MD5sum: 3a8428ed8a0ab8b2a4b24e210b764f7d NeedsCompilation: yes Title: Single-gene classifiers and outlier-resistant detection of differential expression for two-group and survival problems. Description: Messina is a collection of algorithms for constructing optimally robust single-gene classifiers, and for identifying differential expression in the presence of outliers or unknown sample subgroups. The methods have application in identifying lead features to develop into clinical tests (both diagnostic and prognostic), and in identifying differential expression when a fraction of samples show unusual patterns of expression. biocViews: GeneExpression, DifferentialExpression, BiomedicalInformatics, Classification, Survival Author: Mark Pinese [aut], Mark Pinese [cre], Mark Pinese [cph] Maintainer: Mark Pinese VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/messina git_branch: RELEASE_3_10 git_last_commit: 023d865 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/messina_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/messina_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/messina_1.22.0.tgz vignettes: vignettes/messina/inst/doc/messina.pdf vignetteTitles: Using Messina hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/messina/inst/doc/messina.R dependencyCount: 59 Package: metaArray Version: 1.64.0 Imports: Biobase, MergeMaid, graphics, stats License: LGPL-2 MD5sum: 98020a7d9f86f8ebcb72b55588159088 NeedsCompilation: yes Title: Integration of Microarray Data for Meta-analysis Description: 1) Data transformation for meta-analysis of microarray Data: Transformation of gene expression data to signed probability scale (MCMC/EM methods) 2) Combined differential expression on raw scale: Weighted Z-score after stabilizing mean-variance relation within platform biocViews: Microarray, DifferentialExpression Author: Debashis Ghosh Hyungwon Choi Maintainer: Hyungwon Choi git_url: https://git.bioconductor.org/packages/metaArray git_branch: RELEASE_3_10 git_last_commit: dc01992 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/metaArray_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/metaArray_1.64.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/metaArray_1.64.0.tgz vignettes: vignettes/metaArray/inst/doc/metaArray.pdf vignetteTitles: metaArray Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaArray/inst/doc/metaArray.R dependencyCount: 15 Package: Metab Version: 1.20.0 Depends: xcms, R (>= 3.0.1), svDialogs Imports: pander Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: dbc49c53ba2b62af7e5aa11fdd92dd05 NeedsCompilation: no Title: Metab: An R Package for a High-Throughput Analysis of Metabolomics Data Generated by GC-MS. Description: Metab is an R package for high-throughput processing of metabolomics data analysed by the Automated Mass Spectral Deconvolution and Identification System (AMDIS) (http://chemdata.nist.gov/mass-spc/amdis/downloads/). In addition, it performs statistical hypothesis test (t-test) and analysis of variance (ANOVA). Doing so, Metab considerably speed up the data mining process in metabolomics and produces better quality results. Metab was developed using interactive features, allowing users with lack of R knowledge to appreciate its functionalities. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, AMDIS, GCMS Author: Raphael Aggio Maintainer: Raphael Aggio git_url: https://git.bioconductor.org/packages/Metab git_branch: RELEASE_3_10 git_last_commit: 29cfa5f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Metab_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Metab_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Metab_1.20.0.tgz vignettes: vignettes/Metab/inst/doc/MetabPackage.pdf vignetteTitles: Applying Metab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Metab/inst/doc/MetabPackage.R dependencyCount: 100 Package: metabomxtr Version: 1.20.0 Depends: methods,Biobase Imports: optimx, Formula, plyr, multtest, BiocParallel, ggplot2 Suggests: xtable, reshape2 License: GPL-2 Archs: i386, x64 MD5sum: 0a655c70dc69e6b881d1e37b6909d458 NeedsCompilation: no Title: A package to run mixture models for truncated metabolomics data with normal or lognormal distributions Description: The functions in this package return optimized parameter estimates and log likelihoods for mixture models of truncated data with normal or lognormal distributions. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry Author: Michael Nodzenski, Anna Reisetter, Denise Scholtens Maintainer: Michael Nodzenski git_url: https://git.bioconductor.org/packages/metabomxtr git_branch: RELEASE_3_10 git_last_commit: 12a921a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/metabomxtr_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/metabomxtr_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/metabomxtr_1.20.0.tgz vignettes: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.pdf, vignettes/metabomxtr/inst/doc/mixnorm_Vignette.pdf vignetteTitles: metabomxtr, mixnorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.R, vignettes/metabomxtr/inst/doc/mixnorm_Vignette.R dependencyCount: 71 Package: MetaboSignal Version: 1.16.0 Depends: R(>= 3.3) Imports: KEGGgraph, hpar, igraph, RCurl, KEGGREST, EnsDb.Hsapiens.v75, stats, graphics, utils, org.Hs.eg.db, biomaRt, AnnotationDbi, MWASTools, mygene Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 4829d7a5bc2f16a84a8c9a55762b5cd7 NeedsCompilation: no Title: MetaboSignal: a network-based approach to overlay and explore metabolic and signaling KEGG pathways Description: MetaboSignal is an R package that allows merging, analyzing and customizing metabolic and signaling KEGG pathways. It is a network-based approach designed to explore the topological relationship between genes (signaling- or enzymatic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape and regulatory networks of metabolic phenotypes. biocViews: GraphAndNetwork, GeneSignaling, GeneTarget, Network, Pathways, KEGG, Reactome, Software Author: Andrea Rodriguez-Martinez, Rafael Ayala, Joram M. Posma, Ana L. Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas Maintainer: Andrea Rodriguez-Martinez , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaboSignal git_branch: RELEASE_3_10 git_last_commit: 0c950ab git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MetaboSignal_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MetaboSignal_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MetaboSignal_1.16.0.tgz vignettes: vignettes/MetaboSignal/inst/doc/MetaboSignal.html, vignettes/MetaboSignal/inst/doc/MetaboSignal2.html vignetteTitles: MetaboSignal, MetaboSignal 2: merging KEGG with additional interaction resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboSignal/inst/doc/MetaboSignal.R, vignettes/MetaboSignal/inst/doc/MetaboSignal2.R dependencyCount: 188 Package: metaCCA Version: 1.14.0 Suggests: knitr License: MIT + file LICENSE MD5sum: 7bc03201c74b8aff2c4ab36d3f476820 NeedsCompilation: no Title: Summary Statistics-Based Multivariate Meta-Analysis of Genome-Wide Association Studies Using Canonical Correlation Analysis Description: metaCCA performs multivariate analysis of a single or multiple GWAS based on univariate regression coefficients. It allows multivariate representation of both phenotype and genotype. metaCCA extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. biocViews: GenomeWideAssociation, SNP, Genetics, Regression, StatisticalMethod, Software Author: Anna Cichonska Maintainer: Anna Cichonska URL: https://doi.org/10.1093/bioinformatics/btw052 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metaCCA git_branch: RELEASE_3_10 git_last_commit: 5165bee git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/metaCCA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/metaCCA_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/metaCCA_1.14.0.tgz vignettes: vignettes/metaCCA/inst/doc/metaCCA.pdf vignetteTitles: metaCCA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metaCCA/inst/doc/metaCCA.R dependencyCount: 0 Package: MetaCyto Version: 1.8.0 Depends: R (>= 3.4) Imports: flowCore (>= 1.4),tidyr (>= 0.7),fastcluster,ggplot2,metafor,cluster,FlowSOM, grDevices, graphics, stats, utils Suggests: knitr, dplyr License: GPL (>= 2) MD5sum: 1c6aaad46181b43ee0bff3d5f9a52699 NeedsCompilation: no Title: MetaCyto: A package for meta-analysis of cytometry data Description: This package provides functions for preprocessing, automated gating and meta-analysis of cytometry data. It also provides functions that facilitate the collection of cytometry data from the ImmPort database. biocViews: ImmunoOncology, CellBiology, FlowCytometry, Clustering, StatisticalMethod, Software, CellBasedAssays, Preprocessing Author: Zicheng Hu, Chethan Jujjavarapu, Sanchita Bhattacharya, Atul J. Butte Maintainer: Zicheng Hu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaCyto git_branch: RELEASE_3_10 git_last_commit: ab7ea35 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MetaCyto_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MetaCyto_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MetaCyto_1.8.0.tgz vignettes: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.R dependencyCount: 126 Package: metagene Version: 2.18.0 Depends: R (>= 3.5.0), R6 (>= 2.0), GenomicRanges, BiocParallel Imports: rtracklayer, gplots, tools, GenomicAlignments, GenomeInfoDb, GenomicFeatures, IRanges, ggplot2, muStat, Rsamtools, DBChIP, matrixStats, purrr, data.table, magrittr, methods, utils, ensembldb, EnsDb.Hsapiens.v86, stringr Suggests: BiocGenerics, similaRpeak, RUnit, knitr, BiocStyle, rmarkdown, similaRpeak License: Artistic-2.0 | file LICENSE MD5sum: 59d3710035449580d6a0099813a0c40e NeedsCompilation: no Title: A package to produce metagene plots Description: This package produces metagene plots to compare the behavior of DNA-interacting proteins at selected groups of genes/features. Bam files are used to increase the resolution. Multiple combination of group of bam files and/or group of genomic regions can be compared in a single analysis. Bootstraping analysis is used to compare the groups and locate regions with statistically different enrichment profiles. biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment, Sequencing Author: Charles Joly Beauparlant , Fabien Claude Lamaze , Rawane Samb , Cedric Lippens , Astrid Louise Deschenes and Arnaud Droit . Maintainer: Charles Joly Beauparlant VignetteBuilder: knitr BugReports: https://github.com/CharlesJB/metagene/issues git_url: https://git.bioconductor.org/packages/metagene git_branch: RELEASE_3_10 git_last_commit: 161d2cf git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/metagene_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/metagene_2.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/metagene_2.18.0.tgz vignettes: vignettes/metagene/inst/doc/metagene_rnaseq.html, vignettes/metagene/inst/doc/metagene.html vignetteTitles: RNA-seq exp ext, Introduction to metagene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metagene/inst/doc/metagene_rnaseq.R, vignettes/metagene/inst/doc/metagene.R dependsOnMe: Imetagene dependencyCount: 132 Package: metagene2 Version: 1.2.1 Depends: R (>= 3.6), R6 (>= 2.0), GenomicRanges, BiocParallel Imports: rtracklayer, tools, GenomicAlignments, GenomeInfoDb, IRanges, ggplot2, Rsamtools, DBChIP, purrr, data.table, methods, dplyr, magrittr Suggests: BiocGenerics, RUnit, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 05347062997c826954f2cdedaa104c76 NeedsCompilation: no Title: A package to produce metagene plots Description: This package produces metagene plots to compare coverages of sequencing experiments at selected groups of genomic regions. It can be used for such analyses as assessing the binding of DNA-interacting proteins at promoter regions or surveying antisense transcription over the length of a gene. The metagene2 package can manage all aspects of the analysis, from normalization of coverages to plot facetting according to experimental metadata. Bootstraping analysis is used to provide confidence intervals of per-sample mean coverages. biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment, Sequencing Author: Eric Fournier [cre, aut], Charles Joly Beauparlant [aut], Cedric Lippens [aut], Arnaud Droit [aut] Maintainer: Eric Fournier URL: https://github.com/ArnaudDroitLab/metagene2 VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/metagene2/issues git_url: https://git.bioconductor.org/packages/metagene2 git_branch: RELEASE_3_10 git_last_commit: 6778d1b git_last_commit_date: 2019-11-08 Date/Publication: 2019-11-14 source.ver: src/contrib/metagene2_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/metagene2_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/metagene2_1.2.1.tgz vignettes: vignettes/metagene2/inst/doc/metagene2.html vignetteTitles: Introduction to metagene2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagene2/inst/doc/metagene2.R dependencyCount: 105 Package: metagenomeFeatures Version: 2.6.0 Depends: R (>= 3.5), Biobase (>= 2.17.8) Imports: Biostrings (>= 2.36.4), S4Vectors (>= 0.23.18), dplyr (>= 0.7.0), dbplyr(>= 1.0.0), stringr (>= 1.0.0), lazyeval (>= 0.1.10), RSQLite (>= 1.0.0), magrittr (>= 1.5), methods (>= 3.3.1), lattice (>= 0.20.33), ape (>= 3.5), DECIPHER (>= 2.4.0) Suggests: knitr (>= 1.11), testthat (>= 0.10.0), rmarkdown (>= 1.3), devtools (>= 1.13.5), ggtree(>= 1.8.2), BiocStyle (>= 2.8.2), phyloseq (>= 1.24.2), forcats (>= 0.3.0), ggplot2 (>= 3.0.0) License: Artistic-2.0 MD5sum: a3dfd8ccaeba361be75f4b9e232e8829 NeedsCompilation: no Title: Exploration of marker-gene sequence taxonomic annotations Description: metagenomeFeatures was developed for use in exploring the taxonomic annotations for a marker-gene metagenomic sequence dataset. The package can be used to explore the taxonomic composition of a marker-gene database or annotated sequences from a marker-gene metagenome experiment. biocViews: ImmunoOncology, Microbiome, Metagenomics, Annotation, Infrastructure, Sequencing, Software Author: Nathan D. Olson, Joseph Nathaniel Paulson, Hector Corrada Bravo Maintainer: Nathan D. Olson URL: https://github.com/HCBravoLab/metagenomeFeatures VignetteBuilder: knitr BugReports: https://github.com/HCBravoLab/metagenomeFeatures/issues git_url: https://git.bioconductor.org/packages/metagenomeFeatures git_branch: RELEASE_3_10 git_last_commit: 38eddee git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/metagenomeFeatures_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/metagenomeFeatures_2.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/metagenomeFeatures_2.6.0.tgz vignettes: vignettes/metagenomeFeatures/inst/doc/database-explore.html, vignettes/metagenomeFeatures/inst/doc/MgDb_and_mgFeatures_classes.html, vignettes/metagenomeFeatures/inst/doc/retrieve-feature-data.html vignetteTitles: Exploring sequence and phylogenetic diversity for a taxonomic group of interest., `metagenomeFeatures` classes and methods., Using metagenomeFeatures to Retrieve Feature Data. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagenomeFeatures/inst/doc/database-explore.R, vignettes/metagenomeFeatures/inst/doc/MgDb_and_mgFeatures_classes.R, vignettes/metagenomeFeatures/inst/doc/retrieve-feature-data.R dependencyCount: 52 Package: metagenomeSeq Version: 1.28.2 Depends: R(>= 3.0), Biobase, limma, glmnet, methods, RColorBrewer Imports: parallel, matrixStats, foreach, Matrix, gplots, graphics, grDevices, stats, utils, Wrench, IHW Suggests: annotate, BiocGenerics, biomformat, knitr, gss, testthat (>= 0.8), vegan, interactiveDisplay License: Artistic-2.0 MD5sum: e175de6dcab5a5b53761972d0490114c NeedsCompilation: no Title: Statistical analysis for sparse high-throughput sequencing Description: metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations. biocViews: ImmunoOncology, Classification, Clustering, GeneticVariability, DifferentialExpression, Microbiome, Metagenomics, Normalization, Visualization, MultipleComparison, Sequencing, Software Author: Joseph Nathaniel Paulson, Nathan D. Olson, Domenick J. Braccia, Justin Wagner, Hisham Talukder, Mihai Pop, Hector Corrada Bravo Maintainer: Joseph N. Paulson URL: https://github.com/nosson/metagenomeSeq/ VignetteBuilder: knitr BugReports: https://github.com/nosson/metagenomeSeq/issues git_url: https://git.bioconductor.org/packages/metagenomeSeq git_branch: RELEASE_3_10 git_last_commit: 9c1b1a3 git_last_commit_date: 2020-02-02 Date/Publication: 2020-02-03 source.ver: src/contrib/metagenomeSeq_1.28.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/metagenomeSeq_1.28.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/metagenomeSeq_1.28.2.tgz vignettes: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.pdf, vignettes/metagenomeSeq/inst/doc/metagenomeSeq.pdf vignetteTitles: fitTimeSeries: differential abundance analysis through time or location, metagenomeSeq: statistical analysis for sparse high-throughput sequencing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.R, vignettes/metagenomeSeq/inst/doc/metagenomeSeq.R dependsOnMe: metavizr importsMe: Maaslin2 suggestsMe: interactiveDisplay, phyloseq, Wrench dependencyCount: 31 Package: metahdep Version: 1.44.0 Depends: R (>= 2.10), methods Suggests: affyPLM License: GPL-3 MD5sum: e4a5875ae4dbeaf5d6d95cc2efd1aa17 NeedsCompilation: yes Title: Hierarchical Dependence in Meta-Analysis Description: Tools for meta-analysis in the presence of hierarchical (and/or sampling) dependence, including with gene expression studies biocViews: Microarray, DifferentialExpression Author: John R. Stevens, Gabriel Nicholas Maintainer: John R. Stevens git_url: https://git.bioconductor.org/packages/metahdep git_branch: RELEASE_3_10 git_last_commit: 92ed9d7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/metahdep_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/metahdep_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/metahdep_1.44.0.tgz vignettes: vignettes/metahdep/inst/doc/metahdep.pdf vignetteTitles: metahdep Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metahdep/inst/doc/metahdep.R dependencyCount: 1 Package: metaMS Version: 1.22.0 Depends: R (>= 2.10), methods, CAMERA, xcms (>= 1.35) Imports: Matrix, tools, robustbase, BiocGenerics Suggests: metaMSdata, RUnit License: GPL (>= 2) MD5sum: 4bf788f7ca524b09ff5a44a6adb40843 NeedsCompilation: no Title: MS-based metabolomics annotation pipeline Description: MS-based metabolomics data processing and compound annotation pipeline. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Ron Wehrens [aut, cre] (author of GC-MS part), Pietro Franceschi [aut] (author of LC-MS part), Nir Shahaf [ctb], Matthias Scholz [ctb], Georg Weingart [ctb] (development of GC-MS approach), Elisabete Carvalho [ctb] (testing and feedback of GC-MS pipeline) Maintainer: Yann Guitton URL: https://github.com/yguitton/metaMS git_url: https://git.bioconductor.org/packages/metaMS git_branch: RELEASE_3_10 git_last_commit: ee1c99d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/metaMS_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/metaMS_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/metaMS_1.22.0.tgz vignettes: vignettes/metaMS/inst/doc/runGC.pdf, vignettes/metaMS/inst/doc/runLC.pdf vignetteTitles: runGC, runLC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaMS/inst/doc/runGC.R, vignettes/metaMS/inst/doc/runLC.R suggestsMe: CluMSID dependencyCount: 128 Package: MetaNeighbor Version: 1.6.0 Depends: R(>= 3.5) Imports: beanplot (>= 1.2), GenomicRanges (>= 1.34.0), gplots (>= 3.0.1), Matrix (>= 1.2), matrixStats (>= 0.54), Rcpp, RColorBrewer (>= 1.1), stats (>= 3.4), SummarizedExperiment (>= 1.12), utils (>= 3.4) LinkingTo: Rcpp Suggests: knitr (>= 1.17), rmarkdown (>= 1.6), testthat (>= 1.0.2) License: MIT + file LICENSE MD5sum: 365c9076295f65f60898d3e1fe946016 NeedsCompilation: yes Title: Single cell replicability analysis Description: MetaNeighbor allows users to quantify cell type replicability across datasets using neighbor voting. biocViews: ImmunoOncology, GeneExpression, GO, MultipleComparison, SingleCell, Transcriptomics Author: Megan Crow [aut, cre], Sara Ballouz [ctb], Manthan Shah [ctb], Stephan Fischer [ctb], Jesse Gillis [aut] Maintainer: Manthan Shah VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaNeighbor git_branch: RELEASE_3_10 git_last_commit: 6e44380 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MetaNeighbor_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MetaNeighbor_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MetaNeighbor_1.6.0.tgz vignettes: vignettes/MetaNeighbor/inst/doc/MetaNeighbor.pdf vignetteTitles: MetaNeighbor user guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MetaNeighbor/inst/doc/MetaNeighbor.R dependencyCount: 40 Package: metaSeq Version: 1.26.0 Depends: R (>= 2.13.0), NOISeq, snow, Rcpp License: Artistic-2.0 Archs: i386, x64 MD5sum: f19235bd76be69998962118acfe1dfe5 NeedsCompilation: no Title: Meta-analysis of RNA-Seq count data in multiple studies Description: The probabilities by one-sided NOISeq are combined by Fisher's method or Stouffer's method biocViews: RNASeq, DifferentialExpression, Sequencing, ImmunoOncology Author: Koki Tsuyuzaki, Itoshi Nikaido Maintainer: Koki Tsuyuzaki git_url: https://git.bioconductor.org/packages/metaSeq git_branch: RELEASE_3_10 git_last_commit: 454651c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/metaSeq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/metaSeq_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/metaSeq_1.26.0.tgz vignettes: vignettes/metaSeq/inst/doc/metaSeq.pdf vignetteTitles: metaSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaSeq/inst/doc/metaSeq.R dependencyCount: 15 Package: metaseqR Version: 1.26.0 Depends: R (>= 2.13.0), EDASeq, DESeq, limma, qvalue Imports: edgeR, NOISeq, baySeq, NBPSeq, biomaRt, utils, gplots, corrplot, vsn, brew, rjson, log4r Suggests: BiocGenerics, GenomicRanges, rtracklayer, Rsamtools, survcomp, VennDiagram, knitr, zoo, RUnit, BiocManager, BSgenome, RSQLite Enhances: parallel, TCC, RMySQL License: GPL (>= 3) MD5sum: 892c2f693c190dd82d67e6ecf371cfd8 NeedsCompilation: no Title: An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms. Description: Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way. biocViews: ImmunoOncology, Software, GeneExpression, DifferentialExpression, WorkflowStep, Preprocessing, QualityControl, Normalization, ReportWriting, RNASeq Author: Panagiotis Moulos Maintainer: Panagiotis Moulos URL: http://www.fleming.gr VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metaseqR git_branch: RELEASE_3_10 git_last_commit: 3729b71 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/metaseqR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/metaseqR_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/metaseqR_1.26.0.tgz vignettes: vignettes/metaseqR/inst/doc/metaseqr-pdf.pdf vignetteTitles: RNA-Seq data analysis using mulitple statistical algorithms with metaseqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaseqR/inst/doc/metaseqr-pdf.R dependencyCount: 150 Package: metavizr Version: 1.10.0 Depends: R (>= 3.4), metagenomeSeq (>= 1.17.1), methods, data.table, Biobase, digest Imports: epivizr, epivizrData, epivizrServer, epivizrStandalone, vegan, GenomeInfoDb, phyloseq, httr Suggests: knitr, BiocStyle, matrixStats, msd16s (>= 0.109.1), etec16s, testthat, gss, curatedMetagenomicData License: MIT + file LICENSE Archs: i386, x64 MD5sum: 8b272bef7c3e4239fe55fbb3243ed36b NeedsCompilation: no Title: R Interface to the metaviz web app for interactive metagenomics data analysis and visualization Description: This package provides Websocket communication to the metaviz web app (http://metaviz.cbcb.umd.edu) for interactive visualization of metagenomics data. Objects in R/bioc interactive sessions can be displayed in plots and data can be explored using a facetzoom visualization. Fundamental Bioconductor data structures are supported (e.g., MRexperiment objects), while providing an easy mechanism to support other data structures. Visualizations (using d3.js) can be easily added to the web app as well. biocViews: Visualization, Infrastructure, GUI, Metagenomics, ImmunoOncology Author: Hector Corrada Bravo [cre, aut], Florin Chelaru [aut], Justin Wagner [aut], Jayaram Kancherla [aut], Joseph Paulson [aut] Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metavizr git_branch: RELEASE_3_10 git_last_commit: e8518fa git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/metavizr_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/metavizr_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/metavizr_1.10.0.tgz vignettes: vignettes/metavizr/inst/doc/IntroToMetavizr.html vignetteTitles: Introduction to metavizr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metavizr/inst/doc/IntroToMetavizr.R dependencyCount: 162 Package: MetaVolcanoR Version: 1.0.1 Depends: R (>= 3.6.0) Imports: methods, data.table, dplyr, tidyr, plotly, ggplot2, cowplot, parallel, metafor, metap, rlang, topconfects, grDevices, graphics, stats, htmlwidgets Suggests: knitr, testthat License: GPL-3 MD5sum: 7f26024095e9e2db25a7afc1b11a1c34 NeedsCompilation: no Title: Gene Expression Meta-analysis Visualization Tool Description: MetaVolcanoR combines differential gene expression results. It implements three strategies to summarize differential gene expression from different studies. i) Random Effects Model (REM) approach, ii) a p-value combining-approach, and iii) a vote-counting approach. In all cases, MetaVolcano exploits the Volcano plot reasoning to visualize the gene expression meta-analysis results. biocViews: GeneExpression, DifferentialExpression, Transcriptomics, mRNAMicroarray, RNASeq Author: Cesar Prada [aut, cre], Diogenes Lima [aut], Helder Nakaya [aut, ths] Maintainer: Cesar Prada VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaVolcanoR git_branch: RELEASE_3_10 git_last_commit: c0f64a4 git_last_commit_date: 2019-11-04 Date/Publication: 2019-11-04 source.ver: src/contrib/MetaVolcanoR_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/MetaVolcanoR_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MetaVolcanoR_1.0.1.tgz vignettes: vignettes/MetaVolcanoR/inst/doc/MetaVolcano.html, vignettes/MetaVolcanoR/inst/doc/PrepareDatasets.html vignetteTitles: MetaVolcanoR: Differential expression meta-analysis tool, MetaVolcanoR inputs: differential expression examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaVolcanoR/inst/doc/MetaVolcano.R, vignettes/MetaVolcanoR/inst/doc/PrepareDatasets.R dependencyCount: 108 Package: MetCirc Version: 1.16.0 Depends: R (>= 3.5), amap (>= 0.8), circlize (>= 0.3.9), scales (>= 0.3.0), shiny (>= 1.0.0), MSnbase (>= 2.10.1), Imports: ggplot2 (>= 3.2.1), S4Vectors (>= 0.22.0) Suggests: BiocGenerics, graphics (>= 3.5), grDevices (>= 3.5), knitr (>= 1.11), methods (>= 3.5), RUnit (>= 0.4.32), stats (>= 3.5) License: GPL (>= 3) MD5sum: 80cf529442aa638e4e4ece25ffbab203 NeedsCompilation: no Title: Navigating mass spectral similarity in high-resolution MS/MS metabolomics data Description: MetCirc comprises a workflow to interactively explore high-resolution MS/MS metabolomics data. MetCirc uses the Spectrum2 and Spectra infrastructure defined in the package MSnbase that stores MS/MS spectra. MetCirc offers functionality to calculate similarity between precursors based on the normalised dot product, neutral losses or user-defined functions and visualise similarities in a circular layout. Within the interactive framework the user can annotate MS/MS features based on their similarity to (known) related MS/MS features. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, Visualization Author: Thomas Naake and Emmanuel Gaquerel Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetCirc git_branch: RELEASE_3_10 git_last_commit: 731b255 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MetCirc_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MetCirc_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MetCirc_1.16.0.tgz vignettes: vignettes/MetCirc/inst/doc/MetCirc.pdf vignetteTitles: Workflow for Metabolomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetCirc/inst/doc/MetCirc.R dependencyCount: 103 Package: MethCP Version: 1.0.2 Depends: R (>= 3.6.0) Imports: methods, utils, stats, S4Vectors, bsseq, DSS, methylKit, DNAcopy, GenomicRanges, IRanges, GenomeInfoDb, BiocParallel Suggests: testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: 9881c5bec8359c95a3370652b7379ff8 NeedsCompilation: no Title: Differential methylation anlsysis for bisulfite sequencing data Description: MethCP is a differentially methylated region (DMR) detecting method for whole-genome bisulfite sequencing (WGBS) data, which is applicable for a wide range of experimental designs beyond the two-group comparisons, such as time-course data. MethCP identifies DMRs based on change point detection, which naturally segments the genome and provides region-level differential analysis. biocViews: DifferentialMethylation, Sequencing, WholeGenome, TimeCourse Author: Boying Gong [aut, cre] Maintainer: Boying Gong VignetteBuilder: knitr BugReports: https://github.com/boyinggong/methcp/issues git_url: https://git.bioconductor.org/packages/MethCP git_branch: RELEASE_3_10 git_last_commit: badc7e7 git_last_commit_date: 2020-04-07 Date/Publication: 2020-04-07 source.ver: src/contrib/MethCP_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/MethCP_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MethCP_1.0.2.tgz vignettes: vignettes/MethCP/inst/doc/methcp.html vignetteTitles: methcp: User’s Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethCP/inst/doc/methcp.R dependencyCount: 115 Package: methimpute Version: 1.8.0 Depends: R (>= 3.4.0), GenomicRanges, ggplot2 Imports: Rcpp (>= 0.12.4.5), methods, utils, grDevices, stats, GenomeInfoDb, IRanges, Biostrings, reshape2, minpack.lm, data.table LinkingTo: Rcpp Suggests: knitr, BiocStyle License: Artistic-2.0 MD5sum: 30ddcf00c7f84e5a574284403e519493 NeedsCompilation: yes Title: Imputation-guided re-construction of complete methylomes from WGBS data Description: This package implements functions for calling methylation for all cytosines in the genome. biocViews: ImmunoOncology, Software, DNAMethylation, Epigenetics, HiddenMarkovModel, Sequencing, Coverage Author: Aaron Taudt Maintainer: Aaron Taudt VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methimpute git_branch: RELEASE_3_10 git_last_commit: 67c8034 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/methimpute_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/methimpute_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/methimpute_1.8.0.tgz vignettes: vignettes/methimpute/inst/doc/methimpute.pdf vignetteTitles: Methylation status calling with METHimpute hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methimpute/inst/doc/methimpute.R dependencyCount: 73 Package: methInheritSim Version: 1.8.0 Depends: R (>= 3.4) Imports: methylKit, GenomicRanges, GenomeInfoDb, parallel, BiocGenerics, S4Vectors, methods, stats, IRanges, msm Suggests: BiocStyle, knitr, rmarkdown, RUnit, methylInheritance License: Artistic-2.0 MD5sum: 18fa0f742498c95e7305239a27ff1f40 NeedsCompilation: no Title: Simulating Whole-Genome Inherited Bisulphite Sequencing Data Description: Simulate a multigeneration methylation case versus control experiment with inheritance relation using a real control dataset. biocViews: BiologicalQuestion, Epigenetics, DNAMethylation, DifferentialMethylation, MethylSeq, Software, ImmunoOncology, StatisticalMethod, WholeGenome, Sequencing Author: Pascal Belleau, Astrid Deschênes and Arnaud Droit Maintainer: Pascal Belleau URL: https://github.com/belleau/methInheritSim VignetteBuilder: knitr BugReports: https://github.com/belleau/methInheritSim/issues git_url: https://git.bioconductor.org/packages/methInheritSim git_branch: RELEASE_3_10 git_last_commit: 8a85259 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/methInheritSim_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/methInheritSim_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/methInheritSim_1.8.0.tgz vignettes: vignettes/methInheritSim/inst/doc/methInheritSim.html vignetteTitles: Simulating Whole-Genome Inherited Bisulphite Sequencing Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methInheritSim/inst/doc/methInheritSim.R suggestsMe: methylInheritance dependencyCount: 107 Package: MethPed Version: 1.14.0 Depends: R (>= 3.0.0), Biobase Imports: randomForest, grDevices, graphics, stats Suggests: BiocStyle, knitr, markdown, impute License: GPL-2 MD5sum: fe7d6c20cb8faac6f1b1ca4c1abb4585 NeedsCompilation: no Title: A DNA methylation classifier tool for the identification of pediatric brain tumor subtypes Description: Classification of pediatric tumors into biologically defined subtypes is challenging and multifaceted approaches are needed. For this aim, we developed a diagnostic classifier based on DNA methylation profiles. We offer MethPed as an easy-to-use toolbox that allows researchers and clinical diagnosticians to test single samples as well as large cohorts for subclass prediction of pediatric brain tumors. The current version of MethPed can classify the following tumor diagnoses/subgroups: Diffuse Intrinsic Pontine Glioma (DIPG), Ependymoma, Embryonal tumors with multilayered rosettes (ETMR), Glioblastoma (GBM), Medulloblastoma (MB) - Group 3 (MB_Gr3), Group 4 (MB_Gr3), Group WNT (MB_WNT), Group SHH (MB_SHH) and Pilocytic Astrocytoma (PiloAstro). biocViews: ImmunoOncology, DNAMethylation, Classification, Epigenetics Author: Mohammad Tanvir Ahamed [aut, trl], Anna Danielsson [aut], Szilárd Nemes [aut, trl], Helena Carén [aut, cre, cph] Maintainer: Helena Carén VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethPed git_branch: RELEASE_3_10 git_last_commit: bb7d62d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MethPed_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MethPed_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MethPed_1.14.0.tgz vignettes: vignettes/MethPed/inst/doc/MethPed-vignette.html vignetteTitles: MethPed User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethPed/inst/doc/MethPed-vignette.R dependencyCount: 9 Package: methrix Version: 1.0.05 Depends: R (>= 3.6), data.table (>= 1.12.4), SummarizedExperiment Imports: DelayedArray, HDF5Array, BSgenome, rjson, DelayedMatrixStats, parallel, methods, ggplot2, matrixStats, graphics, stats, utils Suggests: knitr, rmarkdown, DSS, bsseq, plotly, BSgenome.Mmusculus.UCSC.mm9, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, GenomicScores, Biostrings, RColorBrewer, GenomicRanges, GenomeInfoDb, IRanges, testthat (>= 2.1.0) License: MIT + file LICENSE Archs: i386, x64 MD5sum: 85612243d3f873ba584968b3f07757a7 NeedsCompilation: no Title: Fast and efficient summarization of generic bedGraph files from Bisufite sequencing Description: Bedgraph files generated by Bisulfite pipelines often come in various flavors. Critical downstream step requires summarization of these files into methylation/coverage matrices. This step of data aggregation is done by Methrix, including many other useful downstream functions. biocViews: DNAMethylation, Sequencing, Coverage Author: Anand Mayakonda [aut, cre], Reka Toth [aut], Maximilian Schönung [ctb], Pavlo Lutsik [ctb], Joschka Hey [ctb] Maintainer: Anand Mayakonda VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methrix git_branch: RELEASE_3_10 git_last_commit: 2ae3aae git_last_commit_date: 2020-01-08 Date/Publication: 2020-01-08 source.ver: src/contrib/methrix_1.0.05.tar.gz win.binary.ver: bin/windows/contrib/3.6/methrix_1.0.05.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/methrix_1.0.05.tgz vignettes: vignettes/methrix/inst/doc/methrix.html vignetteTitles: Methrix tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/methrix/inst/doc/methrix.R dependencyCount: 90 Package: MethTargetedNGS Version: 1.18.0 Depends: R (>= 3.1.2), stringr, seqinr, gplots, Biostrings License: Artistic-2.0 MD5sum: 6d1dda2169d127787422c0b40a33ab80 NeedsCompilation: no Title: Perform Methylation Analysis on Next Generation Sequencing Data Description: Perform step by step methylation analysis of Next Generation Sequencing data. biocViews: ResearchField, Genetics, Sequencing, Alignment, SequenceMatching, DataImport Author: Muhammad Ahmer Jamil with Contribution of Prof. Holger Frohlich and Priv.-Doz. Dr. Osman El-Maarri Maintainer: Muhammad Ahmer Jamil SystemRequirements: HMMER3 git_url: https://git.bioconductor.org/packages/MethTargetedNGS git_branch: RELEASE_3_10 git_last_commit: 1547755 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MethTargetedNGS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MethTargetedNGS_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MethTargetedNGS_1.18.0.tgz vignettes: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.pdf vignetteTitles: Introduction to MethTargetedNGS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.R dependencyCount: 32 Package: methVisual Version: 1.38.0 Depends: R (>= 2.11.0), Biostrings(>= 2.4.8), plotrix,gsubfn, grid,sqldf Imports: Biostrings, ca, graphics, grDevices, grid, gridBase, IRanges, stats, utils License: GPL (>= 2) MD5sum: 53fcecdc49a39209cbc26517ba833d8f NeedsCompilation: no Title: Methods for visualization and statistics on DNA methylation data Description: The package 'methVisual' allows the visualization of DNA methylation data after bisulfite sequencing. biocViews: DNAMethylation, Clustering, Classification Author: A. Zackay, C. Steinhoff Maintainer: Arie Zackay git_url: https://git.bioconductor.org/packages/methVisual git_branch: RELEASE_3_10 git_last_commit: 9a54e46 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/methVisual_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/methVisual_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/methVisual_1.38.0.tgz vignettes: vignettes/methVisual/inst/doc/methVisual.pdf vignetteTitles: Introduction to methVisual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methVisual/inst/doc/methVisual.R dependencyCount: 36 Package: methyAnalysis Version: 1.28.0 Depends: R (>= 2.10), grid, BiocGenerics, IRanges, GenomeInfoDb, GenomicRanges, Biobase (>= 2.34.0), org.Hs.eg.db Imports: grDevices, stats, utils, lumi, methylumi, Gviz, genoset, SummarizedExperiment, IRanges, GenomicRanges, VariantAnnotation, rtracklayer, GenomicFeatures, annotate, Biobase (>= 2.5.5), AnnotationDbi, genefilter, biomaRt, methods, parallel Suggests: FDb.InfiniumMethylation.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: d828e1a53c9b284cdea7b5879c27a744 NeedsCompilation: no Title: DNA methylation data analysis and visualization Description: The methyAnalysis package aims for the DNA methylation data analysis and visualization. A MethyGenoSet class is defined to keep the chromosome location information together with the data. The package also includes functions of estimating the methylation levels from Methy-Seq data. biocViews: Microarray, DNAMethylation, Visualization Author: Pan Du, Richard Bourgon Maintainer: Pan Du , Lei Huang , Gang Feng git_url: https://git.bioconductor.org/packages/methyAnalysis git_branch: RELEASE_3_10 git_last_commit: 7ecad42 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/methyAnalysis_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/methyAnalysis_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/methyAnalysis_1.28.0.tgz vignettes: vignettes/methyAnalysis/inst/doc/methyAnalysis.pdf vignetteTitles: An Introduction to the methyAnalysis package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methyAnalysis/inst/doc/methyAnalysis.R suggestsMe: methylumi dependencyCount: 188 Package: MethylAid Version: 1.20.0 Depends: R (>= 3.4) Imports: Biobase, BiocParallel, BiocGenerics, ggplot2, grid, gridBase, grDevices, graphics, hexbin, matrixStats, minfi (>= 1.22.0), methods, RColorBrewer, shiny, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, MethylAidData, minfiData, minfiDataEPIC, RUnit License: GPL (>= 2) MD5sum: 1f0409f92a419006303b518efb0ce7ee NeedsCompilation: no Title: Visual and interactive quality control of large Illumina DNA Methylation array data sets Description: A visual and interactive web application using RStudio's shiny package. Bad quality samples are detected using sample-dependent and sample-independent controls present on the array and user adjustable thresholds. In depth exploration of bad quality samples can be performed using several interactive diagnostic plots of the quality control probes present on the array. Furthermore, the impact of any batch effect provided by the user can be explored. biocViews: DNAMethylation, MethylationArray, Microarray, TwoChannel, QualityControl, BatchEffect, Visualization, GUI Author: Maarten van Iterson [aut, cre], Elmar Tobi[ctb], Roderick Slieker[ctb], Wouter den Hollander[ctb], Rene Luijk[ctb] and Bas Heijmans[ctb] Maintainer: L.J.Sinke VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethylAid git_branch: RELEASE_3_10 git_last_commit: 98a11f4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MethylAid_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MethylAid_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MethylAid_1.20.0.tgz vignettes: vignettes/MethylAid/inst/doc/MethylAid.pdf vignetteTitles: MethylAid: Visual and Interactive quality control of Illumina Human DNA Methylation array data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylAid/inst/doc/MethylAid.R dependencyCount: 154 Package: methylCC Version: 1.0.0 Depends: R (>= 3.6), FlowSorted.Blood.450k Imports: Biobase, GenomicRanges, IRanges, S4Vectors, dplyr, magrittr, minfi, bsseq, quadprog, plyranges, stats, utils, bumphunter, genefilter, methods, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19 Suggests: knitr, testthat (>= 2.1.0), BiocGenerics, BiocStyle, tidyr, ggplot2 License: CC BY 4.0 MD5sum: 379b27d0b20f977bd53838bd4e216c86 NeedsCompilation: no Title: Estimate the cell composition of whole blood in DNA methylation samples Description: A tool to estimate the cell composition of DNA methylation whole blood sample measured on any platform technology (microarray and sequencing). biocViews: Microarray, Sequencing, DNAMethylation, MethylationArray, MethylSeq, WholeGenome Author: Stephanie C. Hicks [aut, cre] (), Rafael Irizarry [aut] () Maintainer: Stephanie C. Hicks URL: https://github.com/stephaniehicks/methylCC/ VignetteBuilder: knitr BugReports: https://github.com/stephaniehicks/methylCC/ git_url: https://git.bioconductor.org/packages/methylCC git_branch: RELEASE_3_10 git_last_commit: ec73e7c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/methylCC_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/methylCC_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/methylCC_1.0.0.tgz vignettes: vignettes/methylCC/inst/doc/methylCC.html vignetteTitles: The qsmooth user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylCC/inst/doc/methylCC.R dependencyCount: 140 Package: methylGSA Version: 1.4.9 Depends: R (>= 3.5) Imports: RobustRankAggreg, ggplot2, stringr, stats, clusterProfiler, missMethyl, org.Hs.eg.db, reactome.db, BiocParallel, GO.db, AnnotationDbi, shiny, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 Suggests: knitr, rmarkdown, testthat, enrichplot License: GPL-2 MD5sum: ab5f82ad569dea88da61e915e8a02626 NeedsCompilation: no Title: Gene Set Analysis Using the Outcome of Differential Methylation Description: The main functions for methylGSA are methylglm and methylRRA. methylGSA implements logistic regression adjusting number of probes as a covariate. methylRRA adjusts multiple p-values of each gene by Robust Rank Aggregation. For more detailed help information, please see the vignette. biocViews: DNAMethylation,DifferentialMethylation,GeneSetEnrichment,Regression, GeneRegulation,Pathways Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/methylGSA VignetteBuilder: knitr BugReports: https://github.com/reese3928/methylGSA/issues git_url: https://git.bioconductor.org/packages/methylGSA git_branch: RELEASE_3_10 git_last_commit: 15f84c4 git_last_commit_date: 2020-02-23 Date/Publication: 2020-02-24 source.ver: src/contrib/methylGSA_1.4.9.tar.gz win.binary.ver: bin/windows/contrib/3.6/methylGSA_1.4.9.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/methylGSA_1.4.9.tgz vignettes: vignettes/methylGSA/inst/doc/methylGSA-vignette.html vignetteTitles: methylGSA: Gene Set Analysis for DNA Methylation Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylGSA/inst/doc/methylGSA-vignette.R dependencyCount: 197 Package: methylInheritance Version: 1.10.0 Depends: R (>= 3.5) Imports: methylKit, BiocParallel, GenomicRanges, IRanges, S4Vectors, methods, parallel, ggplot2, gridExtra, rebus Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, RUnit, methInheritSim License: Artistic-2.0 MD5sum: 4c0e6f0f005ed4e8cf365c38268b6fb9 NeedsCompilation: no Title: Permutation-Based Analysis associating Conserved Differentially Methylated Elements Across Multiple Generations to a Treatment Effect Description: Permutation analysis, based on Monte Carlo sampling, for testing the hypothesis that the number of conserved differentially methylated elements, between several generations, is associated to an effect inherited from a treatment and that stochastic effect can be dismissed. biocViews: BiologicalQuestion, Epigenetics, DNAMethylation, DifferentialMethylation, MethylSeq, Software, ImmunoOncology, StatisticalMethod, WholeGenome, Sequencing Author: Astrid Deschênes, Pascal Belleau and Arnaud Droit Maintainer: Astrid Deschenes URL: https://github.com/adeschen/methylInheritance VignetteBuilder: knitr BugReports: https://github.com/adeschen/methylInheritance/issues git_url: https://git.bioconductor.org/packages/methylInheritance git_branch: RELEASE_3_10 git_last_commit: 6822d45 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/methylInheritance_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/methylInheritance_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/methylInheritance_1.10.0.tgz vignettes: vignettes/methylInheritance/inst/doc/methylInheritance.html vignetteTitles: Permutation-Based Analysis associating Conserved Differentially Methylated Elements Across Multiple Generations to a Treatment Effect hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylInheritance/inst/doc/methylInheritance.R suggestsMe: methInheritSim dependencyCount: 110 Package: methylKit Version: 1.12.0 Depends: R (>= 3.3.0), GenomicRanges (>= 1.18.1), methods Imports: IRanges, data.table (>= 1.9.6), parallel, S4Vectors (>= 0.13.13), GenomeInfoDb, KernSmooth, qvalue, emdbook, Rsamtools, gtools, fastseg, rtracklayer, mclust, mgcv, Rcpp, R.utils, limma, grDevices, graphics, stats, utils LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc Suggests: testthat,knitr, rmarkdown, genomation, BiocManager License: Artistic-2.0 MD5sum: 5c85fe9214f8269edfce8137944eb52f NeedsCompilation: yes Title: DNA methylation analysis from high-throughput bisulfite sequencing results Description: methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq. Methylation calling can be performed directly from Bismark aligned BAM files. biocViews: DNAMethylation, Sequencing, MethylSeq Author: Altuna Akalin [aut, cre], Matthias Kormaksson [aut], Sheng Li [aut], Arsene Wabo [ctb], Adrian Bierling [aut], Alexander Gosdschan [aut] Maintainer: Altuna Akalin , Alexander Gosdschan URL: http://code.google.com/p/methylkit/ SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylKit git_branch: RELEASE_3_10 git_last_commit: cc00762 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/methylKit_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/methylKit_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/methylKit_1.12.0.tgz vignettes: vignettes/methylKit/inst/doc/methylKit.html vignetteTitles: methylKit: User Guide v`r packageVersion('methylKit')` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylKit/inst/doc/methylKit.R importsMe: MethCP, methInheritSim, methylInheritance dependencyCount: 103 Package: MethylMix Version: 2.16.0 Depends: R (>= 3.2.0) Imports: foreach, RPMM, RColorBrewer, ggplot2, RCurl, impute, data.table, limma, R.matlab, digest Suggests: BiocStyle, doParallel, testthat, knitr, rmarkdown License: GPL-2 MD5sum: 546ce7cae0655dfa35fbfbd2f63489ba NeedsCompilation: no Title: MethylMix: Identifying methylation driven cancer genes Description: MethylMix is an algorithm implemented to identify hyper and hypomethylated genes for a disease. MethylMix is based on a beta mixture model to identify methylation states and compares them with the normal DNA methylation state. MethylMix uses a novel statistic, the Differential Methylation value or DM-value defined as the difference of a methylation state with the normal methylation state. Finally, matched gene expression data is used to identify, besides differential, functional methylation states by focusing on methylation changes that effect gene expression. References: Gevaert 0. MethylMix: an R package for identifying DNA methylation-driven genes. Bioinformatics (Oxford, England). 2015;31(11):1839-41. doi:10.1093/bioinformatics/btv020. Gevaert O, Tibshirani R, Plevritis SK. Pancancer analysis of DNA methylation-driven genes using MethylMix. Genome Biology. 2015;16(1):17. doi:10.1186/s13059-014-0579-8. biocViews: DNAMethylation,StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,DifferentialExpression,Pathways,Network Author: Olivier Gevaert Maintainer: Olivier Gevaert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethylMix git_branch: RELEASE_3_10 git_last_commit: b88e95c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MethylMix_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MethylMix_2.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MethylMix_2.16.0.tgz vignettes: vignettes/MethylMix/inst/doc/vignettes.html vignetteTitles: MethylMix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylMix/inst/doc/vignettes.R dependencyCount: 68 Package: methylMnM Version: 1.24.0 Depends: R (>= 2.12.1), edgeR, statmod License: GPL-3 MD5sum: 90eb32c9e1f3c0b2443466468a0aa7e5 NeedsCompilation: yes Title: detect different methylation level (DMR) Description: To give the exactly p-value and q-value of MeDIP-seq and MRE-seq data for different samples comparation. biocViews: Software, DNAMethylation, Sequencing Author: Yan Zhou, Bo Zhang, Nan Lin, BaoXue Zhang and Ting Wang Maintainer: Yan Zhou git_url: https://git.bioconductor.org/packages/methylMnM git_branch: RELEASE_3_10 git_last_commit: d3c5556 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/methylMnM_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/methylMnM_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/methylMnM_1.24.0.tgz vignettes: vignettes/methylMnM/inst/doc/methylMnM.pdf vignetteTitles: methylMnM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylMnM/inst/doc/methylMnM.R importsMe: SIMD dependencyCount: 12 Package: methylPipe Version: 1.20.0 Depends: R (>= 3.2.0), methods, grDevices, graphics, stats, utils, GenomicRanges, SummarizedExperiment (>= 0.2.0), Rsamtools Imports: marray, gplots, IRanges, BiocGenerics, Gviz, GenomicAlignments, Biostrings, parallel, data.table, GenomeInfoDb, S4Vectors Suggests: BSgenome.Hsapiens.UCSC.hg18, TxDb.Hsapiens.UCSC.hg18.knownGene, knitr, MethylSeekR License: GPL(>=2) Archs: i386, x64 MD5sum: eddecfb8e8c22cfbab97f720d49dbb96 NeedsCompilation: yes Title: Base resolution DNA methylation data analysis Description: Memory efficient analysis of base resolution DNA methylation data in both the CpG and non-CpG sequence context. Integration of DNA methylation data derived from any methodology providing base- or low-resolution data. biocViews: MethylSeq, DNAMethylation, Coverage, Sequencing Author: Kamal Kishore Maintainer: Kamal Kishore VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylPipe git_branch: RELEASE_3_10 git_last_commit: b631764 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/methylPipe_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/methylPipe_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/methylPipe_1.20.0.tgz vignettes: vignettes/methylPipe/inst/doc/methylPipe.pdf vignetteTitles: methylPipe.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylPipe/inst/doc/methylPipe.R importsMe: compEpiTools dependencyCount: 150 Package: MethylSeekR Version: 1.26.0 Depends: rtracklayer (>= 1.16.3), parallel (>= 2.15.1), mhsmm (>= 0.4.4) Imports: IRanges (>= 1.16.3), BSgenome (>= 1.26.1), GenomicRanges (>= 1.10.5), geneplotter (>= 1.34.0), graphics (>= 2.15.2), grDevices (>= 2.15.2), parallel (>= 2.15.2), stats (>= 2.15.2), utils (>= 2.15.2) Suggests: BSgenome.Hsapiens.UCSC.hg18 License: GPL (>=2) MD5sum: 08594097ebb798c3c5e2e7daf2fd7556 NeedsCompilation: no Title: Segmentation of Bis-seq data Description: This is a package for the discovery of regulatory regions from Bis-seq data biocViews: Sequencing, MethylSeq, DNAMethylation Author: Lukas Burger, Dimos Gaidatzis, Dirk Schubeler and Michael Stadler Maintainer: Lukas Burger git_url: https://git.bioconductor.org/packages/MethylSeekR git_branch: RELEASE_3_10 git_last_commit: 508266e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MethylSeekR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MethylSeekR_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MethylSeekR_1.26.0.tgz vignettes: vignettes/MethylSeekR/inst/doc/MethylSeekR.pdf vignetteTitles: MethylSeekR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylSeekR/inst/doc/MethylSeekR.R suggestsMe: methylPipe, RnBeads dependencyCount: 60 Package: methylumi Version: 2.32.0 Depends: Biobase, methods, R (>= 2.13), scales, reshape2, ggplot2, matrixStats, FDb.InfiniumMethylation.hg19 (>= 2.2.0), minfi Imports: BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, Biobase, graphics, lattice, annotate, genefilter, AnnotationDbi, minfi, stats4, illuminaio Suggests: lumi, lattice, limma, xtable, SQN, MASS, matrixStats, parallel, rtracklayer, Biostrings, methyAnalysis, TCGAMethylation450k, IlluminaHumanMethylation450kanno.ilmn12.hg19, FDb.InfiniumMethylation.hg18 (>= 2.2.0), Homo.sapiens, knitr License: GPL-2 Archs: i386, x64 MD5sum: 57d9b0e72e0145434c3f90c4dd2fd680 NeedsCompilation: no Title: Handle Illumina methylation data Description: This package provides classes for holding and manipulating Illumina methylation data. Based on eSet, it can contain MIAME information, sample information, feature information, and multiple matrices of data. An "intelligent" import function, methylumiR can read the Illumina text files and create a MethyLumiSet. methylumIDAT can directly read raw IDAT files from HumanMethylation27 and HumanMethylation450 microarrays. Normalization, background correction, and quality control features for GoldenGate, Infinium, and Infinium HD arrays are also included. biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl, CpGIsland Author: Sean Davis, Pan Du, Sven Bilke, Tim Triche, Jr., Moiz Bootwalla Maintainer: Sean Davis VignetteBuilder: knitr BugReports: https://github.com/seandavi/methylumi/issues/new git_url: https://git.bioconductor.org/packages/methylumi git_branch: RELEASE_3_10 git_last_commit: e2a29c1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/methylumi_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/methylumi_2.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/methylumi_2.32.0.tgz vignettes: vignettes/methylumi/inst/doc/methylumi.pdf, vignettes/methylumi/inst/doc/methylumi450k.pdf vignetteTitles: An Introduction to the methylumi package, Working with Illumina 450k Arrays using methylumi hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylumi/inst/doc/methylumi.R, vignettes/methylumi/inst/doc/methylumi450k.R dependsOnMe: bigmelon, RnBeads, skewr, wateRmelon importsMe: ffpe, lumi, methyAnalysis, missMethyl dependencyCount: 149 Package: methyvim Version: 1.8.0 Depends: R (>= 3.4.0) Imports: stats, cluster, methods, ggplot2, ggsci, gridExtra, superheat, dplyr, gtools, tmle (>= 1.4.0.1), future, doFuture, S4Vectors, BiocGenerics, BiocParallel, SummarizedExperiment, GenomeInfoDb, bumphunter, IRanges, limma, minfi Suggests: testthat, knitr, rmarkdown, BiocStyle, SuperLearner, earth, nnet, gam, arm, snow, parallel, BatchJobs, minfiData, methyvimData License: file LICENSE Archs: i386, x64 MD5sum: 49592ffc7e7ec763810aa02bd5c0b57d NeedsCompilation: no Title: Targeted, Robust, and Model-free Differential Methylation Analysis Description: This package provides facilities for differential methylation analysis based on variable importance measures (VIMs), a class of statistical target parameters that arise in causal inference. The estimation and inference procedures provided are nonparametric, relying on ensemble machine learning to flexibly assess functional relationships among covariates and the outcome of interest. These tools can be applied to differential methylation at the level of CpG sites, to obtain valid statistical inference even after corrections for multiple hypothesis testing. biocViews: Clustering, DNAMethylation, DifferentialMethylation, MethylationArray, MethylSeq Author: Nima Hejazi [aut, cre, cph] (), Rachael Phillips [ctb] (), Mark van der Laan [aut, ths] (), Alan Hubbard [ctb, ths] () Maintainer: Nima Hejazi URL: https://github.com/nhejazi/methyvim VignetteBuilder: knitr BugReports: https://github.com/nhejazi/methyvim/issues git_url: https://git.bioconductor.org/packages/methyvim git_branch: RELEASE_3_10 git_last_commit: 79beaa5 git_last_commit_date: 2019-11-01 Date/Publication: 2019-11-01 source.ver: src/contrib/methyvim_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/methyvim_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/methyvim_1.8.0.tgz vignettes: vignettes/methyvim/inst/doc/using_methyvim.html vignetteTitles: Targeted Data-Adaptive Estimation and Inference for Differential Methylation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/methyvim/inst/doc/using_methyvim.R dependencyCount: 166 Package: MetID Version: 1.4.0 Depends: R (>= 3.5) Imports: utils (>= 3.3.1), stats (>= 3.4.2), devtools (>= 1.13.0), stringr (>= 1.3.0), Matrix (>= 1.2-12), igraph (>= 1.2.1), ChemmineR (>= 2.30.2) Suggests: knitr (>= 1.19), rmarkdown (>= 1.8) License: Artistic-2.0 MD5sum: f8016e43fcfa54e21919e2217db34dc2 NeedsCompilation: no Title: Network-based prioritization of putative metabolite IDs Description: This package uses an innovative network-based approach that will enhance our ability to determine the identities of significant ions detected by LC-MS. biocViews: AssayDomain, BiologicalQuestion, Infrastructure, ResearchField, StatisticalMethod, Technology, WorkflowStep, Network, KEGG Author: Zhenzhi Li Maintainer: Zhenzhi Li URL: https://github.com/ressomlab/MetID VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetID git_branch: RELEASE_3_10 git_last_commit: 703bc92 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MetID_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MetID_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MetID_1.4.0.tgz vignettes: vignettes/MetID/inst/doc/Introduction_to_MetID.html vignetteTitles: Introduction to MetID hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetID/inst/doc/Introduction_to_MetID.R dependencyCount: 110 Package: MetNet Version: 1.4.2 Depends: R (>= 3.5) Imports: bnlearn (>= 4.3), BiocParallel (>= 1.12.0), GENIE3 (>= 1.7.0), methods (>= 3.5), mpmi (>= 0.42), parmigene (>= 1.0.2), ppcor (>= 1.1), sna (>= 2.4), stabs (>= 0.6), stats (>= 3.6) Suggests: BiocGenerics (>= 0.24.0), BiocStyle (>= 2.6.1), glmnet (>= 2.0-18), igraph (>= 1.1.2), knitr (>= 1.11), rmarkdown (>= 1.15), testthat (>= 2.2.1) License: GPL-2 MD5sum: 16078d2e108ff637653dd3fbeb80e9c3 NeedsCompilation: no Title: Inferring metabolic networks from untargeted high-resolution mass spectrometry data Description: MetNet contains functionality to infer metabolic network topologies from quantitative data and high-resolution mass/charge information. Using statistical models (including correlation, mutual information, regression and Bayes statistics) and quantitative data (intensity values of features) adjacency matrices are inferred that can be combined to a consensus matrix. Mass differences calculated between mass/charge values of features will be matched against a data frame of supplied mass/charge differences referring to transformations of enzymatic activities. In a third step, the two matrices are combined to form a adjacency matrix inferred from both quantitative and structure information. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, Network, Regression Author: Thomas Naake [aut, cre] Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetNet git_branch: RELEASE_3_10 git_last_commit: dd24f28 git_last_commit_date: 2020-03-07 Date/Publication: 2020-03-07 source.ver: src/contrib/MetNet_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/MetNet_1.4.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MetNet_1.4.2.tgz vignettes: vignettes/MetNet/inst/doc/MetNet.html vignetteTitles: Workflow for high-resolution metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetNet/inst/doc/MetNet.R dependencyCount: 48 Package: mfa Version: 1.8.0 Depends: R (>= 3.4.0) Imports: methods, stats, ggplot2, Rcpp, dplyr, ggmcmc, MCMCpack, MCMCglmm, coda, magrittr, tibble, Biobase LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) MD5sum: fdfb15540c46249cb1d8396e1e0dada5 NeedsCompilation: yes Title: Bayesian hierarchical mixture of factor analyzers for modelling genomic bifurcations Description: MFA models genomic bifurcations using a Bayesian hierarchical mixture of factor analysers. biocViews: ImmunoOncology, RNASeq, GeneExpression, Bayesian, SingleCell Author: Kieran Campbell [aut, cre] Maintainer: Kieran Campbell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mfa git_branch: RELEASE_3_10 git_last_commit: 2fa5046 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mfa_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mfa_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mfa_1.8.0.tgz vignettes: vignettes/mfa/inst/doc/introduction_to_mfa.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mfa/inst/doc/introduction_to_mfa.R suggestsMe: splatter dependencyCount: 81 Package: Mfuzz Version: 2.46.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), e1071 Imports: tcltk, tkWidgets Suggests: marray License: GPL-2 MD5sum: 2f05b21b05f3ab56a18e7af0c78059df NeedsCompilation: no Title: Soft clustering of time series gene expression data Description: Package for noise-robust soft clustering of gene expression time-series data (including a graphical user interface) biocViews: Microarray, Clustering, TimeCourse, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://mfuzz.sysbiolab.eu/ git_url: https://git.bioconductor.org/packages/Mfuzz git_branch: RELEASE_3_10 git_last_commit: 1e2960d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Mfuzz_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Mfuzz_2.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Mfuzz_2.46.0.tgz vignettes: vignettes/Mfuzz/inst/doc/Mfuzz.pdf vignetteTitles: Introduction to Mfuzz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mfuzz/inst/doc/Mfuzz.R dependsOnMe: cycle suggestsMe: pwOmics dependencyCount: 16 Package: MGFM Version: 1.20.0 Depends: AnnotationDbi,annotate Suggests: hgu133a.db License: GPL-3 MD5sum: 6c616d1129e15371f6b7798d0cc12908 NeedsCompilation: no Title: Marker Gene Finder in Microarray gene expression data Description: The package is designed to detect marker genes from Microarray gene expression data sets biocViews: Genetics, GeneExpression, Microarray Author: Khadija El Amrani Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/MGFM git_branch: RELEASE_3_10 git_last_commit: a0253cc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MGFM_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MGFM_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MGFM_1.20.0.tgz vignettes: vignettes/MGFM/inst/doc/MGFM.pdf vignetteTitles: Using MGFM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MGFM/inst/doc/MGFM.R dependsOnMe: sampleClassifier dependencyCount: 31 Package: MGFR Version: 1.12.0 Depends: R (>= 3.5) Imports: biomaRt, annotate License: GPL-3 Archs: i386, x64 MD5sum: 48d589c78f372ae17213fe20344c0e70 NeedsCompilation: no Title: Marker Gene Finder in RNA-seq data Description: The package is designed to detect marker genes from RNA-seq data. biocViews: ImmunoOncology, Genetics, GeneExpression, RNASeq Author: Khadija El Amrani Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/MGFR git_branch: RELEASE_3_10 git_last_commit: 2bada32 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MGFR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MGFR_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MGFR_1.12.0.tgz vignettes: vignettes/MGFR/inst/doc/MGFR.pdf vignetteTitles: Using MGFR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MGFR/inst/doc/MGFR.R dependsOnMe: sampleClassifier dependencyCount: 62 Package: mgsa Version: 1.34.0 Depends: R (>= 2.14.0), methods, gplots Imports: graphics, stats, utils Suggests: DBI, RSQLite, GO.db, testthat License: Artistic-2.0 MD5sum: 5a8415a83d63b6689df8f88545716501 NeedsCompilation: yes Title: Model-based gene set analysis Description: Model-based Gene Set Analysis (MGSA) is a Bayesian modeling approach for gene set enrichment. The package mgsa implements MGSA and tools to use MGSA together with the Gene Ontology. biocViews: Pathways, GO, GeneSetEnrichment Author: Sebastian Bauer , Julien Gagneur Maintainer: Sebastian Bauer URL: https://github.com/sba1/mgsa-bioc git_url: https://git.bioconductor.org/packages/mgsa git_branch: RELEASE_3_10 git_last_commit: e9c9941 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mgsa_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mgsa_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mgsa_1.34.0.tgz vignettes: vignettes/mgsa/inst/doc/mgsa.pdf vignetteTitles: Overview of the mgsa package. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mgsa/inst/doc/mgsa.R suggestsMe: gCMAP dependencyCount: 10 Package: MiChip Version: 1.40.0 Depends: R (>= 2.3.0), Biobase Imports: Biobase License: GPL (>= 2) MD5sum: e876b00dedff1e52ef1f74aa53754f18 NeedsCompilation: no Title: MiChip Parsing and Summarizing Functions Description: This package takes the MiChip miRNA microarray .grp scanner output files and parses these out, providing summary and plotting functions to analyse MiChip hybridizations. A set of hybridizations is packaged into an ExpressionSet allowing it to be used by other BioConductor packages. biocViews: Microarray, Preprocessing Author: Jonathon Blake Maintainer: Jonathon Blake git_url: https://git.bioconductor.org/packages/MiChip git_branch: RELEASE_3_10 git_last_commit: caea819 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MiChip_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MiChip_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MiChip_1.40.0.tgz vignettes: vignettes/MiChip/inst/doc/MiChip.pdf vignetteTitles: MiChip miRNA Microarray Processing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiChip/inst/doc/MiChip.R dependencyCount: 7 Package: microbiome Version: 1.8.0 Depends: R (>= 3.3.0), phyloseq, ggplot2 Imports: dplyr, reshape2, Rtsne, scales, stats, tidyr, utils, vegan Suggests: BiocGenerics, BiocStyle, Cairo, knitcitations, knitr, rmarkdown, testthat License: BSD_2_clause + file LICENSE MD5sum: bad34f19360b750f8ac6643ce2a6c5c8 NeedsCompilation: no Title: Microbiome Analytics Description: Utilities for microbiome analysis. biocViews: Clustering, Metagenomics, Microbiome, Sequencing,SystemsBiology,ImmunoOncology Author: Leo Lahti [aut, cre], Sudarshan Shetty [aut] Maintainer: Leo Lahti URL: http://microbiome.github.io/microbiome VignetteBuilder: knitr BugReports: https://github.com/microbiome/microbiome/issues git_url: https://git.bioconductor.org/packages/microbiome git_branch: RELEASE_3_10 git_last_commit: 11ae6af git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/microbiome_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/microbiome_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/microbiome_1.8.0.tgz vignettes: vignettes/microbiome/inst/doc/vignette.html vignetteTitles: microbiome R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiome/inst/doc/vignette.R dependencyCount: 93 Package: microbiomeDASim Version: 1.0.0 Depends: R (>= 3.6.0) Imports: graphics, ggplot2, MASS, tmvtnorm, Matrix, mvtnorm, pbapply, stats Suggests: testthat (>= 2.1.0), knitr, devtools License: MIT + file LICENSE Archs: i386, x64 MD5sum: a946db90bbc40630bbd5d0a044088717 NeedsCompilation: no Title: Microbiome Differential Abundance Simulation Description: A toolkit for simulating differential microbiome data designed for longitudinal analyses. Several functional forms may be specified for the mean trend. Observations are drawn from a multivariate normal model. The objective of this package is to be able to simulate data in order to accurately compare different longitudinal methods for differential abundance. biocViews: Microbiome, Visualization, Software Author: Justin Williams, Hector Corrada Bravo, Jennifer Tom, Joseph Nathaniel Paulson Maintainer: Justin Williams URL: https://github.com/williazo/microbiomeDASim VignetteBuilder: knitr BugReports: https://github.com/williazo/microbiomeDASim/issues git_url: https://git.bioconductor.org/packages/microbiomeDASim git_branch: RELEASE_3_10 git_last_commit: aa002e3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/microbiomeDASim_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/microbiomeDASim_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/microbiomeDASim_1.0.0.tgz vignettes: vignettes/microbiomeDASim/inst/doc/microbiomeDASim.pdf vignetteTitles: microbiomeDASim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiomeDASim/inst/doc/microbiomeDASim.R dependencyCount: 62 Package: microRNA Version: 1.44.0 Depends: R (>= 2.10) Imports: Biostrings (>= 2.11.32) License: Artistic-2.0 MD5sum: 64a1cb29fc84c44010c66951a03cbebb NeedsCompilation: yes Title: Data and functions for dealing with microRNAs Description: Different data resources for microRNAs and some functions for manipulating them. biocViews: Infrastructure, GenomeAnnotation, SequenceMatching Author: R. Gentleman, S. Falcon Maintainer: "James F. Reid" git_url: https://git.bioconductor.org/packages/microRNA git_branch: RELEASE_3_10 git_last_commit: 10f7cde git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/microRNA_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/microRNA_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/microRNA_1.44.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Roleswitch suggestsMe: MmPalateMiRNA, rtracklayer dependencyCount: 12 Package: MIGSA Version: 1.10.1 Depends: R (>= 3.4), methods, BiocGenerics Imports: AnnotationDbi, Biobase, BiocParallel, compiler, data.table, edgeR, futile.logger, ggdendro, ggplot2, GO.db, GOstats, graph, graphics, grDevices, grid, GSEABase, ismev, limma, matrixStats, org.Hs.eg.db, RBGL, reshape2, Rgraphviz, RJSONIO, stats, utils, vegan Suggests: breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX, mGSZ, MIGSAdata License: GPL (>= 2) MD5sum: 45caa3073dfe52b029b98548e67e910c NeedsCompilation: no Title: Massive and Integrative Gene Set Analysis Description: Massive and Integrative Gene Set Analysis. The MIGSA package allows to perform a massive and integrative gene set analysis over several expression and gene sets simultaneously. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required to perform both singular and gene set enrichment analyses in an integrative manner by means of the best available methods, i.e. dEnricher and mGSZ respectively. The greatest strengths of this big omics data tool are the availability of several functions to explore, analyze and visualize its results in order to facilitate the data mining task over huge information sources. MIGSA package also provides several functions that allow to easily load the most updated gene sets from several repositories. biocViews: Software, GeneSetEnrichment, Visualization, GeneExpression, Microarray, RNASeq, KEGG Author: Juan C. Rodriguez, Cristobal Fresno, Andrea S. Llera and Elmer A. Fernandez Maintainer: Juan C. Rodriguez URL: https://jcrodriguez.rbind.io/ git_url: https://git.bioconductor.org/packages/MIGSA git_branch: RELEASE_3_10 git_last_commit: a528114 git_last_commit_date: 2020-01-07 Date/Publication: 2020-01-07 source.ver: src/contrib/MIGSA_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/MIGSA_1.10.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MIGSA_1.10.1.tgz vignettes: vignettes/MIGSA/inst/doc/gettingPbcmcData.pdf, vignettes/MIGSA/inst/doc/gettingTcgaData.pdf, vignettes/MIGSA/inst/doc/MIGSA.pdf vignetteTitles: Getting pbcmc datasets, Getting TCGA datasets, Massive and Integrative Gene Set Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIGSA/inst/doc/gettingPbcmcData.R, vignettes/MIGSA/inst/doc/gettingTcgaData.R, vignettes/MIGSA/inst/doc/MIGSA.R dependencyCount: 107 Package: mimager Version: 1.10.0 Depends: Biobase Imports: BiocGenerics, S4Vectors, preprocessCore, grDevices, methods, grid, gtable, scales, DBI, affy, affyPLM, oligo, oligoClasses Suggests: knitr, rmarkdown, BiocStyle, testthat, lintr, Matrix, abind, affydata, hgu95av2cdf, oligoData, pd.hugene.1.0.st.v1 License: MIT + file LICENSE MD5sum: cadc6fde8b95303022ad589b74fcdfa3 NeedsCompilation: no Title: mimager: The Microarray Imager Description: Easily visualize and inspect microarrays for spatial artifacts. biocViews: Infrastructure, Visualization, Microarray Author: Aaron Wolen [aut, cre, cph] Maintainer: Aaron Wolen URL: https://github.com/aaronwolen/mimager VignetteBuilder: knitr BugReports: https://github.com/aaronwolen/mimager/issues git_url: https://git.bioconductor.org/packages/mimager git_branch: RELEASE_3_10 git_last_commit: 89f9cf7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mimager_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mimager_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mimager_1.10.0.tgz vignettes: vignettes/mimager/inst/doc/introduction.html vignetteTitles: mimager overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mimager/inst/doc/introduction.R dependencyCount: 71 Package: MIMOSA Version: 1.24.0 Depends: R (>= 3.0.2), MASS, plyr, reshape, Biobase, ggplot2 Imports: methods, Formula, data.table, pracma, MCMCpack, coda, modeest, testthat, Rcpp, scales, LinkingTo: Rcpp, RcppArmadillo Suggests: parallel, knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: 11a68963e17d75bd8dc8c40d98ccee4c NeedsCompilation: yes Title: Mixture Models for Single-Cell Assays Description: Modeling count data using Dirichlet-multinomial and beta-binomial mixtures with applications to single-cell assays. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays Author: Greg Finak Maintainer: Greg Finak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MIMOSA git_branch: RELEASE_3_10 git_last_commit: 8dd361e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MIMOSA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MIMOSA_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MIMOSA_1.24.0.tgz vignettes: vignettes/MIMOSA/inst/doc/MIMOSA.pdf vignetteTitles: MIMOSA: Mixture Models For Single Cell Assays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIMOSA/inst/doc/MIMOSA.R dependencyCount: 82 Package: MineICA Version: 1.26.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.8), Biobase, plyr, ggplot2, scales, foreach, xtable, biomaRt, gtools, GOstats, cluster, marray, mclust, RColorBrewer, colorspace, igraph, Rgraphviz, graph, annotate, Hmisc, fastICA, JADE Imports: AnnotationDbi, lumi, fpc, lumiHumanAll.db Suggests: biomaRt, GOstats, cluster, hgu133a.db, mclust, igraph, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerVDX Enhances: doMC License: GPL-2 MD5sum: 64b9d80b136635afa6cddf868239414d NeedsCompilation: no Title: Analysis of an ICA decomposition obtained on genomics data Description: The goal of MineICA is to perform Independent Component Analysis (ICA) on multiple transcriptome datasets, integrating additional data (e.g molecular, clinical and pathological). This Integrative ICA helps the biological interpretation of the components by studying their association with variables (e.g sample annotations) and gene sets, and enables the comparison of components from different datasets using correlation-based graph. biocViews: Visualization, MultipleComparison Author: Anne Biton Maintainer: Anne Biton git_url: https://git.bioconductor.org/packages/MineICA git_branch: RELEASE_3_10 git_last_commit: a0e7145 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MineICA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MineICA_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MineICA_1.26.0.tgz vignettes: vignettes/MineICA/inst/doc/MineICA.pdf vignetteTitles: MineICA: Independent component analysis of genomic data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MineICA/inst/doc/MineICA.R dependencyCount: 202 Package: minet Version: 3.44.1 Imports: infotheo License: Artistic-2.0 MD5sum: b79f3ae7fc5843998d3ead1ca578e1f0 NeedsCompilation: yes Title: Mutual Information NETworks Description: This package implements various algorithms for inferring mutual information networks from data. biocViews: Microarray, GraphAndNetwork, Network, NetworkInference Author: Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi Maintainer: Patrick E. Meyer URL: http://minet.meyerp.com git_url: https://git.bioconductor.org/packages/minet git_branch: RELEASE_3_10 git_last_commit: 728eb77 git_last_commit_date: 2020-02-03 Date/Publication: 2020-02-03 source.ver: src/contrib/minet_3.44.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/minet_3.44.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/minet_3.44.1.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BUS, geNetClassifier, netresponse importsMe: coexnet, epiNEM, netbenchmark, RTN suggestsMe: CNORfeeder, predictionet, TCGAbiolinks dependencyCount: 1 Package: minfi Version: 1.32.0 Depends: methods, BiocGenerics (>= 0.15.3), GenomicRanges, SummarizedExperiment (>= 1.1.6), Biostrings, bumphunter (>= 1.1.9) Imports: S4Vectors, GenomeInfoDb, Biobase (>= 2.33.2), IRanges, beanplot, RColorBrewer, lattice, nor1mix, siggenes, limma, preprocessCore, illuminaio (>= 0.23.2), DelayedMatrixStats (>= 1.3.4), mclust, genefilter, nlme, reshape, MASS, quadprog, data.table, GEOquery, stats, grDevices, graphics, utils, DelayedArray (>= 0.9.8), HDF5Array, BiocParallel Suggests: IlluminaHumanMethylation450kmanifest (>= 0.2.0), IlluminaHumanMethylation450kanno.ilmn12.hg19 (>= 0.2.1), minfiData (>= 0.18.0), minfiDataEPIC, FlowSorted.Blood.450k (>= 1.0.1), RUnit, digest, BiocStyle, knitr, rmarkdown, tools License: Artistic-2.0 Archs: i386, x64 MD5sum: 09b488754d663fb975c7cab750327980 NeedsCompilation: no Title: Analyze Illumina Infinium DNA methylation arrays Description: Tools to analyze & visualize Illumina Infinium methylation arrays. biocViews: ImmunoOncology, DNAMethylation, DifferentialMethylation, Epigenetics, Microarray, MethylationArray, MultiChannel, TwoChannel, DataImport, Normalization, Preprocessing, QualityControl Author: Kasper Daniel Hansen [cre, aut], Martin Aryee [aut], Rafael A. Irizarry [aut], Andrew E. Jaffe [ctb], Jovana Maksimovic [ctb], E. Andres Houseman [ctb], Jean-Philippe Fortin [ctb], Tim Triche [ctb], Shan V. Andrews [ctb], Peter F. Hickey [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/hansenlab/minfi VignetteBuilder: knitr BugReports: https://github.com/hansenlab/minfi/issues git_url: https://git.bioconductor.org/packages/minfi git_branch: RELEASE_3_10 git_last_commit: 2b177c3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/minfi_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/minfi_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/minfi_1.32.0.tgz vignettes: vignettes/minfi/inst/doc/minfi.html vignetteTitles: minfi User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/minfi/inst/doc/minfi.R dependsOnMe: bigmelon, ChAMP, compartmap, conumee, DMRcate, methylumi, REMP, shinyMethyl importsMe: funtooNorm, MEAL, MethylAid, methylCC, methylumi, methyvim, missMethyl, quantro, skewr suggestsMe: epivizrChart, Harman, mCSEA, MultiDataSet, RnBeads, sesame dependencyCount: 121 Package: MinimumDistance Version: 1.30.0 Depends: R (>= 3.3), VanillaICE (>= 1.47.1) Imports: methods, BiocGenerics, Biobase, S4Vectors (>= 0.23.18), IRanges, GenomeInfoDb, GenomicRanges (>= 1.17.16), SummarizedExperiment (>= 1.15.4), oligoClasses, DNAcopy, ff, foreach, matrixStats, lattice, data.table, grid, stats, utils Suggests: human610quadv1bCrlmm (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg19, SNPchip, RUnit Enhances: snow, doSNOW License: Artistic-2.0 MD5sum: ce8aef73044b9f39c32fdb5c8cfaab86 NeedsCompilation: no Title: A Package for De Novo CNV Detection in Case-Parent Trios Description: Analysis of de novo copy number variants in trios from high-dimensional genotyping platforms. biocViews: Microarray, SNP, CopyNumberVariation Author: Robert B Scharpf and Ingo Ruczinski Maintainer: Robert B Scharpf git_url: https://git.bioconductor.org/packages/MinimumDistance git_branch: RELEASE_3_10 git_last_commit: a0d8b42 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MinimumDistance_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MinimumDistance_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MinimumDistance_1.30.0.tgz vignettes: vignettes/MinimumDistance/inst/doc/MinimumDistance.pdf vignetteTitles: Detection of de novo copy number alterations in case-parent trios hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MinimumDistance/inst/doc/MinimumDistance.R dependencyCount: 79 Package: MiPP Version: 1.58.0 Depends: R (>= 2.4) Imports: Biobase, e1071, MASS, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: 35d9411a6c0e374e5100453b5dcd250b NeedsCompilation: no Title: Misclassification Penalized Posterior Classification Description: This package finds optimal sets of genes that seperate samples into two or more classes. biocViews: Microarray, Classification Author: HyungJun Cho , Sukwoo Kim , Mat Soukup , and Jae K. Lee Maintainer: Sukwoo Kim URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ git_url: https://git.bioconductor.org/packages/MiPP git_branch: RELEASE_3_10 git_last_commit: e1823e0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MiPP_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MiPP_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MiPP_1.58.0.tgz vignettes: vignettes/MiPP/inst/doc/MiPP.pdf vignetteTitles: MiPP Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 11 Package: MIRA Version: 1.8.0 Depends: R (>= 3.5) Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table, ggplot2, Biobase, stats, bsseq, methods Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown, AnnotationHub, LOLA License: GPL-3 MD5sum: afe7fda8c96626f459f6df253db4ccf8 NeedsCompilation: no Title: Methylation-Based Inference of Regulatory Activity Description: DNA methylation contains information about the regulatory state of the cell. MIRA aggregates genome-scale DNA methylation data into a DNA methylation profile for a given region set with shared biological annotation. Using this profile, MIRA infers and scores the collective regulatory activity for the region set. MIRA facilitates regulatory analysis in situations where classical regulatory assays would be difficult and allows public sources of region sets to be leveraged for novel insight into the regulatory state of DNA methylation datasets. biocViews: ImmunoOncology, DNAMethylation, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, MethylSeq, Sequencing, Epigenetics, Coverage Author: Nathan Sheffield [aut], Christoph Bock [ctb], John Lawson [aut, cre] Maintainer: John Lawson URL: http://databio.org/mira VignetteBuilder: knitr BugReports: https://github.com/databio/MIRA git_url: https://git.bioconductor.org/packages/MIRA git_branch: RELEASE_3_10 git_last_commit: eb5b6ae git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MIRA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MIRA_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MIRA_1.8.0.tgz vignettes: vignettes/MIRA/inst/doc/BiologicalApplication.html, vignettes/MIRA/inst/doc/GettingStarted.html vignetteTitles: Applying MIRA to a Biological Question, Getting Started with Methylation-based Inference of Regulatory Activity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIRA/inst/doc/BiologicalApplication.R, vignettes/MIRA/inst/doc/GettingStarted.R importsMe: COCOA dependencyCount: 98 Package: MiRaGE Version: 1.28.0 Depends: R (>= 3.1.0), Biobase(>= 2.23.3) Imports: BiocGenerics, S4Vectors, AnnotationDbi, BiocManager Suggests: seqinr (>= 3.0.7), biomaRt (>= 2.19.1), GenomicFeatures (>= 1.15.4), Biostrings (>= 2.31.3), BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, miRNATarget, humanStemCell, IRanges, GenomicRanges (>= 1.8.3), BSgenome, beadarrayExampleData License: GPL MD5sum: 8418c9ff436b9effe97a5f41584e98f1 NeedsCompilation: no Title: MiRNA Ranking by Gene Expression Description: The package contains functions for inferece of target gene regulation by miRNA, based on only target gene expression profile. biocViews: ImmunoOncology, Microarray, GeneExpression, RNASeq, Sequencing, SAGE Author: Y-h. Taguchi Maintainer: Y-h. Taguchi git_url: https://git.bioconductor.org/packages/MiRaGE git_branch: RELEASE_3_10 git_last_commit: b47fa2d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MiRaGE_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MiRaGE_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MiRaGE_1.28.0.tgz vignettes: vignettes/MiRaGE/inst/doc/MiRaGE.pdf vignetteTitles: How to use MiRaGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiRaGE/inst/doc/MiRaGE.R dependencyCount: 27 Package: miRBaseConverter Version: 1.10.1 Depends: R (>= 3.4) Imports: stats Suggests: BiocGenerics, RUnit, knitr, rtracklayer, utils License: GPL (>= 2) MD5sum: 61125aeaee330b578a71fc6e3a74f2dd NeedsCompilation: no Title: A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions Description: A comprehensive tool for converting and retrieving the miRNA Name, Accession, Sequence, Version, History and Family information in different miRBase versions. It can process a huge number of miRNAs in a short time without other depends. biocViews: Software, miRNA Author: Taosheng Xu, Thuc Le Maintainer: Taosheng Xu URL: https://github.com/taoshengxu/miRBaseConverter VignetteBuilder: knitr BugReports: https://github.com/taoshengxu/miRBaseConverter/issues git_url: https://git.bioconductor.org/packages/miRBaseConverter git_branch: RELEASE_3_10 git_last_commit: dddf277 git_last_commit_date: 2020-04-04 Date/Publication: 2020-04-04 source.ver: src/contrib/miRBaseConverter_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/miRBaseConverter_1.10.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/miRBaseConverter_1.10.1.tgz vignettes: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.html vignetteTitles: "miRBaseConverter: A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.R dependencyCount: 1 Package: miRcomp Version: 1.16.0 Depends: R (>= 3.2), Biobase (>= 2.22.0), miRcompData Imports: utils, methods, graphics, KernSmooth, stats Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics, shiny License: GPL-3 | file LICENSE MD5sum: fa25f0b38e3506890f79640f6d6d292a NeedsCompilation: no Title: Tools to assess and compare miRNA expression estimatation methods Description: Based on a large miRNA dilution study, this package provides tools to read in the raw amplification data and use these data to assess the performance of methods that estimate expression from the amplification curves. biocViews: Software, qPCR, Preprocessing, QualityControl Author: Matthew N. McCall , Lauren Kemperman Maintainer: Matthew N. McCall VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRcomp git_branch: RELEASE_3_10 git_last_commit: 2595591 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/miRcomp_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/miRcomp_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/miRcomp_1.16.0.tgz vignettes: vignettes/miRcomp/inst/doc/miRcomp.html vignetteTitles: Assessment and comparison of miRNA expression estimation methods (miRcomp) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miRcomp/inst/doc/miRcomp.R dependencyCount: 9 Package: mirIntegrator Version: 1.16.0 Depends: R (>= 3.3) Imports: graph,ROntoTools, ggplot2, org.Hs.eg.db, AnnotationDbi, Rgraphviz Suggests: RUnit, BiocGenerics License: GPL (>=3) MD5sum: 422064cf4bbc164786b6329bb8e5e1aa NeedsCompilation: no Title: Integrating microRNA expression into signaling pathways for pathway analysis Description: Tools for augmenting signaling pathways to perform pathway analysis of microRNA and mRNA expression levels. biocViews: Network, Microarray, GraphAndNetwork, Pathways, KEGG Author: Diana Diaz Maintainer: Diana Diaz URL: http://datad.github.io/mirIntegrator/ git_url: https://git.bioconductor.org/packages/mirIntegrator git_branch: RELEASE_3_10 git_last_commit: 1744e2b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mirIntegrator_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mirIntegrator_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mirIntegrator_1.16.0.tgz vignettes: vignettes/mirIntegrator/inst/doc/mirIntegrator.pdf vignetteTitles: mirIntegrator Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mirIntegrator/inst/doc/mirIntegrator.R dependencyCount: 90 Package: miRLAB Version: 1.16.0 Imports: methods, stats, utils, RCurl, httr, stringr, Hmisc, energy, entropy, Roleswitch, gplots, glmnet, impute, limma, pcalg,TCGAbiolinks,dplyr,SummarizedExperiment, ctc, heatmap.plus, InvariantCausalPrediction, Category, GOstats, org.Hs.eg.db Suggests: knitr,BiocGenerics, AnnotationDbi,RUnit License: GPL (>=2) MD5sum: fdc8a090ca9b3fc3e487500888dcadc8 NeedsCompilation: no Title: Dry lab for exploring miRNA-mRNA relationships Description: Provide tools exploring miRNA-mRNA relationships, including popular miRNA target prediction methods, ensemble methods that integrate individual methods, functions to get data from online resources, functions to validate the results, and functions to conduct enrichment analyses. biocViews: miRNA, GeneExpression, NetworkInference, Network Author: Thuc Duy Le, Junpeng Zhang, Mo Chen, Vu Viet Hoang Pham Maintainer: Thuc Duy Le URL: https://github.com/pvvhoang/miRLAB VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRLAB git_branch: RELEASE_3_10 git_last_commit: 7fe84d5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/miRLAB_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/miRLAB_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/miRLAB_1.16.0.tgz vignettes: vignettes/miRLAB/inst/doc/miRLAB-vignette.html vignetteTitles: miRLAB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRLAB/inst/doc/miRLAB-vignette.R dependencyCount: 237 Package: miRmine Version: 1.8.0 Depends: R (>= 3.4), SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, DESeq2 License: GPL (>= 3) MD5sum: 14e79fff145df5025246db3ecfd25fc3 NeedsCompilation: no Title: Data package with miRNA-seq datasets from miRmine database as RangedSummarizedExperiment Description: miRmine database is a collection of expression profiles from different publicly available miRNA-seq datasets, Panwar et al (2017) miRmine: A Database of Human miRNA Expression, prepared with this data package as RangedSummarizedExperiment. biocViews: Homo_sapiens_Data, RNASeqData, SequencingData, ExpressionData Author: Dusan Randjelovic [aut, cre] Maintainer: Dusan Randjelovic VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRmine git_branch: RELEASE_3_10 git_last_commit: 5603e2a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/miRmine_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/miRmine_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/miRmine_1.8.0.tgz vignettes: vignettes/miRmine/inst/doc/miRmine.html vignetteTitles: miRmine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRmine/inst/doc/miRmine.R dependencyCount: 32 Package: miRNAmeConverter Version: 1.14.0 Depends: miRBaseVersions.db Imports: DBI, AnnotationDbi, reshape2 Suggests: methods, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: 712522c5c799e746e65c3f0c543a4c66 NeedsCompilation: no Title: Convert miRNA Names to Different miRBase Versions Description: Translating mature miRNA names to different miRBase versions, sequence retrieval, checking names for validity and detecting miRBase version of a given set of names (data from http://www.mirbase.org/). biocViews: Preprocessing, miRNA Author: Stefan Haunsberger [aut, cre] Maintainer: Stefan J. Haunsberger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRNAmeConverter git_branch: RELEASE_3_10 git_last_commit: 241f39d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/miRNAmeConverter_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/miRNAmeConverter_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/miRNAmeConverter_1.14.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 34 Package: miRNApath Version: 1.46.0 Depends: methods, R(>= 2.7.0) License: LGPL-2.1 MD5sum: 82d75bb0520bc381f6755a1ce8207f94 NeedsCompilation: no Title: miRNApath: Pathway Enrichment for miRNA Expression Data Description: This package provides pathway enrichment techniques for miRNA expression data. Specifically, the set of methods handles the many-to-many relationship between miRNAs and the multiple genes they are predicted to target (and thus affect.) It also handles the gene-to-pathway relationships separately. Both steps are designed to preserve the additive effects of miRNAs on genes, many miRNAs affecting one gene, one miRNA affecting multiple genes, or many miRNAs affecting many genes. biocViews: Annotation, Pathways, DifferentialExpression, NetworkEnrichment, miRNA Author: James M. Ward with contributions from Yunling Shi, Cindy Richards, John P. Cogswell Maintainer: James M. Ward git_url: https://git.bioconductor.org/packages/miRNApath git_branch: RELEASE_3_10 git_last_commit: 2e760d2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/miRNApath_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/miRNApath_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/miRNApath_1.46.0.tgz vignettes: vignettes/miRNApath/inst/doc/miRNApath.pdf vignetteTitles: miRNApath: Pathway Enrichment for miRNA Expression Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNApath/inst/doc/miRNApath.R dependencyCount: 1 Package: miRNAtap Version: 1.20.0 Depends: R (>= 3.3.0), AnnotationDbi Imports: DBI, RSQLite, stringr, sqldf, plyr, methods Suggests: topGO, org.Hs.eg.db, miRNAtap.db, testthat License: GPL-2 MD5sum: 106ed3a75d4cd2d2aac5e667225e529e NeedsCompilation: no Title: miRNAtap: microRNA Targets - Aggregated Predictions Description: The package facilitates implementation of workflows requiring miRNA predictions, it allows to integrate ranked miRNA target predictions from multiple sources available online and aggregate them with various methods which improves quality of predictions above any of the single sources. Currently predictions are available for Homo sapiens, Mus musculus and Rattus norvegicus (the last one through homology translation). biocViews: Software, Classification, Microarray, Sequencing, miRNA Author: Maciej Pajak, T. Ian Simpson Maintainer: T. Ian Simpson git_url: https://git.bioconductor.org/packages/miRNAtap git_branch: RELEASE_3_10 git_last_commit: c5974b4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/miRNAtap_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/miRNAtap_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/miRNAtap_1.20.0.tgz vignettes: vignettes/miRNAtap/inst/doc/miRNAtap.pdf vignetteTitles: miRNAtap hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNAtap/inst/doc/miRNAtap.R importsMe: SpidermiR dependencyCount: 35 Package: miRSM Version: 1.4.1 Depends: R (>= 3.5.0) Imports: WGCNA, flashClust, dynamicTreeCut, GFA, igraph, linkcomm, MCL, NMF, biclust, runibic, iBBiG, fabia, BicARE, isa2, s4vd, BiBitR, rqubic, Biobase, PMA, stats, dbscan, subspace, mclust, SOMbrero, ppclust, miRspongeR, Rcpp, utils, SummarizedExperiment, GSEABase, org.Hs.eg.db, MatrixCorrelation, energy Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 9713a4e180bae88f20a9a3c8fd2bb272 NeedsCompilation: yes Title: Inferring miRNA sponge modules by integrating expression data and miRNA-target binding information Description: The package aims to identify miRNA sponge modules by integrating expression data and miRNA-target binding information. It provides several functions to study miRNA sponge modules, including popular methods for inferring gene modules (candidate miRNA sponge modules), and a function to identify miRNA sponge modules, as well as several functions to conduct modular analysis of miRNA sponge modules. biocViews: GeneExpression, BiomedicalInformatics, Clustering, GeneSetEnrichment, Microarray, Software, GeneRegulation, GeneTarget Author: Junpeng Zhang [aut, cre] Maintainer: Junpeng Zhang URL: https://github.com/zhangjunpeng411/miRSM VignetteBuilder: knitr BugReports: https://github.com/zhangjunpeng411/miRSM/issues git_url: https://git.bioconductor.org/packages/miRSM git_branch: RELEASE_3_10 git_last_commit: 5441072 git_last_commit_date: 2020-02-01 Date/Publication: 2020-02-02 source.ver: src/contrib/miRSM_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/miRSM_1.4.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/miRSM_1.4.1.tgz vignettes: vignettes/miRSM/inst/doc/miRSM.html vignetteTitles: miRSM: inferring miRNA sponge modules by integrating expression data and miRNA-target binding information hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRSM/inst/doc/miRSM.R dependencyCount: 237 Package: miRspongeR Version: 1.12.0 Depends: R (>= 3.5.0) Imports: corpcor, parallel, igraph, MCL, clusterProfiler, ReactomePA, DOSE, survival, grDevices, graphics, stats, varhandle, linkcomm, utils, Rcpp, org.Hs.eg.db Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 82ffaa1e8c048505f8bc9d9c27867de5 NeedsCompilation: yes Title: Identification and analysis of miRNA sponge interaction networks and modules Description: This package provides several functions to study miRNA sponge (also called ceRNA or miRNA decoy), including popular methods for identifying miRNA sponge interactions, and the integrative method to integrate miRNA sponge interactions from different methods, as well as the functions to validate miRNA sponge interactions, and infer miRNA sponge modules, conduct enrichment analysis of modules, and conduct survival analysis of modules. biocViews: GeneExpression, BiomedicalInformatics, NetworkEnrichment, Survival, Microarray, Software Author: Junpeng Zhang Maintainer: Junpeng Zhang URL: VignetteBuilder: knitr BugReports: https://github.com/zhangjunpeng411/miRspongeR/issues git_url: https://git.bioconductor.org/packages/miRspongeR git_branch: RELEASE_3_10 git_last_commit: f115a40 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/miRspongeR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/miRspongeR_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/miRspongeR_1.12.0.tgz vignettes: vignettes/miRspongeR/inst/doc/miRspongeR.html vignetteTitles: miRspongeR: identification and analysis of miRNA sponge interaction networks and modules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRspongeR/inst/doc/miRspongeR.R importsMe: miRSM dependencyCount: 138 Package: Mirsynergy Version: 1.22.0 Depends: R (>= 3.0.2), igraph, ggplot2 Imports: graphics, grDevices, gridExtra, Matrix, parallel, RColorBrewer, reshape, scales, utils Suggests: glmnet, RUnit, BiocGenerics, knitr License: GPL-2 MD5sum: 66c78f6daca2d3d12eeefc2fb60eca34 NeedsCompilation: no Title: Mirsynergy Description: Detect synergistic miRNA regulatory modules by overlapping neighbourhood expansion. biocViews: Clustering Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/Mirsynergy.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Mirsynergy git_branch: RELEASE_3_10 git_last_commit: 72c8d0c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Mirsynergy_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Mirsynergy_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Mirsynergy_1.22.0.tgz vignettes: vignettes/Mirsynergy/inst/doc/Mirsynergy.pdf vignetteTitles: Mirsynergy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mirsynergy/inst/doc/Mirsynergy.R dependencyCount: 59 Package: missMethyl Version: 1.20.4 Depends: R (>= 2.3.0) Imports: limma, minfi, methylumi, IlluminaHumanMethylation450kmanifest, statmod, ruv, stringr, IlluminaHumanMethylation450kanno.ilmn12.hg19, org.Hs.eg.db, AnnotationDbi, BiasedUrn, GO.db, IlluminaHumanMethylationEPICmanifest, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 Suggests: minfiData, BiocStyle, knitr, rmarkdown, edgeR, tweeDEseqCountData License: GPL-2 Archs: i386, x64 MD5sum: 5056e78661b8d4b239e58b0ae56716d9 NeedsCompilation: no Title: Analysing Illumina HumanMethylation BeadChip Data Description: Normalisation and testing for differential variability and differential methylation for data from Illumina's Infinium HumanMethylation450 array. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array. biocViews: Normalization, DNAMethylation, MethylationArray, GenomicVariation, GeneticVariability, DifferentialMethylation, GeneSetEnrichment Author: Belinda Phipson and Jovana Maksimovic Maintainer: Belinda Phipson , Jovana Maksimovic , Andrew Lonsdale VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/missMethyl git_branch: RELEASE_3_10 git_last_commit: 6865392 git_last_commit_date: 2020-01-27 Date/Publication: 2020-01-28 source.ver: src/contrib/missMethyl_1.20.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/missMethyl_1.20.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/missMethyl_1.20.4.tgz vignettes: vignettes/missMethyl/inst/doc/missMethyl.html vignetteTitles: missMethyl: Analysing Illumina HumanMethylation BeadChip Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missMethyl/inst/doc/missMethyl.R importsMe: DMRcate, MEAL, methylGSA suggestsMe: RnBeads dependencyCount: 159 Package: missRows Version: 1.6.0 Depends: R (>= 3.5), methods, ggplot2, grDevices, MultiAssayExperiment Imports: plyr, stats, gtools, S4Vectors Suggests: BiocStyle, knitr, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 5a1cd9d03f9e9795fce266e555c55deb NeedsCompilation: no Title: Handling Missing Individuals in Multi-Omics Data Integration Description: The missRows package implements the MI-MFA method to deal with missing individuals ('biological units') in multi-omics data integration. The MI-MFA method generates multiple imputed datasets from a Multiple Factor Analysis model, then the yield results are combined in a single consensus solution. The package provides functions for estimating coordinates of individuals and variables, imputing missing individuals, and various diagnostic plots to inspect the pattern of missingness and visualize the uncertainty due to missing values. biocViews: Software, StatisticalMethod, DimensionReduction, PrincipalComponent, MathematicalBiology, Visualization Author: Ignacio Gonzalez and Valentin Voillet Maintainer: Gonzalez Ignacio VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/missRows git_branch: RELEASE_3_10 git_last_commit: b695202 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/missRows_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/missRows_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/missRows_1.6.0.tgz vignettes: vignettes/missRows/inst/doc/missRows.pdf vignetteTitles: missRows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missRows/inst/doc/missRows.R dependencyCount: 86 Package: mitoODE Version: 1.24.0 Depends: R (>= 2.14.0), minpack.lm, MASS, parallel, mitoODEdata, KernSmooth License: LGPL MD5sum: 9b96fe4c33b7c52f22cb63f2b98dd2d9 NeedsCompilation: yes Title: Implementation of the differential equation model described in "Dynamical modelling of phenotypes in a genome-wide RNAi live-cell imaging assay" Description: The package contains the methods to fit a cell-cycle model on cell count data and the code to reproduce the results shown in our paper "Dynamical modelling of phenotypes in a genome-wide RNAi live-cell imaging assay" by Pau, G., Walter, T., Neumann, B., Heriche, J.-K., Ellenberg, J., & Huber, W., BMC Bioinformatics (2013), 14(1), 308. doi:10.1186/1471-2105-14-308 biocViews: ImmunoOncology, ExperimentData, TimeCourse, CellBasedAssays, Preprocessing Author: Gregoire Pau Maintainer: Gregoire Pau SystemRequirements: git_url: https://git.bioconductor.org/packages/mitoODE git_branch: RELEASE_3_10 git_last_commit: 7c8cfdd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mitoODE_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mitoODE_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mitoODE_1.24.0.tgz vignettes: vignettes/mitoODE/inst/doc/mitoODE-introduction.pdf vignetteTitles: mitoODE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mitoODE/inst/doc/mitoODE-introduction.R dependencyCount: 10 Package: mixOmics Version: 6.10.9 Depends: R (>= 3.5.0), MASS, lattice, ggplot2 Imports: igraph, ellipse, corpcor, RColorBrewer, parallel, dplyr, tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra, grDevices, graphics, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, rgl License: GPL (>= 2) MD5sum: 7998fe9cfd97c73d8e5e8faccdeb0b85 NeedsCompilation: no Title: Omics Data Integration Project Description: Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares. biocViews: ImmunoOncology, Microarray, Sequencing, Metabolomics, Metagenomics, Proteomics, GenePrediction, MultipleComparison, Classification, Regression Author: Kim-Anh Le Cao [aut, cre], Florian Rohart [aut], Ignacio Gonzalez [aut], Sebastien Dejean [aut], Al Abadi [ctb], Benoit Gautier [ctb], Francois Bartolo [ctb], Pierre Monget [ctb], Jeff Coquery [ctb], FangZou Yao [ctb], Benoit Liquet [ctb] Maintainer: Kim-Anh Le Cao URL: http://www.mixOmics.org VignetteBuilder: knitr BugReports: https://github.com/mixOmicsTeam/mixOmics/issues/ git_url: https://git.bioconductor.org/packages/mixOmics git_branch: RELEASE_3_10 git_last_commit: b399f33 git_last_commit_date: 2020-03-29 Date/Publication: 2020-03-30 source.ver: src/contrib/mixOmics_6.10.9.tar.gz win.binary.ver: bin/windows/contrib/3.6/mixOmics_6.10.9.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mixOmics_6.10.9.tgz vignettes: vignettes/mixOmics/inst/doc/vignette.html vignetteTitles: mixOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mixOmics/inst/doc/vignette.R dependsOnMe: compartmap importsMe: DepecheR dependencyCount: 73 Package: MLInterfaces Version: 1.66.5 Depends: R (>= 3.5), methods, BiocGenerics (>= 0.13.11), Biobase, annotate, cluster Imports: gdata, pls, sfsmisc, MASS, rpart, genefilter, fpc, ggvis, shiny, gbm, RColorBrewer, hwriter, threejs (>= 0.2.2), mlbench, stats4, tools, grDevices, graphics, stats Suggests: class, e1071, ipred, randomForest, gpls, pamr, nnet, ALL, hgu95av2.db, som, hu6800.db, lattice, caret (>= 5.07), golubEsets, ada, keggorthology, kernlab, mboost, party Enhances: parallel, rda License: LGPL MD5sum: 67dfa6d329a9f34eff9573e786712f68 NeedsCompilation: no Title: Uniform interfaces to R machine learning procedures for data in Bioconductor containers Description: This package provides uniform interfaces to machine learning code for data in R and Bioconductor containers. biocViews: Classification, Clustering Author: Vince Carey , Robert Gentleman, Jess Mar, and contributions from Jason Vertrees and Laurent Gatto Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/MLInterfaces git_branch: RELEASE_3_10 git_last_commit: 1549f50 git_last_commit_date: 2020-03-07 Date/Publication: 2020-03-07 source.ver: src/contrib/MLInterfaces_1.66.5.tar.gz win.binary.ver: bin/windows/contrib/3.6/MLInterfaces_1.66.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MLInterfaces_1.66.5.tgz vignettes: vignettes/MLInterfaces/inst/doc/MLint_devel.pdf, vignettes/MLInterfaces/inst/doc/MLInterfaces.pdf, vignettes/MLInterfaces/inst/doc/MLprac2_2.pdf, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.pdf vignetteTitles: MLInterfaces devel for schema-based MLearn, MLInterfaces Primer, A machine learning tutorial: applications of the Bioconductor MLInterfaces package to expression and ChIP-Seq data, MLInterfaces Computer Cluster hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLInterfaces/inst/doc/MLint_devel.R, vignettes/MLInterfaces/inst/doc/MLInterfaces.R, vignettes/MLInterfaces/inst/doc/MLprac2_2.R, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.R dependsOnMe: a4Classif, pRoloc, SigCheck suggestsMe: BiocCaseStudies dependencyCount: 93 Package: mlm4omics Version: 1.3.0 Depends: R (>= 3.5.0), Rcpp (>= 0.12.17), methods, stats Imports: rstan (>= 2.17.3),rstantools (>= 1.5.0),MASS,Matrix,stats4,ggplot2 LinkingTo: StanHeaders (>= 2.17.2), rstan (>= 2.17.3), BH (>= 1.66.0-1), Rcpp (>= 0.12.17), RcppEigen (>= 0.3.3.4.0) Suggests: testthat, BiocStyle, knitr, rmarkdown, roxygen2 (>= 5.0.0) License: GPL-3 Archs: i386, x64 MD5sum: 75d192355f185ddbf202f1fa0ab81076 NeedsCompilation: yes Title: Multilevel Model for Multivariate Responses with Missing Values Description: To conduct Bayesian inference regression for responses with multilevel explanatory variables and missing values; It uses function from 'Stan', a software to implement posterior sampling using Hamiltonian MC and its variation Non-U-Turn algorithms. It implements the posterior sampling of regression coefficients from the multilevel regression models. The package has two main functions to handle not-missing-at-random missing responses and left-censored with not-missing-at random responses. The purpose is to provide a similar format as the other R regression functions but using 'Stan' models. biocViews: ImmunoOncology, Bayesian,CopyNumberVariation,Classification,Regression,MassSpectrometry,Proteomics,Software Author: Irene Zeng [aut, cre], Thomas Lumley [ctb] Maintainer: Irene SL Zeng URL: https://doi.org/10.1101/153049 SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mlm4omics git_branch: master git_last_commit: 754d8b6 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-02 source.ver: src/contrib/mlm4omics_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mlm4omics_1.3.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mlm4omics_1.3.0.tgz vignettes: vignettes/mlm4omics/inst/doc/vigettes_mlm4omics.html vignetteTitles: Introduction of mlm4omics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mlm4omics/inst/doc/vigettes_mlm4omics.R dependencyCount: 66 Package: MLP Version: 1.34.0 Depends: AnnotationDbi, affy, plotrix, gplots, gmodels, gdata, gtools Suggests: GO.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Cf.eg.db, KEGG.db, annotate, Rgraphviz, GOstats, limma, mouse4302.db, reactome.db License: GPL-3 MD5sum: 07a485d8598c65452fc1af6f5a45d103 NeedsCompilation: no Title: MLP Description: Mean Log P Analysis biocViews: Genetics, Reactome, KEGG Author: Nandini Raghavan, Tobias Verbeke, An De Bondt with contributions by Javier Cabrera, Dhammika Amaratunga, Tine Casneuf and Willem Ligtenberg Maintainer: Tobias Verbeke git_url: https://git.bioconductor.org/packages/MLP git_branch: RELEASE_3_10 git_last_commit: 1e2cd2a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MLP_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MLP_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MLP_1.34.0.tgz vignettes: vignettes/MLP/inst/doc/UsingMLP.pdf vignetteTitles: UsingMLP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLP/inst/doc/UsingMLP.R importsMe: esetVis suggestsMe: a4 dependencyCount: 41 Package: MLSeq Version: 2.4.0 Depends: caret, ggplot2 Imports: methods, DESeq2, edgeR, limma, Biobase, SummarizedExperiment, plyr, foreach, utils, sSeq, xtable Suggests: knitr, testthat, BiocStyle, VennDiagram, pamr License: GPL(>=2) MD5sum: 2cb943dc4a7d5ccbf86ef83c94fda2ac NeedsCompilation: no Title: Machine Learning Interface for RNA-Seq Data Description: This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART to RNA-Seq data. biocViews: ImmunoOncology, Sequencing, RNASeq, Classification, Clustering Author: Gokmen Zararsiz [aut, cre], Dincer Goksuluk [aut], Selcuk Korkmaz [aut], Vahap Eldem [aut], Izzet Parug Duru [ctb], Ahmet Ozturk [aut], Ahmet Ergun Karaagaoglu [aut, ths] Maintainer: Gokmen Zararsiz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MLSeq git_branch: RELEASE_3_10 git_last_commit: ddade0c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MLSeq_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MLSeq_2.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MLSeq_2.4.0.tgz vignettes: vignettes/MLSeq/inst/doc/MLSeq.pdf vignetteTitles: Beginner's guide to the "MLSeq" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLSeq/inst/doc/MLSeq.R importsMe: GARS dependencyCount: 149 Package: MMAPPR2 Version: 1.0.0 Depends: R (>= 3.6.0) Imports: ensemblVEP (>= 1.20.0), gmapR, Rsamtools, VariantAnnotation, BiocParallel, Biobase, BiocGenerics, dplyr, GenomeInfoDb, GenomicRanges, IRanges, S4Vectors, tidyr, VariantTools, magrittr, methods, grDevices, graphics, stats, utils, stringr, data.table Suggests: testthat, mockery, roxygen2, knitr, rmarkdown, BiocStyle, MMAPPR2data License: GPL-3 OS_type: unix MD5sum: af68b0f8af62f38d4929de39986a6a5e NeedsCompilation: no Title: Mutation Mapping Analysis Pipeline for Pooled RNA-Seq Description: MMAPPR2 maps mutations resulting from pooled RNA-seq data from the F2 cross of forward genetic screens. Its predecessor is described in a paper published in Genome Research (Hill et al. 2013). MMAPPR2 accepts aligned BAM files as well as a reference genome as input, identifies loci of high sequence disparity between the control and mutant RNA sequences, predicts variant effects using Ensembl's Variant Effect Predictor, and outputs a ranked list of candidate mutations. biocViews: RNASeq, PooledScreens, DNASeq, VariantDetection Author: Kyle Johnsen [aut], Nathaniel Jenkins [aut], Jonathon Hill [cre] Maintainer: Jonathon Hill URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3613585/, https://github.com/kjohnsen/MMAPPR2 SystemRequirements: Ensembl VEP, Samtools VignetteBuilder: knitr BugReports: https://github.com/kjohnsen/MMAPPR2/issues git_url: https://git.bioconductor.org/packages/MMAPPR2 git_branch: RELEASE_3_10 git_last_commit: 50c8222 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MMAPPR2_1.0.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MMAPPR2_1.0.0.tgz vignettes: vignettes/MMAPPR2/inst/doc/MMAPPR2.html vignetteTitles: An Introduction to MMAPPR2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MMAPPR2/inst/doc/MMAPPR2.R dependencyCount: 90 Package: MMDiff2 Version: 1.14.0 Depends: R (>= 3.3), Rsamtools, Biobase, Imports: GenomicRanges, locfit, BSgenome, Biostrings, shiny, ggplot2, RColorBrewer, graphics, grDevices, parallel, S4Vectors, methods Suggests: MMDiffBamSubset, MotifDb, knitr, BiocStyle, BSgenome.Mmusculus.UCSC.mm9 License: Artistic-2.0 Archs: i386, x64 MD5sum: 653169209bfcf633f42238a3225a5f64 NeedsCompilation: no Title: Statistical Testing for ChIP-Seq data sets Description: This package detects statistically significant differences between read enrichment profiles in different ChIP-Seq samples. To take advantage of shape differences it uses Kernel methods (Maximum Mean Discrepancy, MMD). biocViews: ChIPSeq, DifferentialPeakCalling, Sequencing, Software Author: Gabriele Schweikert [cre, aut], David Kuo [aut] Maintainer: Gabriele Schweikert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MMDiff2 git_branch: RELEASE_3_10 git_last_commit: c886107 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MMDiff2_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MMDiff2_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MMDiff2_1.14.0.tgz vignettes: vignettes/MMDiff2/inst/doc/MMDiff2.pdf vignetteTitles: An Introduction to the MMDiff2 method hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MMDiff2/inst/doc/MMDiff2.R dependencyCount: 95 Package: MmPalateMiRNA Version: 1.36.0 Depends: R (>= 2.13.0), methods, Biobase, xtable, limma, statmod, lattice, vsn Imports: limma, lattice, Biobase Suggests: GOstats, graph, Category, org.Mm.eg.db, microRNA, targetscan.Mm.eg.db, RSQLite, DBI, AnnotationDbi, clValid, class, cluster, multtest, RColorBrewer, latticeExtra License: GPL-3 MD5sum: 2e66a9518fd38ced3f145b22cfd62e79 NeedsCompilation: no Title: Murine Palate miRNA Expression Analysis Description: R package compendium for the analysis of murine palate miRNA two-color expression data. biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, DifferentialExpression, MultipleComparison, Clustering, GO, Pathways, ReportWriting, SequenceMatching Author: Guy Brock , Partha Mukhopadhyay , Vasyl Pihur , Robert M. Greene , and M. Michele Pisano Maintainer: Guy Brock git_url: https://git.bioconductor.org/packages/MmPalateMiRNA git_branch: RELEASE_3_10 git_last_commit: 8d64193 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MmPalateMiRNA_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MmPalateMiRNA_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MmPalateMiRNA_1.36.0.tgz vignettes: vignettes/MmPalateMiRNA/inst/doc/MmPalateMiRNA.pdf vignetteTitles: Palate miRNA Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MmPalateMiRNA/inst/doc/MmPalateMiRNA.R dependencyCount: 66 Package: MMUPHin Version: 1.0.0 Depends: R (>= 3.6) Imports: Maaslin2, metafor, fpc, igraph, ggplot2, dplyr, tidyr, cowplot, utils, stats, grDevices Suggests: testthat, BiocStyle, knitr, rmarkdown, magrittr, vegan, phyloseq, curatedMetagenomicData, genefilter License: MIT + file LICENSE MD5sum: 2f3b3816c123bfceabf431d639ef790f NeedsCompilation: no Title: Meta-analysis Methods with Uniform Pipeline for Heterogeneity in Microbiome Studies Description: MMUPHin is an R package for meta-analysis tasks of microbiome cohorts. It has function interfaces for: a) covariate-controlled batch- and cohort effect adjustment, b) meta-analysis differential abundance testing, c) meta-analysis unsupervised discrete structure (clustering) discovery, and d) meta-analysis unsupervised continuous structure discovery. biocViews: Metagenomics, Microbiome, BatchEffect Author: Siyuan Ma Maintainer: Siyuan MA VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MMUPHin git_branch: RELEASE_3_10 git_last_commit: 2a6c283 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MMUPHin_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MMUPHin_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MMUPHin_1.0.0.tgz vignettes: vignettes/MMUPHin/inst/doc/MMUPHin.html vignetteTitles: MMUPHin hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MMUPHin/inst/doc/MMUPHin.R dependencyCount: 161 Package: mnem Version: 1.2.0 Depends: R (>= 3.6) Imports: cluster, nem, epiNEM, graph, Rgraphviz, flexclust, lattice, naturalsort, snowfall, stats4, tsne, methods, graphics, stats, utils, Linnorm, data.table, Rcpp, RcppEigen, matrixStats, grDevices LinkingTo: Rcpp, RcppEigen Suggests: knitr, devtools, rmarkdown, BiocGenerics, RUnit License: GPL-3 MD5sum: 74fdf3785b1bcdd07f94b68494ea6547 NeedsCompilation: yes Title: Mixture Nested Effects Models Description: Mixture Nested Effects Models (mnem) is an extension of Nested Effects Models and allows for the analysis of single cell perturbation data provided by methods like Perturb-Seq (Dixit et al., 2016) or Crop-Seq (Datlinger et al., 2017). In those experiments each of many cells is perturbed by a knock-down of a specific gene, i.e. several cells are perturbed by a knock-down of gene A, several by a knock-down of gene B, ... and so forth. The observed read-out has to be multi-trait and in the case of the Perturb-/Crop-Seq gene are expression profiles for each cell. mnem uses a mixture model to simultaneously cluster the cell population into k clusters and and infer k networks causally linking the perturbed genes for each cluster. The mixture components are inferred via an expectation maximization algorithm. biocViews: Pathways, SystemsBiology, NetworkInference, Network, RNASeq, PooledScreens, SingleCell, CRISPR, ATACSeq, DNASeq, GeneExpression Author: Martin Pirkl Maintainer: Martin Pirkl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mnem git_branch: RELEASE_3_10 git_last_commit: 1210f5a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mnem_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mnem_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mnem_1.2.0.tgz vignettes: vignettes/mnem/inst/doc/mnem.pdf vignetteTitles: mnem hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mnem/inst/doc/mnem.R dependencyCount: 120 Package: MODA Version: 1.12.0 Depends: R (>= 3.3) Imports: grDevices, graphics, stats, utils, WGCNA, dynamicTreeCut, igraph, cluster, AMOUNTAIN, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 51d05f3feca6bde4f0e0f4f38d62036d NeedsCompilation: no Title: MODA: MOdule Differential Analysis for weighted gene co-expression network Description: MODA can be used to estimate and construct condition-specific gene co-expression networks, and identify differentially expressed subnetworks as conserved or condition specific modules which are potentially associated with relevant biological processes. biocViews: GeneExpression, Microarray, DifferentialExpression, Network Author: Dong Li, James B. Brown, Luisa Orsini, Zhisong Pan, Guyu Hu and Shan He Maintainer: Dong Li git_url: https://git.bioconductor.org/packages/MODA git_branch: RELEASE_3_10 git_last_commit: 3b853a3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MODA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MODA_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MODA_1.12.0.tgz vignettes: vignettes/MODA/inst/doc/MODA.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 110 Package: Modstrings Version: 1.2.1 Depends: R (>= 3.6), Biostrings (>= 2.51.5) Imports: methods, assertive, BiocGenerics, GenomicRanges, S4Vectors, IRanges, XVector, stringi, stringr Suggests: BiocStyle, knitr, rmarkdown, testthat, usethis License: Artistic-2.0 MD5sum: ffbfcc955251ad30dd77b0ece5f6f156 NeedsCompilation: no Title: Implementation of Biostrings to work with nucleotide sequences containing modified nucleotides Description: Representing nucleotide modifications in a nucleotide sequence is usually done via special characters from a number of sources. This represents a challenge to work with in R and the Biostrings package. The Modstrings package implements this functionallity for RNA and DNA sequences containing modified nucleotides by translating the character internally in order to work with the infrastructure of the Biostrings package. For this the ModRNAString and ModDNAString classes and derivates and functions to construct and modify these objects despite the encoding issues are implemenented. In addition the conversion from sequences to list like location information (and the reverse operation) is implemented as well. biocViews: DataImport, DataRepresentation, Infrastructure, Sequencing, Software Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/Modstrings/issues git_url: https://git.bioconductor.org/packages/Modstrings git_branch: RELEASE_3_10 git_last_commit: 43e07f1 git_last_commit_date: 2020-02-01 Date/Publication: 2020-02-01 source.ver: src/contrib/Modstrings_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/Modstrings_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Modstrings_1.2.1.tgz vignettes: vignettes/Modstrings/inst/doc/ModDNAString-alphabet.html, vignettes/Modstrings/inst/doc/ModRNAString-alphabet.html, vignettes/Modstrings/inst/doc/Modstrings.html vignetteTitles: Modstrings-DNA-alphabet, Modstrings-RNA-alphabet, Modstrings hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Modstrings/inst/doc/ModDNAString-alphabet.R, vignettes/Modstrings/inst/doc/ModRNAString-alphabet.R, vignettes/Modstrings/inst/doc/Modstrings.R dependsOnMe: RNAmodR, tRNAdbImport importsMe: tRNA dependencyCount: 46 Package: MOFA Version: 1.2.0 Depends: R (>= 3.5) Imports: rhdf5, dplyr, reshape2, pheatmap, corrplot, ggplot2, ggbeeswarm, methods, scales, GGally, RColorBrewer, cowplot, ggrepel, MultiAssayExperiment, Biobase, doParallel, foreach, reticulate, grDevices, stats, utils Suggests: knitr, MOFAdata License: LGPL-3 | file LICENSE Archs: i386, x64 MD5sum: 6beb191501160fffbad9eee7869a8cd2 NeedsCompilation: yes Title: Multi-Omics Factor Analysis (MOFA) Description: Multi-Omics Factor Analysis: an unsupervised framework for the integration of multi-omics data sets. biocViews: DimensionReduction, Bayesian, Visualization Author: Ricard Argelaguet, Britta Velten, Damien Arnol, Florian Buettner, Wolfgang Huber, Oliver Stegle Maintainer: Britta Velten SystemRequirements: Python (>=2.7.0), numpy, pandas, h5py, scipy, sklearn, mofapy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MOFA git_branch: RELEASE_3_10 git_last_commit: cbc3ced git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MOFA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MOFA_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MOFA_1.2.0.tgz vignettes: vignettes/MOFA/inst/doc/MOFA_example_CLL.html, vignettes/MOFA/inst/doc/MOFA_example_scMT.html, vignettes/MOFA/inst/doc/MOFA_example_simulated.html, vignettes/MOFA/inst/doc/MOFA.html vignetteTitles: MOFA: applications to a multi-omics data set of CLL patients, MOFA: Application to a single-cell multi-omics data set, MOFA: How to assess model robustness and do model selection, Introduction to MOFA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MOFA/inst/doc/MOFA_example_CLL.R, vignettes/MOFA/inst/doc/MOFA_example_scMT.R, vignettes/MOFA/inst/doc/MOFA_example_simulated.R, vignettes/MOFA/inst/doc/MOFA.R dependencyCount: 107 Package: mogsa Version: 1.20.0 Depends: R (>= 3.4.0) Imports: methods, graphite, genefilter, BiocGenerics, gplots, GSEABase, Biobase, parallel, corpcor, svd, cluster, grDevices, graphics, stats, utils Suggests: BiocStyle, knitr, org.Hs.eg.db License: GPL-2 MD5sum: 108805b80e56b9d18e138c4fceed319c NeedsCompilation: no Title: Multiple omics data integrative clustering and gene set analysis Description: This package provide a method for doing gene set analysis based on multiple omics data. biocViews: GeneExpression, PrincipalComponent, StatisticalMethod, Clustering, Software Author: Chen Meng Maintainer: Chen Meng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mogsa git_branch: RELEASE_3_10 git_last_commit: 4777a93 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mogsa_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mogsa_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mogsa_1.20.0.tgz vignettes: vignettes/mogsa/inst/doc/moCluster-knitr.pdf, vignettes/mogsa/inst/doc/mogsa-knitr.pdf vignetteTitles: moCluster: Integrative clustering using multiple omics data, mogsa: gene set analysis on multiple omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mogsa/inst/doc/moCluster-knitr.R, vignettes/mogsa/inst/doc/mogsa-knitr.R dependencyCount: 61 Package: monocle Version: 2.14.0 Depends: R (>= 2.10.0), methods, Matrix (>= 1.2-6), Biobase, ggplot2 (>= 1.0.0), VGAM (>= 1.0-6), DDRTree (>= 0.1.4), Imports: parallel, igraph (>= 1.0.1), BiocGenerics, HSMMSingleCell (>= 0.101.5), plyr, cluster, combinat, fastICA, grid, irlba (>= 2.0.0), matrixStats, densityClust (>= 0.3), Rtsne, MASS, reshape2, limma, tibble, dplyr, qlcMatrix, pheatmap, stringr, proxy, slam, viridis, stats, biocViews, RANN(>= 2.5), Rcpp (>= 0.12.0) LinkingTo: Rcpp Suggests: destiny, Hmisc, knitr, Seurat, scater, testthat License: Artistic-2.0 MD5sum: f3d411e989b56ecd931473137d5bdc1e NeedsCompilation: yes Title: Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq Description: Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well. biocViews: ImmunoOncology, Sequencing, RNASeq, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Clustering, MultipleComparison, QualityControl Author: Cole Trapnell Maintainer: Cole Trapnell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/monocle git_branch: RELEASE_3_10 git_last_commit: 9b7ba93 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/monocle_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/monocle_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/monocle_2.14.0.tgz vignettes: vignettes/monocle/inst/doc/monocle-vignette.pdf vignetteTitles: Monocle: Cell counting,, differential expression,, and trajectory analysis for single-cell RNA-Seq experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/monocle/inst/doc/monocle-vignette.R dependsOnMe: cicero, phemd importsMe: uSORT suggestsMe: M3Drop, scran, sincell dependencyCount: 99 Package: MoonlightR Version: 1.12.0 Depends: R (>= 3.5), doParallel, foreach Imports: parmigene, randomForest, SummarizedExperiment, gplots, circlize, RColorBrewer, HiveR, clusterProfiler, DOSE, Biobase, limma, grDevices, graphics, TCGAbiolinks, GEOquery, stats, RISmed, grid, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2, png License: GPL (>= 3) MD5sum: e4172acc77107eabf979dd799a43b263 NeedsCompilation: no Title: Identify oncogenes and tumor suppressor genes from omics data Description: Motivation: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). Results: We present an R/bioconductor package called MoonlightR which returns a list of candidate driver genes for specific cancer types on the basis of TCGA expression data. The method first infers gene regulatory networks and then carries out a functional enrichment analysis (FEA) (implementing an upstream regulator analysis, URA) to score the importance of well-known biological processes with respect to the studied cancer type. Eventually, by means of random forests, MoonlightR predicts two specific roles for the candidate driver genes: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, MoonlightR can be used to discover OCGs and TSGs in the same cancer type. This may help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV) in breast cancer. In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Survival, GeneSetEnrichment, NetworkEnrichment Author: Antonio Colaprico*, Catharina Olsen*, Claudia Cava, Thilde Terkelsen, Laura Cantini, Andre Olsen, Gloria Bertoli, Andrei Zinovyev, Emmanuel Barillot, Isabella Castiglioni, Elena Papaleo, Gianluca Bontempi Maintainer: Antonio Colaprico , Catharina Olsen URL: https://github.com/ibsquare/MoonlightR VignetteBuilder: knitr BugReports: https://github.com/ibsquare/MoonlightR/issues git_url: https://git.bioconductor.org/packages/MoonlightR git_branch: RELEASE_3_10 git_last_commit: f6cce34 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MoonlightR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MoonlightR_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MoonlightR_1.12.0.tgz vignettes: vignettes/MoonlightR/inst/doc/Moonlight.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MoonlightR/inst/doc/Moonlight.R dependencyCount: 230 Package: MoPS Version: 1.20.0 Imports: Biobase License: GPL-3 MD5sum: 6d3b59fd43f62596a50216dd4b13b571 NeedsCompilation: no Title: MoPS - Model-based Periodicity Screening Description: Identification and characterization of periodic fluctuations in time-series data. biocViews: GeneRegulation,Classification,TimeCourse,Regression Author: Philipp Eser, Achim Tresch Maintainer: Philipp Eser git_url: https://git.bioconductor.org/packages/MoPS git_branch: RELEASE_3_10 git_last_commit: b31440c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MoPS_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MoPS_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MoPS_1.20.0.tgz vignettes: vignettes/MoPS/inst/doc/MoPS.pdf vignetteTitles: Model-based Periodicity Screening hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MoPS/inst/doc/MoPS.R dependencyCount: 7 Package: mosaics Version: 2.24.0 Depends: R (>= 3.0.0), methods, graphics, Rcpp Imports: MASS, splines, lattice, IRanges, GenomicRanges, GenomicAlignments, Rsamtools, GenomeInfoDb, S4Vectors LinkingTo: Rcpp Suggests: mosaicsExample Enhances: parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: 86cddecbfe1aa2a138dacdf1850a1ffa NeedsCompilation: yes Title: MOSAiCS (MOdel-based one and two Sample Analysis and Inference for ChIP-Seq) Description: This package provides functions for fitting MOSAiCS and MOSAiCS-HMM, a statistical framework to analyze one-sample or two-sample ChIP-seq data of transcription factor binding and histone modification. biocViews: ChIPseq, Sequencing, Transcription, Genetics, Bioinformatics Author: Dongjun Chung, Pei Fen Kuan, Rene Welch, Sunduz Keles Maintainer: Dongjun Chung URL: http://groups.google.com/group/mosaics_user_group SystemRequirements: Perl git_url: https://git.bioconductor.org/packages/mosaics git_branch: RELEASE_3_10 git_last_commit: 558fa2c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mosaics_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mosaics_2.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mosaics_2.24.0.tgz vignettes: vignettes/mosaics/inst/doc/mosaics-example.pdf vignetteTitles: MOSAiCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mosaics/inst/doc/mosaics-example.R dependencyCount: 39 Package: MOSim Version: 1.0.2 Depends: R (>= 3.6) Imports: HiddenMarkov, zoo, methods, matrixStats, dplyr, stringi, lazyeval, rlang, stats, utils, purrr, scales, stringr, tibble, tidyr, ggplot2, Biobase, IRanges, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: cdc7b4d553fefe006a145d7407dc424d NeedsCompilation: no Title: Multi-Omics Simulation (MOSim) Description: MOSim package simulates multi-omic experiments that mimic regulatory mechanisms within the cell, allowing flexible experimental design including time course and multiple groups. biocViews: Software, TimeCourse, ExperimentalDesign, RNASeq Author: Carlos Martínez [cre, aut], Sonia Tarazona [aut] Maintainer: Carlos Martínez URL: https://github.com/Neurergus/MOSim VignetteBuilder: knitr BugReports: https://github.com/Neurergus/MOSim/issues git_url: https://git.bioconductor.org/packages/MOSim git_branch: RELEASE_3_10 git_last_commit: 753625b git_last_commit_date: 2020-04-03 Date/Publication: 2020-04-03 source.ver: src/contrib/MOSim_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/MOSim_1.0.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MOSim_1.0.2.tgz vignettes: vignettes/MOSim/inst/doc/MOSim.pdf vignetteTitles: MOSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MOSim/inst/doc/MOSim.R dependencyCount: 72 Package: motifbreakR Version: 2.0.0 Depends: R (>= 3.5.0), grid, MotifDb Imports: methods, compiler, grDevices, grImport, stringr, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, Biostrings, BSgenome, rtracklayer, VariantAnnotation, BiocParallel, motifStack, Gviz, matrixStats, TFMPvalue, SummarizedExperiment Suggests: BSgenome.Hsapiens.UCSC.hg19, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP142.GRCh37, knitr, rmarkdown, BSgenome.Drerio.UCSC.danRer7, BiocStyle License: GPL-2 MD5sum: 235326b824fe9163b6d7a997bae37cac NeedsCompilation: no Title: A Package For Predicting The Disruptiveness Of Single Nucleotide Polymorphisms On Transcription Factor Binding Sites Description: We introduce motifbreakR, which allows the biologist to judge in the first place whether the sequence surrounding the polymorphism is a good match, and in the second place how much information is gained or lost in one allele of the polymorphism relative to another. MotifbreakR is both flexible and extensible over previous offerings; giving a choice of algorithms for interrogation of genomes with motifs from public sources that users can choose from; these are 1) a weighted-sum probability matrix, 2) log-probabilities, and 3) weighted by relative entropy. MotifbreakR can predict effects for novel or previously described variants in public databases, making it suitable for tasks beyond the scope of its original design. Lastly, it can be used to interrogate any genome curated within Bioconductor (currently there are 22). biocViews: ChIPSeq, Visualization, MotifAnnotation Author: Simon Gert Coetzee [aut, cre], Dennis J. Hazelett [aut] Maintainer: Simon Gert Coetzee VignetteBuilder: knitr BugReports: https://github.com/Simon-Coetzee/motifbreakR/issues git_url: https://git.bioconductor.org/packages/motifbreakR git_branch: RELEASE_3_10 git_last_commit: ffe8057 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/motifbreakR_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/motifbreakR_2.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/motifbreakR_2.0.0.tgz vignettes: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.html vignetteTitles: motifbreakR: an Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.R dependencyCount: 156 Package: motifcounter Version: 1.10.0 Depends: R(>= 3.0) Imports: Biostrings, methods Suggests: knitr, rmarkdown, testthat, MotifDb, seqLogo, prettydoc License: GPL-2 Archs: i386, x64 MD5sum: baec624f1247d7c85982b1fd30a2959a NeedsCompilation: yes Title: R package for analysing TFBSs in DNA sequences Description: 'motifcounter' provides motif matching, motif counting and motif enrichment functionality based on position frequency matrices. The main features of the packages include the utilization of higher-order background models and accounting for self-overlapping motif matches when determining motif enrichment. The background model allows to capture dinucleotide (or higher-order nucleotide) composition adequately which may reduced model biases and misleading results compared to using simple GC background models. When conducting a motif enrichment analysis based on the motif match count, the package relies on a compound Poisson distribution or alternatively a combinatorial model. These distribution account for self-overlapping motif structures as exemplified by repeat-like or palindromic motifs, and allow to determine the p-value and fold-enrichment for a set of observed motif matches. biocViews: Transcription,MotifAnnotation,SequenceMatching,Software Author: Wolfgang Kopp [aut, cre] Maintainer: Wolfgang Kopp VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifcounter git_branch: RELEASE_3_10 git_last_commit: 961aea0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/motifcounter_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/motifcounter_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/motifcounter_1.10.0.tgz vignettes: vignettes/motifcounter/inst/doc/motifcounter.html vignetteTitles: Introduction to the `motifcounter` package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifcounter/inst/doc/motifcounter.R dependencyCount: 12 Package: MotifDb Version: 1.28.0 Depends: R (>= 3.5.0), methods, BiocGenerics, S4Vectors, IRanges, Biostrings Imports: rtracklayer, splitstackshape Suggests: RUnit License: Artistic-2.0 | file LICENSE License_is_FOSS: no License_restricts_use: yes MD5sum: 995a35861aeb026c36a00c1f65efe977 NeedsCompilation: no Title: An Annotated Collection of Protein-DNA Binding Sequence Motifs Description: More than 9900 annotated position frequency matrices from 14 public sources, for multiple organisms. biocViews: MotifAnnotation Author: Paul Shannon, Matt Richards Maintainer: Paul Shannon git_url: https://git.bioconductor.org/packages/MotifDb git_branch: RELEASE_3_10 git_last_commit: 3602d2b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MotifDb_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MotifDb_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MotifDb_1.28.0.tgz vignettes: vignettes/MotifDb/inst/doc/MotifDb.pdf vignetteTitles: %%MotifDb Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MotifDb/inst/doc/MotifDb.R dependsOnMe: motifbreakR, trena importsMe: igvR, rTRMui suggestsMe: ATACseqQC, DiffLogo, MMDiff2, motifcounter, motifStack, profileScoreDist, PWMEnrich, rTRM, TFutils, universalmotif, vtpnet dependencyCount: 40 Package: motifmatchr Version: 1.8.0 Depends: R (>= 3.3) Imports: Matrix, Rcpp, methods, TFBSTools, Biostrings, BSgenome, S4Vectors, SummarizedExperiment, GenomicRanges, IRanges, Rsamtools, GenomeInfoDb LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 + file LICENSE MD5sum: d3412907d7f469c0a003f7e1fee646f4 NeedsCompilation: yes Title: Fast Motif Matching in R Description: Quickly find motif matches for many motifs and many sequences. Wraps C++ code from the MOODS motif calling library, which was developed by Pasi Rastas, Janne Korhonen, and Petri Martinmäki. biocViews: MotifAnnotation Author: Alicia Schep [aut, cre], Stanford University [cph] Maintainer: Alicia Schep SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifmatchr git_branch: RELEASE_3_10 git_last_commit: a576d99 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/motifmatchr_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/motifmatchr_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/motifmatchr_1.8.0.tgz vignettes: vignettes/motifmatchr/inst/doc/motifmatchr.html vignetteTitles: motifmatchr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/motifmatchr/inst/doc/motifmatchr.R importsMe: enrichTF, esATAC suggestsMe: chromVAR dependencyCount: 124 Package: motifRG Version: 1.30.0 Depends: R (>= 2.15), Biostrings (>= 2.26), IRanges, seqLogo, parallel, methods, grid, graphics, BSgenome, XVector, BSgenome.Hsapiens.UCSC.hg19 Imports: Biostrings,IRanges,seqLogo,parallel,methods,grid,graphics,XVector License: Artistic-2.0 Archs: i386, x64 MD5sum: 6a6357a10a8e96520ae92583ba871065 NeedsCompilation: no Title: A package for discriminative motif discovery, designed for high throughput sequencing dataset Description: Tools for discriminative motif discovery using regression methods biocViews: Transcription,MotifDiscovery Author: Zizhen Yao Maintainer: Zizhen Yao git_url: https://git.bioconductor.org/packages/motifRG git_branch: RELEASE_3_10 git_last_commit: 7ec8603 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/motifRG_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/motifRG_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/motifRG_1.30.0.tgz vignettes: vignettes/motifRG/inst/doc/motifRG.pdf vignetteTitles: motifRG: regression-based discriminative motif discovery hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifRG/inst/doc/motifRG.R dependsOnMe: RCAS dependencyCount: 41 Package: motifStack Version: 1.30.0 Depends: R (>= 2.15.1), methods, grImport2, grid, MotIV, ade4, Biostrings Imports: XML, scales, htmlwidgets,grDevices, stats, stats4, graphics, utils, ggplot2 Suggests: RUnit, BiocGenerics, MotifDb, RColorBrewer, BiocStyle, knitr, httr, htmltools License: GPL (>= 2) MD5sum: 376f31f934f060fd704887b80230607f NeedsCompilation: no Title: Plot stacked logos for single or multiple DNA, RNA and amino acid sequence Description: The motifStack package is designed for graphic representation of multiple motifs with different similarity scores. It works with both DNA/RNA sequence motif and amino acid sequence motif. In addition, it provides the flexibility for users to customize the graphic parameters such as the font type and symbol colors. biocViews: SequenceMatching, Visualization, Sequencing, Microarray, Alignment, ChIPchip, ChIPSeq, MotifAnnotation, DataImport Author: Jianhong Ou, Michael Brodsky, Scot Wolfe and Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifStack git_branch: RELEASE_3_10 git_last_commit: e807768 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/motifStack_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/motifStack_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/motifStack_1.30.0.tgz vignettes: vignettes/motifStack/inst/doc/motifStack_HTML.html vignetteTitles: motifStack Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifStack/inst/doc/motifStack_HTML.R dependsOnMe: dagLogo importsMe: ATACseqQC, atSNP, LowMACA, motifbreakR suggestsMe: ChIPpeakAnno, TFutils, universalmotif dependencyCount: 98 Package: MotIV Version: 1.42.0 Depends: R (>= 2.10), graphics, BiocGenerics, GenomicRanges Imports: methods, stats, grid, S4Vectors, IRanges (>= 1.13.5), Biostrings, lattice, rGADEM, utils Suggests: rtracklayer License: GPL-2 Archs: i386, x64 MD5sum: 3ce2d5cca1fe4c4802b58458371514d2 NeedsCompilation: yes Title: Motif Identification and Validation Description: This package makes use of STAMP for comparing a set of motifs to a given database (e.g. JASPAR). It can also be used to visualize motifs, motif distributions, modules and filter motifs. biocViews: Microarray, ChIPchip, ChIPSeq, GenomicSequence, MotifAnnotation Author: Eloi Mercier, Raphael Gottardo Maintainer: Eloi Mercier , Raphael Gottardo SystemRequirements: GNU Scientific Library >= 1.6 (http://www.gnu.org/software/gsl/) git_url: https://git.bioconductor.org/packages/MotIV git_branch: RELEASE_3_10 git_last_commit: 8c9b4d3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MotIV_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MotIV_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MotIV_1.42.0.tgz vignettes: vignettes/MotIV/inst/doc/MotIV.pdf vignetteTitles: The MotIV users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MotIV/inst/doc/MotIV.R dependsOnMe: motifStack dependencyCount: 41 Package: MPFE Version: 1.22.0 License: GPL (>= 3) MD5sum: 95b3b62a755a216af6bc590ec733423f NeedsCompilation: no Title: Estimation of the amplicon methylation pattern distribution from bisulphite sequencing data Description: Estimate distribution of methylation patterns from a table of counts from a bisulphite sequencing experiment given a non-conversion rate and read error rate. biocViews: HighThroughputSequencingData, DNAMethylation, MethylSeq Author: Peijie Lin, Sylvain Foret, Conrad Burden Maintainer: Conrad Burden git_url: https://git.bioconductor.org/packages/MPFE git_branch: RELEASE_3_10 git_last_commit: bb2f0be git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MPFE_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MPFE_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MPFE_1.22.0.tgz vignettes: vignettes/MPFE/inst/doc/MPFE.pdf vignetteTitles: MPFE hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPFE/inst/doc/MPFE.R dependencyCount: 0 Package: mpra Version: 1.8.0 Depends: R (>= 3.4.0), methods, BiocGenerics, SummarizedExperiment, limma Imports: S4Vectors, scales, stats, graphics, statmod Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: 2959c3818cbd092e93e7535d45aef6ed NeedsCompilation: no Title: Analyze massively parallel reporter assays Description: Tools for data management, count preprocessing, and differential analysis in massively parallel report assays (MPRA). biocViews: Software, GeneRegulation, Sequencing, FunctionalGenomics Author: Leslie Myint [cre, aut], Kasper D. Hansen [aut] Maintainer: Leslie Myint URL: https://github.com/hansenlab/mpra VignetteBuilder: knitr BugReports: https://github.com/hansenlab/mpra/issues git_url: https://git.bioconductor.org/packages/mpra git_branch: RELEASE_3_10 git_last_commit: 3c52f8e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mpra_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mpra_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mpra_1.8.0.tgz vignettes: vignettes/mpra/inst/doc/mpra.html vignetteTitles: mpra User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mpra/inst/doc/mpra.R dependencyCount: 45 Package: MPRAnalyze Version: 1.4.0 Imports: BiocParallel, methods, progress, stats, SummarizedExperiment Suggests: knitr License: GPL-3 Archs: i386, x64 MD5sum: f8ef998bc611c8ddfb78d6b7a28d4023 NeedsCompilation: no Title: Statistical Analysis of MPRA data Description: MPRAnalyze provides statistical framework for the analysis of data generated by Massively Parallel Reporter Assays (MPRAs), used to directly measure enhancer activity. MPRAnalyze can be used for quantification of enhancer activity, classification of active enhancers and comparative analyses of enhancer activity between conditions. MPRAnalyze construct a nested pair of generalized linear models (GLMs) to relate the DNA and RNA observations, easily adjustable to various experimental designs and conditions, and provides a set of rigorous statistical testig schemes. biocViews: ImmunoOncology, Software, StatisticalMethod, Sequencing, GeneExpression, CellBiology, CellBasedAssays, DifferentialExpression Author: Tal Ashuach [aut, cre], David S Fischer [aut], Nir Yosef [ctb], Fabian J Theis [ctb], Maintainer: Tal Ashuach URL: https://github.com/YosefLab/MPRAnalyze VignetteBuilder: knitr BugReports: https://github.com/YosefLab/MPRAnalyze git_url: https://git.bioconductor.org/packages/MPRAnalyze git_branch: RELEASE_3_10 git_last_commit: 309358e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MPRAnalyze_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MPRAnalyze_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MPRAnalyze_1.4.0.tgz vignettes: vignettes/MPRAnalyze/inst/doc/vignette.html vignetteTitles: Analyzing MPRA data with MPRAnalyze hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPRAnalyze/inst/doc/vignette.R dependencyCount: 43 Package: msa Version: 1.18.0 Depends: R (>= 3.1.0), methods, Biostrings (>= 2.40.0) Imports: Rcpp (>= 0.11.1), BiocGenerics, IRanges (>= 1.20.0), S4Vectors, tools LinkingTo: Rcpp Suggests: Biobase, knitr, seqinr, ape, phangorn License: GPL (>= 2) MD5sum: 8ad1412d804619e5e6d5ab5da9378831 NeedsCompilation: yes Title: Multiple Sequence Alignment Description: The 'msa' package provides a unified R/Bioconductor interface to the multiple sequence alignment algorithms ClustalW, ClustalOmega, and Muscle. All three algorithms are integrated in the package, therefore, they do not depend on any external software tools and are available for all major platforms. The multiple sequence alignment algorithms are complemented by a function for pretty-printing multiple sequence alignments using the LaTeX package TeXshade. biocViews: MultipleSequenceAlignment, Alignment, MultipleComparison, Sequencing Author: Enrico Bonatesta, Christoph Horejs-Kainrath, Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/msa/ SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/msa git_branch: RELEASE_3_10 git_last_commit: bfbaad2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/msa_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/msa_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/msa_1.18.0.tgz vignettes: vignettes/msa/inst/doc/msa.pdf vignetteTitles: msa - An R Package for Multiple Sequence Alignment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msa/inst/doc/msa.R importsMe: LymphoSeq, odseq dependencyCount: 14 Package: msgbsR Version: 1.10.0 Depends: R (>= 3.4), GenomicRanges, methods Imports: BSgenome, easyRNASeq, edgeR, GenomicAlignments, GenomicFeatures, GenomeInfoDb, ggbio, ggplot2, IRanges, parallel, plyr, Rsamtools, R.utils, stats, SummarizedExperiment, S4Vectors, utils Suggests: roxygen2, BSgenome.Rnorvegicus.UCSC.rn6 License: GPL-2 MD5sum: d85c2df4c4c9b2499cf8d9e40a118be3 NeedsCompilation: no Title: msgbsR: methylation sensitive genotyping by sequencing (MS-GBS) R functions Description: Pipeline for the anaysis of a MS-GBS experiment. biocViews: ImmunoOncology, DifferentialMethylation, DataImport, Epigenetics, MethylSeq Author: Benjamin Mayne Maintainer: Benjamin Mayne git_url: https://git.bioconductor.org/packages/msgbsR git_branch: RELEASE_3_10 git_last_commit: da0eb60 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/msgbsR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/msgbsR_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/msgbsR_1.10.0.tgz vignettes: vignettes/msgbsR/inst/doc/msgbsR_Vignette.pdf vignetteTitles: msgbsR_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msgbsR/inst/doc/msgbsR_Vignette.R dependencyCount: 168 Package: MSGFgui Version: 1.20.0 Depends: mzR, xlsx Imports: shiny, mzID (>= 1.2), MSGFplus, shinyFiles (>= 0.4.0), tools Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 13914be4568d1ec3bbfba81b21468250 NeedsCompilation: no Title: A shiny GUI for MSGFplus Description: This package makes it possible to perform analyses using the MSGFplus package in a GUI environment. Furthermore it enables the user to investigate the results using interactive plots, summary statistics and filtering. Lastly it exposes the current results to another R session so the user can seamlessly integrate the gui into other workflows. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, GUI, Visualization Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MSGFgui git_branch: RELEASE_3_10 git_last_commit: bc2a587 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MSGFgui_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MSGFgui_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MSGFgui_1.20.0.tgz vignettes: vignettes/MSGFgui/inst/doc/Using_MSGFgui.html vignetteTitles: Using MSGFgui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSGFgui/inst/doc/Using_MSGFgui.R dependencyCount: 55 Package: MSGFplus Version: 1.20.0 Depends: methods Imports: mzID, ProtGenerics Suggests: gWidgets, knitr, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: e8dbf9dea28ec4232f2b0af786873448 NeedsCompilation: no Title: An interface between R and MS-GF+ Description: This package contains function to perform peptide identification using the MS-GF+ algorithm. The package contains functionality for building up a parameter set both in code and through a simple GUI, as well as running the algorithm in batches, potentially asynchronously. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen SystemRequirements: Java (>= 1.7) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MSGFplus git_branch: RELEASE_3_10 git_last_commit: f795758 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MSGFplus_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MSGFplus_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MSGFplus_1.20.0.tgz vignettes: vignettes/MSGFplus/inst/doc/Using_MSGFplus.html vignetteTitles: Using MSGFgui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSGFplus/inst/doc/Using_MSGFplus.R importsMe: MSGFgui dependencyCount: 12 Package: msmsEDA Version: 1.24.0 Depends: R (>= 3.0.1), MSnbase Imports: MASS, gplots, RColorBrewer License: GPL-2 MD5sum: dda431279d446cb7bc3c4660a9de4e11 NeedsCompilation: no Title: Exploratory Data Analysis of LC-MS/MS data by spectral counts Description: Exploratory data analysis to assess the quality of a set of LC-MS/MS experiments, and visualize de influence of the involved factors. biocViews: ImmunoOncology, Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori git_url: https://git.bioconductor.org/packages/msmsEDA git_branch: RELEASE_3_10 git_last_commit: e24b462 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/msmsEDA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/msmsEDA_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/msmsEDA_1.24.0.tgz vignettes: vignettes/msmsEDA/inst/doc/msmsData-Vignette.pdf vignetteTitles: msmsEDA: Batch effects detection in LC-MSMS experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msmsEDA/inst/doc/msmsData-Vignette.R dependsOnMe: msmsTests suggestsMe: Harman dependencyCount: 95 Package: msmsTests Version: 1.24.0 Depends: R (>= 3.0.1), MSnbase, msmsEDA Imports: edgeR, qvalue License: GPL-2 MD5sum: 4da226a627d823da001d41b2358f455d NeedsCompilation: no Title: LC-MS/MS Differential Expression Tests Description: Statistical tests for label-free LC-MS/MS data by spectral counts, to discover differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package.The three models admit blocking factors to control for nuissance variables.To assure a good level of reproducibility a post-test filter is available, where we may set the minimum effect size considered biologicaly relevant, and the minimum expression of the most abundant condition. biocViews: ImmunoOncology, Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori i Font git_url: https://git.bioconductor.org/packages/msmsTests git_branch: RELEASE_3_10 git_last_commit: 9a90d6d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/msmsTests_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/msmsTests_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/msmsTests_1.24.0.tgz vignettes: vignettes/msmsTests/inst/doc/msmsTests-Vignette.pdf, vignettes/msmsTests/inst/doc/msmsTests-Vignette2.pdf vignetteTitles: msmsTests: post test filters to improve reproducibility, msmsTests: controlling batch effects by blocking hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msmsTests/inst/doc/msmsTests-Vignette.R, vignettes/msmsTests/inst/doc/msmsTests-Vignette2.R suggestsMe: MSnID dependencyCount: 102 Package: MSnbase Version: 2.12.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.7.1), Biobase (>= 2.15.2), mzR (>= 2.19.6), S4Vectors, ProtGenerics (>= 1.17.4) Imports: BiocParallel, IRanges (>= 2.13.28), plyr, preprocessCore, vsn, grid, stats4, affy, impute, pcaMethods, MALDIquant (>= 1.16), mzID (>= 1.5.2), digest, lattice, ggplot2, XML, scales, MASS, Rcpp LinkingTo: Rcpp Suggests: testthat, pryr, gridExtra, microbenchmark, zoo, knitr (>= 1.1.0), rols, Rdisop, pRoloc, pRolocdata (>= 1.7.1), msdata (>= 0.19.3), roxygen2, rgl, rpx, AnnotationHub, BiocStyle (>= 2.5.19), rmarkdown, imputeLCMD, norm, gplots, shiny, magrittr, SummarizedExperiment License: Artistic-2.0 MD5sum: aad6649b829ad868e32f6945ac4afb82 NeedsCompilation: yes Title: Base Functions and Classes for Mass Spectrometry and Proteomics Description: MSnbase provides infrastructure for manipulation, processing and visualisation of mass spectrometry and proteomics data, ranging from raw to quantitative and annotated data. biocViews: ImmunoOncology, Infrastructure, Proteomics, MassSpectrometry, QualityControl, DataImport Author: Laurent Gatto, Johannes Rainer and Sebastian Gibb with contributions from Guangchuang Yu, Samuel Wieczorek, Vasile-Cosmin Lazar, Vladislav Petyuk, Thomas Naake, Richie Cotton, Arne Smits, Martina Fisher, Ludger Goeminne, Adriaan Sticker and Lieven Clement. Maintainer: Laurent Gatto URL: https://lgatto.github.io/MSnbase VignetteBuilder: knitr BugReports: https://github.com/lgatto/MSnbase/issues git_url: https://git.bioconductor.org/packages/MSnbase git_branch: RELEASE_3_10 git_last_commit: c9e328c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MSnbase_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MSnbase_2.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MSnbase_2.12.0.tgz vignettes: vignettes/MSnbase/inst/doc/v01-MSnbase-demo.html, vignettes/MSnbase/inst/doc/v02-MSnbase-io.html, vignettes/MSnbase/inst/doc/v03-MSnbase-centroiding.html, vignettes/MSnbase/inst/doc/v04-benchmarking.html, vignettes/MSnbase/inst/doc/v05-MSnbase-development.html vignetteTitles: Base Functions and Classes for MS-based Proteomics, MSnbase IO capabilities, MSnbase: centroiding of profile-mode MS data, MSnbase benchmarking, A short introduction to `MSnbase` development hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnbase/inst/doc/v01-MSnbase-demo.R, vignettes/MSnbase/inst/doc/v02-MSnbase-io.R, vignettes/MSnbase/inst/doc/v03-MSnbase-centroiding.R, vignettes/MSnbase/inst/doc/v04-benchmarking.R, vignettes/MSnbase/inst/doc/v05-MSnbase-development.R dependsOnMe: MetCirc, msmsEDA, msmsTests, pRoloc, pRolocGUI, proteoQC, qPLEXanalyzer, synapter, xcms importsMe: Autotuner, cliqueMS, CluMSID, DAPAR, DEP, MSnID, MSstatsQC, Pbase, peakPantheR, PrInCE, ProteomicsAnnotationHubData, topdownr suggestsMe: AnnotationHub, biobroom, BiocGenerics, isobar, proDA, qcmetrics, readat, rpx dependencyCount: 88 Package: MSnID Version: 1.20.0 Depends: R (>= 2.10), Rcpp Imports: MSnbase (>= 1.12.1), mzID (>= 1.3.5), R.cache, foreach, doParallel, parallel, methods, iterators, data.table, Biobase, ProtGenerics, reshape2, dplyr, mzR Suggests: BiocStyle, msmsTests, ggplot2, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 1a35f20a76f3b66d1fea3c80f486f17a NeedsCompilation: no Title: Utilities for Exploration and Assessment of Confidence of LC-MSn Proteomics Identifications Description: Extracts MS/MS ID data from mzIdentML (leveraging mzID package) or text files. After collating the search results from multiple datasets it assesses their identification quality and optimize filtering criteria to achieve the maximum number of identifications while not exceeding a specified false discovery rate. Also contains a number of utilities to explore the MS/MS results and assess missed and irregular enzymatic cleavages, mass measurement accuracy, etc. biocViews: Proteomics, MassSpectrometry, ImmunoOncology Author: Vlad Petyuk with contributions from Laurent Gatto Maintainer: Vlad Petyuk git_url: https://git.bioconductor.org/packages/MSnID git_branch: RELEASE_3_10 git_last_commit: ad1f290 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MSnID_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MSnID_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MSnID_1.20.0.tgz vignettes: vignettes/MSnID/inst/doc/msnid_vignette.pdf vignetteTitles: MSnID Package for Handling MS/MS Identifications hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnID/inst/doc/msnid_vignette.R dependencyCount: 101 Package: msPurity Version: 1.12.2 Depends: Rcpp Imports: plyr, dplyr, dbplyr, magrittr, foreach, parallel, doSNOW, stringr, mzR, reshape2, fastcluster, ggplot2, DBI, RSQLite, uuid, jsonlite Suggests: testthat, xcms, BiocStyle, knitr, rmarkdown, msPurityData, CAMERA, RPostgres, RMySQL License: GPL (>= 2) MD5sum: 0b7bfa854a266f47a1b8926b8c1df0a1 NeedsCompilation: no Title: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry Based Fragmentation in Metabolomics Description: msPurity R package was developed to: 1) Assess the spectral quality of fragmentation spectra by evaluating the "precursor ion purity". 2) Process fragmentation spectra. 3) Perform spectral matching. What is precursor ion purity? -What we call "Precursor ion purity" is a measure of the contribution of a selected precursor peak in an isolation window used for fragmentation. The simple calculation involves dividing the intensity of the selected precursor peak by the total intensity of the isolation window. When assessing MS/MS spectra this calculation is done before and after the MS/MS scan of interest and the purity is interpolated at the recorded time of the MS/MS acquisition. Additionally, isotopic peaks can be removed, low abundance peaks are removed that are thought to have limited contribution to the resulting MS/MS spectra and the isolation efficiency of the mass spectrometer can be used to normalise the intensities used for the calculation. biocViews: MassSpectrometry, Metabolomics, Software Author: Thomas N. Lawson, Ralf Weber, Martin Jones, Mark Viant, Warwick Dunn Maintainer: Thomas N. Lawson VignetteBuilder: knitr BugReports: https://github.com/computational-metabolomics/msPurity/issues/new git_url: https://git.bioconductor.org/packages/msPurity git_branch: RELEASE_3_10 git_last_commit: 2c0f0f1 git_last_commit_date: 2020-03-26 Date/Publication: 2020-03-26 source.ver: src/contrib/msPurity_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/msPurity_1.12.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/msPurity_1.12.2.tgz vignettes: vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.html, vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.html, vignettes/msPurity/inst/doc/msPurity-vignette.html vignetteTitles: msPurity spectral matching, msPurity spectral database schema, msPurity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.R, vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.R, vignettes/msPurity/inst/doc/msPurity-vignette.R dependencyCount: 86 Package: MSstats Version: 3.18.5 Depends: R (>= 3.6) Imports: lme4, marray, limma, gplots, ggplot2, methods, grid, ggrepel, preprocessCore, reshape2, survival, statmod, minpack.lm, utils, grDevices, graphics, stats, doSNOW, snow, foreach, data.table, MASS, dplyr, tidyr, broom, purrr, stringr Suggests: BiocStyle, knitr, rmarkdown, MSstatsBioData License: Artistic-2.0 Archs: i386, x64 MD5sum: 00d09ee969adab7c63f2f988cf430545 NeedsCompilation: no Title: Protein Significance Analysis in DDA, SRM and DIA for Label-free or Label-based Proteomics Experiments Description: A set of tools for statistical relative protein significance analysis in DDA, SRM and DIA experiments. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, Normalization, QualityControl, TimeCourse Author: Meena Choi [aut, cre], Tsung-Heng Tsai [aut], Cyril Galitzine [aut], Olga Vitek [aut] Maintainer: Meena Choi URL: http://msstats.org VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstats git_branch: RELEASE_3_10 git_last_commit: ea676d7 git_last_commit_date: 2020-03-02 Date/Publication: 2020-03-02 source.ver: src/contrib/MSstats_3.18.5.tar.gz win.binary.ver: bin/windows/contrib/3.6/MSstats_3.18.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MSstats_3.18.5.tgz vignettes: vignettes/MSstats/inst/doc/MSstats.html vignetteTitles: MSstats: Protein/Peptide significance analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstats/inst/doc/MSstats.R importsMe: artMS, MSstatsSampleSize, MSstatsTMT dependencyCount: 91 Package: MSstatsQC Version: 2.4.0 Imports: dplyr,plotly,RecordLinkage,ggplot2,ggExtra, stats,grid, MSnbase, qcmetrics Suggests: knitr,rmarkdown, testthat, RforProteomics License: Artistic License 2.0 MD5sum: 91f53b4e18dc8cb7c9f057e957a46098 NeedsCompilation: no Title: Longitudinal system suitability monitoring and quality control for proteomic experiments Description: MSstatsQC is an R package which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments. biocViews: ImmunoOncology, Software, QualityControl, Proteomics, MassSpectrometry Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut] Maintainer: Eralp Dogu URL: http://msstats.org/msstatsqc VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstatsqc git_url: https://git.bioconductor.org/packages/MSstatsQC git_branch: RELEASE_3_10 git_last_commit: 814301d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MSstatsQC_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MSstatsQC_2.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MSstatsQC_2.4.0.tgz vignettes: vignettes/MSstatsQC/inst/doc/MSstatsQC.html vignetteTitles: MSstatsQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsQC/inst/doc/MSstatsQC.R importsMe: MSstatsQCgui dependencyCount: 153 Package: MSstatsQCgui Version: 1.6.0 Imports: shiny, MSstatsQC, ggExtra, gridExtra, plotly, dplyr, RecordLinkage, grid Suggests: knitr License: Artistic License 2.0 MD5sum: b7c7451034c037e2019890e9028277c4 NeedsCompilation: no Title: A graphical user interface for MSstatsQC package Description: MSstatsQCgui is a Shiny app which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments. biocViews: ImmunoOncology, Software, QualityControl, Proteomics, MassSpectrometry, GUI Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut] Maintainer: Eralp Dogu URL: http://msstats.org/msstatsqc VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstatsqc git_url: https://git.bioconductor.org/packages/MSstatsQCgui git_branch: RELEASE_3_10 git_last_commit: cb794ef git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MSstatsQCgui_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MSstatsQCgui_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MSstatsQCgui_1.6.0.tgz vignettes: vignettes/MSstatsQCgui/inst/doc/MSstatsQCgui.html vignetteTitles: MSstatsQCgui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsQCgui/inst/doc/MSstatsQCgui.R dependencyCount: 155 Package: MSstatsSampleSize Version: 1.0.1 Depends: R (>= 3.6) Imports: ggplot2, BiocParallel, caret, gridExtra, reshape2, stats, utils, grDevices, graphics, MSstats Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: cddfd197ec5fc66f327928b4389fe4ca NeedsCompilation: no Title: Simulation tool for optimal design of high-dimensional MS-based proteomics experiment Description: The packages estimates the variance in the input protein abundance data and simulates data with predefined number of biological replicates based on the variance estimation. It reports the mean predictive accuracy of the classifier and mean protein importance over multiple iterations of the simulation. biocViews: MassSpectrometry, Proteomics, Software, DifferentialExpression, Classification, PrincipalComponent, ExperimentalDesign, Visualization Author: Ting Huang [aut, cre], Meena Choi [aut], Olga Vitek [aut] Maintainer: Ting Huang URL: http://msstats.org VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstatsSampleSize git_branch: RELEASE_3_10 git_last_commit: 7b43632 git_last_commit_date: 2020-03-04 Date/Publication: 2020-03-04 source.ver: src/contrib/MSstatsSampleSize_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/MSstatsSampleSize_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MSstatsSampleSize_1.0.1.tgz vignettes: vignettes/MSstatsSampleSize/inst/doc/MSstatsSampleSize.html vignetteTitles: MSstatsSampleSize User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsSampleSize/inst/doc/MSstatsSampleSize.R dependencyCount: 114 Package: MSstatsTMT Version: 1.4.6 Depends: R (>= 3.6) Imports: limma, lme4, lmerTest, dplyr, tidyr, statmod, methods, reshape2, data.table, matrixStats, stats, utils, ggplot2, grDevices, graphics, MSstats Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: b4f9b7fd22b07db215abcd1ae12b74c0 NeedsCompilation: no Title: Protein Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling Description: Tools for protein significance analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software Author: Ting Huang [aut, cre], Meena Choi [aut], Sicheng Hao [aut], Olga Vitek [aut] Maintainer: Ting Huang URL: http://msstats.org/msstatstmt/ VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstatsTMT git_branch: RELEASE_3_10 git_last_commit: c6193cc git_last_commit_date: 2020-04-14 Date/Publication: 2020-04-14 source.ver: src/contrib/MSstatsTMT_1.4.6.tar.gz win.binary.ver: bin/windows/contrib/3.6/MSstatsTMT_1.4.6.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MSstatsTMT_1.4.6.tgz vignettes: vignettes/MSstatsTMT/inst/doc/MSstatsTMT.html vignetteTitles: MSstatsTMT User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsTMT/inst/doc/MSstatsTMT.R dependencyCount: 95 Package: MTseeker Version: 1.3.0 Depends: viridis, S4Vectors, GenomeInfoDb, GenomicAlignments, VariantAnnotation Imports: xml2, utils, gmapR, methods, IRanges, Biobase, circlize, jsonlite, graphics, Rsamtools, grDevices, Biostrings, rtracklayer, VariantTools, Homo.sapiens, BiocGenerics, GenomicRanges, GenomicFeatures, SummarizedExperiment Suggests: MTseekerData, BiocStyle, rmarkdown, ggthemes, ggplot2, pkgdown, knitr, rsvg License: GPL-3 MD5sum: 65f9efdacb7aa51bbfa1593643b2b0d3 NeedsCompilation: no Title: Bioconductor Tools for Human Mitochondrial Variant Analysis Description: Variant analysis tools for mitochondrial genetics. biocViews: Genetics, Metabolomics, VariantAnnotation Author: Tim Triche [cre, aut], Noor Sohail [aut], Ben Johnson [aut], Tim Vickers [ctb], Azif Zubair [ctb] Maintainer: Tim Triche VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MTseeker git_branch: master git_last_commit: 8678848 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-02 source.ver: src/contrib/MTseeker_1.3.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MTseeker_1.3.0.tgz vignettes: vignettes/MTseeker/inst/doc/oncocytomas.html vignetteTitles: MTseeker example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MTseeker/inst/doc/oncocytomas.R dependencyCount: 128 Package: Mulcom Version: 1.36.0 Depends: R (>= 2.10), Biobase Imports: graphics, grDevices, stats, methods, fields License: GPL-2 MD5sum: d599cda1545fa10ed21087836d92007b NeedsCompilation: yes Title: Calculates Mulcom test Description: Identification of differentially expressed genes and false discovery rate (FDR) calculation by Multiple Comparison test. biocViews: StatisticalMethod, MultipleComparison, Microarray, DifferentialExpression, GeneExpression Author: Claudio Isella Maintainer: Claudio Isella git_url: https://git.bioconductor.org/packages/Mulcom git_branch: RELEASE_3_10 git_last_commit: 5e6b2f8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Mulcom_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Mulcom_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Mulcom_1.36.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 13 Package: MultiAssayExperiment Version: 1.12.6 Depends: R (>= 3.6.0), SummarizedExperiment (>= 1.3.81) Imports: methods, GenomicRanges (>= 1.25.93), BiocGenerics, S4Vectors (>= 0.23.19), IRanges, Biobase, stats, tidyr, utils Suggests: BiocStyle, testthat, knitr, rmarkdown, R.rsp, HDF5Array, RaggedExperiment, UpSetR, survival, survminer License: Artistic-2.0 MD5sum: 3202f76f0fb0db87ae3189859c03677a NeedsCompilation: no Title: Software for the integration of multi-omics experiments in Bioconductor Description: MultiAssayExperiment harmonizes data management of multiple assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of standard data classes for individual assays, and allowing subsetting by genomic ranges or rownames. biocViews: Infrastructure, DataRepresentation Author: Marcel Ramos [aut, cre], Levi Waldron [aut], MultiAssay SIG [ctb] Maintainer: Marcel Ramos URL: http://waldronlab.io/MultiAssayExperiment/ VignetteBuilder: knitr, R.rsp BugReports: https://github.com/waldronlab/MultiAssayExperiment/issues git_url: https://git.bioconductor.org/packages/MultiAssayExperiment git_branch: RELEASE_3_10 git_last_commit: b3daa27 git_last_commit_date: 2020-03-23 Date/Publication: 2020-03-23 source.ver: src/contrib/MultiAssayExperiment_1.12.6.tar.gz win.binary.ver: bin/windows/contrib/3.6/MultiAssayExperiment_1.12.6.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MultiAssayExperiment_1.12.6.tgz vignettes: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment_cheatsheet.pdf, vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.html, vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html, vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.html vignetteTitles: MultiAssayExperiment_cheatsheet.pdf, Coordinating Analysis of Multi-Assay Experiments, Quick-start Guide, HDF5Array and MultiAssayExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.R, vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.R, vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.R dependsOnMe: CAGEr, ClassifyR, evaluomeR, glmSparseNet, hipathia, InTAD, missRows importsMe: AffiXcan, AMARETTO, animalcules, ELMER, GOpro, LinkHD, MOFA, OMICsPCA, omicsPrint, TCGAutils suggestsMe: BiocOncoTK, CNVRanger, deco, MultiDataSet, RaggedExperiment dependencyCount: 55 Package: multiClust Version: 1.16.0 Imports: mclust, ctc, survival, cluster, dendextend, amap, graphics, grDevices Suggests: knitr, gplots, RUnit, BiocGenerics, preprocessCore, Biobase, GEOquery License: GPL (>= 2) MD5sum: b51a8169fffe6faf014333fddc722d81 NeedsCompilation: no Title: multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles Description: Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies. biocViews: FeatureExtraction, Clustering, GeneExpression, Survival Author: Nathan Lawlor [aut, cre], Peiyong Guan [aut], Alec Fabbri [aut], Krish Karuturi [aut], Joshy George [aut] Maintainer: Nathan Lawlor VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/multiClust git_branch: RELEASE_3_10 git_last_commit: c53eb70 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/multiClust_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/multiClust_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/multiClust_1.16.0.tgz vignettes: vignettes/multiClust/inst/doc/multiClust.html vignetteTitles: "A Guide to multiClust" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiClust/inst/doc/multiClust.R dependencyCount: 63 Package: MultiDataSet Version: 1.14.0 Depends: R (>= 3.3), Biobase Imports: BiocGenerics, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, methods, utils, ggplot2, ggrepel, qqman, limma Suggests: brgedata, minfi, minfiData, knitr, rmarkdown, testthat, omicade4, iClusterPlus, GEOquery, MultiAssayExperiment, BiocStyle, RaggedExperiment License: file LICENSE Archs: i386, x64 MD5sum: ba9b0dd6133035c6a16bbae989e060f3 NeedsCompilation: no Title: Implementation of MultiDataSet and ResultSet Description: Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and ResultSet. MultiDataSet is designed for integrating multi omics data sets and ResultSet is a container for omics results. This package contains base classes for MEAL and rexposome packages. biocViews: Software, DataRepresentation Author: Carlos Ruiz-Arenas [aut, cre], Carles Hernandez-Ferrer [aut], Juan R. Gonzalez [aut] Maintainer: Carlos Ruiz-Arenas VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultiDataSet git_branch: RELEASE_3_10 git_last_commit: 157af5f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MultiDataSet_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MultiDataSet_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MultiDataSet_1.14.0.tgz vignettes: vignettes/MultiDataSet/inst/doc/MultiDataSet_3party_Integration.html, vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.html, vignettes/MultiDataSet/inst/doc/MultiDataSet.html vignetteTitles: Using MultiDataSet with third party R packages, Adding a new type of data to MultiDataSet objects, Introduction to MultiDataSet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiDataSet/inst/doc/MultiDataSet_3party_Integration.R, vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.R, vignettes/MultiDataSet/inst/doc/MultiDataSet.R dependsOnMe: MEAL importsMe: biosigner, omicRexposome, ropls dependencyCount: 81 Package: multiHiCcompare Version: 1.4.0 Depends: R (>= 3.5.0) Imports: data.table, dplyr, HiCcompare, edgeR, BiocParallel, qqman, pheatmap, methods, metap, GenomicRanges, graphics, stats, utils, pbapply, GenomeInfoDbData, BLMA, GenomeInfoDb Suggests: knitr, rmarkdown, testthat, BiocStyle License: MIT + file LICENSE MD5sum: fcd3c85e612e7f9768f1c9e71079b2d5 NeedsCompilation: no Title: Normalize and detect differences between Hi-C datasets when replicates of each experimental condition are available Description: multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner. biocViews: Software, HiC, Sequencing, Normalization Author: John Stansfield [aut, cre], Mikhail Dozmorov [aut] Maintainer: John Stansfield VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/multiHiCcompare git_branch: RELEASE_3_10 git_last_commit: d05906c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/multiHiCcompare_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/multiHiCcompare_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/multiHiCcompare_1.4.0.tgz vignettes: vignettes/multiHiCcompare/inst/doc/juiceboxVisualization.html, vignettes/multiHiCcompare/inst/doc/multiHiCcompare.html vignetteTitles: Visualizing results in Juicebox, multiHiCcompare Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/multiHiCcompare/inst/doc/juiceboxVisualization.R, vignettes/multiHiCcompare/inst/doc/multiHiCcompare.R suggestsMe: HiCcompare dependencyCount: 170 Package: MultiMed Version: 2.8.0 Depends: R (>= 3.1.0) Suggests: RUnit, BiocGenerics License: GPL (>= 2) + file LICENSE MD5sum: 766c6cbb686bafe486cd61fce4a8a072 NeedsCompilation: no Title: Testing multiple biological mediators simultaneously Description: Implements methods for testing multiple mediators biocViews: MultipleComparison, StatisticalMethod, Software Author: Simina M. Boca, Ruth Heller, Joshua N. Sampson Maintainer: Simina M. Boca git_url: https://git.bioconductor.org/packages/MultiMed git_branch: RELEASE_3_10 git_last_commit: d9a56b6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MultiMed_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MultiMed_2.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MultiMed_2.8.0.tgz vignettes: vignettes/MultiMed/inst/doc/MultiMed.pdf vignetteTitles: MultiMedTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiMed/inst/doc/MultiMed.R dependencyCount: 0 Package: multiMiR Version: 1.8.0 Depends: R (>= 3.4) Imports: stats, XML, RCurl, purrr (>= 0.2.2), tibble (>= 1.2), methods, BiocGenerics, AnnotationDbi, dplyr, Suggests: BiocStyle, edgeR, knitr, rmarkdown, testthat (>= 1.0.2) License: MIT + file LICENSE MD5sum: d104b662b30cf12d7ac493c465476ddc NeedsCompilation: no Title: Integration of multiple microRNA-target databases with their disease and drug associations Description: A collection of microRNAs/targets from external resources, including validated microRNA-target databases (miRecords, miRTarBase and TarBase), predicted microRNA-target databases (DIANA-microT, ElMMo, MicroCosm, miRanda, miRDB, PicTar, PITA and TargetScan) and microRNA-disease/drug databases (miR2Disease, Pharmaco-miR VerSe and PhenomiR). biocViews: miRNAData, Homo_sapiens_Data, Mus_musculus_Data, Rattus_norvegicus_Data, OrganismData Author: Yuanbin Ru [aut], Matt Mulvahill [cre, aut], Spencer Mahaffey [aut], Katerina Kechris [aut, cph, ths] Maintainer: Matt Mulvahill URL: https://github.com/KechrisLab/multiMiR VignetteBuilder: knitr BugReports: https://github.com/KechrisLab/multiMiR/issues git_url: https://git.bioconductor.org/packages/multiMiR git_branch: RELEASE_3_10 git_last_commit: 221900e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/multiMiR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/multiMiR_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/multiMiR_1.8.0.tgz vignettes: vignettes/multiMiR/inst/doc/multiMiR.html vignetteTitles: The multiMiR user's guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/multiMiR/inst/doc/multiMiR.R dependencyCount: 44 Package: multiOmicsViz Version: 1.10.0 Depends: R (>= 3.3.2) Imports: methods, parallel, doParallel, foreach, grDevices, graphics, utils, SummarizedExperiment, stats Suggests: BiocGenerics License: LGPL MD5sum: 33b0803136e1784355cd1e95a7188b43 NeedsCompilation: no Title: Plot the effect of one omics data on other omics data along the chromosome Description: Calculate the spearman correlation between the source omics data and other target omics data, identify the significant correlations and plot the significant correlations on the heat map in which the x-axis and y-axis are ordered by the chromosomal location. biocViews: Software, Visualization, SystemsBiology Author: Jing Wang Maintainer: Jing Wang git_url: https://git.bioconductor.org/packages/multiOmicsViz git_branch: RELEASE_3_10 git_last_commit: 6187cc2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/multiOmicsViz_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/multiOmicsViz_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/multiOmicsViz_1.10.0.tgz vignettes: vignettes/multiOmicsViz/inst/doc/multiOmicsViz.pdf vignetteTitles: multiOmicsViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiOmicsViz/inst/doc/multiOmicsViz.R dependencyCount: 36 Package: multiscan Version: 1.46.0 Depends: R (>= 2.3.0) Imports: Biobase, utils License: GPL (>= 2) MD5sum: 3ab2e55b5268b6914152988362b3c9fa NeedsCompilation: yes Title: R package for combining multiple scans Description: Estimates gene expressions from several laser scans of the same microarray biocViews: Microarray, Preprocessing Author: Mizanur Khondoker , Chris Glasbey, Bruce Worton. Maintainer: Mizanur Khondoker git_url: https://git.bioconductor.org/packages/multiscan git_branch: RELEASE_3_10 git_last_commit: 4972c59 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/multiscan_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/multiscan_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/multiscan_1.46.0.tgz vignettes: vignettes/multiscan/inst/doc/multiscan.pdf vignetteTitles: An R Package for Estimating Gene Expressions using Multiple Scans hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiscan/inst/doc/multiscan.R dependencyCount: 7 Package: multtest Version: 2.42.0 Depends: R (>= 2.10), methods, BiocGenerics, Biobase Imports: survival, MASS, stats4 Suggests: snow License: LGPL MD5sum: 0c1317c032c7c973726464df9211bfc1 NeedsCompilation: yes Title: Resampling-based multiple hypothesis testing Description: Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Katherine S. Pollard, Houston N. Gilbert, Yongchao Ge, Sandra Taylor, Sandrine Dudoit Maintainer: Katherine S. Pollard git_url: https://git.bioconductor.org/packages/multtest git_branch: RELEASE_3_10 git_last_commit: 048f6b6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/multtest_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/multtest_2.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/multtest_2.42.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4Base, aCGH, BicARE, iPAC, KCsmart, LMGene, PREDA, rain, REDseq, SAGx, siggenes, webbioc importsMe: ABarray, aCGH, adSplit, ALDEx2, anota, ChIPpeakAnno, IsoGeneGUI, KnowSeq, mAPKL, metabomxtr, nethet, OCplus, phyloseq, REDseq, RTopper, singleCellTK, synapter, webbioc, xcms suggestsMe: annaffy, BiocCaseStudies, ecolitk, factDesign, GGtools, GOstats, gQTLstats, GSEAlm, maigesPack, MmPalateMiRNA, pcot2, ropls, topGO dependencyCount: 15 Package: muscat Version: 1.0.1 Depends: R (>= 3.6), scater (>= 1.13) Imports: BiocParallel, blme, ComplexHeatmap, data.table, DESeq2, doParallel, dplyr, edgeR, ggplot2, glmmTMB, grDevices, grid, limma, lmerTest, lme4, magrittr, Matrix, matrixStats, methods, parallel, progress, purrr, reshape2, scales, sctransform, stats, SingleCellExperiment, SummarizedExperiment, S4Vectors, tibble, variancePartition, viridis Suggests: BiocStyle, cowplot, ExperimentHub, knitr, RColorBrewer, rmarkdown, testthat, UpSetR License: GPL (>= 2) MD5sum: 879a11f3a2281833cfc43d5bfd1961ef NeedsCompilation: no Title: Multi-sample multi-group scRNA-seq data analysis tools Description: `muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data. biocViews: DifferentialExpression, Sequencing, SingleCell, Software, StatisticalMethod, Visualization Author: Helena L. Crowell [aut, cre], Charlotte Soneson [aut], Pierre-Luc Germain [aut], Mark D. Robinson [aut, fnd] Maintainer: Helena L. Crowell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/muscat git_branch: RELEASE_3_10 git_last_commit: 9202144 git_last_commit_date: 2020-03-31 Date/Publication: 2020-03-31 source.ver: src/contrib/muscat_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/muscat_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/muscat_1.0.1.tgz vignettes: vignettes/muscat/inst/doc/vignette.html vignetteTitles: Untitled hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/muscat/inst/doc/vignette.R dependencyCount: 181 Package: muscle Version: 3.28.0 Depends: Biostrings License: Unlimited Archs: i386, x64 MD5sum: 38e8c4ae4ce1656a92be53b932b5dde8 NeedsCompilation: yes Title: Multiple Sequence Alignment with MUSCLE Description: MUSCLE performs multiple sequence alignments of nucleotide or amino acid sequences. biocViews: MultipleSequenceAlignment, Alignment, Sequencing, Genetics, SequenceMatching, DataImport Author: Algorithm by Robert C. Edgar. R port by Alex T. Kalinka. Maintainer: Alex T. Kalinka URL: http://www.drive5.com/muscle/ git_url: https://git.bioconductor.org/packages/muscle git_branch: RELEASE_3_10 git_last_commit: 82ca783 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/muscle_3.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/muscle_3.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/muscle_3.28.0.tgz vignettes: vignettes/muscle/inst/doc/muscle-vignette.pdf vignetteTitles: A guide to using muscle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/muscle/inst/doc/muscle-vignette.R dependencyCount: 12 Package: MutationalPatterns Version: 1.12.0 Depends: R (>= 3.4.0), GenomicRanges (>= 1.24.0), NMF (>= 0.20.6) Imports: stats, parallel, S4Vectors, BiocGenerics (>= 0.18.0), VariantAnnotation (>= 1.18.1), reshape2 (>= 1.4.1), plyr (>= 1.8.3), ggplot2 (>= 2.1.0), pracma (>= 1.8.8), SummarizedExperiment (>= 1.2.2), IRanges (>= 2.6.0), GenomeInfoDb (>= 1.12.0), Biostrings (>= 2.40.0), ggdendro (>= 0.1-20), cowplot (>= 0.9.2) Suggests: BSgenome (>= 1.40.0), BSgenome.Hsapiens.1000genomes.hs37d5 (>= 0.99.1), BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), BiocStyle (>= 2.0.3), TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2), biomaRt (>= 2.28.0), gridExtra (>= 2.2.1), rtracklayer (>= 1.32.2), testthat License: MIT + file LICENSE Archs: i386, x64 MD5sum: deda3fb884fa97d7dbd2af285afa4890 NeedsCompilation: no Title: Comprehensive genome-wide analysis of mutational processes Description: An extensive toolset for the characterization and visualization of a wide range of mutational patterns in base substitution catalogs. biocViews: Genetics, SomaticMutation Author: Francis Blokzijl, Roel Janssen, Ruben van Boxtel, Edwin Cuppen Maintainer: Roel Janssen , Francis Blokzijl URL: https://doi.org/10.1186/s13073-018-0539-0 git_url: https://git.bioconductor.org/packages/MutationalPatterns git_branch: RELEASE_3_10 git_last_commit: 226bd0a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MutationalPatterns_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MutationalPatterns_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MutationalPatterns_1.12.0.tgz vignettes: vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.pdf vignetteTitles: Introduction to MutationalPatterns hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.R dependencyCount: 130 Package: MVCClass Version: 1.60.0 Depends: R (>= 2.1.0), methods License: LGPL MD5sum: f539cd619b2fc8e209b8d73bd9ac4f6b NeedsCompilation: no Title: Model-View-Controller (MVC) Classes Description: Creates classes used in model-view-controller (MVC) design biocViews: Visualization, Infrastructure, GraphAndNetwork Author: Elizabeth Whalen Maintainer: Elizabeth Whalen git_url: https://git.bioconductor.org/packages/MVCClass git_branch: RELEASE_3_10 git_last_commit: 7540b56 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MVCClass_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MVCClass_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MVCClass_1.60.0.tgz vignettes: vignettes/MVCClass/inst/doc/MVCClass.pdf vignetteTitles: MVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BioMVCClass dependencyCount: 1 Package: MWASTools Version: 1.10.0 Depends: R(>= 3.4) Imports: glm2, ppcor, qvalue, car, boot, grid, ggplot2, gridExtra, igraph, SummarizedExperiment, KEGGgraph, RCurl, KEGGREST, ComplexHeatmap, stats, utils Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown License: CC BY-NC-ND 4.0 Archs: i386, x64 MD5sum: 4f302694636ea418a41584475bcd9d83 NeedsCompilation: no Title: MWASTools: an integrated pipeline to perform metabolome-wide association studies Description: MWASTools provides a complete pipeline to perform metabolome-wide association studies. Key functionalities of the package include: quality control analysis of metabonomic data; MWAS using different association models (partial correlations; generalized linear models); model validation using non-parametric bootstrapping; visualization of MWAS results; NMR metabolite identification using STOCSY; and biological interpretation of MWAS results. biocViews: Metabolomics, Lipidomics, Cheminformatics, SystemsBiology, QualityControl Author: Andrea Rodriguez-Martinez, Joram M. Posma, Rafael Ayala, Ana L. Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas Maintainer: Andrea Rodriguez-Martinez , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MWASTools git_branch: RELEASE_3_10 git_last_commit: 243d7c9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/MWASTools_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/MWASTools_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/MWASTools_1.10.0.tgz vignettes: vignettes/MWASTools/inst/doc/MWASTools.html vignetteTitles: MWASTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MWASTools/inst/doc/MWASTools.R importsMe: MetaboSignal dependencyCount: 139 Package: mygene Version: 1.22.0 Depends: R (>= 3.2.1), GenomicFeatures, Imports: httr (>= 0.3), jsonlite (>= 0.9.7), S4Vectors, Hmisc, sqldf, plyr Suggests: BiocStyle License: Artistic-2.0 MD5sum: a26911dd782f84025dfc7475d62d4c17 NeedsCompilation: no Title: Access MyGene.Info_ services Description: MyGene.Info_ provides simple-to-use REST web services to query/retrieve gene annotation data. It's designed with simplicity and performance emphasized. *mygene*, is an easy-to-use R wrapper to access MyGene.Info_ services. biocViews: Annotation Author: Adam Mark, Ryan Thompson, Cyrus Afrasiabi, Chunlei Wu Maintainer: Adam Mark, Cyrus Afrasiabi, Chunlei Wu git_url: https://git.bioconductor.org/packages/mygene git_branch: RELEASE_3_10 git_last_commit: 40c7415 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mygene_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mygene_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mygene_1.22.0.tgz vignettes: vignettes/mygene/inst/doc/mygene.pdf vignetteTitles: Using mygene.R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mygene/inst/doc/mygene.R importsMe: MetaboSignal dependencyCount: 139 Package: myvariant Version: 1.16.0 Depends: R (>= 3.2.1), VariantAnnotation Imports: httr, jsonlite, S4Vectors, Hmisc, plyr, magrittr, GenomeInfoDb Suggests: BiocStyle License: Artistic-2.0 MD5sum: 364bb66d18ce9b2fecbadeb664d6dfc9 NeedsCompilation: no Title: Accesses MyVariant.info variant query and annotation services Description: MyVariant.info is a comprehensive aggregation of variant annotation resources. myvariant is a wrapper for querying MyVariant.info services biocViews: VariantAnnotation, Annotation, GenomicVariation Author: Adam Mark Maintainer: Adam Mark, Chunlei Wu git_url: https://git.bioconductor.org/packages/myvariant git_branch: RELEASE_3_10 git_last_commit: 472b4d1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/myvariant_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/myvariant_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/myvariant_1.16.0.tgz vignettes: vignettes/myvariant/inst/doc/myvariant.pdf vignetteTitles: Using MyVariant.R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/myvariant/inst/doc/myvariant.R dependencyCount: 137 Package: mzID Version: 1.24.0 Depends: methods Imports: XML, plyr, parallel, doParallel, foreach, iterators, ProtGenerics Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 71469960bbd2b9fc78aade51bf2a27f0 NeedsCompilation: no Title: An mzIdentML parser for R Description: A parser for mzIdentML files implemented using the XML package. The parser tries to be general and able to handle all types of mzIdentML files with the drawback of having less 'pretty' output than a vendor specific parser. Please contact the maintainer with any problems and supply an mzIdentML file so the problems can be fixed quickly. biocViews: ImmunoOncology, DataImport, MassSpectrometry, Proteomics Author: Laurent Gatto [ctb, cre] (), Thomas Pedersen [aut] (), Vladislav Petyuk [ctb] Maintainer: Laurent Gatto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mzID git_branch: RELEASE_3_10 git_last_commit: 25c38b7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mzID_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mzID_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mzID_1.24.0.tgz vignettes: vignettes/mzID/inst/doc/HOWTO_mzID.pdf vignetteTitles: Using mzID hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzID/inst/doc/HOWTO_mzID.R importsMe: MSGFgui, MSGFplus, MSnbase, MSnID, Pbase suggestsMe: mzR dependencyCount: 11 Package: mzR Version: 2.20.0 Depends: Rcpp (>= 0.10.1), methods, utils Imports: Biobase, BiocGenerics (>= 0.13.6), ProtGenerics (>= 1.17.3), ncdf4 LinkingTo: Rcpp, zlibbioc, Rhdf5lib (>= 1.1.4) Suggests: msdata (>= 0.15.1), RUnit, mzID, BiocStyle (>= 2.5.19), knitr, XML License: Artistic-2.0 MD5sum: 721f2e859382e79ff086a0955e55ce2b NeedsCompilation: yes Title: parser for netCDF, mzXML, mzData and mzML and mzIdentML files (mass spectrometry data) Description: mzR provides a unified API to the common file formats and parsers available for mass spectrometry data. It comes with a wrapper for the ISB random access parser for mass spectrometry mzXML, mzData and mzML files. The package contains the original code written by the ISB, and a subset of the proteowizard library for mzML and mzIdentML. The netCDF reading code has previously been used in XCMS. biocViews: ImmunoOncology, Infrastructure, DataImport, Proteomics, Metabolomics, MassSpectrometry Author: Bernd Fischer, Steffen Neumann, Laurent Gatto, Qiang Kou, Johannes Rainer Maintainer: Steffen Neumann , Laurent Gatto , Qiang Kou URL: https://github.com/sneumann/mzR/ SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: https://github.com/sneumann/mzR/issues/ git_url: https://git.bioconductor.org/packages/mzR git_branch: RELEASE_3_10 git_last_commit: a66df47 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/mzR_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/mzR_2.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/mzR_2.20.0.tgz vignettes: vignettes/mzR/inst/doc/mzR.html vignetteTitles: Accessin raw mass spectrometry and identification data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzR/inst/doc/mzR.R dependsOnMe: MSGFgui, MSnbase importsMe: adductomicsR, Autotuner, CluMSID, MSnID, msPurity, Pbase, peakPantheR, ProteomicsAnnotationHubData, SIMAT, topdownr, xcms, yamss suggestsMe: AnnotationHub, qcmetrics dependencyCount: 12 Package: NADfinder Version: 1.10.0 Depends: R (>= 3.4), BiocGenerics, IRanges, GenomicRanges, S4Vectors, SummarizedExperiment Imports: graphics, methods, baseline, signal, GenomicAlignments, GenomeInfoDb, rtracklayer, limma, trackViewer, stats, utils, Rsamtools, metap, EmpiricalBrownsMethod,ATACseqQC, corrplot, csaw Suggests: RUnit, BiocStyle, knitr, BSgenome.Mmusculus.UCSC.mm10, testthat, BiocManager License: GPL (>= 2) MD5sum: 9c6599c342c88e61aebac319ceb1016e NeedsCompilation: no Title: Call wide peaks for sequencing data Description: Nucleolus is an important structure inside the nucleus in eukaryotic cells. It is the site for transcribing rDNA into rRNA and for assembling ribosomes, aka ribosome biogenesis. In addition, nucleoli are dynamic hubs through which numerous proteins shuttle and contact specific non-rDNA genomic loci. Deep sequencing analyses of DNA associated with isolated nucleoli (NAD- seq) have shown that specific loci, termed nucleolus- associated domains (NADs) form frequent three- dimensional associations with nucleoli. NAD-seq has been used to study the biological functions of NAD and the dynamics of NAD distribution during embryonic stem cell (ESC) differentiation. Here, we developed a Bioconductor package NADfinder for bioinformatic analysis of the NAD-seq data, including baseline correction, smoothing, normalization, peak calling, and annotation. biocViews: Sequencing, DNASeq, GeneRegulation, PeakDetection Author: Jianhong Ou, Haibo Liu, Jun Yu, Hervé Pagès, Paul Kaufman, Lihua Julie Zhu Maintainer: Jianhong Ou , Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NADfinder git_branch: RELEASE_3_10 git_last_commit: 6891ac8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/NADfinder_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/NADfinder_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/NADfinder_1.10.0.tgz vignettes: vignettes/NADfinder/inst/doc/NADfinder.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NADfinder/inst/doc/NADfinder.R dependencyCount: 212 Package: NanoStringDiff Version: 1.16.0 Depends: Biobase Imports: matrixStats, methods, Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle License: GPL MD5sum: 9212300147eb95cd82661effc9028744 NeedsCompilation: yes Title: Differential Expression Analysis of NanoString nCounter Data Description: This Package utilizes a generalized linear model(GLM) of the negative binomial family to characterize count data and allows for multi-factor design. NanoStrongDiff incorporate size factors, calculated from positive controls and housekeeping controls, and background level, obtained from negative controls, in the model framework so that all the normalization information provided by NanoString nCounter Analyzer is fully utilized. biocViews: DifferentialExpression, Normalization Author: hong wang , tingting zhai , chi wang Maintainer: tingting zhai ,hong wang git_url: https://git.bioconductor.org/packages/NanoStringDiff git_branch: RELEASE_3_10 git_last_commit: 1f10952 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/NanoStringDiff_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/NanoStringDiff_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/NanoStringDiff_1.16.0.tgz vignettes: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.pdf vignetteTitles: NanoStringDiff Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.R dependencyCount: 9 Package: NanoStringQCPro Version: 1.18.0 Depends: R (>= 3.2), methods Imports: AnnotationDbi (>= 1.26.0), org.Hs.eg.db (>= 2.14.0), Biobase (>= 2.24.0), knitr (>= 1.12), NMF (>= 0.20.5), RColorBrewer (>= 1.0-5), png (>= 0.1-7) Suggests: roxygen2 (>= 4.0.1), testthat, BiocStyle, knitr, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 37d749988c90afcbf718659d9e5e389a NeedsCompilation: no Title: Quality metrics and data processing methods for NanoString mRNA gene expression data Description: NanoStringQCPro provides a set of quality metrics that can be used to assess the quality of NanoString mRNA gene expression data -- i.e. to identify outlier probes and outlier samples. It also provides different background subtraction and normalization approaches for this data. It outputs suggestions for flagging samples/probes and an easily sharable html quality control output. biocViews: Microarray, mRNAMicroarray, Preprocessing, Normalization, QualityControl, ReportWriting Author: Dorothee Nickles , Thomas Sandmann , Robert Ziman , Richard Bourgon Maintainer: Robert Ziman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoStringQCPro git_branch: RELEASE_3_10 git_last_commit: f862b69 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/NanoStringQCPro_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/NanoStringQCPro_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/NanoStringQCPro_1.18.0.tgz vignettes: vignettes/NanoStringQCPro/inst/doc/vignetteNanoStringQCPro.pdf vignetteTitles: vignetteNanoStringQCPro.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 94 Package: nanotatoR Version: 1.2.0 Depends: R (>= 3.6) Imports: hash(>= 2.2.6), openxlsx(>= 4.0.17), rentrez(>= 1.1.0), stats, grDevices, graphics, stringr, knitr, testthat, utils, AnnotationDbi, httr, org.Hs.eg.db, rtracklayer Suggests: rmarkdown, yaml License: file LICENSE MD5sum: 28ea2320bf7525a940a8006ac84bfa21 NeedsCompilation: no Title: nanotatoR: next generation structural variant annotation and classification Description: Whole genome sequencing (WGS) has successfully been used to identify single-nucleotide variants (SNV), small insertions and deletions and, more recently, small copy number variants. However, due to utilization of short reads, it is not well suited for identification of structural variants (SV) and the majority of SV calling tools having high false positive and negative rates.Optical next-generation mapping (NGM) utilizes long fluorescently labeled native-state DNA molecules for de novo genome assembly to overcome the limitations of WGS. NGM allows for a significant increase in SV detection capability. However, accuracy of SV annotation is highly important for variant classification and filtration to determine pathogenicity.Here we create a new tool in R, for SV annotation, including population frequency and gene function and expression, using publicly available datasets. We use DGV (Database of Genomic Variants), to calculate the population frequency of the SVs identified by the Bionano SVCaller in the NGM dataset of a cohort of 50 undiagnosed patients with a variety of phenotypes. The new annotation tool, nanotatoR, also calculates the internal frequency of SVs, which could be beneficial in identification of potential false positive or common calls. The software creates a primary gene list (PG) from NCBI databases based on patient phenotype and compares the list to the set of genes overlapping the patient’s SVs generated by SVCaller, providing analysts with an easy way to identify variants affecting genes in the PG. The output is given in an Excel file format, which is subdivided into multiple sheets based on SV type. Users then have a choice to filter SVs using the provided annotation for identification of inherited, de novo or rare variants. nanotatoR provides integrated annotation and the expression patterns to enable users to identify potential pathogenic SVs with greater precision and faster turnaround times. biocViews: Software, WorkflowStep, GenomeAssembly, VariantAnnotation Author: Surajit Bhattacharya,Hayk Barsheghyan, Emmanuele C Delot and Eric Vilain Maintainer: Surajit Bhattacharya URL: https://github.com/VilainLab/Nanotator VignetteBuilder: knitr BugReports: https://github.com/VilainLab/Nanotator/issues git_url: https://git.bioconductor.org/packages/nanotatoR git_branch: RELEASE_3_10 git_last_commit: 274d1b2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/nanotatoR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/nanotatoR_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/nanotatoR_1.2.0.tgz vignettes: vignettes/nanotatoR/inst/doc/nanotatoR.html vignetteTitles: nanotatoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/nanotatoR/inst/doc/nanotatoR.R dependencyCount: 92 Package: NarrowPeaks Version: 1.30.0 Depends: R (>= 2.10.0), splines Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, GenomeInfoDb, fda, CSAR, ICSNP Suggests: rtracklayer, BiocStyle, GenomicRanges, CSAR License: Artistic-2.0 Archs: i386, x64 MD5sum: f7d362205e7b0a8edfc32f42704a027c NeedsCompilation: yes Title: Shape-based Analysis of Variation in ChIP-seq using Functional PCA Description: The package applies a functional version of principal component analysis (FPCA) to: (1) Postprocess data in wiggle track format, commonly produced by generic ChIP-seq peak callers, by applying FPCA over a set of read-enriched regions (ChIP-seq peaks). This is done to study variability of the the peaks, or to shorten their genomic locations accounting for a given proportion of variation among the enrichment-score profiles. (2) Analyse differential variation between multiple ChIP-seq samples with replicates. The function 'narrowpeaksDiff' quantifies differences between the shapes, and uses Hotelling's T2 tests on the functional principal component scores to identify significant differences across conditions. An application of the package for Arabidopsis datasets is described in Mateos, Madrigal, et al. (2015) Genome Biology: 16:31. biocViews: Visualization, ChIPSeq, Transcription, Genetics, Sequencing, Sequencing Author: Pedro Madrigal , Pawel Krajewski Maintainer: Pedro Madrigal git_url: https://git.bioconductor.org/packages/NarrowPeaks git_branch: RELEASE_3_10 git_last_commit: 8feaaa8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/NarrowPeaks_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/NarrowPeaks_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/NarrowPeaks_1.30.0.tgz vignettes: vignettes/NarrowPeaks/inst/doc/NarrowPeaks.pdf vignetteTitles: NarrowPeaks Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NarrowPeaks/inst/doc/NarrowPeaks.R dependencyCount: 33 Package: NBAMSeq Version: 1.2.1 Depends: R (>= 3.6), SummarizedExperiment, S4Vectors Imports: DESeq2, mgcv(>= 1.8-24), BiocParallel, genefilter, methods, stats, Suggests: knitr, rmarkdown, testthat, ggplot2 License: GPL-2 MD5sum: 3ca1118a825c74c270f34654a2261c38 NeedsCompilation: no Title: Negative Binomial Additive Model for RNA-Seq Data Description: High-throughput sequencing experiments followed by differential expression analysis is a widely used approach to detect genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. NBAMSeq a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation. biocViews: RNASeq, DifferentialExpression, GeneExpression, Sequencing, Coverage Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/NBAMSeq VignetteBuilder: knitr BugReports: https://github.com/reese3928/NBAMSeq/issues git_url: https://git.bioconductor.org/packages/NBAMSeq git_branch: RELEASE_3_10 git_last_commit: 27a036c git_last_commit_date: 2020-02-18 Date/Publication: 2020-02-18 source.ver: src/contrib/NBAMSeq_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/NBAMSeq_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/NBAMSeq_1.2.1.tgz vignettes: vignettes/NBAMSeq/inst/doc/NBAMSeq-vignette.html vignetteTitles: Negative Binomial Additive Model for RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NBAMSeq/inst/doc/NBAMSeq-vignette.R dependencyCount: 121 Package: NBSplice Version: 1.4.0 Depends: R (>= 3.5), methods Imports: edgeR, stats, MASS, car, mppa, BiocParallel, ggplot2, reshape2 Suggests: knitr, RUnit, BiocGenerics License: GPL (>=2) MD5sum: b955adacfd44c949df6b6edbbe1c0a22 NeedsCompilation: no Title: Negative Binomial Models to detect Differential Splicing Description: The package proposes a differential splicing evaluation method based on isoform quantification. It applies generalized linear models with negative binomial distribution to infer changes in isoform relative expression. biocViews: Software, StatisticalMethod, AlternativeSplicing, Regression, DifferentialExpression, DifferentialSplicing, RNASeq, ImmunoOncology Author: Gabriela A. Merino and Elmer A. Fernandez Maintainer: Gabriela Merino URL: http://www.bdmg.com.ar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NBSplice git_branch: RELEASE_3_10 git_last_commit: eea7609 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/NBSplice_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/NBSplice_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/NBSplice_1.4.0.tgz vignettes: vignettes/NBSplice/inst/doc/NBSplice-vignette.html vignetteTitles: NBSplice-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NBSplice/inst/doc/NBSplice-vignette.R dependencyCount: 103 Package: ncdfFlow Version: 2.32.0 Depends: R (>= 2.14.0), flowCore(>= 1.51.7), RcppArmadillo, methods, BH Imports: Biobase,BiocGenerics,flowCore,zlibbioc LinkingTo: Rcpp,RcppArmadillo,BH, Rhdf5lib Suggests: testthat,parallel,flowStats License: Artistic-2.0 Archs: i386, x64 MD5sum: eb3d4b57a226897083436bc2807f40d8 NeedsCompilation: yes Title: ncdfFlow: A package that provides HDF5 based storage for flow cytometry data. Description: Provides HDF5 storage based methods and functions for manipulation of flow cytometry data. biocViews: ImmunoOncology, FlowCytometry Author: Mike Jiang,Greg Finak,N. Gopalakrishnan Maintainer: Mike Jiang git_url: https://git.bioconductor.org/packages/ncdfFlow git_branch: RELEASE_3_10 git_last_commit: 1c01749 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ncdfFlow_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ncdfFlow_2.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ncdfFlow_2.32.0.tgz vignettes: vignettes/ncdfFlow/inst/doc/ncdfFlow.pdf vignetteTitles: Basic Functions for Flow Cytometry Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncdfFlow/inst/doc/ncdfFlow.R dependsOnMe: ggcyto importsMe: CytoML, flowStats, flowWorkspace suggestsMe: COMPASS, cydar dependencyCount: 17 Package: ncGTW Version: 1.0.0 Depends: methods, BiocParallel, xcms Imports: Rcpp, grDevices, graphics, stats LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat, rmarkdown License: GPL-2 MD5sum: b2877541ca5cc088ab48053a8e924cff NeedsCompilation: yes Title: Alignment of LC-MS Profiles by Neighbor-wise Compound-specific Graphical Time Warping with Misalignment Detection Description: The purpose of ncGTW is to help XCMS for LC-MS data alignment. Currently, ncGTW can detect the misaligned feature groups by XCMS, and the user can choose to realign these feature groups by ncGTW or not. biocViews: Software, MassSpectrometry, Metabolomics, Alignment Author: Chiung-Ting Wu Maintainer: Chiung-Ting Wu VignetteBuilder: knitr BugReports: https://github.com/ChiungTingWu/ncGTW/issues git_url: https://git.bioconductor.org/packages/ncGTW git_branch: RELEASE_3_10 git_last_commit: 66bafd5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ncGTW_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ncGTW_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ncGTW_1.0.0.tgz vignettes: vignettes/ncGTW/inst/doc/ncGTW.html vignetteTitles: ncGTW User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncGTW/inst/doc/ncGTW.R dependencyCount: 97 Package: NCIgraph Version: 1.34.0 Depends: R (>= 2.10.0) Imports: graph, KEGGgraph, methods, RBGL, RCy3, R.methodsS3 Suggests: Rgraphviz Enhances: DEGraph License: GPL-3 MD5sum: 66753fdfb17e84c6971fda51365eb5b9 NeedsCompilation: no Title: Pathways from the NCI Pathways Database Description: Provides various methods to load the pathways from the NCI Pathways Database in R graph objects and to re-format them. biocViews: Pathways, GraphAndNetwork Author: Laurent Jacob Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/NCIgraph git_branch: RELEASE_3_10 git_last_commit: 77331da git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/NCIgraph_1.34.0.tar.gz vignettes: vignettes/NCIgraph/inst/doc/NCIgraph.pdf vignetteTitles: NCIgraph: networks from the NCI pathway integrated database as graphNEL objects. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NCIgraph/inst/doc/NCIgraph.R importsMe: DEGraph suggestsMe: DEGraph dependencyCount: 35 Package: ndexr Version: 1.8.0 Depends: igraph Imports: httr, jsonlite, plyr, tidyr Suggests: BiocStyle, testthat, knitr, rmarkdown License: BSD MD5sum: b85877e94568499759bab8bfd14336e8 NeedsCompilation: no Title: NDEx R client library Description: This package offers an interface to NDEx servers, e.g. the public server at http://ndexbio.org/. It can retrieve and save networks via the API. Networks are offered as RCX object and as igraph representation. biocViews: Pathways, DataImport, Network Author: Florian Auer , Frank Kramer , Alex Ishkin , Dexter Pratt Maintainer: Florian Auer URL: https://github.com/frankkramer-lab/ndexr VignetteBuilder: knitr BugReports: https://github.com/frankkramer-lab/ndexr/issues git_url: https://git.bioconductor.org/packages/ndexr git_branch: RELEASE_3_10 git_last_commit: 3c35165 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ndexr_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ndexr_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ndexr_1.8.0.tgz vignettes: vignettes/ndexr/inst/doc/ndexr-vignette.html vignetteTitles: NDExR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ndexr/inst/doc/ndexr-vignette.R dependencyCount: 42 Package: NeighborNet Version: 1.4.0 Depends: methods Imports: graph, stats License: CC BY-NC-ND 4.0 Archs: i386, x64 MD5sum: 8fc20e833ed7e61210bc572bcff1fb2d NeedsCompilation: no Title: Neighbor_net analysis Description: Identify the putative mechanism explaining the active interactions between genes in the investigated phenotype. biocViews: Software, GeneExpression, StatisticalMethod, GraphAndNetwork Author: Sahar Ansari and Sorin Draghici Maintainer: Sahar Ansari git_url: https://git.bioconductor.org/packages/NeighborNet git_branch: RELEASE_3_10 git_last_commit: b7c0e63 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/NeighborNet_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/NeighborNet_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/NeighborNet_1.4.0.tgz vignettes: vignettes/NeighborNet/inst/doc/neighborNet.pdf vignetteTitles: NeighborNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NeighborNet/inst/doc/neighborNet.R dependencyCount: 8 Package: nem Version: 2.60.0 Depends: R (>= 3.0) Imports: boot, e1071, graph, graphics, grDevices, methods, RBGL (>= 1.8.1), RColorBrewer, stats, utils, Rgraphviz, statmod, plotrix, limma Suggests: Biobase (>= 1.10) Enhances: doMC, snow, parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: 48a59d2b81ff8dcac7dadf89eebdd395 NeedsCompilation: yes Title: (Dynamic) Nested Effects Models and Deterministic Effects Propagation Networks to reconstruct phenotypic hierarchies Description: The package 'nem' allows to reconstruct features of pathways from the nested structure of perturbation effects. It takes as input (1.) a set of pathway components, which were perturbed, and (2.) phenotypic readout of these perturbations (e.g. gene expression, protein expression). The output is a directed graph representing the phenotypic hierarchy. biocViews: Microarray, Bioinformatics, GraphsAndNetworks, Pathways, SystemsBiology, NetworkInference Author: Holger Froehlich, Florian Markowetz, Achim Tresch, Theresa Niederberger, Christian Bender, Matthias Maneck, Claudio Lottaz, Tim Beissbarth Maintainer: Holger Froehlich URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/nem git_branch: RELEASE_3_10 git_last_commit: ded87f8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/nem_2.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/nem_2.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/nem_2.60.0.tgz vignettes: vignettes/nem/inst/doc/markowetz-thesis-2006.pdf, vignettes/nem/inst/doc/nem.pdf vignetteTitles: markowetz-thesis-2006.pdf, Nested Effects Models - An example in Drosophila immune response hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nem/inst/doc/nem.R dependsOnMe: lpNet importsMe: epiNEM, mnem, OncoSimulR, birte suggestsMe: rBiopaxParser dependencyCount: 21 Package: netbenchmark Version: 1.18.0 Depends: grndata (>= 0.99.3) Imports: Rcpp (>= 0.11.0), minet, GENIE3, c3net, PCIT, GeneNet, tools, pracma, Matrix, corpcor, fdrtool LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, knitr, graph License: CC BY-NC-SA 4.0 MD5sum: e86e6d1e708f4512437f57b0a8867bbd NeedsCompilation: yes Title: Benchmarking of several gene network inference methods Description: This package implements a benchmarking of several gene network inference algorithms from gene expression data. biocViews: Microarray, GraphAndNetwork, Network, NetworkInference, GeneExpression Author: Pau Bellot, Catharina Olsen, Patrick Meyer Maintainer: Pau Bellot URL: https://imatge.upc.edu/netbenchmark/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/netbenchmark git_branch: RELEASE_3_10 git_last_commit: 68b2e30 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/netbenchmark_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/netbenchmark_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/netbenchmark_1.18.0.tgz vignettes: vignettes/netbenchmark/inst/doc/netbenchmark.html vignetteTitles: Netbenchmark hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/netbenchmark/inst/doc/netbenchmark.R dependencyCount: 29 Package: netbiov Version: 1.20.0 Depends: R (>= 3.1.0), igraph (>= 0.7.1) Suggests: BiocStyle,RUnit,BiocGenerics,Matrix License: GPL (>= 2) MD5sum: 1ce344902d6354bf9b09a75924544e8f NeedsCompilation: no Title: A package for visualizing complex biological network Description: A package that provides an effective visualization of large biological networks biocViews: GraphAndNetwork, Network, Software, Visualization Author: Shailesh tripathi and Frank Emmert-Streib Maintainer: Shailesh tripathi URL: http://www.bio-complexity.com git_url: https://git.bioconductor.org/packages/netbiov git_branch: RELEASE_3_10 git_last_commit: 2ad8567 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/netbiov_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/netbiov_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/netbiov_1.20.0.tgz vignettes: vignettes/netbiov/inst/doc/netbiov-intro.pdf vignetteTitles: netbiov: An R package for visualizing biological networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netbiov/inst/doc/netbiov-intro.R dependencyCount: 11 Package: netboost Version: 1.2.2 Depends: R (>= 3.6.0) Imports: Rcpp, RcppParallel, parallel, grDevices, graphics, stats, utils, dynamicTreeCut, WGCNA, impute, colorspace, methods, R.utils LinkingTo: Rcpp, RcppParallel Suggests: knitr License: GPL-3 OS_type: unix MD5sum: 0a16d26871c90410aae618e267443cfe NeedsCompilation: yes Title: Network Analysis Supported by Boosting Description: Boosting supported network analysis for high-dimensional omics applications. This package comes bundled with the MC-UPGMA clustering package by Yaniv Loewenstein. biocViews: Software, StatisticalMethod, GraphAndNetwork, Network, Clustering, DimensionReduction, BiomedicalInformatics, Epigenetics, Metabolomics, Transcriptomics Author: Pascal Schlosser [aut, cre], Jochen Knaus [aut, ctb], Yaniv Loewenstein [aut] Maintainer: Pascal Schlosser URL: https://bioconductor.org/packages/release/bioc/html/netboost.html SystemRequirements: GNU make, Bash, Perl, Gzip VignetteBuilder: knitr BugReports: https://github.com/PascalSchlosser/netboost/issues git_url: https://git.bioconductor.org/packages/netboost git_branch: RELEASE_3_10 git_last_commit: 86ac4bd git_last_commit_date: 2019-11-27 Date/Publication: 2019-11-27 source.ver: src/contrib/netboost_1.2.2.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/netboost_1.2.2.tgz vignettes: vignettes/netboost/inst/doc/netboost.html vignetteTitles: The Netboost users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netboost/inst/doc/netboost.R dependencyCount: 112 Package: nethet Version: 1.18.0 Imports: glasso, mvtnorm, parcor, GeneNet, huge, CompQuadForm, ggm, mclust, parallel, GSA, limma, multtest, ICSNP, glmnet, network, ggplot2, grDevices, graphics, stats, utils Suggests: knitr, xtable, BiocStyle, testthat License: GPL-2 Archs: i386, x64 MD5sum: b3565a8d5944b393643b904e2afeb7b9 NeedsCompilation: yes Title: A bioconductor package for high-dimensional exploration of biological network heterogeneity Description: Package nethet is an implementation of statistical solid methodology enabling the analysis of network heterogeneity from high-dimensional data. It combines several implementations of recent statistical innovations useful for estimation and comparison of networks in a heterogeneous, high-dimensional setting. In particular, we provide code for formal two-sample testing in Gaussian graphical models (differential network and GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel network-based clustering algorithm available (mixed graphical lasso, Stadler and Mukherjee, 2013). biocViews: Clustering, GraphAndNetwork Author: Nicolas Staedler, Frank Dondelinger Maintainer: Nicolas Staedler , Frank Dondelinger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nethet git_branch: RELEASE_3_10 git_last_commit: a7e4ace git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/nethet_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/nethet_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/nethet_1.18.0.tgz vignettes: vignettes/nethet/inst/doc/nethet.pdf vignetteTitles: nethet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nethet/inst/doc/nethet.R dependencyCount: 97 Package: NetPathMiner Version: 1.22.0 Depends: R (>= 3.0.2), igraph (>= 1.0) Suggests: rBiopaxParser (>= 2.1), RCurl, graph, knitr, rmarkdown, BiocStyle License: GPL (>= 2) Archs: i386, x64 MD5sum: 5ab050356ecc71b57399bc3bccc066ab NeedsCompilation: yes Title: NetPathMiner for Biological Network Construction, Path Mining and Visualization Description: NetPathMiner is a general framework for network path mining using genome-scale networks. It constructs networks from KGML, SBML and BioPAX files, providing three network representations, metabolic, reaction and gene representations. NetPathMiner finds active paths and applies machine learning methods to summarize found paths for easy interpretation. It also provides static and interactive visualizations of networks and paths to aid manual investigation. biocViews: GraphAndNetwork, Pathways, Network, Clustering, Classification Author: Ahmed Mohamed , Tim Hancock , Ichigaku Takigawa , Nicolas Wicker Maintainer: Ahmed Mohamed URL: https://github.com/ahmohamed/NetPathMiner SystemRequirements: libxml2, libSBML (>= 5.5) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetPathMiner git_branch: RELEASE_3_10 git_last_commit: c6d0baa git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/NetPathMiner_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/NetPathMiner_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/NetPathMiner_1.22.0.tgz vignettes: vignettes/NetPathMiner/inst/doc/NPMVignette.html vignetteTitles: NetPathMiner Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetPathMiner/inst/doc/NPMVignette.R dependencyCount: 11 Package: netprioR Version: 1.12.0 Depends: methods, graphics, R(>= 3.3) Imports: stats, Matrix, dplyr, doParallel, foreach, parallel, sparseMVN, ggplot2, gridExtra, pROC Suggests: knitr, BiocStyle, pander License: GPL-3 MD5sum: 839dacbea48b1548176dc22f26706356 NeedsCompilation: no Title: A model for network-based prioritisation of genes Description: A model for semi-supervised prioritisation of genes integrating network data, phenotypes and additional prior knowledge about TP and TN gene labels from the literature or experts. biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, Network Author: Fabian Schmich Maintainer: Fabian Schmich URL: http://bioconductor.org/packages/netprioR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/netprioR git_branch: RELEASE_3_10 git_last_commit: ea16fc7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/netprioR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/netprioR_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/netprioR_1.12.0.tgz vignettes: vignettes/netprioR/inst/doc/netprioR.html vignetteTitles: netprioR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netprioR/inst/doc/netprioR.R dependencyCount: 68 Package: netReg Version: 1.10.0 Depends: R(>= 3.4) Imports: Rcpp, stats LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, BiocStyle, lintr, lassoshooting License: GPL-3 | BSL-1.0 + file LICENSE Archs: i386, x64 MD5sum: 7033d29b3af78f45f287a3776619c079 NeedsCompilation: yes Title: Network-Regularized Regression Models Description: netReg fits linear regression models using network-penalization. Graph prior knowledge, in the form of biological networks, is being incorporated into the loss function of the linear model. The networks describe biological relationships such as co-regulation or dependency of the same transcription factors/metabolites/etc. yielding a part sparse and part smooth solution for coefficient profiles. biocViews: Software, StatisticalMethod, Regression, FeatureExtraction, Network, GraphAndNetwork Author: Simon Dirmeier [aut, cre] Maintainer: Simon Dirmeier URL: https://github.com/dirmeier/netReg SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/dirmeier/netReg/issues git_url: https://git.bioconductor.org/packages/netReg git_branch: RELEASE_3_10 git_last_commit: 8a63f73 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/netReg_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/netReg_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/netReg_1.10.0.tgz vignettes: vignettes/netReg/inst/doc/netReg.html vignetteTitles: netReg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/netReg/inst/doc/netReg.R dependencyCount: 5 Package: netresponse Version: 1.46.0 Depends: R (>= 2.15.1), Rgraphviz, methods, minet, mclust, reshape2 Imports: dmt, ggplot2, graph, igraph, parallel, plyr, qvalue, RColorBrewer License: GPL (>=2) Archs: i386, x64 MD5sum: 22ac6fb3d2b56b930e879c97c40f4da4 NeedsCompilation: yes Title: Functional Network Analysis Description: Algorithms for functional network analysis. Includes an implementation of a variational Dirichlet process Gaussian mixture model for nonparametric mixture modeling. biocViews: CellBiology, Clustering, GeneExpression, Genetics, Network, GraphAndNetwork, DifferentialExpression, Microarray, Transcription Author: Leo Lahti, Olli-Pekka Huovilainen, Antonio Gusmao and Juuso Parkkinen Maintainer: Leo Lahti URL: https://github.com/antagomir/netresponse BugReports: https://github.com/antagomir/netresponse/issues git_url: https://git.bioconductor.org/packages/netresponse git_branch: RELEASE_3_10 git_last_commit: c9b4039 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/netresponse_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/netresponse_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/netresponse_1.46.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 70 Package: NetSAM Version: 1.26.0 Depends: R (>= 2.15.1), methods, igraph (>= 0.6-1), seriation (>= 1.0-6), graph (>= 1.34.0) Imports: methods Suggests: RUnit, BiocGenerics License: LGPL MD5sum: 4cfe1f447691f48421c87412a42740b0 NeedsCompilation: no Title: Network Seriation And Modularization Description: The NetSAM (Network Seriation and Modularization) package takes an edge-list representation of a network as an input, performs network seriation and modularization analysis, and generates as files that can be used as an input for the one-dimensional network visualization tool NetGestalt (http://www.netgestalt.org) or other network analysis. biocViews: Visualization, Network Author: Jing Wang Maintainer: Bing Zhang git_url: https://git.bioconductor.org/packages/NetSAM git_branch: RELEASE_3_10 git_last_commit: 16896fb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/NetSAM_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/NetSAM_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/NetSAM_1.26.0.tgz vignettes: vignettes/NetSAM/inst/doc/NetSAM.pdf vignetteTitles: NetSAM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetSAM/inst/doc/NetSAM.R dependencyCount: 78 Package: netSmooth Version: 1.6.0 Depends: R (>= 3.5), scater (>= 1.9.20), clusterExperiment (>= 2.1.6) Imports: entropy, SummarizedExperiment, SingleCellExperiment, Matrix, cluster, data.table, stats, methods, DelayedArray, HDF5Array Suggests: knitr, testthat, Rtsne, biomaRt, igraph, STRINGdb, NMI, pheatmap, ggplot2, BiocStyle, rmarkdown, BiocParallel, uwot License: GPL-3 Archs: i386, x64 MD5sum: 84f44df4e5aafaa64bf27fd929634dfd NeedsCompilation: no Title: Network smoothing for scRNAseq Description: netSmooth is an R package for network smoothing of single cell RNA sequencing data. Using bio networks such as protein-protein interactions as priors for gene co-expression, netsmooth improves cell type identification from noisy, sparse scRNAseq data. biocViews: Network, GraphAndNetwork, SingleCell, RNASeq, GeneExpression, Sequencing, Transcriptomics, Normalization, Preprocessing, Clustering, DimensionReduction Author: Jonathan Ronen [aut, cre], Altuna Akalin [aut] Maintainer: Jonathan Ronen URL: https://github.com/BIMSBbioinfo/netSmooth VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/netSmooth/issues git_url: https://git.bioconductor.org/packages/netSmooth git_branch: RELEASE_3_10 git_last_commit: 7b74eac git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/netSmooth_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/netSmooth_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/netSmooth_1.6.0.tgz vignettes: vignettes/netSmooth/inst/doc/buildingPPIsFromStringDB.html, vignettes/netSmooth/inst/doc/netSmoothIntro.html vignetteTitles: Generation of PPI graph, netSmooth example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netSmooth/inst/doc/buildingPPIsFromStringDB.R, vignettes/netSmooth/inst/doc/netSmoothIntro.R dependencyCount: 161 Package: networkBMA Version: 2.26.0 Depends: R (>= 2.15.0), stats, utils, BMA, Rcpp (>= 0.10.3), RcppArmadillo (>= 0.3.810.2), RcppEigen (>= 0.3.1.2.1), leaps LinkingTo: Rcpp, RcppArmadillo, RcppEigen, BH License: GPL (>= 2) Archs: i386, x64 MD5sum: 336f4d0a9905d3ab71d892d92ad2081b NeedsCompilation: yes Title: Regression-based network inference using Bayesian Model Averaging Description: An extension of Bayesian Model Averaging (BMA) for network construction using time series gene expression data. Includes assessment functions and sample test data. biocViews: GraphsAndNetwork, NetworkInference, GeneExpression, GeneTarget, Network, Bayesian Author: Chris Fraley, Wm. Chad Young, Ling-Hong Hung, Kaiyuan Shi, Ka Yee Yeung, Adrian Raftery (with contributions from Kenneth Lo) Maintainer: Ka Yee Yeung SystemRequirements: liblapack-dev git_url: https://git.bioconductor.org/packages/networkBMA git_branch: RELEASE_3_10 git_last_commit: e8f23a7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/networkBMA_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/networkBMA_2.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/networkBMA_2.26.0.tgz vignettes: vignettes/networkBMA/inst/doc/networkBMA.pdf vignetteTitles: networkBMA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/networkBMA/inst/doc/networkBMA.R dependencyCount: 23 Package: ngsReports Version: 1.2.0 Depends: R (>= 3.6.0), BiocGenerics, ggplot2, tibble (>= 1.3.1) Imports: Biostrings, checkmate, dplyr (>= 0.8.0), FactoMineR, ggdendro, grDevices, grid, kableExtra, lubridate, methods, pander, parallel, plotly, readr, reshape2, rmarkdown, Rsamtools, scales, ShortRead, stats, stringr, tidyr, tidyselect (>= 0.2.3), truncnorm, utils, viridisLite, XVector, zoo Suggests: BiocStyle, Cairo, knitr, testthat License: file LICENSE MD5sum: e243eed10e89cbc863afd2f4e5224111 NeedsCompilation: no Title: Load FastqQC reports and other NGS related files Description: This package provides methods and object classes for parsing FastQC reports and output summaries from other NGS tools into R, as well as visualising the data loaded from these files. biocViews: QualityControl, ReportWriting Author: Steve Pederson [aut, cre], Christopher Ward [aut], Thu-Hien To [aut] Maintainer: Steve Pederson URL: https://github.com/UofABioinformaticsHub/ngsReports VignetteBuilder: knitr BugReports: https://github.com/UofABioinformaticsHub/ngsReports/issues git_url: https://git.bioconductor.org/packages/ngsReports git_branch: RELEASE_3_10 git_last_commit: 9fe5358 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ngsReports_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ngsReports_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ngsReports_1.2.0.tgz vignettes: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.html vignetteTitles: An Introduction To ngsReports hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.R dependencyCount: 167 Package: nnNorm Version: 2.50.0 Depends: R(>= 2.2.0), marray Imports: graphics, grDevices, marray, methods, nnet, stats License: LGPL MD5sum: 3bfd80c05fc7efa7de30a13519cd96c7 NeedsCompilation: no Title: Spatial and intensity based normalization of cDNA microarray data based on robust neural nets Description: This package allows to detect and correct for spatial and intensity biases with two-channel microarray data. The normalization method implemented in this package is based on robust neural networks fitting. biocViews: Microarray, TwoChannel, Preprocessing Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/tarca/ git_url: https://git.bioconductor.org/packages/nnNorm git_branch: RELEASE_3_10 git_last_commit: fb8dcea git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/nnNorm_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/nnNorm_2.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/nnNorm_2.50.0.tgz vignettes: vignettes/nnNorm/inst/doc/nnNorm.pdf vignetteTitles: nnNorm Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nnNorm/inst/doc/nnNorm.R dependencyCount: 8 Package: NOISeq Version: 2.30.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.13.11), splines (>= 3.0.1), Matrix (>= 1.2) License: Artistic-2.0 Archs: i386, x64 MD5sum: c3222f5a6a1000f7443e7d9039140f34 NeedsCompilation: no Title: Exploratory analysis and differential expression for RNA-seq data Description: Analysis of RNA-seq expression data or other similar kind of data. Exploratory plots to evualuate saturation, count distribution, expression per chromosome, type of detected features, features length, etc. Differential expression between two experimental conditions with no parametric assumptions. biocViews: ImmunoOncology, RNASeq, DifferentialExpression, Visualization, Sequencing Author: Sonia Tarazona, Pedro Furio-Tari, Maria Jose Nueda, Alberto Ferrer and Ana Conesa Maintainer: Sonia Tarazona git_url: https://git.bioconductor.org/packages/NOISeq git_branch: RELEASE_3_10 git_last_commit: 596c7f2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/NOISeq_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/NOISeq_2.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/NOISeq_2.30.0.tgz vignettes: vignettes/NOISeq/inst/doc/NOISeq.pdf, vignettes/NOISeq/inst/doc/QCreport.pdf vignetteTitles: NOISeq User's Guide, QCreport.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NOISeq/inst/doc/NOISeq.R dependsOnMe: metaSeq importsMe: CNVPanelizer, metaseqR suggestsMe: compcodeR dependencyCount: 12 Package: nondetects Version: 2.16.0 Depends: R (>= 3.2), Biobase (>= 2.22.0) Imports: limma, mvtnorm, utils, methods, arm, HTqPCR (>= 1.16.0) Suggests: knitr, rmarkdown, BiocStyle (>= 1.0.0), RUnit, BiocGenerics (>= 0.8.0) License: GPL-3 MD5sum: b344491cb7eb3939b58de492e073912e NeedsCompilation: no Title: Non-detects in qPCR data Description: Methods to model and impute non-detects in the results of qPCR experiments. biocViews: Software, AssayDomain, GeneExpression, Technology, qPCR, WorkflowStep, Preprocessing Author: Matthew N. McCall , Valeriia Sherina Maintainer: Valeriia Sherina VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nondetects git_branch: RELEASE_3_10 git_last_commit: 736c983 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/nondetects_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/nondetects_2.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/nondetects_2.16.0.tgz vignettes: vignettes/nondetects/inst/doc/nondetects.html vignetteTitles: Title of your vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nondetects/inst/doc/nondetects.R dependencyCount: 40 Package: normalize450K Version: 1.14.0 Depends: R (>= 3.3), Biobase, illuminaio, quadprog Imports: utils License: BSD_2_clause + file LICENSE MD5sum: ab26e3c7547c2db7606952b0586192bb NeedsCompilation: no Title: Preprocessing of Illumina Infinium 450K data Description: Precise measurements are important for epigenome-wide studies investigating DNA methylation in whole blood samples, where effect sizes are expected to be small in magnitude. The 450K platform is often affected by batch effects and proper preprocessing is recommended. This package provides functions to read and normalize 450K '.idat' files. The normalization corrects for dye bias and biases related to signal intensity and methylation of probes using local regression. No adjustment for probe type bias is performed to avoid the trade-off of precision for accuracy of beta-values. biocViews: Normalization, DNAMethylation, Microarray, TwoChannel, Preprocessing, MethylationArray Author: Jonathan Alexander Heiss Maintainer: Jonathan Alexander Heiss git_url: https://git.bioconductor.org/packages/normalize450K git_branch: RELEASE_3_10 git_last_commit: 683daf6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/normalize450K_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/normalize450K_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/normalize450K_1.14.0.tgz vignettes: vignettes/normalize450K/inst/doc/read_and_normalize450K.pdf vignetteTitles: Normalization of 450K data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/normalize450K/inst/doc/read_and_normalize450K.R dependencyCount: 13 Package: NormalyzerDE Version: 1.4.0 Depends: R (>= 3.6) Imports: vsn, preprocessCore, limma, MASS, ape, car, ggplot2, methods, Biobase, RcmdrMisc, raster, utils, stats, SummarizedExperiment, matrixStats, ggforce Suggests: knitr, testthat, rmarkdown, roxygen2, hexbin, BiocStyle License: Artistic-2.0 MD5sum: 044052e2ad90d3d76557b2c7004227fb NeedsCompilation: no Title: Evaluation of normalization methods and calculation of differential expression analysis statistics Description: NormalyzerDE provides screening of normalization methods for LC-MS based expression data. It calculates a range of normalized matrices using both existing approaches and a novel time-segmented approach, calculates performance measures and generates an evaluation report. Furthermore, it provides an easy utility for Limma- or ANOVA- based differential expression analysis. biocViews: Normalization, MultipleComparison, Visualization, Bayesian, Proteomics, Metabolomics, DifferentialExpression Author: Jakob Willforss Maintainer: Jakob Willforss URL: https://github.com/ComputationalProteomics/NormalyzerDE VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NormalyzerDE git_branch: RELEASE_3_10 git_last_commit: 96750a1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/NormalyzerDE_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/NormalyzerDE_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/NormalyzerDE_1.4.0.tgz vignettes: vignettes/NormalyzerDE/inst/doc/vignette.pdf vignetteTitles: Differential expression and countering technical biases using NormalyzerDE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NormalyzerDE/inst/doc/vignette.R dependencyCount: 153 Package: NormqPCR Version: 1.32.0 Depends: R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR, qpcR License: LGPL-3 MD5sum: 0dcfda841f46fbe1e9022e203189e939 NeedsCompilation: no Title: Functions for normalisation of RT-qPCR data Description: Functions for the selection of optimal reference genes and the normalisation of real-time quantitative PCR data. biocViews: MicrotitrePlateAssay, GeneExpression, qPCR Author: Matthias Kohl, James Perkins, Nor Izayu Abdul Rahman Maintainer: James Perkins URL: www.bioconductor.org/packages/release/bioc/html/NormqPCR.html git_url: https://git.bioconductor.org/packages/NormqPCR git_branch: RELEASE_3_10 git_last_commit: 38573f3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/NormqPCR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/NormqPCR_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/NormqPCR_1.32.0.tgz vignettes: vignettes/NormqPCR/inst/doc/NormqPCR.pdf vignetteTitles: NormqPCR: Functions for normalisation of RT-qPCR data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NormqPCR/inst/doc/NormqPCR.R dependencyCount: 57 Package: normr Version: 1.12.0 Depends: R (>= 3.3.0) Imports: methods, stats, utils, grDevices, parallel, GenomeInfoDb, GenomicRanges, IRanges, Rcpp (>= 0.11), qvalue (>= 2.2), bamsignals (>= 1.4), rtracklayer (>= 1.32) LinkingTo: Rcpp Suggests: BiocStyle, testthat (>= 1.0), knitr, rmarkdown Enhances: BiocParallel License: GPL-2 MD5sum: e8b5ce4c475045ce9c166b1f3964f71c NeedsCompilation: yes Title: Normalization and difference calling in ChIP-seq data Description: Robust normalization and difference calling procedures for ChIP-seq and alike data. Read counts are modeled jointly as a binomial mixture model with a user-specified number of components. A fitted background estimate accounts for the effect of enrichment in certain regions and, therefore, represents an appropriate null hypothesis. This robust background is used to identify significantly enriched or depleted regions. biocViews: Bayesian, DifferentialPeakCalling, Classification, DataImport, ChIPSeq, RIPSeq, FunctionalGenomics, Genetics, MultipleComparison, Normalization, PeakDetection, Preprocessing, Alignment Author: Johannes Helmuth [aut, cre], Ho-Ryun Chung [aut] Maintainer: Johannes Helmuth URL: https://github.com/your-highness/normR SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/your-highness/normR/issues git_url: https://git.bioconductor.org/packages/normr git_branch: RELEASE_3_10 git_last_commit: 1168ee9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/normr_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/normr_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/normr_1.12.0.tgz vignettes: vignettes/normr/inst/doc/normr.html vignetteTitles: Introduction to the normR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/normr/inst/doc/normr.R dependencyCount: 89 Package: npGSEA Version: 1.22.0 Depends: GSEABase (>= 1.24.0) Imports: Biobase, methods, BiocGenerics, graphics, stats Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: b5d8fa65e72df93708e37e149daa2e84 NeedsCompilation: no Title: Permutation approximation methods for gene set enrichment analysis (non-permutation GSEA) Description: Current gene set enrichment methods rely upon permutations for inference. These approaches are computationally expensive and have minimum achievable p-values based on the number of permutations, not on the actual observed statistics. We have derived three parametric approximations to the permutation distributions of two gene set enrichment test statistics. We are able to reduce the computational burden and granularity issues of permutation testing with our method, which is implemented in this package. npGSEA calculates gene set enrichment statistics and p-values without the computational cost of permutations. It is applicable in settings where one or many gene sets are of interest. There are also built-in plotting functions to help users visualize results. biocViews: GeneSetEnrichment, Microarray, StatisticalMethod, Pathways Author: Jessica Larson and Art Owen Maintainer: Jessica Larson git_url: https://git.bioconductor.org/packages/npGSEA git_branch: RELEASE_3_10 git_last_commit: 202dda1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/npGSEA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/npGSEA_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/npGSEA_1.22.0.tgz vignettes: vignettes/npGSEA/inst/doc/npGSEA.pdf vignetteTitles: Running gene set enrichment analysis with the "npGSEA" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/npGSEA/inst/doc/npGSEA.R dependencyCount: 33 Package: NTW Version: 1.36.0 Depends: R (>= 2.3.0) Imports: mvtnorm, stats, utils License: GPL-2 MD5sum: 35488e3009d1ff3caddc8d2d658552d0 NeedsCompilation: no Title: Predict gene network using an Ordinary Differential Equation (ODE) based method Description: This package predicts the gene-gene interaction network and identifies the direct transcriptional targets of the perturbation using an ODE (Ordinary Differential Equation) based method. biocViews: Preprocessing Author: Wei Xiao, Yin Jin, Darong Lai, Xinyi Yang, Yuanhua Liu, Christine Nardini Maintainer: Yuanhua Liu git_url: https://git.bioconductor.org/packages/NTW git_branch: RELEASE_3_10 git_last_commit: 94df79e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/NTW_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/NTW_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/NTW_1.36.0.tgz vignettes: vignettes/NTW/inst/doc/NTW.pdf vignetteTitles: NTW vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NTW/inst/doc/NTW.R dependencyCount: 4 Package: nucleoSim Version: 1.14.0 Imports: stats, IRanges, S4Vectors, graphics, methods Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 6f0ba371bd1f10f827cd4294e2a80f3e NeedsCompilation: no Title: Generate synthetic nucleosome maps Description: This package can generate a synthetic map with reads covering the nucleosome regions as well as a synthetic map with forward and reverse reads emulating next-generation sequencing. The user has choice between three different distributions for the read positioning: Normal, Student and Uniform. biocViews: Genetics, Sequencing, Software, StatisticalMethod, Alignment Author: Rawane Samb [aut], Astrid Deschênes [cre, aut], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschenes URL: https://github.com/arnauddroitlab/nucleoSim VignetteBuilder: knitr BugReports: https://github.com/arnauddroitlab/nucleoSim/issues git_url: https://git.bioconductor.org/packages/nucleoSim git_branch: RELEASE_3_10 git_last_commit: 47db809 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/nucleoSim_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/nucleoSim_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/nucleoSim_1.14.0.tgz vignettes: vignettes/nucleoSim/inst/doc/nucleoSim.html vignetteTitles: Generate synthetic nucleosome maps hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleoSim/inst/doc/nucleoSim.R suggestsMe: RJMCMCNucleosomes dependencyCount: 9 Package: nucleR Version: 2.18.4 Depends: methods Imports: Biobase, BiocGenerics, Biostrings, GenomeInfoDb, GenomicRanges, IRanges, Rsamtools, S4Vectors, ShortRead, dplyr, ggplot2, magrittr, parallel, stats, utils Suggests: Starr, BiocStyle, knitr, rmarkdown, testthat License: LGPL (>= 3) MD5sum: 55b3e13325a87ca79ed15084ad2783e3 NeedsCompilation: no Title: Nucleosome positioning package for R Description: Nucleosome positioning for Tiling Arrays and NGS experiments. biocViews: NucleosomePositioning, Coverage, ChIPSeq, Microarray, Sequencing, Genetics, QualityControl, DataImport Author: Oscar Flores, Ricard Illa Maintainer: Diego Gallego VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nucleR git_branch: RELEASE_3_10 git_last_commit: 106a3d5 git_last_commit_date: 2020-01-07 Date/Publication: 2020-01-07 source.ver: src/contrib/nucleR_2.18.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/nucleR_2.18.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/nucleR_2.18.4.tgz vignettes: vignettes/nucleR/inst/doc/nucleR.pdf vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleR/inst/doc/nucleR.R dependencyCount: 90 Package: nuCpos Version: 1.4.0 Depends: R (>= 3.5.1) Imports: graphics, methods Suggests: NuPoP, Biostrings, testthat License: GPL-2 MD5sum: 803b7ccaeaf4b616ca8970bda0a9bfed NeedsCompilation: yes Title: An R package for prediction of nucleosome positions Description: nuCpos, a derivative of NuPoP, is an R package for prediction of nucleosome positions. In nuCpos, a duration hidden Markov model is trained with a chemical map of nucleosomes either from budding yeast, fission yeast, or mouse embryonic stem cells. nuCpos outputs the Viterbi (most probable) path of nucleosome-linker states, predicted nucleosome occupancy scores and histone binding affinity (HBA) scores as NuPoP does. nuCpos can also calculate local and whole nucleosomal HBA scores for a given 147-bp sequence. Furthermore, effect of genetic alterations on nucleosome occupancy can be predicted with this package. The parental package NuPoP, which is based on an MNase-seq-based map of budding yeast nucleosomes, was developed by Ji-Ping Wang and Liqun Xi, licensed under GPL-2. biocViews: Genetics, Epigenetics, NucleosomePositioning, HiddenMarkovModel, ImmunoOncology Author: Hiroaki Kato, Takeshi Urano Maintainer: Hiroaki Kato git_url: https://git.bioconductor.org/packages/nuCpos git_branch: RELEASE_3_10 git_last_commit: c8f56d7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/nuCpos_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/nuCpos_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/nuCpos_1.4.0.tgz vignettes: vignettes/nuCpos/inst/doc/nuCpos-intro.pdf vignetteTitles: An R package for prediction of nucleosome positioning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/nuCpos/inst/doc/nuCpos-intro.R dependencyCount: 2 Package: NuPoP Version: 1.36.0 Depends: R (>= 2.10) License: GPL-2 MD5sum: 41ada647df363beb66a9ba2b8fb9da4b NeedsCompilation: yes Title: An R package for nucleosome positioning prediction Description: NuPoP is an R package for Nucleosome Positioning Prediction.This package is built upon a duration hidden Markov model proposed in Xi et al, 2010; Wang et al, 2008. The core of the package was written in Fotran. In addition to the R package, a stand-alone Fortran software tool is also available at http://nucleosome.stats.northwestern.edu. biocViews: Genetics,Visualization,Classification Author: Ji-Ping Wang ; Liqun Xi Maintainer: Ji-Ping Wang git_url: https://git.bioconductor.org/packages/NuPoP git_branch: RELEASE_3_10 git_last_commit: c80b597 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/NuPoP_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/NuPoP_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/NuPoP_1.36.0.tgz vignettes: vignettes/NuPoP/inst/doc/NuPoP-intro.pdf vignetteTitles: An R package for Nucleosome positioning prediction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NuPoP/inst/doc/NuPoP-intro.R suggestsMe: nuCpos dependencyCount: 0 Package: occugene Version: 1.46.0 Depends: R (>= 2.0.0) License: GPL (>= 2) MD5sum: fe5292d00ad6cc6e4d37ab160a845dac NeedsCompilation: no Title: Functions for Multinomial Occupancy Distribution Description: Statistical tools for building random mutagenesis libraries for prokaryotes. The package has functions for handling the occupancy distribution for a multinomial and for estimating the number of essential genes in random transposon mutagenesis libraries. biocViews: Annotation, Pathways Author: Oliver Will Maintainer: Oliver Will git_url: https://git.bioconductor.org/packages/occugene git_branch: RELEASE_3_10 git_last_commit: b27ed7f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/occugene_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/occugene_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/occugene_1.46.0.tgz vignettes: vignettes/occugene/inst/doc/occugene.pdf vignetteTitles: occugene hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/occugene/inst/doc/occugene.R dependencyCount: 0 Package: OCplus Version: 1.60.0 Depends: R (>= 2.1.0) Imports: multtest (>= 1.7.3), graphics, grDevices, stats, akima License: LGPL Archs: i386, x64 MD5sum: 9adcf66101d0e53dbd9711727de871ea NeedsCompilation: no Title: Operating characteristics plus sample size and local fdr for microarray experiments Description: This package allows to characterize the operating characteristics of a microarray experiment, i.e. the trade-off between false discovery rate and the power to detect truly regulated genes. The package includes tools both for planned experiments (for sample size assessment) and for already collected data (identification of differentially expressed genes). biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Yudi Pawitan and Alexander Ploner Maintainer: Alexander Ploner git_url: https://git.bioconductor.org/packages/OCplus git_branch: RELEASE_3_10 git_last_commit: ddb509d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/OCplus_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/OCplus_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OCplus_1.60.0.tgz vignettes: vignettes/OCplus/inst/doc/OCplus.pdf vignetteTitles: OCplus Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OCplus/inst/doc/OCplus.R dependencyCount: 18 Package: odseq Version: 1.14.0 Depends: R (>= 3.2.3) Imports: msa (>= 1.2.1), kebabs (>= 1.4.1), mclust (>= 5.1) Suggests: knitr(>= 1.11) License: MIT + file LICENSE Archs: i386, x64 MD5sum: ffad0baa771c6b23c2155a45c0e2ff81 NeedsCompilation: no Title: Outlier detection in multiple sequence alignments Description: Performs outlier detection of sequences in a multiple sequence alignment using bootstrap of predefined distance metrics. Outlier sequences can make downstream analyses unreliable or make the alignments less accurate while they are being constructed. This package implements the OD-seq algorithm proposed by Jehl et al (doi 10.1186/s12859-015-0702-1) for aligned sequences and a variant using string kernels for unaligned sequences. biocViews: Alignment, MultipleSequenceAlignment Author: José Jiménez Maintainer: José Jiménez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/odseq git_branch: RELEASE_3_10 git_last_commit: 12889be git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/odseq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/odseq_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/odseq_1.14.0.tgz vignettes: vignettes/odseq/inst/doc/vignette.pdf vignetteTitles: A quick tutorial to outlier detection in MSAs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/odseq/inst/doc/vignette.R dependencyCount: 27 Package: OGSA Version: 1.16.0 Depends: R (>= 3.2.0) Imports: gplots(>= 2.8.0), limma(>= 3.18.13), Biobase License: GPL (== 2) MD5sum: 2fb12f9db88876ec0600d10a8c4ab516 NeedsCompilation: no Title: Outlier Gene Set Analysis Description: OGSA provides a global estimate of pathway deregulation in cancer subtypes by integrating the estimates of significance for individual pathway members that have been identified by outlier analysis. biocViews: GeneExpression, Microarray, CopyNumberVariation Author: Michael F. Ochs Maintainer: Michael F. Ochs git_url: https://git.bioconductor.org/packages/OGSA git_branch: RELEASE_3_10 git_last_commit: 9bed527 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/OGSA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/OGSA_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OGSA_1.16.0.tgz vignettes: vignettes/OGSA/inst/doc/OGSAUsersManual.pdf vignetteTitles: OGSA Users Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OGSA/inst/doc/OGSAUsersManual.R dependencyCount: 15 Package: oligo Version: 1.50.0 Depends: R (>= 3.2.0), BiocGenerics (>= 0.13.11), oligoClasses (>= 1.29.6), Biobase (>= 2.27.3), Biostrings (>= 2.35.12) Imports: affyio (>= 1.35.0), affxparser (>= 1.39.4), DBI (>= 0.3.1), ff, graphics, methods, preprocessCore (>= 1.29.0), RSQLite (>= 1.0.0), splines, stats, stats4, utils, zlibbioc LinkingTo: preprocessCore Suggests: BSgenome.Hsapiens.UCSC.hg18, hapmap100kxba, pd.hg.u95av2, pd.mapping50k.xba240, pd.huex.1.0.st.v2, pd.hg18.60mer.expr, pd.hugene.1.0.st.v1, maqcExpression4plex, genefilter, limma, RColorBrewer, oligoData, BiocStyle, knitr, RUnit, biomaRt, AnnotationDbi, GenomeGraphs, RCurl, ACME, biomaRt, AnnotationDbi, GenomeGraphs, RCurl Enhances: ff, doMC, doMPI License: LGPL (>= 2) MD5sum: 0f866d5a1a15d170f8812c82302af14b NeedsCompilation: yes Title: Preprocessing tools for oligonucleotide arrays Description: A package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files). biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, SNP, DifferentialExpression, ExonArray, GeneExpression, DataImport Author: Benilton Carvalho and Rafael Irizarry Maintainer: Benilton Carvalho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oligo git_branch: RELEASE_3_10 git_last_commit: 9e5e8d4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/oligo_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/oligo_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/oligo_1.50.0.tgz vignettes: vignettes/oligo/inst/doc/oug.pdf vignetteTitles: oligo User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder, puma, SCAN.UPC, waveTiling importsMe: ArrayExpress, cn.farms, crossmeta, frma, ITALICS, mimager suggestsMe: BiocGenerics, fastseg, frmaTools dependencyCount: 57 Package: oligoClasses Version: 1.48.0 Depends: R (>= 2.14) Imports: BiocGenerics (>= 0.27.1), Biobase (>= 2.17.8), methods, graphics, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), SummarizedExperiment, Biostrings (>= 2.23.6), affyio (>= 1.23.2), ff, foreach, BiocManager, utils, S4Vectors (>= 0.9.25), RSQLite, DBI Suggests: hapmapsnp5, hapmapsnp6, pd.genomewidesnp.6, pd.genomewidesnp.5, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.mapping250k.sty, pd.mapping250k.nsp, genomewidesnp6Crlmm (>= 1.0.7), genomewidesnp5Crlmm (>= 1.0.6), RUnit, human370v1cCrlmm, SNPchip, VanillaICE, crlmm Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: GPL (>= 2) MD5sum: 6249313b60b24d2f8c3a00c2ed666f0a NeedsCompilation: no Title: Classes for high-throughput arrays supported by oligo and crlmm Description: This package contains class definitions, validity checks, and initialization methods for classes used by the oligo and crlmm packages. biocViews: Infrastructure Author: Benilton Carvalho and Robert Scharpf Maintainer: Benilton Carvalho and Robert Scharpf git_url: https://git.bioconductor.org/packages/oligoClasses git_branch: RELEASE_3_10 git_last_commit: 2f8c8dc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/oligoClasses_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/oligoClasses_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/oligoClasses_1.48.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cn.farms, crlmm, mBPCR, oligo, puma, waveTiling importsMe: affycoretools, ArrayTV, frma, ITALICS, mimager, MinimumDistance, pdInfoBuilder, puma, SNPchip, VanillaICE suggestsMe: BiocGenerics dependencyCount: 53 Package: OLIN Version: 1.64.0 Depends: R (>= 2.10), methods, locfit, marray Imports: graphics, grDevices, limma, marray, methods, stats Suggests: convert License: GPL-2 MD5sum: 3917ad145739a555cc41d6996521d2c3 NeedsCompilation: no Title: Optimized local intensity-dependent normalisation of two-color microarrays Description: Functions for normalisation of two-color microarrays by optimised local regression and for detection of artefacts in microarray data biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://olin.sysbiolab.eu git_url: https://git.bioconductor.org/packages/OLIN git_branch: RELEASE_3_10 git_last_commit: a61230b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/OLIN_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/OLIN_1.64.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OLIN_1.64.0.tgz vignettes: vignettes/OLIN/inst/doc/OLIN.pdf vignetteTitles: Introduction to OLIN hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OLIN/inst/doc/OLIN.R dependsOnMe: OLINgui importsMe: OLINgui suggestsMe: maigesPack dependencyCount: 10 Package: OLINgui Version: 1.60.0 Depends: R (>= 2.0.0), OLIN (>= 1.4.0) Imports: graphics, marray, OLIN, tcltk, tkWidgets, widgetTools License: GPL-2 Archs: i386, x64 MD5sum: f244e795e26dbbfd683f9895e297a0bf NeedsCompilation: no Title: Graphical user interface for OLIN Description: Graphical user interface for the OLIN package biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://olin.sysbiolab.eu git_url: https://git.bioconductor.org/packages/OLINgui git_branch: RELEASE_3_10 git_last_commit: 90a4fa2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/OLINgui_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/OLINgui_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OLINgui_1.60.0.tgz vignettes: vignettes/OLINgui/inst/doc/OLINgui.pdf vignetteTitles: Introduction to OLINgui hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OLINgui/inst/doc/OLINgui.R dependencyCount: 16 Package: OmaDB Version: 2.2.0 Depends: R (>= 3.5), httr (>= 1.2.1), plyr(>= 1.8.4) Imports: utils, ape, Biostrings, GenomicRanges, IRanges, methods, topGO, jsonlite Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 7a7cb406bac6ba23d480587a02f76ceb NeedsCompilation: no Title: R wrapper for the OMA REST API Description: A package for the orthology prediction data download from OMA database. biocViews: Software, ComparativeGenomics, FunctionalGenomics, Genetics, Annotation, GO, FunctionalPrediction Author: Klara Kaleb Maintainer: Klara Kaleb , Adrian Altenhoff URL: https://github.com/DessimozLab/OmaDB VignetteBuilder: knitr BugReports: https://github.com/DessimozLab/OmaDB/issues git_url: https://git.bioconductor.org/packages/OmaDB git_branch: RELEASE_3_10 git_last_commit: 66d06e5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/OmaDB_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/OmaDB_2.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OmaDB_2.2.0.tgz vignettes: vignettes/OmaDB/inst/doc/exploring_hogs.html, vignettes/OmaDB/inst/doc/OmaDB.html, vignettes/OmaDB/inst/doc/sequence_mapping.html, vignettes/OmaDB/inst/doc/tree_visualisation.html vignetteTitles: Exploring Hierarchical orthologous groups with OmaDB, Get started with OmaDB, Sequence Mapping with OmaDB, Exploring Taxonomic trees with OmaDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmaDB/inst/doc/exploring_hogs.R, vignettes/OmaDB/inst/doc/OmaDB.R, vignettes/OmaDB/inst/doc/sequence_mapping.R, vignettes/OmaDB/inst/doc/tree_visualisation.R importsMe: PhyloProfile dependencyCount: 54 Package: omicade4 Version: 1.26.0 Depends: R (>= 3.0.0), ade4 Imports: made4 Suggests: BiocStyle License: GPL-2 MD5sum: 16d90baff7336b42890b77bfa5dda3bb NeedsCompilation: no Title: Multiple co-inertia analysis of omics datasets Description: Multiple co-inertia analysis of omics datasets biocViews: Software, Clustering, Classification, MultipleComparison Author: Chen Meng, Aedin Culhane, Amin M. Gholami. Maintainer: Chen Meng git_url: https://git.bioconductor.org/packages/omicade4 git_branch: RELEASE_3_10 git_last_commit: 11055e5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/omicade4_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/omicade4_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/omicade4_1.26.0.tgz vignettes: vignettes/omicade4/inst/doc/omicade4.pdf vignetteTitles: Using omicade4 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicade4/inst/doc/omicade4.R importsMe: omicRexposome suggestsMe: biosigner, MultiDataSet, ropls dependencyCount: 20 Package: OmicCircos Version: 1.24.0 Depends: R (>= 2.14.0), methods,GenomicRanges License: GPL-2 MD5sum: 9219b51c3890a89ae60fb0ff30b551e1 NeedsCompilation: no Title: High-quality circular visualization of omics data Description: OmicCircos is an R application and package for generating high-quality circular plots for omics data. biocViews: Visualization,Statistics,Annotation Author: Ying Hu Chunhua Yan Maintainer: Ying Hu git_url: https://git.bioconductor.org/packages/OmicCircos git_branch: RELEASE_3_10 git_last_commit: e388c99 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/OmicCircos_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/OmicCircos_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OmicCircos_1.24.0.tgz vignettes: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.pdf vignetteTitles: OmicCircos vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.R dependencyCount: 16 Package: omicplotR Version: 1.6.1 Depends: R (>= 3.6), ALDEx2 (>= 1.18.0) Imports: compositions, DT, grDevices, knitr, jsonlite, matrixStats, rmarkdown, shiny, stats, vegan, zCompositions License: MIT + file LICENSE MD5sum: 6b54161c7ce07378d4d6004368f51633 NeedsCompilation: no Title: Visual Exploration of Omic Datasets Using a Shiny App Description: A Shiny app for visual exploration of omic datasets as compositions, and differential abundance analysis using ALDEx2. Useful for exploring RNA-seq, meta-RNA-seq, 16s rRNA gene sequencing with visualizations such as principal component analysis biplots (coloured using metadata for visualizing each variable), dendrograms and stacked bar plots, and effect plots (ALDEx2). Input is a table of counts and metadata file (if metadata exists), with options to filter data by count or by metadata to remove low counts, or to visualize select samples according to selected metadata. biocViews: Software, DifferentialExpression, GeneExpression, GUI, RNASeq, DNASeq, Metagenomics, Transcriptomics, Bayesian, Microbiome, Visualization, Sequencing, ImmunoOncology Author: Daniel Giguere [aut, cre], Jean Macklaim [aut], Brandon Lieng [aut], Greg Gloor [aut] Maintainer: Daniel Giguere VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicplotR git_branch: RELEASE_3_10 git_last_commit: ffd45dd git_last_commit_date: 2019-11-12 Date/Publication: 2019-11-13 source.ver: src/contrib/omicplotR_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/omicplotR_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/omicplotR_1.6.1.tgz vignettes: vignettes/omicplotR/inst/doc/omicplotR.html vignetteTitles: omicplotR: A tool for visualization of omic datasets as compositions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omicplotR/inst/doc/omicplotR.R dependencyCount: 83 Package: omicRexposome Version: 1.8.0 Depends: R (>= 3.4), Biobase Imports: stats, utils, grDevices, graphics, methods, rexposome, limma, sva, ggplot2, ggrepel, PMA, omicade4, gridExtra, MultiDataSet, SmartSVA, isva, parallel, SummarizedExperiment, stringr Suggests: BiocStyle, knitr, rmarkdown, snpStats, brgedata License: MIT + file LICENSE MD5sum: 8938b5824f6dd59b120e67959fe66c5b NeedsCompilation: no Title: Exposome and omic data associatin and integration analysis Description: omicRexposome systematizes the association evaluation between exposures and omic data, taking advantage of MultiDataSet for coordinated data management, rexposome for exposome data definition and limma for association testing. Also to perform data integration mixing exposome and omic data using multi co-inherent analysis (omicade4) and multi-canonical correlation analysis (PMA). biocViews: ImmunoOncology, WorkflowStep, MultipleComparison, Visualization, GeneExpression, DifferentialExpression, DifferentialMethylation, GeneRegulation, Epigenetics, Proteomics, Transcriptomics, StatisticalMethod, Regression Author: Carles Hernandez-Ferrer [aut, cre], Juan R. González [aut] Maintainer: Carles Hernandez-Ferrer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicRexposome git_branch: RELEASE_3_10 git_last_commit: b57ede7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/omicRexposome_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/omicRexposome_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/omicRexposome_1.8.0.tgz vignettes: vignettes/omicRexposome/inst/doc/exposome_omic_integration.html vignetteTitles: Exposome Data Integration with Omic Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omicRexposome/inst/doc/exposome_omic_integration.R dependencyCount: 202 Package: OmicsLonDA Version: 1.2.2 Depends: R(>= 3.6) Imports: SummarizedExperiment, gss, plyr, zoo, pracma, ggplot2, BiocParallel, parallel, grDevices, graphics, stats, utils, methods, BiocGenerics Suggests: knitr, rmarkdown, testthat, devtools, BiocManager License: MIT + file LICENSE MD5sum: f3576c207764ce57a75d93df159c4af8 NeedsCompilation: no Title: Omics Longitudinal Differential Analysis Description: Statistical method that provides robust identification of time intervals where omics features (such as proteomics, lipidomics, metabolomics, transcriptomics, microbiome, as well as physiological parameters captured by wearable sensors such as heart rhythm, body temperature, and activity level) are significantly different between groups. biocViews: TimeCourse, Survival, Microbiome, Metabolomics, Proteomics, Lipidomics, Transcriptomics, Regression Author: Ahmed A. Metwally, Tom Zhang, Michael Snyder Maintainer: Ahmed A. Metwally URL: https://github.com/aametwally/OmicsLonDA VignetteBuilder: knitr BugReports: https://github.com/aametwally/OmicsLonDA/issues git_url: https://git.bioconductor.org/packages/OmicsLonDA git_branch: RELEASE_3_10 git_last_commit: 0f2a795 git_last_commit_date: 2019-12-19 Date/Publication: 2019-12-19 source.ver: src/contrib/OmicsLonDA_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/OmicsLonDA_1.2.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OmicsLonDA_1.2.2.tgz vignettes: vignettes/OmicsLonDA/inst/doc/OmicsLonDA.html vignetteTitles: OmicsLonDA Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OmicsLonDA/inst/doc/OmicsLonDA.R dependencyCount: 81 Package: OmicsMarkeR Version: 1.18.0 Depends: R (>= 3.2.0) Imports: graphics, stats, utils, plyr (>= 1.8), data.table (>= 1.9.4), caret (>= 6.0-37), DiscriMiner (>= 0.1-29), e1071 (>= 1.6-1), randomForest (>= 4.6-10), gbm (>= 2.1), pamr (>= 1.54.1), glmnet (>= 1.9-5), caTools (>= 1.14), foreach (>= 1.4.1), permute (>= 0.7-0), assertive (>= 0.3-0), assertive.base (>= 0.0-1) Suggests: testthat, BiocStyle, knitr License: GPL-3 MD5sum: 81a17e0a5214995f2335004988a10554 NeedsCompilation: no Title: Classification and Feature Selection for 'Omics' Datasets Description: Tools for classification and feature selection for 'omics' level datasets. It is a tool to provide multiple multivariate classification and feature selection techniques complete with multiple stability metrics and aggregation techniques. It is primarily designed for analysis of metabolomics datasets but potentially extendable to proteomics and transcriptomics applications. biocViews: Metabolomics, Classification, FeatureExtraction Author: Charles E. Determan Jr. Maintainer: Charles E. Determan Jr. URL: http://github.com/cdeterman/OmicsMarkeR VignetteBuilder: knitr BugReports: http://github.com/cdeterman/OmicsMarkeR/issues/new git_url: https://git.bioconductor.org/packages/OmicsMarkeR git_branch: RELEASE_3_10 git_last_commit: 76b46b7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/OmicsMarkeR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/OmicsMarkeR_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OmicsMarkeR_1.18.0.tgz vignettes: vignettes/OmicsMarkeR/inst/doc/OmicsMarkeR.pdf vignetteTitles: A Short Introduction to the OmicMarkeR Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmicsMarkeR/inst/doc/OmicsMarkeR.R dependencyCount: 122 Package: OMICsPCA Version: 1.4.0 Depends: R (>= 3.5.0), OMICsPCAdata Imports: HelloRanges, fpc, stats, MultiAssayExperiment, pdftools, methods, grDevices, utils,clValid, NbClust, cowplot, rmarkdown, kableExtra, rtracklayer, IRanges, GenomeInfoDb, reshape2, ggplot2, factoextra, rgl, corrplot, MASS, graphics, FactoMineR, PerformanceAnalytics, tidyr, data.table, cluster, magick Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: ff5b945a6af53a43960e940f9ffe9f3c NeedsCompilation: no Title: An R package for quantitative integration and analysis of multiple omics assays from heterogeneous samples Description: OMICsPCA is an analysis pipeline designed to integrate multi OMICs experiments done on various subjects (e.g. Cell lines, individuals), treatments (e.g. disease/control) or time points and to analyse such integrated data from various various angles and perspectives. In it's core OMICsPCA uses Principal Component Analysis (PCA) to integrate multiomics experiments from various sources and thus has ability to over data insufficiency issues by using the ingegrated data as representatives. OMICsPCA can be used in various application including analysis of overall distribution of OMICs assays across various samples /individuals /time points; grouping assays by user-defined conditions; identification of source of variation, similarity/dissimilarity between assays, variables or individuals. biocViews: ImmunoOncology, MultipleComparison, PrincipalComponent, DataRepresentation, Workflow, Visualization, DimensionReduction, Clustering, BiologicalQuestion, EpigeneticsWorkflow, Transcription, GeneticVariability, GUI, BiomedicalInformatics, Epigenetics, FunctionalGenomics, SingleCell Author: Subhadeep Das [aut, cre], Dr. Sucheta Tripathy [ctb] Maintainer: Subhadeep Das VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OMICsPCA git_branch: RELEASE_3_10 git_last_commit: 9033c55 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/OMICsPCA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/OMICsPCA_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OMICsPCA_1.4.0.tgz vignettes: vignettes/OMICsPCA/inst/doc/vignettes.html vignetteTitles: OMICsPCA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OMICsPCA/inst/doc/vignettes.R dependencyCount: 212 Package: omicsPrint Version: 1.6.0 Depends: R (>= 3.5), MASS Imports: methods, matrixStats, graphics, stats, SummarizedExperiment, MultiAssayExperiment, RaggedExperiment Suggests: BiocStyle, knitr, rmarkdown, testthat, GEOquery, VariantAnnotation, Rsamtools, BiocParallel, GenomicRanges, FDb.InfiniumMethylation.hg19, snpStats License: GPL (>= 2) MD5sum: f6fdab706cfa58b3f99e2ec9feda8864 NeedsCompilation: no Title: Cross omic genetic fingerprinting Description: omicsPrint provides functionality for cross omic genetic fingerprinting, for example, to verify sample relationships between multiple omics data types, i.e. genomic, transcriptomic and epigenetic (DNA methylation). biocViews: QualityControl, Genetics, Epigenetics, Transcriptomics, DNAMethylation, Transcription, GeneticVariability, ImmunoOncology Author: Maarten van Iterson [aut], Davy Cats [cre] Maintainer: Davy Cats VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicsPrint git_branch: RELEASE_3_10 git_last_commit: 302a97b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/omicsPrint_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/omicsPrint_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/omicsPrint_1.6.0.tgz vignettes: vignettes/omicsPrint/inst/doc/omicsPrint.html vignetteTitles: omicsPrint: detection of data linkage errors in multiple omics studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicsPrint/inst/doc/omicsPrint.R dependencyCount: 58 Package: OmnipathR Version: 1.0.1 Depends: igraph, graphics, methods, utils Imports: dplyr, rlang Suggests: tidyr, dnet, gprofiler2, BiocStyle, testthat License: MIT + file LICENSE MD5sum: daf1c5869b3d0ab48df8acae3e7de826 NeedsCompilation: no Title: Import Omnipath network Description: Import data from https://www.omnipathdb.org webservice. It also includes functions to transform and print this data. biocViews: GraphAndNetwork, Network, Pathways, Software, ThirdPartyClient, DataImport, DataRepresentation Author: Attila Gabor, Denes Turei, Alberto Valdeolivas Maintainer: Alberto Valdeolivas Urbelz URL: https://github.com/saezlab/OmnipathR git_url: https://git.bioconductor.org/packages/OmnipathR git_branch: RELEASE_3_10 git_last_commit: f0dd8eb git_last_commit_date: 2020-03-26 Date/Publication: 2020-03-26 source.ver: src/contrib/OmnipathR_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/OmnipathR_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OmnipathR_1.0.1.tgz vignettes: vignettes/OmnipathR/inst/doc/OmnipathR.pdf vignetteTitles: OmnipathR: utility functions to work with Omnipath in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OmnipathR/inst/doc/OmnipathR.R dependencyCount: 32 Package: Onassis Version: 1.8.2 Depends: R (>= 3.4), rJava, OnassisJavaLibs Imports: GEOmetadb, RSQLite, data.table, methods, tools, utils, AnnotationDbi, RCurl, stats, DT, data.table, knitr, Rtsne, dendextend, clusteval, ggplot2, ggfortify Suggests: BiocStyle, rmarkdown, htmltools, org.Hs.eg.db, gplots, GenomicRanges, kableExtra License: GPL-2 MD5sum: 4bfacb1fde686a8a369935419bfa3bc4 NeedsCompilation: no Title: OnASSIs Ontology Annotation and Semantic SImilarity software Description: A package that allows the annotation of text with ontology terms (mainly from OBO ontologies) and the computation of semantic similarity measures based on the structure of the ontology between different annotated samples. biocViews: Annotation, DataImport, Clustering, Network, Software, GeneTarget Author: Eugenia Galeota Maintainer: Eugenia Galeota SystemRequirements: Java (>= 1.8) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Onassis git_branch: RELEASE_3_10 git_last_commit: e66cf3b git_last_commit_date: 2019-12-19 Date/Publication: 2019-12-19 source.ver: src/contrib/Onassis_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/Onassis_1.8.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Onassis_1.8.2.tgz vignettes: vignettes/Onassis/inst/doc/Onassis.html vignetteTitles: Onassis: Ontology Annotation and Semantic Similarity software hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Onassis/inst/doc/Onassis.R dependencyCount: 114 Package: oncomix Version: 1.8.0 Depends: R (>= 3.4.0) Imports: ggplot2, ggrepel, RColorBrewer, mclust, stats, SummarizedExperiment Suggests: knitr, rmarkdown, testthat, RMySQL License: GPL-3 MD5sum: 2b0a45ca8fc5b01b55a3d5de0eb9ab83 NeedsCompilation: no Title: Identifying Genes Overexpressed in Subsets of Tumors from Tumor-Normal mRNA Expression Data Description: This package helps identify mRNAs that are overexpressed in subsets of tumors relative to normal tissue. Ideal inputs would be paired tumor-normal data from the same tissue from many patients (>15 pairs). This unsupervised approach relies on the observation that oncogenes are characteristically overexpressed in only a subset of tumors in the population, and may help identify oncogene candidates purely based on differences in mRNA expression between previously unknown subtypes. biocViews: GeneExpression, Sequencing Author: Daniel Pique, John Greally, Jessica Mar Maintainer: Daniel Pique VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oncomix git_branch: RELEASE_3_10 git_last_commit: 53dbab9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/oncomix_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/oncomix_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/oncomix_1.8.0.tgz vignettes: vignettes/oncomix/inst/doc/oncomix.html vignetteTitles: OncoMix Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oncomix/inst/doc/oncomix.R dependencyCount: 79 Package: OncoScore Version: 1.14.0 Depends: R (>= 3.6), Imports: biomaRt, grDevices, graphics, utils, methods, Suggests: BiocGenerics, BiocStyle, knitr, testthat, License: file LICENSE MD5sum: 02cab682c92546c2ce9d11252a65bb08 NeedsCompilation: no Title: A tool to identify potentially oncogenic genes Description: OncoScore is a tool to measure the association of genes to cancer based on citation frequencies in biomedical literature. The score is evaluated from PubMed literature by dynamically updatable web queries. biocViews: BiomedicalInformatics Author: Luca De Sano [aut], Carlo Gambacorti Passerini [ctb], Rocco Piazza [ctb], Daniele Ramazzotti [aut, cre], Roberta Spinelli [ctb] Maintainer: Luca De Sano URL: https://github.com/danro9685/OncoScore VignetteBuilder: knitr BugReports: https://github.com/danro9685/OncoScore git_url: https://git.bioconductor.org/packages/OncoScore git_branch: RELEASE_3_10 git_last_commit: 7846a2c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/OncoScore_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/OncoScore_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OncoScore_1.14.0.tgz vignettes: vignettes/OncoScore/inst/doc/vignette.pdf vignetteTitles: OncoScore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OncoScore/inst/doc/vignette.R dependencyCount: 58 Package: OncoSimulR Version: 2.16.1 Depends: R (>= 3.3.0) Imports: Rcpp (>= 0.12.4), parallel, data.table, graph, Rgraphviz, gtools, igraph, methods, RColorBrewer, grDevices, car, dplyr, smatr, ggplot2, ggrepel, nem LinkingTo: Rcpp Suggests: BiocStyle, knitr, Oncotree, testthat (>= 1.0.0), rmarkdown, bookdown, pander License: GPL (>= 3) MD5sum: 2caed068fd7747e84afb7ec3b74a5562 NeedsCompilation: yes Title: Forward Genetic Simulation of Cancer Progression with Epistasis Description: Functions for forward population genetic simulation in asexual populations, with special focus on cancer progression. Fitness can be an arbitrary function of genetic interactions between multiple genes or modules of genes, including epistasis, order restrictions in mutation accumulation, and order effects. Mutation rates can differ between genes, and we can include mutator/antimutator genes (to model mutator phenotypes). Simulations use continuous-time models and can include driver and passenger genes and modules. Also included are functions for: simulating random DAGs of the type found in Oncogenetic Trees, Conjunctive Bayesian Networks, and other cancer progression models; plotting and sampling from single or multiple realizations of the simulations, including single-cell sampling; plotting the parent-child relationships of the clones; generating random fitness landscapes (Rough Mount Fuji, House of Cards, and additive models) and plotting them. biocViews: BiologicalQuestion, SomaticMutation Author: Ramon Diaz-Uriarte [aut, cre], Mark Taylor [ctb] Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/OncoSimul, https://popmodels.cancercontrol.cancer.gov/gsr/packages/oncosimulr/ VignetteBuilder: knitr BugReports: https://github.com/rdiaz02/OncoSimul/issues git_url: https://git.bioconductor.org/packages/OncoSimulR git_branch: RELEASE_3_10 git_last_commit: 2d6b985 git_last_commit_date: 2020-04-10 Date/Publication: 2020-04-11 source.ver: src/contrib/OncoSimulR_2.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/OncoSimulR_2.16.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OncoSimulR_2.16.1.tgz vignettes: vignettes/OncoSimulR/inst/doc/OncoSimulR.html vignetteTitles: OncoSimulR: forward genetic simulation in asexual populations with arbitrary epistatic interactions and a focus on modeling tumor progression. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OncoSimulR/inst/doc/OncoSimulR.R dependencyCount: 106 Package: oneSENSE Version: 1.8.0 Depends: R (>= 3.4), webshot, shiny, shinyFiles, scatterplot3d Imports: Rtsne, plotly, gplots, grDevices, graphics, stats, utils, methods, flowCore Suggests: knitr, rmarkdown License: GPL (>=3) MD5sum: 7dd7c1e12e2280810e837ce9a81c5474 NeedsCompilation: no Title: One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding (OneSENSE) Description: A graphical user interface that facilitates the dimensional reduction method based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm, for categorical analysis of mass cytometry data. With One-SENSE, measured parameters are grouped into predefined categories, and cells are projected onto a space composed of one dimension for each category. Each dimension is informative and can be annotated through the use of heatplots aligned in parallel to each axis, allowing for simultaneous visualization of two catergories across a two-dimensional plot. The cellular occupancy of the resulting plots alllows for direct assessment of the relationships between the categories. biocViews: ImmunoOncology, Software, FlowCytometry, GUI, DimensionReduction Author: Cheng Yang, Evan Newell, Yong Kee Tan Maintainer: Yong Kee Tan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oneSENSE git_branch: RELEASE_3_10 git_last_commit: e87f028 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/oneSENSE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/oneSENSE_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/oneSENSE_1.8.0.tgz vignettes: vignettes/oneSENSE/inst/doc/quickstart.html vignetteTitles: Introduction to oneSENSE GUI hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/oneSENSE/inst/doc/quickstart.R dependencyCount: 102 Package: onlineFDR Version: 1.4.0 Imports: stats Suggests: knitr, rmarkdown, testthat, covr License: GPL-3 MD5sum: 40fdea75bc18b5bb270ed4db81d9bc68 NeedsCompilation: no Title: Online error control Description: This package allows users to control the false discovery rate (FDR) or familywise error rate (FWER) for online hypothesis testing, where hypotheses arrive sequentially in a stream. In this framework, a null hypothesis is rejected based only on the previous decisions, as the future p-values and the number of hypotheses to be tested are unknown. biocViews: MultipleComparison, Software, StatisticalMethod Author: David S. Robertson [aut, cre], Aaditya Ramdas [aut], Adel Javanmard [aut], Andrea Montanari [aut], Jinjin Tian [aut], Tijana Zrnic [aut], Natasha A. Karp [aut] Maintainer: David S. Robertson URL: https://dsrobertson.github.io/onlineFDR/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/onlineFDR git_branch: RELEASE_3_10 git_last_commit: b44094b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/onlineFDR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/onlineFDR_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/onlineFDR_1.4.0.tgz vignettes: vignettes/onlineFDR/inst/doc/onlineFDR-vignette.html vignetteTitles: Using the onlineFDR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/onlineFDR/inst/doc/onlineFDR-vignette.R dependencyCount: 1 Package: ontoProc Version: 1.8.1 Depends: R (>= 3.5), ontologyIndex Imports: Biobase, S4Vectors, methods, AnnotationDbi, stats, utils, shiny, graph, Rgraphviz, ontologyPlot, dplyr, magrittr Suggests: knitr, org.Hs.eg.db, org.Mm.eg.db, testthat, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: e5a739ee8cc68836457ececa0c01ac4d NeedsCompilation: no Title: processing of ontologies of anatomy, cell lines, and so on Description: Support harvesting of diverse bioinformatic ontologies, making particular use of the ontologyIndex package on CRAN. We provide snapshots of key ontologies for terms about cells, cell lines, chemical compounds, and anatomy, to help analyze genome-scale experiments, particularly cell x compound screens. Another purpose is to strengthen development of compelling use cases for richer interfaces to emerging ontologies. biocViews: Infrastructure, GO Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ontoProc git_branch: RELEASE_3_10 git_last_commit: 5447ef6 git_last_commit_date: 2020-03-08 Date/Publication: 2020-03-08 source.ver: src/contrib/ontoProc_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/ontoProc_1.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ontoProc_1.8.1.tgz vignettes: vignettes/ontoProc/inst/doc/ontoProc.html vignetteTitles: ontoProc: some ontology-oriented utilites with single-cell focus for Bioconductor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ontoProc/inst/doc/ontoProc.R importsMe: pogos, tenXplore suggestsMe: BiocOncoTK, TxRegInfra dependencyCount: 57 Package: openCyto Version: 1.24.0 Depends: R (>= 3.5.0) Imports: methods,Biobase,BiocGenerics,gtools,flowCore(>= 1.31.17),flowViz,ncdfFlow(>= 2.11.34),flowWorkspace(>= 3.33.1),flowStats(>= 3.29.1),flowClust(>= 3.11.4),MASS,clue,plyr,RBGL,graph,data.table,ks,RColorBrewer,lattice,rrcov,R.utils LinkingTo: Rcpp Suggests: flowWorkspaceData, knitr, testthat, utils, tools, parallel, ggcyto, CytoML License: Artistic-2.0 MD5sum: 597019e35478b2e15ff7a9e16920cdc5 NeedsCompilation: yes Title: Hierarchical Gating Pipeline for flow cytometry data Description: This package is designed to facilitate the automated gating methods in sequential way to mimic the manual gating strategy. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang, John Ramey, Greg Finak, Raphael Gottardo Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openCyto git_branch: RELEASE_3_10 git_last_commit: 9a81423 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/openCyto_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/openCyto_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/openCyto_1.24.0.tgz vignettes: vignettes/openCyto/inst/doc/HowToAutoGating.html, vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.html, vignettes/openCyto/inst/doc/openCytoVignette.html vignetteTitles: How to use different auto gating functions, How to write a csv gating template, An Introduction to the openCyto package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openCyto/inst/doc/HowToAutoGating.R, vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.R, vignettes/openCyto/inst/doc/openCytoVignette.R importsMe: CytoML suggestsMe: flowClust, flowCore, flowStats, flowWorkspace, ggcyto dependencyCount: 94 Package: openPrimeR Version: 1.8.0 Depends: R (>= 3.4.0) Imports: Biostrings (>= 2.38.4), XML (>= 3.98-1.4), scales (>= 0.4.0), reshape2 (>= 1.4.1), seqinr (>= 3.3-3), IRanges (>= 2.4.8), GenomicRanges (>= 1.22.4), ggplot2 (>= 2.1.0), plyr (>= 1.8.4), dplyr (>= 0.5.0), stringdist (>= 0.9.4.1), stringr (>= 1.0.0), RColorBrewer (>= 1.1-2), DECIPHER (>= 1.16.1), lpSolveAPI (>= 5.5.2.0-17), digest (>= 0.6.9), Hmisc (>= 3.17-4), ape (>= 3.5), BiocGenerics (>= 0.16.1), S4Vectors (>= 0.8.11), foreach (>= 1.4.3), magrittr (>= 1.5), distr (>= 2.6), distrEx (>= 2.6), fitdistrplus (>= 1.0-7), uniqtag (>= 1.0), openxlsx (>= 4.0.17), grid (>= 3.1.0), grDevices (>= 3.1.0), stats (>= 3.1.0), utils (>= 3.1.0), methods (>= 3.1.0), tinytex (>= 0.5) Suggests: testthat (>= 1.0.2), knitr (>= 1.13), rmarkdown (>= 1.0), devtools (>= 1.12.0), doParallel (>= 1.0.10), pander (>= 0.6.0), learnr (>= 0.9) License: GPL-2 MD5sum: a0cb2c8c53c02fd6487a68da63cc3449 NeedsCompilation: no Title: Multiplex PCR Primer Design and Analysis Description: An implementation of methods for designing, evaluating, and comparing primer sets for multiplex PCR. Primers are designed by solving a set cover problem such that the number of covered template sequences is maximized with the smallest possible set of primers. To guarantee that high-quality primers are generated, only primers fulfilling constraints on their physicochemical properties are selected. A Shiny app providing a user interface for the functionalities of this package is provided by the 'openPrimeRui' package. biocViews: Software, Technology Author: Matthias Döring [aut, cre], Nico Pfeifer [aut] Maintainer: Matthias Döring SystemRequirements: MAFFT (>= 7.305), OligoArrayAux (>= 3.8), ViennaRNA (>= 2.4.1), MELTING (>= 5.1.1), Pandoc (>= 1.12.3) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openPrimeR git_branch: RELEASE_3_10 git_last_commit: 9789d80 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/openPrimeR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/openPrimeR_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/openPrimeR_1.8.0.tgz vignettes: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.html vignetteTitles: openPrimeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.R dependsOnMe: openPrimeRui dependencyCount: 132 Package: openPrimeRui Version: 1.8.0 Depends: R (>= 3.4.0), openPrimeR (>= 0.99.0) Imports: shiny (>= 1.0.2), shinyjs (>= 0.9), shinyBS (>= 0.61), DT (>= 0.2), rmarkdown (>= 1.0) Suggests: knitr (>= 1.13) License: GPL-2 MD5sum: 1fe9975230fdfa86b65e47bbb7611859 NeedsCompilation: no Title: Shiny Application for Multiplex PCR Primer Design and Analysis Description: A Shiny application providing methods for designing, evaluating, and comparing primer sets for multiplex polymerase chain reaction. Primers are designed by solving a set cover problem such that the number of covered template sequences is maximized with the smallest possible set of primers. To guarantee that high-quality primers are generated, only primers fulfilling constraints on their physicochemical properties are selected. biocViews: Software, Technology Author: Matthias Döring [aut, cre], Nico Pfeifer [aut] Maintainer: Matthias Döring VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openPrimeRui git_branch: RELEASE_3_10 git_last_commit: c56da29 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/openPrimeRui_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/openPrimeRui_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/openPrimeRui_1.8.0.tgz vignettes: vignettes/openPrimeRui/inst/doc/openPrimeRui_vignette.html vignetteTitles: openPrimeRui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openPrimeRui/inst/doc/openPrimeRui_vignette.R dependencyCount: 146 Package: oposSOM Version: 2.4.0 Depends: R (>= 3.0), igraph (>= 1.0.0) Imports: fastICA, tsne, scatterplot3d, pixmap, fdrtool, ape, biomaRt, Biobase, RcppParallel, Rcpp LinkingTo: RcppParallel, Rcpp License: GPL (>=2) Archs: i386, x64 MD5sum: 00770d8ba0b26755359a8f4c208318f9 NeedsCompilation: yes Title: Comprehensive analysis of transciptome data Description: This package translates microarray expression data into metadata of reduced dimension. It provides various sample-centered and group-centered visualizations, sample similarity analyses and functional enrichment analyses. The underlying SOM algorithm combines feature clustering, multidimensional scaling and dimension reduction, along with strong visualization capabilities. It enables extraction and description of functional expression modules inherent in the data. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DataRepresentation, Visualization Author: Henry Loeffler-Wirth , Hoang Thanh Le and Martin Kalcher Maintainer: Henry Loeffler-Wirth URL: http://som.izbi.uni-leipzig.de git_url: https://git.bioconductor.org/packages/oposSOM git_branch: RELEASE_3_10 git_last_commit: 0f519fe git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/oposSOM_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/oposSOM_2.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/oposSOM_2.4.0.tgz vignettes: vignettes/oposSOM/inst/doc/Vignette.pdf vignetteTitles: The oposSOM users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oposSOM/inst/doc/Vignette.R dependencyCount: 70 Package: oppar Version: 1.14.0 Depends: R (>= 3.3) Imports: Biobase, methods, GSEABase, GSVA Suggests: knitr, rmarkdown, limma, org.Hs.eg.db, GO.db, snow, parallel License: GPL-2 MD5sum: 5e82af7d4a976889a57034bd4d06a564 NeedsCompilation: yes Title: Outlier profile and pathway analysis in R Description: The R implementation of mCOPA package published by Wang et al. (2012). Oppar provides methods for Cancer Outlier profile Analysis. Although initially developed to detect outlier genes in cancer studies, methods presented in oppar can be used for outlier profile analysis in general. In addition, tools are provided for gene set enrichment and pathway analysis. biocViews: Pathways, GeneSetEnrichment, SystemsBiology, GeneExpression, Software Author: Chenwei Wang [aut], Alperen Taciroglu [aut], Stefan R Maetschke [aut], Colleen C Nelson [aut], Mark Ragan [aut], Melissa Davis [aut], Soroor Hediyeh zadeh [cre], Momeneh Foroutan [ctr] Maintainer: Soroor Hediyeh zadeh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oppar git_branch: RELEASE_3_10 git_last_commit: 1b3eb6f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/oppar_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/oppar_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/oppar_1.14.0.tgz vignettes: vignettes/oppar/inst/doc/oppar.html vignetteTitles: OPPAR: Outlier Profile and Pathway Analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oppar/inst/doc/oppar.R dependencyCount: 53 Package: oppti Version: 1.0.0 Depends: R (>= 3.6) Imports: limma, stats, reshape, ggplot2, grDevices, RColorBrewer, pheatmap, knitr, methods, devtools License: MIT MD5sum: ae688bb7bf60b63d9ac6ff2f727ab14e NeedsCompilation: no Title: Outlier Protein and Phosphosite Target Identifier Description: The aim of oppti is to analyze protein (and phosphosite) expressions to find outlying markers for each sample in the given cohort(s) for the discovery of personalized actionable targets. biocViews: Proteomics, Regression, DifferentialExpression, BiomedicalInformatics, GeneTarget, GeneExpression, Network Author: Abdulkadir Elmas Maintainer: Abdulkadir Elmas URL: https://github.com/Huang-lab/oppti VignetteBuilder: knitr BugReports: https://github.com/Huang-lab/oppti/issues git_url: https://git.bioconductor.org/packages/oppti git_branch: RELEASE_3_10 git_last_commit: 93192cd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/oppti_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/oppti_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/oppti_1.0.0.tgz vignettes: vignettes/oppti/inst/doc/analysis.html vignetteTitles: Outlier Protein and Phosphosite Target Identifier hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oppti/inst/doc/analysis.R dependencyCount: 102 Package: OPWeight Version: 1.8.0 Depends: R (>= 3.4.0), Imports: graphics, qvalue, MASS, tibble, stats, Suggests: airway, BiocStyle, cowplot, DESeq2, devtools, ggplot2, gridExtra, knitr, Matrix, rmarkdown, scales, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 9881964b0e3da5824b07790facdc2704 NeedsCompilation: no Title: Optimal p-value weighting with independent information Description: This package perform weighted-pvalue based multiple hypothesis test and provides corresponding information such as ranking probability, weight, significant tests, etc . To conduct this testing procedure, the testing method apply a probabilistic relationship between the test rank and the corresponding test effect size. biocViews: ImmunoOncology, BiomedicalInformatics, MultipleComparison, Regression, RNASeq, SNP Author: Mohamad Hasan [aut, cre], Paul Schliekelman [aut] Maintainer: Mohamad Hasan URL: https://github.com/mshasan/OPWeight VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OPWeight git_branch: RELEASE_3_10 git_last_commit: af5f8fa git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/OPWeight_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/OPWeight_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OPWeight_1.8.0.tgz vignettes: vignettes/OPWeight/inst/doc/OPWeight.html vignetteTitles: "Introduction to OPWeight" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OPWeight/inst/doc/OPWeight.R dependencyCount: 59 Package: OrderedList Version: 1.58.0 Depends: R (>= 2.1.0), Biobase (>= 1.5.12), twilight (>= 1.9.2), methods Imports: Biobase, graphics, methods, stats, twilight License: GPL (>= 2) Archs: i386, x64 MD5sum: 908318a3315439650280213689521924 NeedsCompilation: no Title: Similarities of Ordered Gene Lists Description: Detection of similarities between ordered lists of genes. Thereby, either simple lists can be compared or gene expression data can be used to deduce the lists. Significance of similarities is evaluated by shuffling lists or by resampling in microarray data, respectively. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Xinan Yang, Stefanie Scheid, Claudio Lottaz Maintainer: Claudio Lottaz URL: http://compdiag.molgen.mpg.de/software/index.shtml git_url: https://git.bioconductor.org/packages/OrderedList git_branch: RELEASE_3_10 git_last_commit: 715f465 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/OrderedList_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/OrderedList_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OrderedList_1.58.0.tgz vignettes: vignettes/OrderedList/inst/doc/tr_2006_01.pdf vignetteTitles: Similarities of Ordered Gene Lists hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrderedList/inst/doc/tr_2006_01.R dependencyCount: 10 Package: ORFik Version: 1.6.9 Depends: R (>= 3.6.0), IRanges (>= 2.17.1), GenomicRanges (>= 1.35.1), GenomicAlignments (>= 1.19.0) Imports: S4Vectors (>= 0.21.3), GenomeInfoDb (>= 1.15.5), GenomicFeatures (>= 1.31.10), AnnotationDbi (>= 1.45.0), rtracklayer (>= 1.43.0), Rcpp (>= 1.0.0), data.table (>= 1.11.8), Biostrings (>= 2.51.1), stats, tools, Rsamtools (>= 1.35.0), BiocGenerics (>= 0.29.1), BiocParallel (>= 1.19.0), SummarizedExperiment (>= 1.14.0), DESeq2 (>= 1.24.0), ggplot2 (>= 2.2.1), gridExtra (>= 2.3), GGally (>= 1.4.0), methods (>= 3.6.0) LinkingTo: Rcpp Suggests: testthat, rmarkdown, knitr, BiocStyle, BSgenome, BSgenome.Hsapiens.UCSC.hg19 License: MIT + file LICENSE MD5sum: 234681ea77cb08968ad56800fac06707 NeedsCompilation: yes Title: Open Reading Frames in Genomics Description: Tools for manipulation of sequence-, RiboSeq-, RNASeq- and CageSeq data. ORFik is extremely fast through use of C, data.table and GenomicRanges. Package allows to reassign starts of the transcripts with the use of CageSeq data, automatic shifting of RiboSeq reads, finding of Open Reading Frames for whole genomes and much more. biocViews: ImmunoOncology, Software, Sequencing, RiboSeq, RNASeq, FunctionalGenomics, Coverage, Alignment, DataImport Author: Haakon Tjeldnes [aut, cre, dtc], Kornel Labun [aut, cph], Katarzyna Chyzynska [ctb, dtc], Evind Valen [ths, fnd] Maintainer: Haakon Tjeldnes URL: https://github.com/Roleren/ORFik VignetteBuilder: knitr BugReports: https://github.com/Roleren/ORFik/issues git_url: https://git.bioconductor.org/packages/ORFik git_branch: RELEASE_3_10 git_last_commit: 3cc10e4 git_last_commit_date: 2020-02-06 Date/Publication: 2020-02-06 source.ver: src/contrib/ORFik_1.6.9.tar.gz win.binary.ver: bin/windows/contrib/3.6/ORFik_1.6.9.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ORFik_1.6.9.tgz vignettes: vignettes/ORFik/inst/doc/ORFikOverview.html vignetteTitles: ORFik Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ORFik/inst/doc/ORFikOverview.R dependencyCount: 144 Package: Organism.dplyr Version: 1.14.0 Depends: R (>= 3.4), dplyr (>= 0.7.0), AnnotationFilter (>= 1.1.3) Imports: RSQLite, S4Vectors, GenomeInfoDb, IRanges, GenomicRanges, GenomicFeatures, AnnotationDbi, methods, tools, utils, BiocFileCache, DBI, dbplyr Suggests: org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.ensGene, testthat, knitr, rmarkdown, BiocStyle, ggplot2 License: Artistic-2.0 MD5sum: 7b263e5559d1cda838cd41b40a005039 NeedsCompilation: no Title: dplyr-based Access to Bioconductor Annotation Resources Description: This package provides an alternative interface to Bioconductor 'annotation' resources, in particular the gene identifier mapping functionality of the 'org' packages (e.g., org.Hs.eg.db) and the genome coordinate functionality of the 'TxDb' packages (e.g., TxDb.Hsapiens.UCSC.hg38.knownGene). biocViews: Annotation, Sequencing, GenomeAnnotation Author: Martin Morgan [aut, cre], Yubo Cheng [ctb] Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Organism.dplyr git_branch: RELEASE_3_10 git_last_commit: 6121d67 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Organism.dplyr_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Organism.dplyr_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Organism.dplyr_1.14.0.tgz vignettes: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.html vignetteTitles: Organism.dplyr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.R importsMe: Ularcirc dependencyCount: 85 Package: OrganismDbi Version: 1.28.0 Depends: R (>= 2.14.0), methods, BiocGenerics (>= 0.15.10), AnnotationDbi (>= 1.33.15), GenomicFeatures (>= 1.23.31) Imports: Biobase, BiocManager, GenomicRanges (>= 1.31.13), graph, IRanges, RBGL, DBI, S4Vectors (>= 0.9.25), stats Suggests: Homo.sapiens, Rattus.norvegicus, BSgenome.Hsapiens.UCSC.hg19, AnnotationHub, FDb.UCSC.tRNAs, rtracklayer, biomaRt, RUnit, RMariaDB License: Artistic-2.0 MD5sum: f108c744e565cbadc9a2e62a86f3d25a NeedsCompilation: no Title: Software to enable the smooth interfacing of different database packages Description: The package enables a simple unified interface to several annotation packages each of which has its own schema by taking advantage of the fact that each of these packages implements a select methods. biocViews: Annotation, Infrastructure Author: Marc Carlson, Hervé Pagès, Martin Morgan, Valerie Obenchain Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/OrganismDbi git_branch: RELEASE_3_10 git_last_commit: 72d16a5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/OrganismDbi_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/OrganismDbi_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OrganismDbi_1.28.0.tgz vignettes: vignettes/OrganismDbi/inst/doc/OrganismDbi.pdf vignetteTitles: OrganismDbi: A meta framework for Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrganismDbi/inst/doc/OrganismDbi.R importsMe: AnnotationHubData, epivizrData, ggbio, gpart suggestsMe: ChIPpeakAnno, epivizrStandalone dependencyCount: 86 Package: OSAT Version: 1.34.0 Depends: methods,stats Suggests: xtable, Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 2fad042da6596db79aa947301f08cd8c NeedsCompilation: no Title: OSAT: Optimal Sample Assignment Tool Description: A sizable genomics study such as microarray often involves the use of multiple batches (groups) of experiment due to practical complication. To minimize batch effects, a careful experiment design should ensure the even distribution of biological groups and confounding factors across batches. OSAT (Optimal Sample Assignment Tool) is developed to facilitate the allocation of collected samples to different batches. With minimum steps, it produces setup that optimizes the even distribution of samples in groups of biological interest into different batches, reducing the confounding or correlation between batches and the biological variables of interest. It can also optimize the even distribution of confounding factors across batches. Our tool can handle challenging instances where incomplete and unbalanced sample collections are involved as well as ideal balanced RCBD. OSAT provides a number of predefined layout for some of the most commonly used genomics platform. Related paper can be find at http://www.biomedcentral.com/1471-2164/13/689 . biocViews: DataRepresentation, Visualization, ExperimentalDesign, QualityControl Author: Li Yan Maintainer: Li Yan URL: http://www.biomedcentral.com/1471-2164/13/689 git_url: https://git.bioconductor.org/packages/OSAT git_branch: RELEASE_3_10 git_last_commit: 4b73336 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/OSAT_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/OSAT_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OSAT_1.34.0.tgz vignettes: vignettes/OSAT/inst/doc/OSAT.pdf vignetteTitles: An introduction to OSAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OSAT/inst/doc/OSAT.R dependencyCount: 2 Package: Oscope Version: 1.16.0 Depends: EBSeq, cluster, testthat, BiocParallel Suggests: BiocStyle License: Artistic-2.0 MD5sum: 358eeb905549c2b04109a63f98a0c5c8 NeedsCompilation: no Title: Oscope - A statistical pipeline for identifying oscillatory genes in unsynchronized single cell RNA-seq Description: Oscope is a statistical pipeline developed to identifying and recovering the base cycle profiles of oscillating genes in an unsynchronized single cell RNA-seq experiment. The Oscope pipeline includes three modules: a sine model module to search for candidate oscillator pairs; a K-medoids clustering module to cluster candidate oscillators into groups; and an extended nearest insertion module to recover the base cycle order for each oscillator group. biocViews: ImmunoOncology, StatisticalMethod,RNASeq, Sequencing, GeneExpression Author: Ning Leng Maintainer: Ning Leng git_url: https://git.bioconductor.org/packages/Oscope git_branch: RELEASE_3_10 git_last_commit: 9ce80ce git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Oscope_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Oscope_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Oscope_1.16.0.tgz vignettes: vignettes/Oscope/inst/doc/Oscope_vignette.pdf vignetteTitles: Oscope_vigette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Oscope/inst/doc/Oscope_vignette.R dependencyCount: 50 Package: OTUbase Version: 1.36.0 Depends: R (>= 2.9.0), methods, S4Vectors, IRanges, ShortRead (>= 1.23.15), Biobase, vegan Imports: Biostrings License: Artistic-2.0 MD5sum: 8d228ac0dd481e14ef89dd8a5912feff NeedsCompilation: no Title: Provides structure and functions for the analysis of OTU data Description: Provides a platform for Operational Taxonomic Unit based analysis biocViews: Sequencing, DataImport Author: Daniel Beck, Matt Settles, and James A. Foster Maintainer: Daniel Beck git_url: https://git.bioconductor.org/packages/OTUbase git_branch: RELEASE_3_10 git_last_commit: 57176d3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/OTUbase_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/OTUbase_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OTUbase_1.36.0.tgz vignettes: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.pdf vignetteTitles: An introduction to OTUbase hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.R dependsOnMe: mcaGUI dependencyCount: 49 Package: OutlierD Version: 1.50.0 Depends: R (>= 2.3.0), Biobase, quantreg License: GPL (>= 2) MD5sum: 8a938ab2a8643791be6846301009dbd4 NeedsCompilation: no Title: Outlier detection using quantile regression on the M-A scatterplots of high-throughput data Description: This package detects outliers using quantile regression on the M-A scatterplots of high-throughput data. biocViews: Microarray Author: HyungJun Cho Maintainer: Sukwoo Kim URL: http://www.korea.ac.kr/~stat2242/ git_url: https://git.bioconductor.org/packages/OutlierD git_branch: RELEASE_3_10 git_last_commit: d0b0f6f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/OutlierD_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/OutlierD_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OutlierD_1.50.0.tgz vignettes: vignettes/OutlierD/inst/doc/OutlierD.pdf vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OutlierD/inst/doc/OutlierD.R dependencyCount: 14 Package: OUTRIDER Version: 1.4.2 Depends: R (>= 3.6), BiocParallel, GenomicFeatures, SummarizedExperiment, data.table, methods Imports: BBmisc, Biobase, BiocGenerics, compiler, DESeq2 (>= 1.16.1), GenomicRanges, ggplot2, grDevices, heatmaply, pheatmap, gplots, graphics, IRanges, matrixStats, plotly, plyr, pcaMethods, PRROC, RColorBrewer, Rcpp, reshape2, S4Vectors, scales, splines, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, RMariaDB, AnnotationDbi, beeswarm, covr License: MIT + file LICENSE MD5sum: 522e0c0a4dcc2909a89ce6350ceb0368 NeedsCompilation: yes Title: OUTRIDER - OUTlier in RNA-Seq fInDER Description: Identification of aberrant gene expression in RNA-seq data. Read count expectations are modeled by an autoencoder to control for confounders in the data. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. Furthermore, OUTRIDER provides useful plotting functions to analyze and visualize the results. biocViews: ImmunoOncology, RNASeq, Transcriptomics, Alignment, Sequencing, GeneExpression, Genetics Author: Felix Brechtmann [aut], Christian Mertes [aut, cre], Agne Matuseviciute [aut], Michaela Fee Müller [ctb], Vicente Yepez [aut], Julien Gagneur [aut] Maintainer: Christian Mertes URL: https://github.com/gagneurlab/OUTRIDER VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OUTRIDER git_branch: RELEASE_3_10 git_last_commit: c1700e1 git_last_commit_date: 2020-04-14 Date/Publication: 2020-04-14 source.ver: src/contrib/OUTRIDER_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/OUTRIDER_1.4.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OUTRIDER_1.4.2.tgz vignettes: vignettes/OUTRIDER/inst/doc/OUTRIDER.pdf vignetteTitles: OUTRIDER: OUTlier in RNA-seq fInDER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OUTRIDER/inst/doc/OUTRIDER.R dependencyCount: 173 Package: OVESEG Version: 1.2.0 Depends: R (>= 3.6) Imports: stats, utils, methods, BiocParallel, SummarizedExperiment, limma, fdrtool, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat, ggplot2, gridExtra, grid, reshape2, scales License: GPL-2 MD5sum: 63aa7c4309f8ba52115e8a66c549d6d0 NeedsCompilation: yes Title: OVESEG-test to detect tissue/cell-specific markers Description: An R package for multiple-group comparison to detect tissue/cell-specific marker genes among subtypes. It provides functions to compute OVESEG-test statistics, derive component weights in the mixture null distribution model and estimate p-values from weightedly aggregated permutations. Obtained posterior probabilities of component null hypotheses can also portrait all kinds of upregulation patterns among subtypes. biocViews: Software, MultipleComparison, CellBiology, GeneExpression Author: Lulu Chen Maintainer: Lulu Chen SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Lululuella/OVESEG git_url: https://git.bioconductor.org/packages/OVESEG git_branch: RELEASE_3_10 git_last_commit: 777ea8a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/OVESEG_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/OVESEG_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/OVESEG_1.2.0.tgz vignettes: vignettes/OVESEG/inst/doc/OVESEG.html vignetteTitles: OVESEG User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OVESEG/inst/doc/OVESEG.R dependencyCount: 35 Package: PAA Version: 1.20.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.6) Imports: e1071, gplots, gtools, limma, MASS, mRMRe, randomForest, ROCR, sva LinkingTo: Rcpp Suggests: BiocStyle, RUnit, BiocGenerics, vsn License: BSD_3_clause + file LICENSE MD5sum: ac500aee387ea1f1c83916de9cec1911 NeedsCompilation: yes Title: PAA (Protein Array Analyzer) Description: PAA imports single color (protein) microarray data that has been saved in gpr file format - esp. ProtoArray data. After preprocessing (background correction, batch filtering, normalization) univariate feature preselection is performed (e.g., using the "minimum M statistic" approach - hereinafter referred to as "mMs"). Subsequently, a multivariate feature selection is conducted to discover biomarker candidates. Therefore, either a frequency-based backwards elimination aproach or ensemble feature selection can be used. PAA provides a complete toolbox of analysis tools including several different plots for results examination and evaluation. biocViews: Classification, Microarray, OneChannel, Proteomics Author: Michael Turewicz [aut, cre], Martin Eisenacher [ctb, cre] Maintainer: Michael Turewicz , Martin Eisenacher URL: http://www.ruhr-uni-bochum.de/mpc/software/PAA/ SystemRequirements: C++ software package Random Jungle git_url: https://git.bioconductor.org/packages/PAA git_branch: RELEASE_3_10 git_last_commit: 25f78bc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PAA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PAA_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PAA_1.20.0.tgz vignettes: vignettes/PAA/inst/doc/PAA_1.7.1.pdf, vignettes/PAA/inst/doc/PAA_vignette.pdf vignetteTitles: PAA_1.7.1.pdf, PAA tutorial hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAA/inst/doc/PAA_vignette.R dependencyCount: 62 Package: PADOG Version: 1.28.0 Depends: R (>= 3.0.0), KEGGdzPathwaysGEO, methods,Biobase Imports: limma, AnnotationDbi, GSA, foreach, doRNG, hgu133plus2.db, hgu133a.db, KEGG.db, nlme Suggests: doParallel, parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: 84a2ce49de9336b876fa0e698badf2fc NeedsCompilation: no Title: Pathway Analysis with Down-weighting of Overlapping Genes (PADOG) Description: This package implements a general purpose gene set analysis method called PADOG that downplays the importance of genes that apear often accross the sets of genes to be analyzed. The package provides also a benchmark for gene set analysis methods in terms of sensitivity and ranking using 24 public datasets from KEGGdzPathwaysGEO package. biocViews: Microarray, OneChannel, TwoChannel Author: Adi Laurentiu Tarca ; Zhonghui Xu Maintainer: Adi Laurentiu Tarca git_url: https://git.bioconductor.org/packages/PADOG git_branch: RELEASE_3_10 git_last_commit: 512431c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PADOG_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PADOG_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PADOG_1.28.0.tgz vignettes: vignettes/PADOG/inst/doc/PADOG.pdf vignetteTitles: PADOG hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PADOG/inst/doc/PADOG.R dependsOnMe: BLMA importsMe: EGSEA dependencyCount: 42 Package: PAIRADISE Version: 1.2.0 Depends: R (>= 3.6), nloptr Imports: SummarizedExperiment, S4Vectors, stats, methods, abind, BiocParallel Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE Archs: i386, x64 MD5sum: 3f8bac560aa30db720642565f009d6c9 NeedsCompilation: no Title: PAIRADISE: Paired analysis of differential isoform expression Description: This package implements the PAIRADISE procedure for detecting differential isoform expression between matched replicates in paired RNA-Seq data. biocViews: RNASeq, DifferentialExpression, AlternativeSplicing, StatisticalMethod, ImmunoOncology Author: Levon Demirdjian, Ying Nian Wu, Yi Xing Maintainer: Qiang Hu , Levon Demirdjian VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PAIRADISE git_branch: RELEASE_3_10 git_last_commit: 28802c5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PAIRADISE_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PAIRADISE_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PAIRADISE_1.2.0.tgz vignettes: vignettes/PAIRADISE/inst/doc/pairadise.html vignetteTitles: PAIRADISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAIRADISE/inst/doc/pairadise.R dependencyCount: 34 Package: paircompviz Version: 1.24.0 Depends: R (>= 2.10), Rgraphviz Imports: Rgraphviz Suggests: multcomp, reshape, rpart, plyr, xtable License: GPL (>=3.0) Archs: i386, x64 MD5sum: 04d76de30b7c3d1fcb3df86d3cd9a202 NeedsCompilation: no Title: Multiple comparison test visualization Description: This package provides visualization of the results from the multiple (i.e. pairwise) comparison tests such as pairwise.t.test, pairwise.prop.test or pairwise.wilcox.test. The groups being compared are visualized as nodes in Hasse diagram. Such approach enables very clear and vivid depiction of which group is significantly greater than which others, especially if comparing a large number of groups. biocViews: GraphAndNetwork Author: Michal Burda Maintainer: Michal Burda git_url: https://git.bioconductor.org/packages/paircompviz git_branch: RELEASE_3_10 git_last_commit: 2aa6da5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/paircompviz_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/paircompviz_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/paircompviz_1.24.0.tgz vignettes: vignettes/paircompviz/inst/doc/vignette.pdf vignetteTitles: Using paircompviz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paircompviz/inst/doc/vignette.R dependencyCount: 11 Package: pandaR Version: 1.18.0 Depends: R (>= 3.0.0), methods, Biobase, BiocGenerics, Imports: matrixStats, igraph, ggplot2, grid, reshape, plyr, RUnit, hexbin Suggests: knitr License: GPL-2 MD5sum: 98ae7d26991f2ae261f184a43f43b817 NeedsCompilation: no Title: PANDA Algorithm Description: Runs PANDA, an algorithm for discovering novel network structure by combining information from multiple complementary data sources. biocViews: StatisticalMethod, GraphAndNetwork, Microarray, GeneRegulation, NetworkInference, GeneExpression, Transcription, Network Author: Dan Schlauch, Joseph N. Paulson, Albert Young, John Quackenbush, Kimberly Glass Maintainer: Joseph N. Paulson , Dan Schlauch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pandaR git_branch: RELEASE_3_10 git_last_commit: 85c61b6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pandaR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pandaR_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pandaR_1.18.0.tgz vignettes: vignettes/pandaR/inst/doc/pandaR.html vignetteTitles: pandaR Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pandaR/inst/doc/pandaR.R dependencyCount: 63 Package: panelcn.mops Version: 1.8.0 Depends: R (>= 3.4), cn.mops, methods, utils, stats, graphics Imports: GenomicRanges, Rsamtools, IRanges, S4Vectors, GenomeInfoDb, grDevices Suggests: knitr, rmarkdown, RUnit, BiocGenerics License: LGPL (>= 2.0) MD5sum: 4ffdbf23392358963141a5ac2047dc0f NeedsCompilation: no Title: CNV detection tool for targeted NGS panel data Description: CNV detection tool for targeted NGS panel data. Extension of the cn.mops package. biocViews: Sequencing, CopyNumberVariation, CellBiology, GenomicVariation, VariantDetection, Genetics Author: Verena Haunschmid, Gundula Povysil Maintainer: Gundula Povysil VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/panelcn.mops git_branch: RELEASE_3_10 git_last_commit: 3f3f728 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/panelcn.mops_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/panelcn.mops_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/panelcn.mops_1.8.0.tgz vignettes: vignettes/panelcn.mops/inst/doc/panelcn.mops.pdf vignetteTitles: panelcn.mops: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/panelcn.mops/inst/doc/panelcn.mops.R suggestsMe: CopyNumberPlots dependencyCount: 30 Package: panp Version: 1.56.0 Depends: R (>= 2.10), affy (>= 1.23.4), Biobase (>= 2.5.5) Imports: Biobase, methods, stats, utils Suggests: gcrma License: GPL (>= 2) MD5sum: 08f1e14d25dbd742f0309e2c7676d4e6 NeedsCompilation: no Title: Presence-Absence Calls from Negative Strand Matching Probesets Description: A function to make gene presence/absence calls based on distance from negative strand matching probesets (NSMP) which are derived from Affymetrix annotation. PANP is applied after gene expression values are created, and therefore can be used after any preprocessing method such as MAS5 or GCRMA, or PM-only methods like RMA. NSMP sets have been established for the HGU133A and HGU133-Plus-2.0 chipsets to date. biocViews: Infrastructure Author: Peter Warren Maintainer: Peter Warren git_url: https://git.bioconductor.org/packages/panp git_branch: RELEASE_3_10 git_last_commit: 360491b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/panp_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/panp_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/panp_1.56.0.tgz vignettes: vignettes/panp/inst/doc/panp.pdf vignetteTitles: gene presence/absence calls hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/panp/inst/doc/panp.R dependencyCount: 13 Package: PANR Version: 1.32.0 Depends: R (>= 2.14), igraph Imports: graphics, grDevices, MASS, methods, pvclust, stats, utils, RedeR Suggests: snow License: Artistic-2.0 MD5sum: e0e50b3ff222e4ecf700123918734f9e NeedsCompilation: no Title: Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations Description: This package provides S4 classes and methods for inferring functional gene networks with edges encoding posterior beliefs of gene association types and nodes encoding perturbation effects. biocViews: ImmunoOncology, NetworkInference, Visualization, GraphAndNetwork, Clustering, CellBasedAssays Author: Xin Wang Maintainer: Xin Wang git_url: https://git.bioconductor.org/packages/PANR git_branch: RELEASE_3_10 git_last_commit: f28599d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PANR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PANR_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PANR_1.32.0.tgz vignettes: vignettes/PANR/inst/doc/PANR-Vignette.pdf vignetteTitles: Main vignette:Posterior association network and enriched functional gene modules inferred from rich phenotypes of gene perturbations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PANR/inst/doc/PANR-Vignette.R dependencyCount: 14 Package: PanVizGenerator Version: 1.14.0 Depends: methods Imports: shiny, tools, jsonlite, pcaMethods, FindMyFriends, igraph, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, digest License: GPL (>= 2) MD5sum: e88ad7d2bcce62ac6e5febcd267deb33 NeedsCompilation: no Title: Generate PanViz visualisations from your pangenome Description: PanViz is a JavaScript based visualisation tool for functionaly annotated pangenomes. PanVizGenerator is a companion for PanViz that facilitates the necessary data preprocessing step necessary to create a working PanViz visualization. The output is fully self-contained so the recipient of the visualization does not need R or PanVizGenerator installed. biocViews: ComparativeGenomics, GUI, Visualization Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen URL: https://github.com/thomasp85/PanVizGenerator VignetteBuilder: knitr BugReports: https://github.com/thomasp85/PanVizGenerator/issues git_url: https://git.bioconductor.org/packages/PanVizGenerator git_branch: RELEASE_3_10 git_last_commit: 73b7e25 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PanVizGenerator_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PanVizGenerator_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PanVizGenerator_1.14.0.tgz vignettes: vignettes/PanVizGenerator/inst/doc/panviz_howto.html vignetteTitles: Creating PanViz visualizations with PanVizGenerator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PanVizGenerator/inst/doc/panviz_howto.R dependencyCount: 99 Package: PAPi Version: 1.26.0 Depends: R (>= 2.15.2), svDialogs, KEGGREST License: GPL(>= 2) MD5sum: 7a13dd668c01d8665fb1ce591194f4c4 NeedsCompilation: no Title: Predict metabolic pathway activity based on metabolomics data Description: The Pathway Activity Profiling - PAPi - is an R package for predicting the activity of metabolic pathways based solely on a metabolomics data set containing a list of metabolites identified and their respective abundances in different biological samples. PAPi generates hypothesis that improves the final biological interpretation. See Aggio, R.B.M; Ruggiero, K. and Villas-Boas, S.G. (2010) - Pathway Activity Profiling (PAPi): from metabolite profile to metabolic pathway activity. Bioinformatics. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Raphael Aggio Maintainer: Raphael Aggio git_url: https://git.bioconductor.org/packages/PAPi git_branch: RELEASE_3_10 git_last_commit: 43a0675 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PAPi_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PAPi_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PAPi_1.26.0.tgz vignettes: vignettes/PAPi/inst/doc/PAPi.pdf, vignettes/PAPi/inst/doc/PAPiPackage.pdf vignetteTitles: PAPi.pdf, Applying PAPi hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PAPi/inst/doc/PAPiPackage.R dependencyCount: 26 Package: parglms Version: 1.18.0 Depends: methods Imports: BiocGenerics, BatchJobs, foreach, doParallel Suggests: RUnit, sandwich, MASS, knitr, GenomeInfoDb, GenomicRanges, gQTLBase, gQTLstats, geuvStore2, gwascat, BiocStyle License: Artistic-2.0 MD5sum: b27e99ae63d16b40b2c6d6ebbc822cc4 NeedsCompilation: no Title: support for parallelized estimation of GLMs/GEEs Description: support for parallelized estimation of GLMs/GEEs, catering for dispersed data Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/parglms git_branch: RELEASE_3_10 git_last_commit: 8d95155 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/parglms_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/parglms_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/parglms_1.18.0.tgz vignettes: vignettes/parglms/inst/doc/parglms.pdf vignetteTitles: parglms: parallelized GLM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/parglms/inst/doc/parglms.R dependencyCount: 35 Package: parody Version: 1.44.0 Depends: R (>= 2.5.0), methods, tools, utils License: Artistic-2.0 MD5sum: ff34c1705004dcb8f448de024ac4bd2b NeedsCompilation: no Title: Parametric And Resistant Outlier DYtection Description: routines for univariate and multivariate outlier detection with a focus on parametric methods, but support for some methods based on resistant statistics biocViews: MultipleComparison Author: VJ Carey Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/parody git_branch: RELEASE_3_10 git_last_commit: 4891131 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/parody_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/parody_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/parody_1.44.0.tgz vignettes: vignettes/parody/inst/doc/parody.pdf vignetteTitles: parody: parametric and resistant outlier detection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/parody/inst/doc/parody.R dependsOnMe: arrayMvout dependencyCount: 3 Package: PAST Version: 1.2.8 Depends: R (>= 3.6) Imports: stats, utils, dplyr, rlang, iterators, parallel, foreach, doParallel, qvalue, rtracklayer, ggplot2, GenomicRanges, S4Vectors Suggests: knitr, rmarkdown License: GPL (>=3) + file LICENSE MD5sum: b0a3f3c653348cf059daa6fa3ef983bb NeedsCompilation: no Title: Pathway Association Study Tool (PAST) Description: PAST takes GWAS output and assigns SNPs to genes, uses those genes to find pathways associated with the genes, and plots pathways based on significance. Implements methods for reading GWAS input data, finding genes associated with SNPs, calculating enrichment score and significance of pathways, and plotting pathways. biocViews: Pathways, GeneSetEnrichment Author: Thrash Adam [cre, aut], DeOrnellis Mason [aut] Maintainer: Thrash Adam URL: https://github.com/IGBB/past VignetteBuilder: knitr BugReports: https://github.com/IGBB/past/issues git_url: https://git.bioconductor.org/packages/PAST git_branch: RELEASE_3_10 git_last_commit: 5072e56 git_last_commit_date: 2020-03-16 Date/Publication: 2020-03-16 source.ver: src/contrib/PAST_1.2.8.tar.gz win.binary.ver: bin/windows/contrib/3.6/PAST_1.2.8.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PAST_1.2.8.tgz vignettes: vignettes/PAST/inst/doc/past.html vignetteTitles: PAST hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAST/inst/doc/past.R dependencyCount: 96 Package: Path2PPI Version: 1.16.0 Depends: R (>= 3.2.1), igraph (>= 1.0.1), methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: 0ead5df8382400389dea70b5496719db NeedsCompilation: no Title: Prediction of pathway-related protein-protein interaction networks Description: Package to predict protein-protein interaction (PPI) networks in target organisms for which only a view information about PPIs is available. Path2PPI predicts PPI networks based on sets of proteins which can belong to a certain pathway from well-established model organisms. It helps to combine and transfer information of a certain pathway or biological process from several reference organisms to one target organism. Path2PPI only depends on the sequence similarity of the involved proteins. biocViews: NetworkInference, SystemsBiology, Network, Proteomics, Pathways Author: Oliver Philipp [aut, cre], Ina Koch [ctb] Maintainer: Oliver Philipp URL: http://www.bioinformatik.uni-frankfurt.de/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Path2PPI git_branch: RELEASE_3_10 git_last_commit: fce9685 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Path2PPI_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Path2PPI_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Path2PPI_1.16.0.tgz vignettes: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.html vignetteTitles: Path2PPI - A brief tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.R dependencyCount: 11 Package: pathifier Version: 1.24.0 Imports: R.oo, princurve (>= 2.0.4) License: Artistic-1.0 MD5sum: c0dea678b9f452ca9d94ea3332c11510 NeedsCompilation: no Title: Quantify deregulation of pathways in cancer Description: Pathifier is an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample. biocViews: Network Author: Yotam Drier Maintainer: Assif Yitzhaky git_url: https://git.bioconductor.org/packages/pathifier git_branch: RELEASE_3_10 git_last_commit: 9876cee git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pathifier_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pathifier_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pathifier_1.24.0.tgz vignettes: vignettes/pathifier/inst/doc/Overview.pdf vignetteTitles: Quantify deregulation of pathways in cancer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathifier/inst/doc/Overview.R dependencyCount: 9 Package: PathoStat Version: 1.12.0 Depends: R (>= 3.5) Imports: limma, corpcor,matrixStats, reshape2, scales, ggplot2, rentrez, DT, tidyr, plyr, dplyr, phyloseq, shiny, stats, methods, XML, graphics, utils, BiocStyle, edgeR, DESeq2, ComplexHeatmap, plotly, webshot, vegan, shinyjs, glmnet, gmodels, ROCR, RColorBrewer, knitr, devtools, ape Suggests: rmarkdown, testthat License: GPL (>= 2) MD5sum: 86b182e7f988286b95987b3e16e11b83 NeedsCompilation: no Title: PathoStat Statistical Microbiome Analysis Package Description: The purpose of this package is to perform Statistical Microbiome Analysis on metagenomics results from sequencing data samples. In particular, it supports analyses on the PathoScope generated report files. PathoStat provides various functionalities including Relative Abundance charts, Diversity estimates and plots, tests of Differential Abundance, Time Series visualization, and Core OTU analysis. biocViews: Microbiome, Metagenomics, GraphAndNetwork, Microarray, PatternLogic, PrincipalComponent, Sequencing, Software, Visualization, RNASeq, ImmunoOncology Author: Solaiappan Manimaran , Matthew Bendall , Sandro Valenzuela Diaz , Eduardo Castro , Tyler Faits , Yue Zhao , Anthony Nicholas Federico , W. Evan Johnson Maintainer: Solaiappan Manimaran , Yue Zhao URL: https://github.com/mani2012/PathoStat VignetteBuilder: knitr BugReports: https://github.com/mani2012/PathoStat/issues git_url: https://git.bioconductor.org/packages/PathoStat git_branch: RELEASE_3_10 git_last_commit: 57b4183 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PathoStat_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PathoStat_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PathoStat_1.12.0.tgz vignettes: vignettes/PathoStat/inst/doc/PathoStat-vignette.html vignetteTitles: PathoStat intro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathoStat/inst/doc/PathoStat-vignette.R dependencyCount: 205 Package: pathprint Version: 1.16.0 Depends: R (>= 3.4) Imports: stats, utils Suggests: ALL, GEOquery, pathprintGEOData, SummarizedExperiment License: GPL MD5sum: c69ef9dd86a2a97b46d6445debbf24cc NeedsCompilation: no Title: Pathway fingerprinting for analysis of gene expression arrays Description: Algorithms to convert a gene expression array provided as an expression table or a GEO reference to a 'pathway fingerprint', a vector of discrete ternary scores representing high (1), low(-1) or insignificant (0) expression in a suite of pathways. biocViews: Transcription, GeneExpression, KEGG, Reactome Author: Gabriel Altschuler, Sokratis Kariotis Maintainer: Sokratis Kariotis git_url: https://git.bioconductor.org/packages/pathprint git_branch: RELEASE_3_10 git_last_commit: 4e1d7c0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pathprint_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pathprint_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pathprint_1.16.0.tgz vignettes: vignettes/pathprint/inst/doc/exampleFingerprint.pdf vignetteTitles: pathprint hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathprint/inst/doc/exampleFingerprint.R dependencyCount: 2 Package: pathRender Version: 1.54.0 Depends: graph, Rgraphviz, RColorBrewer, cMAP, AnnotationDbi, methods, stats4 Suggests: ALL, hgu95av2.db License: LGPL MD5sum: c42f97459d702ae8226d328f39c7365f NeedsCompilation: no Title: Render molecular pathways Description: build graphs from pathway databases, render them by Rgraphviz. biocViews: GraphAndNetwork, Pathways, Visualization Author: Li Long Maintainer: Vince Carey URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/pathRender git_branch: RELEASE_3_10 git_last_commit: 2b0cf50 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pathRender_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pathRender_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pathRender_1.54.0.tgz vignettes: vignettes/pathRender/inst/doc/pathRender.pdf, vignettes/pathRender/inst/doc/plotExG.pdf vignetteTitles: pathRender overview, pathway graphs colored by expression map hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathRender/inst/doc/pathRender.R, vignettes/pathRender/inst/doc/plotExG.R dependencyCount: 32 Package: pathVar Version: 1.16.0 Depends: R (>= 3.3.0), methods, ggplot2, gridExtra Imports: EMT, mclust, Matching, data.table, stats, grDevices, graphics, utils License: LGPL (>= 2.0) MD5sum: dea6507521b555169679eaa3e05391e5 NeedsCompilation: no Title: Methods to Find Pathways with Significantly Different Variability Description: This package contains the functions to find the pathways that have significantly different variability than a reference gene set. It also finds the categories from this pathway that are significant where each category is a cluster of genes. The genes are separated into clusters by their level of variability. biocViews: GeneticVariability, GeneSetEnrichment, Pathways Author: Laurence de Torrente, Samuel Zimmerman, Jessica Mar Maintainer: Samuel Zimmerman git_url: https://git.bioconductor.org/packages/pathVar git_branch: RELEASE_3_10 git_last_commit: f3a638c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pathVar_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pathVar_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pathVar_1.16.0.tgz vignettes: vignettes/pathVar/inst/doc/pathVar.pdf vignetteTitles: Tutorial on How to Use the Functions in the \texttt{PathVar} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathVar/inst/doc/pathVar.R dependencyCount: 59 Package: pathview Version: 1.26.0 Depends: R (>= 2.10), org.Hs.eg.db Imports: KEGGgraph, XML, Rgraphviz, graph, png, AnnotationDbi, KEGGREST, methods, utils Suggests: gage, org.Mm.eg.db, RUnit, BiocGenerics License: GPL (>=3.0) MD5sum: 1ac1f359523bfbdce048a212d651be14 NeedsCompilation: no Title: a tool set for pathway based data integration and visualization Description: Pathview is a tool set for pathway based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, Pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis. biocViews: Pathways, GraphAndNetwork, Visualization, GeneSetEnrichment, DifferentialExpression, GeneExpression, Microarray, RNASeq, Genetics, Metabolomics, Proteomics, SystemsBiology, Sequencing Author: Weijun Luo Maintainer: Weijun Luo URL: https://pathview.uncc.edu/ git_url: https://git.bioconductor.org/packages/pathview git_branch: RELEASE_3_10 git_last_commit: 0affb03 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pathview_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pathview_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pathview_1.26.0.tgz vignettes: vignettes/pathview/inst/doc/pathview.pdf vignetteTitles: Pathview: pathway based data integration and visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathview/inst/doc/pathview.R dependsOnMe: BioNetStat, EGSEA, RNASeqR, SBGNview importsMe: CompGO, debrowser, EnrichmentBrowser, GDCRNATools, KnowSeq, MAGeCKFlute, TCGAbiolinksGUI suggestsMe: gage, Pi, TCGAbiolinks dependencyCount: 49 Package: pathwayPCA Version: 1.2.0 Depends: R (>= 3.6) Imports: lars, methods, parallel, stats, survival, utils Suggests: airway, circlize, grDevices, knitr, RCurl, reshape2, rmarkdown, SummarizedExperiment, survminer, testthat, tidyverse License: GPL-3 MD5sum: 8fab9e58ca4256fd482fd99d9e6668d3 NeedsCompilation: no Title: Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection Description: pathwayPCA is an integrative analysis tool that implements the principal component analysis (PCA) based pathway analysis approaches described in Chen et al. (2008), Chen et al. (2010), and Chen (2011). pathwayPCA allows users to: (1) Test pathway association with binary, continuous, or survival phenotypes. (2) Extract relevant genes in the pathways using the SuperPCA and AES-PCA approaches. (3) Compute principal components (PCs) based on the selected genes. These estimated latent variables represent pathway activities for individual subjects, which can then be used to perform integrative pathway analysis, such as multi-omics analysis. (4) Extract relevant genes that drive pathway significance as well as data corresponding to these relevant genes for additional in-depth analysis. (5) Perform analyses with enhanced computational efficiency with parallel computing and enhanced data safety with S4-class data objects. (6) Analyze studies with complex experimental designs, with multiple covariates, and with interaction effects, e.g., testing whether pathway association with clinical phenotype is different between male and female subjects. Citations: Chen et al. (2008) ; Chen et al. (2010) ; and Chen (2011) . biocViews: CopyNumberVariation, DNAMethylation, GeneExpression, SNP, Transcription, GenePrediction, GeneSetEnrichment, GeneSignaling, GeneTarget, GenomeWideAssociation, GenomicVariation, CellBiology, Epigenetics, FunctionalGenomics, Genetics, Lipidomics, Metabolomics, Proteomics, SystemsBiology, Transcriptomics, Classification, DimensionReduction, FeatureExtraction, PrincipalComponent, Regression, Survival, MultipleComparison, Pathways Author: Gabriel Odom [aut, cre], James Ban [aut], Lizhong Liu [aut], Lily Wang [aut], Steven Chen [aut] Maintainer: Gabriel Odom URL: VignetteBuilder: knitr BugReports: https://github.com/gabrielodom/pathwayPCA/issues git_url: https://git.bioconductor.org/packages/pathwayPCA git_branch: RELEASE_3_10 git_last_commit: 3ee316f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pathwayPCA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pathwayPCA_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pathwayPCA_1.2.0.tgz vignettes: vignettes/pathwayPCA/inst/doc/Introduction_to_pathwayPCA.html, vignettes/pathwayPCA/inst/doc/Supplement1-Quickstart_Guide.html, vignettes/pathwayPCA/inst/doc/Supplement2-Importing_Data.html, vignettes/pathwayPCA/inst/doc/Supplement3-Create_Omics_Objects.html, vignettes/pathwayPCA/inst/doc/Supplement4-Methods_Walkthrough.html, vignettes/pathwayPCA/inst/doc/Supplement5-Analyse_Results.html vignetteTitles: Integrative Pathway Analysis with pathwayPCA, Suppl. 1. Quickstart Guide, Suppl. 2. Importing Data, Suppl. 3. Create Data Objects, Suppl. 4. Test Pathway Significance, Suppl. 5. Visualizing the Results hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathwayPCA/inst/doc/Introduction_to_pathwayPCA.R, vignettes/pathwayPCA/inst/doc/Supplement1-Quickstart_Guide.R, vignettes/pathwayPCA/inst/doc/Supplement2-Importing_Data.R, vignettes/pathwayPCA/inst/doc/Supplement3-Create_Omics_Objects.R, vignettes/pathwayPCA/inst/doc/Supplement4-Methods_Walkthrough.R, vignettes/pathwayPCA/inst/doc/Supplement5-Analyse_Results.R importsMe: fcoex dependencyCount: 12 Package: PathwaySplice Version: 1.10.0 Depends: R (>= 3.5.0) Imports: goseq, Biobase, DOSE, reshape2, igraph, org.Hs.eg.db, org.Mm.eg.db, BiocGenerics, AnnotationDbi, JunctionSeq, BiasedUrn, GO.db,gdata, geneLenDataBase, grDevices, graphics, stats, utils, VennDiagram, RColorBrewer, ensembldb, AnnotationHub, S4Vectors, dplyr, plotly, webshot, htmlwidgets , mgcv ,gridExtra, grid ,gplots, tibble , EnrichmentBrowser, annotate , KEGGREST Suggests: testthat, knitr, rmarkdown License: LGPL(>=2) MD5sum: 10d172465fb939c0767da56a5d8440f4 NeedsCompilation: no Title: An R Package for Unbiased Splicing Pathway Analysis Description: Pathway analysis of alternative splicing would be biased without accounting for the different number of exons associated with each gene, because genes with higher number of exons are more likely to be included in the 'significant' gene list in alternative splicing. PathwaySplice is an R package that: (1) performs pathway analysis that explicitly adjusts for the number of exons associated with each gene (2) visualizes selection bias due to different number of exons for each gene (3) formally tests for presence of bias using logistic regression (4) supports gene sets based on the Gene Ontology terms, as well as more broadly defined gene sets (e.g. MSigDB) or user defined gene sets (5) identifies the significant genes driving pathway significance (6) organizes significant pathways with an enrichment map, where pathways with large number of overlapping genes are grouped together in a network graph biocViews: ImmunoOncology, AlternativeSplicing, DifferentialSplicing, GeneSetEnrichment, GO, RNASeq, Sequencing, Software, Visualization, NetworkEnrichment, Network, Pathways, GraphAndNetwork, Regression Author: Aimin Yan, Xi Chen, Lily Wang Maintainer: Aimin Yan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PathwaySplice git_branch: RELEASE_3_10 git_last_commit: c31f96d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PathwaySplice_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PathwaySplice_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PathwaySplice_1.10.0.tgz vignettes: vignettes/PathwaySplice/inst/doc/tutorial.html vignetteTitles: PathwaySplice hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathwaySplice/inst/doc/tutorial.R dependencyCount: 207 Package: paxtoolsr Version: 1.20.0 Depends: R (>= 3.2), rJava (>= 0.9-8), XML Imports: utils, httr, igraph, plyr, rjson, R.utils, jsonlite, readr Suggests: testthat, knitr, BiocStyle, rmarkdown, RColorBrewer, biomaRt, estrogen, affy, hgu95av2, hgu95av2cdf, limma, foreach, doSNOW, parallel, org.Hs.eg.db License: LGPL-3 MD5sum: 7355d762ac9ddcb397f819256d0a0e1b NeedsCompilation: no Title: PaxtoolsR: Access Pathways from Multiple Databases through BioPAX and Pathway Commons Description: The package provides a set of R functions for interacting with BioPAX OWL files using Paxtools and the querying Pathway Commons (PC) molecular interaction database that are hosted by the Computational Biology Center at Memorial Sloan-Kettering Cancer Center (MSKCC). Pathway Commons databases include: BIND, BioGRID, CORUM, CTD, DIP, DrugBank, HPRD, HumanCyc, IntAct, KEGG, MirTarBase, Panther, PhosphoSitePlus, Reactome, RECON, TRANSFAC. biocViews: GeneSetEnrichment, GraphAndNetwork, Pathways, Software, SystemsBiology, NetworkEnrichment, Network, Reactome, KEGG Author: Augustin Luna Maintainer: Augustin Luna URL: https://github.com/BioPAX/paxtoolsr SystemRequirements: Java (>= 1.6) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/paxtoolsr git_branch: RELEASE_3_10 git_last_commit: bd307cf git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-30 source.ver: src/contrib/paxtoolsr_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/paxtoolsr_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/paxtoolsr_1.20.0.tgz vignettes: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.html vignetteTitles: Using PaxtoolsR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.R dependencyCount: 45 Package: Pbase Version: 0.26.0 Depends: R (>= 2.10), methods, BiocGenerics, Rcpp, Gviz Imports: cleaver (>= 1.3.6), Biobase, Biostrings (>= 2.47.5), IRanges (>= 2.13.11), S4Vectors (>= 0.17.24), mzID, mzR (>= 1.99.1), MSnbase (>= 1.15.5), Pviz, biomaRt, GenomicRanges (>= 1.31.7), rtracklayer (>= 1.39.6), ensembldb (>= 1.99.13), BiocParallel, AnnotationFilter Suggests: testthat (>= 0.8), ggplot2, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, AnnotationHub, knitr, rmarkdown, BiocStyle, EnsDb.Hsapiens.v86 (>= 2.0.0) License: GPL-3 MD5sum: 0d8b793646f1a3c918ae270e806eeed0 NeedsCompilation: no Title: Manipulating and exploring protein and proteomics data Description: A set of classes and functions to investigate and understand protein sequence data in the context of a proteomics experiment. biocViews: ImmunoOncology, Infrastructure, Proteomics, MassSpectrometry, Visualization, DataImport, DataRepresentation Author: Laurent Gatto [aut, cre], Johannes Rainer [aut], Sebastian Gibb [aut] Maintainer: Laurent Gatto URL: https://github.com/ComputationalProteomicsUnit/Pbase VignetteBuilder: knitr BugReports: https://github.com/ComputationalProteomicsUnit/Pbase/issues PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/Pbase git_branch: RELEASE_3_10 git_last_commit: fffa65a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Pbase_0.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Pbase_0.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Pbase_0.26.0.tgz vignettes: vignettes/Pbase/inst/doc/Pbase-data.html vignetteTitles: Pbase data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pbase/inst/doc/Pbase-data.R dependencyCount: 164 Package: pcaExplorer Version: 2.12.0 Imports: DESeq2, SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, genefilter, ggplot2 (>= 2.0.0), d3heatmap, scales, NMF, plyr, topGO, limma, GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0), shinydashboard, shinyBS, ggrepel, DT, shinyAce, threejs, biomaRt, pheatmap, knitr, rmarkdown, base64enc, tidyr, grDevices, methods Suggests: testthat, BiocStyle, airway, org.Hs.eg.db, htmltools License: MIT + file LICENSE MD5sum: 9707449000bd66453b3a1bdf89a02538 NeedsCompilation: no Title: Interactive Visualization of RNA-seq Data Using a Principal Components Approach Description: This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis. biocViews: ImmunoOncology, Visualization, RNASeq, DimensionReduction, PrincipalComponent, QualityControl, GUI, ReportWriting Author: Federico Marini [aut, cre] () Maintainer: Federico Marini URL: https://github.com/federicomarini/pcaExplorer, https://federicomarini.github.io/pcaExplorer/ VignetteBuilder: knitr BugReports: https://github.com/federicomarini/pcaExplorer/issues git_url: https://git.bioconductor.org/packages/pcaExplorer git_branch: RELEASE_3_10 git_last_commit: 832d95f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pcaExplorer_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pcaExplorer_2.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pcaExplorer_2.12.0.tgz vignettes: vignettes/pcaExplorer/inst/doc/pcaExplorer.html, vignettes/pcaExplorer/inst/doc/upandrunning.html vignetteTitles: pcaExplorer User Guide, Up and running with pcaExplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pcaExplorer/inst/doc/pcaExplorer.R, vignettes/pcaExplorer/inst/doc/upandrunning.R importsMe: ideal dependencyCount: 181 Package: pcaGoPromoter Version: 1.30.0 Depends: R (>= 2.14.0), ellipse, Biostrings Imports: AnnotationDbi Suggests: Rgraphviz, GO.db, hgu133plus2.db, mouse4302.db, rat2302.db, hugene10sttranscriptcluster.db, mogene10sttranscriptcluster.db, pcaGoPromoter.Hs.hg19, pcaGoPromoter.Mm.mm9, pcaGoPromoter.Rn.rn4, serumStimulation, parallel License: GPL (>= 2) MD5sum: 517485f39d6558d93c6df7313c841d3f NeedsCompilation: no Title: pcaGoPromoter is used to analyze DNA micro array data Description: This package contains functions to ease the analyses of DNA micro arrays. It utilizes principal component analysis as the initial multivariate analysis, followed by functional interpretation of the principal component dimensions with overrepresentation analysis for GO terms and regulatory interpretations using overrepresentation analysis of predicted transcription factor binding sites with the primo algorithm. biocViews: GeneExpression, Microarray, GO , Visualization Author: Morten Hansen, Jorgen Olsen Maintainer: Morten Hansen git_url: https://git.bioconductor.org/packages/pcaGoPromoter git_branch: RELEASE_3_10 git_last_commit: 3b333b3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pcaGoPromoter_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pcaGoPromoter_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pcaGoPromoter_1.30.0.tgz vignettes: vignettes/pcaGoPromoter/inst/doc/pcaGoPromoter.pdf vignetteTitles: pcaGoPromoter hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcaGoPromoter/inst/doc/pcaGoPromoter.R dependencyCount: 30 Package: pcaMethods Version: 1.78.0 Depends: Biobase, methods Imports: BiocGenerics, Rcpp (>= 0.11.3), MASS LinkingTo: Rcpp Suggests: matrixStats, lattice, ggplot2 License: GPL (>= 3) MD5sum: 053352a2282185c49e0ed314a43c783c NeedsCompilation: yes Title: A collection of PCA methods Description: Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany. biocViews: Bayesian Author: Wolfram Stacklies, Henning Redestig, Kevin Wright Maintainer: Henning Redestig URL: https://github.com/hredestig/pcamethods SystemRequirements: Rcpp BugReports: https://github.com/hredestig/pcamethods/issues git_url: https://git.bioconductor.org/packages/pcaMethods git_branch: RELEASE_3_10 git_last_commit: 0ae2f05 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pcaMethods_1.78.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pcaMethods_1.78.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pcaMethods_1.78.0.tgz vignettes: vignettes/pcaMethods/inst/doc/missingValues.pdf, vignettes/pcaMethods/inst/doc/outliers.pdf, vignettes/pcaMethods/inst/doc/pcaMethods.pdf vignetteTitles: Missing value imputation, Data with outliers, Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcaMethods/inst/doc/missingValues.R, vignettes/pcaMethods/inst/doc/outliers.R, vignettes/pcaMethods/inst/doc/pcaMethods.R dependsOnMe: DeconRNASeq importsMe: CompGO, consensusDE, destiny, MSnbase, OUTRIDER, PanVizGenerator, scde, SomaticSignatures dependencyCount: 10 Package: PCAN Version: 1.14.0 Depends: R (>= 3.3), BiocParallel Imports: grDevices, stats Suggests: BiocStyle, knitr, rmarkdown, reactome.db, STRINGdb License: CC BY-NC-ND 4.0 MD5sum: 12a7ba5cba21a77575518f77e15e19a3 NeedsCompilation: no Title: Phenotype Consensus ANalysis (PCAN) Description: Phenotypes comparison based on a pathway consensus approach. Assess the relationship between candidate genes and a set of phenotypes based on additional genes related to the candidate (e.g. Pathways or network neighbors). biocViews: Annotation, Sequencing, Genetics, FunctionalPrediction, VariantAnnotation, Pathways, Network Author: Matthew Page and Patrice Godard Maintainer: Matthew Page and Patrice Godard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PCAN git_branch: RELEASE_3_10 git_last_commit: 65380e6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PCAN_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PCAN_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PCAN_1.14.0.tgz vignettes: vignettes/PCAN/inst/doc/PCAN.html vignetteTitles: Assessing gene relevance for a set of phenotypes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCAN/inst/doc/PCAN.R dependencyCount: 12 Package: PCAtools Version: 1.2.0 Depends: ggplot2, ggrepel, reshape2, lattice, grDevices, cowplot Imports: methods, stats, utils, Matrix, DelayedMatrixStats, DelayedArray, BiocSingular, BiocParallel, Rcpp, dqrng LinkingTo: Rcpp, beachmat, BH, dqrng Suggests: testthat, scran, BiocGenerics, knitr, Biobase, GEOquery, biomaRt, ggplotify, beachmat License: GPL-3 MD5sum: b721ed796c1c461a7b869a58dd176753 NeedsCompilation: yes Title: PCAtools: Everything Principal Components Analysis Description: Principal Components Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated, i.e., the principal components, whilst at the same time being capable of easy interpretation on the original data. biocViews: RNASeq, GeneExpression, Transcription Author: Kevin Blighe [aut, cre], Myles Lewis [ctb], Aaron Lun [ctb] Maintainer: Kevin Blighe URL: https://github.com/kevinblighe/PCAtools SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PCAtools git_branch: RELEASE_3_10 git_last_commit: 638f4c1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PCAtools_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PCAtools_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PCAtools_1.2.0.tgz vignettes: vignettes/PCAtools/inst/doc/PCAtools.html vignetteTitles: PCAtools: everything Principal Component Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCAtools/inst/doc/PCAtools.R dependencyCount: 84 Package: pcot2 Version: 1.54.0 Depends: R (>= 2.0.0), grDevices, Biobase, amap Suggests: multtest, hu6800.db, KEGG.db, mvtnorm License: GPL (>= 2) Archs: i386, x64 MD5sum: c0c446e2aedf25749b6f190c139dd740 NeedsCompilation: no Title: Principal Coordinates and Hotelling's T-Square method Description: PCOT2 is a permutation-based method for investigating changes in the activity of multi-gene networks. It utilizes inter-gene correlation information to detect significant alterations in gene network activities. Currently it can be applied to two-sample comparisons. biocViews: Microarray, DifferentialExpression, KEGG, GeneExpression, Network Author: Sarah Song, Mik Black Maintainer: Sarah Song git_url: https://git.bioconductor.org/packages/pcot2 git_branch: RELEASE_3_10 git_last_commit: 48bc314 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pcot2_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pcot2_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pcot2_1.54.0.tgz vignettes: vignettes/pcot2/inst/doc/pcot2.pdf vignetteTitles: PCOT2 Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcot2/inst/doc/pcot2.R dependencyCount: 9 Package: PCpheno Version: 1.48.0 Depends: R (>= 2.10), Category, ScISI (>= 1.3.0), SLGI, ppiStats, ppiData, annotate (>= 1.17.4) Imports: AnnotationDbi, Biobase, Category, GO.db, graph, graphics, GSEABase, KEGG.db, methods, ScISI, stats, stats4 Suggests: KEGG.db, GO.db, org.Sc.sgd.db License: Artistic-2.0 MD5sum: 9d27d6533f80a13c153cb297bdedb9f2 NeedsCompilation: no Title: Phenotypes and cellular organizational units Description: Tools to integrate, annotate, and link phenotypes to cellular organizational units such as protein complexes and pathways. biocViews: GraphAndNetwork, Proteomics, Network Author: Nolwenn Le Meur and Robert Gentleman Maintainer: Nolwenn Le Meur git_url: https://git.bioconductor.org/packages/PCpheno git_branch: RELEASE_3_10 git_last_commit: 7c6d2ba git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PCpheno_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PCpheno_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PCpheno_1.48.0.tgz vignettes: vignettes/PCpheno/inst/doc/PCpheno.pdf vignetteTitles: PCpheno Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCpheno/inst/doc/PCpheno.R dependencyCount: 54 Package: pcxn Version: 2.8.0 Depends: R (>= 3.4), pcxnData Imports: methods, grDevices, utils, pheatmap Suggests: igraph, annotate, org.Hs.eg.db License: MIT + file LICENSE Archs: i386, x64 MD5sum: 97d1aa16a803ff040d52f0d8ce3d4e88 NeedsCompilation: no Title: Exploring, analyzing and visualizing functions utilizing the pcxnData package Description: Discover the correlated pathways/gene sets of a single pathway/gene set or discover correlation relationships among multiple pathways/gene sets. Draw a heatmap or create a network of your query and extract members of each pathway/gene set found in the available collections (MSigDB H hallmark, MSigDB C2 Canonical pathways, MSigDB C5 GO BP and Pathprint). biocViews: ExperimentData, ExpressionData, MicroarrayData, GEO, Homo_sapiens_Data, OneChannelData, PathwayInteractionDatabase Author: Sokratis Kariotis, Yered Pita-Juarez, Winston Hide, Wenbin Wei Maintainer: Sokratis Kariotis git_url: https://git.bioconductor.org/packages/pcxn git_branch: RELEASE_3_10 git_last_commit: f4ec77c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pcxn_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pcxn_2.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pcxn_2.8.0.tgz vignettes: vignettes/pcxn/inst/doc/using_pcxn.pdf vignetteTitles: pcxn hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pcxn/inst/doc/using_pcxn.R dependencyCount: 20 Package: pdInfoBuilder Version: 1.50.0 Depends: R (>= 3.2.0), methods, Biobase (>= 2.27.3), RSQLite (>= 1.0.0), affxparser (>= 1.39.4), oligo (>= 1.31.5) Imports: Biostrings (>= 2.35.12), BiocGenerics (>= 0.13.11), DBI (>= 0.3.1), IRanges (>= 2.1.43), oligoClasses (>= 1.29.6), S4Vectors (>= 0.5.22) License: Artistic-2.0 MD5sum: 705392a466c603850be4de53a3632ab7 NeedsCompilation: yes Title: Platform Design Information Package Builder Description: Builds platform design information packages. These consist of a SQLite database containing feature-level data such as x, y position on chip and featureSet ID. The database also incorporates featureSet-level annotation data. The products of this packages are used by the oligo pkg. biocViews: Annotation, Infrastructure Author: Seth Falcon, Vince Carey, Matt Settles, Kristof de Beuf, Benilton Carvalho Maintainer: Benilton Carvalho git_url: https://git.bioconductor.org/packages/pdInfoBuilder git_branch: RELEASE_3_10 git_last_commit: 896b3de git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pdInfoBuilder_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pdInfoBuilder_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pdInfoBuilder_1.50.0.tgz vignettes: vignettes/pdInfoBuilder/inst/doc/BuildingPDInfoPkgs.pdf, vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.pdf vignetteTitles: Building Annotation Packages with pdInfoBuilder for Use with the oligo Package, PDInfo Package Building Affymetrix Mapping Chips hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.R dependencyCount: 58 Package: peakPantheR Version: 1.0.0 Depends: R (>= 3.6.0) Imports: foreach (>= 1.4.4), doParallel (>= 1.0.11), ggplot2 (>= 2.2.1), gridExtra (>= 2.3), MSnbase (>= 2.4.0), mzR (>= 2.12.0), stringr (>= 1.2.0), methods (>= 3.4.0), XML (>= 3.98.1.10), minpack.lm (>= 1.2.1), scales(>= 0.5.0), utils Suggests: testthat, faahKO, msdata, knitr, rmarkdown, pander, BiocStyle License: GPL-3 MD5sum: ba72d93ef88bf142aa874881aafeff64 NeedsCompilation: no Title: Peak Picking and Annotation of High Resolution Experiments Description: An automated pipeline for the detection, integration and reporting of predefined features across a large number of mass spectrometry data files. biocViews: MassSpectrometry, Metabolomics, PeakDetection Author: Arnaud Wolfer [aut, cre] (), Goncalo Correia [aut] (), Jake Pearce [ctb], Caroline Sands [ctb] Maintainer: Arnaud Wolfer URL: https://github.com/phenomecentre/peakPantheR VignetteBuilder: knitr BugReports: https://github.com/phenomecentre/peakPantheR/issues/new git_url: https://git.bioconductor.org/packages/peakPantheR git_branch: RELEASE_3_10 git_last_commit: e9614bf git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/peakPantheR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/peakPantheR_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/peakPantheR_1.0.0.tgz vignettes: vignettes/peakPantheR/inst/doc/getting-started.html, vignettes/peakPantheR/inst/doc/parallel-annotation.html, vignettes/peakPantheR/inst/doc/real-time-annotation.html vignetteTitles: Getting Started with the peakPantheR package, Parallel Annotation, Real Time Annotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/peakPantheR/inst/doc/getting-started.R, vignettes/peakPantheR/inst/doc/parallel-annotation.R, vignettes/peakPantheR/inst/doc/real-time-annotation.R dependencyCount: 93 Package: PECA Version: 1.22.0 Depends: R (>= 3.3) Imports: ROTS, limma, affy, genefilter, preprocessCore, aroma.affymetrix, aroma.core Suggests: SpikeIn License: GPL (>= 2) MD5sum: 399bb169c909f578e7517067a4cb79f0 NeedsCompilation: no Title: Probe-level Expression Change Averaging Description: Calculates Probe-level Expression Change Averages (PECA) to identify differential expression in Affymetrix gene expression microarray studies or in proteomic studies using peptide-level mesurements respectively. biocViews: Software, Proteomics, Microarray, DifferentialExpression, GeneExpression, ExonArray, DifferentialSplicing Author: Tomi Suomi, Jukka Hiissa, Laura L. Elo Maintainer: Tomi Suomi git_url: https://git.bioconductor.org/packages/PECA git_branch: RELEASE_3_10 git_last_commit: 7ba1964 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PECA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PECA_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PECA_1.22.0.tgz vignettes: vignettes/PECA/inst/doc/PECA.pdf vignetteTitles: PECA: Probe-level Expression Change Averaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PECA/inst/doc/PECA.R dependencyCount: 68 Package: PepsNMR Version: 1.4.0 Depends: R (>= 3.5) Imports: Matrix, ptw, ggplot2, gridExtra, matrixStats, reshape2, methods, graphics, stats Suggests: knitr, markdown, rmarkdown, BiocStyle, PepsNMRData License: GPL-2 | file LICENSE Archs: i386, x64 MD5sum: df26561ab0cefd48376f75726c498b07 NeedsCompilation: no Title: Pre-process 1H-NMR FID signals Description: This package provides R functions for common pre-procssing steps that are applied on 1H-NMR data. It also provides a function to read the FID signals directly in the Bruker format. biocViews: Software, Preprocessing, Visualization, Metabolomics, DataImport Author: Manon Martin [aut, cre], Bernadette Govaerts [aut, ths], Benoît Legat [aut], Pascal de Tullio [dtc], Bruno Boulanger [ctb], Paul H.C. Eilers [ctb], Julien Vanwinsberghe [ctb] Maintainer: Manon Martin URL: https://github.com/ManonMartin/PepsNMR VignetteBuilder: knitr BugReports: https://github.com/ManonMartin/PepsNMR/issues git_url: https://git.bioconductor.org/packages/PepsNMR git_branch: RELEASE_3_10 git_last_commit: 7db5372 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PepsNMR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PepsNMR_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PepsNMR_1.4.0.tgz vignettes: vignettes/PepsNMR/inst/doc/PepsNMR_minimal_example.html vignetteTitles: Application of PepsNMR on the Human Serum dataset hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PepsNMR/inst/doc/PepsNMR_minimal_example.R importsMe: ASICS dependencyCount: 62 Package: pepStat Version: 1.20.0 Depends: R (>= 3.0.0), Biobase, IRanges Imports: limma, fields, GenomicRanges, ggplot2, plyr, tools, methods, data.table Suggests: pepDat, Pviz, knitr, shiny License: Artistic-2.0 MD5sum: 38c9989a8032ac56305e0f633829bc52 NeedsCompilation: no Title: Statistical analysis of peptide microarrays Description: Statistical analysis of peptide microarrays biocViews: Microarray, Preprocessing Author: Raphael Gottardo, Gregory C Imholte, Renan Sauteraud, Mike Jiang Maintainer: Gregory C Imholte URL: https://github.com/RGLab/pepStat VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pepStat git_branch: RELEASE_3_10 git_last_commit: 364a303 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pepStat_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pepStat_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pepStat_1.20.0.tgz vignettes: vignettes/pepStat/inst/doc/pepStat.pdf vignetteTitles: Full peptide microarray analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pepStat/inst/doc/pepStat.R dependencyCount: 74 Package: pepXMLTab Version: 1.20.0 Depends: R (>= 3.0.1) Imports: XML(>= 3.98-1.1) Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 1e99d76c2695d8e1f0cf04b3af21fb91 NeedsCompilation: no Title: Parsing pepXML files and filter based on peptide FDR. Description: Parsing pepXML files based one XML package. The package tries to handle pepXML files generated from different softwares. The output will be a peptide-spectrum-matching tabular file. The package also provide function to filter the PSMs based on FDR. biocViews: ImmunoOncology, Proteomics, MassSpectrometry Author: Xiaojing Wang Maintainer: Xiaojing Wang git_url: https://git.bioconductor.org/packages/pepXMLTab git_branch: RELEASE_3_10 git_last_commit: 2f2dbb7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pepXMLTab_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pepXMLTab_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pepXMLTab_1.20.0.tgz vignettes: vignettes/pepXMLTab/inst/doc/pepXMLTab.pdf vignetteTitles: Introduction to pepXMLTab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pepXMLTab/inst/doc/pepXMLTab.R dependencyCount: 3 Package: PERFect Version: 1.0.0 Depends: R (>= 3.6.0), sn (>= 1.5.2) Imports: ggplot2 (>= 3.0.0), phyloseq (>= 1.28.0), zoo (>= 1.8.3), psych (>= 1.8.4), stats (>= 3.6.0), Matrix (>= 1.2.14), fitdistrplus (>= 1.0.12), parallel (>= 3.6.0) Suggests: knitr, rmarkdown, kableExtra, ggpubr License: Artistic-2.0 MD5sum: 3510fc3b8271ae2238d68636dd6718b0 NeedsCompilation: no Title: Permutation filtration for microbiome data Description: PERFect is a novel permutation filtering approach designed to address two unsolved problems in microbiome data processing: (i) define and quantify loss due to filtering by implementing thresholds, and (ii) introduce and evaluate a permutation test for filtering loss to provide a measure of excessive filtering. biocViews: Software, Microbiome, Sequencing, Classification, Metagenomics Author: Ekaterina Smirnova , Quy Cao Maintainer: Quy Cao URL: https://github.com/cxquy91/PERFect VignetteBuilder: knitr BugReports: https://github.com/cxquy91/PERFect/issues git_url: https://git.bioconductor.org/packages/PERFect git_branch: RELEASE_3_10 git_last_commit: d2f503a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PERFect_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PERFect_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PERFect_1.0.0.tgz vignettes: vignettes/PERFect/inst/doc/MethodIllustration.html vignetteTitles: Method Illustration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PERFect/inst/doc/MethodIllustration.R dependencyCount: 97 Package: perturbatr Version: 1.6.0 Depends: R (>= 3.5), methods, stats Imports: dplyr, ggplot2, tidyr, assertthat, lme4, splines, igraph, foreach, parallel, doParallel, diffusr, lazyeval, tibble, grid, utils, graphics, scales, magrittr, formula.tools, rlang Suggests: testthat, lintr, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 181f34566229478c038b92e3a465cadf NeedsCompilation: no Title: Statistical Analysis of High-Throughput Genetic Perturbation Screens Description: perturbatr does stage-wise analysis of large-scale genetic perturbation screens for integrated data sets consisting of multiple screens. For multiple integrated perturbation screens a hierarchical model that considers the variance between different biological conditions is fitted. The resulting list of gene effects is then further extended using a network propagation algorithm to correct for false negatives. biocViews: ImmunoOncology, Regression, CellBasedAssays, Network Author: Simon Dirmeier [aut, cre] Maintainer: Simon Dirmeier URL: https://github.com/cbg-ethz/perturbatr VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/perturbatr/issues git_url: https://git.bioconductor.org/packages/perturbatr git_branch: RELEASE_3_10 git_last_commit: 7c959ed git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/perturbatr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/perturbatr_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/perturbatr_1.6.0.tgz vignettes: vignettes/perturbatr/inst/doc/perturbatr.html vignetteTitles: perturbatr cookbook hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/perturbatr/inst/doc/perturbatr.R dependencyCount: 77 Package: PGA Version: 1.16.0 Depends: R (>= 3.5.0), IRanges, GenomicRanges, Biostrings (>= 2.26.3), data.table, rTANDEM Imports: S4Vectors (>= 0.9.25), Rsamtools (>= 1.10.2), GenomicFeatures (>= 1.19.8), biomaRt (>= 2.17.1), stringr, RCurl, Nozzle.R1, VariantAnnotation (>= 1.7.28), rtracklayer, RSQLite, ggplot2, AnnotationDbi, customProDB (>= 1.21.5), pheatmap, dplyr, processx, readr, seqinr Suggests: RMariaDB, BSgenome.Hsapiens.UCSC.hg19, RUnit, BiocGenerics, BiocStyle, knitr, R.utils License: GPL-2 MD5sum: a1ddf4925994b24566c63a61d06c2087 NeedsCompilation: no Title: An package for identification of novel peptides by customized database derived from RNA-Seq Description: This package provides functions for construction of customized protein databases based on RNA-Seq data with/without genome guided, database searching, post-processing and report generation. This kind of customized protein database includes both the reference database (such as Refseq or ENSEMBL) and the novel peptide sequences form RNA-Seq data. biocViews: Proteomics, ImmunoOncology, MassSpectrometry, Software, ReportWriting, RNASeq, Sequencing Author: Shaohang Xu, Bo Wen Maintainer: Bo Wen , Shaohang Xu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PGA git_branch: RELEASE_3_10 git_last_commit: 9acdd39 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PGA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PGA_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PGA_1.16.0.tgz vignettes: vignettes/PGA/inst/doc/PGA.pdf vignetteTitles: PGA tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PGA/inst/doc/PGA.R dependencyCount: 126 Package: pgca Version: 1.10.0 Imports: utils, stats Suggests: knitr, testthat License: GPL (>= 2) MD5sum: f6c8b1514590f7aded4eed2a49a39027 NeedsCompilation: no Title: PGCA: An Algorithm to Link Protein Groups Created from MS/MS Data Description: Protein Group Code Algorithm (PGCA) is a computationally inexpensive algorithm to merge protein summaries from multiple experimental quantitative proteomics data. The algorithm connects two or more groups with overlapping accession numbers. In some cases, pairwise groups are mutually exclusive but they may still be connected by another group (or set of groups) with overlapping accession numbers. Thus, groups created by PGCA from multiple experimental runs (i.e., global groups) are called "connected" groups. These identified global protein groups enable the analysis of quantitative data available for protein groups instead of unique protein identifiers. biocViews: WorkflowStep,AssayDomain,Proteomics,MassSpectrometry,ImmunoOncology Author: Gabriela Cohen-Freue Maintainer: Gabriela Cohen-Freue VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pgca git_branch: RELEASE_3_10 git_last_commit: 4354a3f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pgca_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pgca_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pgca_1.10.0.tgz vignettes: vignettes/pgca/inst/doc/intro.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pgca/inst/doc/intro.R dependencyCount: 2 Package: PGSEA Version: 1.60.0 Depends: R (>= 2.10), GO.db, KEGG.db, AnnotationDbi, annaffy, methods, Biobase (>= 2.5.5) Suggests: GSEABase, GEOquery, org.Hs.eg.db, hgu95av2.db, limma License: GPL-2 MD5sum: f7c7a4a83b03ec4489cfb7b6836f564f NeedsCompilation: no Title: Parametric Gene Set Enrichment Analysis Description: Parametric Analysis of Gene Set Enrichment biocViews: Microarray Author: Kyle Furge and Karl Dykema Maintainer: Karl Dykema git_url: https://git.bioconductor.org/packages/PGSEA git_branch: RELEASE_3_10 git_last_commit: 61ff68a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PGSEA_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PGSEA_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PGSEA_1.60.0.tgz vignettes: vignettes/PGSEA/inst/doc/PGSEA.pdf, vignettes/PGSEA/inst/doc/PGSEA2.pdf vignetteTitles: HOWTO: PGSEA, HOWTO: PGSEA Example Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PGSEA/inst/doc/PGSEA.R, vignettes/PGSEA/inst/doc/PGSEA2.R dependsOnMe: GeneExpressionSignature dependencyCount: 29 Package: phantasus Version: 1.6.0 Depends: R (>= 3.5) Imports: ggplot2, protolite, Biobase, GEOquery, Rook, htmltools, httpuv, jsonlite, limma, opencpu, assertthat, methods, httr, rhdf5, utils, parallel, stringr, fgsea (>= 1.9.4), svglite, gtable, stats, Matrix, Matrix.utils, pheatmap, scales, ccaPP, grid, grDevices, AnnotationDbi Suggests: testthat, BiocStyle, knitr, rmarkdown, data.table License: MIT + file LICENSE MD5sum: d514f254c059f3f84269aa45dc24b797 NeedsCompilation: no Title: Visual and interactive gene expression analysis Description: Phantasus is a web-application for visual and interactive gene expression analysis. Phantasus is based on Morpheus – a web-based software for heatmap visualisation and analysis, which was integrated with an R environment via OpenCPU API. Aside from basic visualization and filtering methods, R-based methods such as k-means clustering, principal component analysis or differential expression analysis with limma package are supported. biocViews: GeneExpression, GUI, Visualization, DataRepresentation, Transcriptomics, RNASeq, Microarray, Normalization, Clustering, DifferentialExpression, PrincipalComponent, ImmunoOncology Author: Daria Zenkova [aut], Vladislav Kamenev [aut], Rita Sablina [ctb], Maxim Artyomov [aut], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://genome.ifmo.ru/phantasus, https://artyomovlab.wustl.edu/phantasus VignetteBuilder: knitr BugReports: https://github.com/ctlab/phantasus/issues git_url: https://git.bioconductor.org/packages/phantasus git_branch: RELEASE_3_10 git_last_commit: 80c7b42 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/phantasus_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/phantasus_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/phantasus_1.6.0.tgz vignettes: vignettes/phantasus/inst/doc/phantasus-tutorial.html vignetteTitles: Using phantasus application hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/phantasus/inst/doc/phantasus-tutorial.R dependencyCount: 129 Package: PharmacoGx Version: 1.17.1 Depends: R (>= 3.6) Imports: Biobase, piano, magicaxis, RColorBrewer, parallel, caTools, methods, downloader, stats, utils, graphics, grDevices, lsa, reshape2 Suggests: xtable, testthat License: Artistic-2.0 MD5sum: ee84085ea398bdbe2463583ace7360c3 NeedsCompilation: no Title: Analysis of Large-Scale Pharmacogenomic Data Description: Contains a set of functions to perform large-scale analysis of pharmacogenomic data. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification Author: Petr Smirnov, Zhaleh Safikhani, Mark Freeman, Benjamin Haibe-Kains Maintainer: Benjamin Haibe-Kains BugReports: https://github.com/bhklab/PharmacoGx/issues git_url: https://git.bioconductor.org/packages/PharmacoGx git_branch: RELEASE_3_10 git_last_commit: e34f5b8 git_last_commit_date: 2020-01-29 Date/Publication: 2020-01-29 source.ver: src/contrib/PharmacoGx_1.17.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/PharmacoGx_1.17.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PharmacoGx_1.17.1.tgz vignettes: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.pdf, vignettes/PharmacoGx/inst/doc/PharmacoGx.pdf vignetteTitles: Creating a PharmacoSet object, PharmacoGx: an R package for analysis of large pharmacogenomic datasets hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.R, vignettes/PharmacoGx/inst/doc/PharmacoGx.R importsMe: Xeva dependencyCount: 116 Package: phemd Version: 1.1.1 Depends: R (>= 3.6), monocle Imports: SingleCellExperiment, RColorBrewer, igraph, transport, pracma, cluster, Rtsne, destiny, Seurat, RANN, ggplot2, maptree, pheatmap, scatterplot3d, VGAM, methods, grDevices, graphics, stats, utils, cowplot, S4Vectors, BiocGenerics, SummarizedExperiment, Biobase Suggests: knitr License: GPL-2 MD5sum: a2fd862da8160dee366da2e066c60ef4 NeedsCompilation: no Title: Phenotypic EMD for comparison of single-cell samples Description: Package for comparing and generating a low-dimensional embedding of multiple single-cell samples. biocViews: Clustering, ComparativeGenomics, Proteomics, Transcriptomics, Sequencing, DimensionReduction, SingleCell, DataRepresentation, Visualization, MultipleComparison Author: William S Chen Maintainer: William S Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phemd git_branch: master git_last_commit: a72896c git_last_commit_date: 2019-05-07 Date/Publication: 2019-05-09 source.ver: src/contrib/phemd_1.1.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/phemd_1.1.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/phemd_1.2.0.tgz vignettes: vignettes/phemd/inst/doc/phemd.html vignetteTitles: PhEMD vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phemd/inst/doc/phemd.R dependencyCount: 222 Package: phenopath Version: 1.10.0 Imports: Rcpp (>= 0.12.8), SummarizedExperiment, methods, stats, dplyr, tibble, ggplot2, tidyr LinkingTo: Rcpp Suggests: knitr, rmarkdown, forcats, testthat, BiocStyle, SingleCellExperiment License: Apache License (== 2.0) MD5sum: 1d1e2f8e72b599f996cac8b274f177d3 NeedsCompilation: yes Title: Genomic trajectories with heterogeneous genetic and environmental backgrounds Description: PhenoPath infers genomic trajectories (pseudotimes) in the presence of heterogeneous genetic and environmental backgrounds and tests for interactions between them. biocViews: ImmunoOncology, RNASeq, GeneExpression, Bayesian, SingleCell, PrincipalComponent Author: Kieran Campbell Maintainer: Kieran Campbell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phenopath git_branch: RELEASE_3_10 git_last_commit: 0ff6e18 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/phenopath_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/phenopath_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/phenopath_1.10.0.tgz vignettes: vignettes/phenopath/inst/doc/introduction_to_phenopath.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenopath/inst/doc/introduction_to_phenopath.R suggestsMe: splatter dependencyCount: 83 Package: phenoTest Version: 1.34.0 Depends: R (>= 2.12.0), Biobase, methods, annotate, Heatplus, BMA, ggplot2 Imports: survival, limma, Hmisc, gplots, Category, AnnotationDbi, hopach, biomaRt, GSEABase, genefilter, xtable, annotate, mgcv, SNPchip, hgu133a.db, HTSanalyzeR, ellipse Suggests: GSEABase, KEGG.db, GO.db Enhances: parallel, org.Ce.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Hs.eg.db, org.Dm.eg.db License: GPL (>=2) MD5sum: 35f930938e96fd137a4d714d6a9bf30c NeedsCompilation: no Title: Tools to test association between gene expression and phenotype in a way that is efficient, structured, fast and scalable. We also provide tools to do GSEA (Gene set enrichment analysis) and copy number variation. Description: Tools to test correlation between gene expression and phenotype in a way that is efficient, structured, fast and scalable. GSEA is also provided. biocViews: Microarray, DifferentialExpression, MultipleComparison, Clustering, Classification Author: Evarist Planet Maintainer: Evarist Planet git_url: https://git.bioconductor.org/packages/phenoTest git_branch: RELEASE_3_10 git_last_commit: 977621c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/phenoTest_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/phenoTest_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/phenoTest_1.34.0.tgz vignettes: vignettes/phenoTest/inst/doc/phenoTest.pdf vignetteTitles: Manual for the phenoTest library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenoTest/inst/doc/phenoTest.R importsMe: canceR dependencyCount: 177 Package: PhenStat Version: 2.22.0 Depends: R (>= 3.5.0) Imports: SmoothWin, methods, car, nlme, nortest, MASS, msgps, logistf, knitr, tools, pingr, ggplot2, reshape, corrplot, graph, lme4, graphics, grDevices, utils, stats Suggests: RUnit, BiocGenerics License: file LICENSE MD5sum: c2db406790784f559059f8e19eeb164b NeedsCompilation: no Title: Statistical analysis of phenotypic data Description: Package contains methods for statistical analysis of phenotypic data. biocViews: StatisticalMethod Author: Natalja Kurbatova, Natasha Karp, Jeremy Mason, Hamed Haselimashhadi Maintainer: Hamed Haselimashhadi git_url: https://git.bioconductor.org/packages/PhenStat git_branch: RELEASE_3_10 git_last_commit: 85472df git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PhenStat_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PhenStat_2.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PhenStat_2.22.0.tgz vignettes: vignettes/PhenStat/inst/doc/PhenStat.pdf vignetteTitles: PhenStat Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhenStat/inst/doc/PhenStat.R dependencyCount: 119 Package: philr Version: 1.12.0 Imports: ape, phangorn, tidyr, ggplot2, ggtree Suggests: testthat, knitr, rmarkdown, BiocStyle, phyloseq, glmnet, dplyr License: GPL-3 Archs: i386, x64 MD5sum: 8ea2ff54fb886e88319a27ac40a3ba9e NeedsCompilation: no Title: Phylogenetic partitioning based ILR transform for metagenomics data Description: PhILR is short for Phylogenetic Isometric Log-Ratio Transform. This package provides functions for the analysis of compositional data (e.g., data representing proportions of different variables/parts). Specifically this package allows analysis of compositional data where the parts can be related through a phylogenetic tree (as is common in microbiota survey data) and makes available the Isometric Log Ratio transform built from the phylogenetic tree and utilizing a weighted reference measure. biocViews: ImmunoOncology, Sequencing, Microbiome, Metagenomics, Software Author: Justin Silverman Maintainer: Justin Silverman URL: https://github.com/jsilve24/philr VignetteBuilder: knitr BugReports: https://github.com/jsilve24/philr/issues git_url: https://git.bioconductor.org/packages/philr git_branch: RELEASE_3_10 git_last_commit: c9be279 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/philr_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/philr_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/philr_1.12.0.tgz vignettes: vignettes/philr/inst/doc/philr-intro.html vignetteTitles: Introduction to PhILR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/philr/inst/doc/philr-intro.R dependencyCount: 74 Package: phosphonormalizer Version: 1.10.0 Depends: R (>= 3.4.0) Imports: plyr, stats, graphics, matrixStats Suggests: knitr, rmarkdown, testthat Enhances: MSnbase License: GPL (>= 2) MD5sum: 00830f27cee472ccd3d558b5648a2396 NeedsCompilation: no Title: Compensates for the bias introduced by median normalization in phosphoproteomics Description: It uses the overlap between enriched and non-enriched datasets to compensate for the bias introduced in global phosphorylation after applying median normalization. biocViews: Software, StatisticalMethod, WorkflowStep, Normalization, Proteomics Author: Sohrab Saraei, Tomi Suomi, Otto Kauko, Laura L. Elo Maintainer: Sohrab Saraei VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phosphonormalizer git_branch: RELEASE_3_10 git_last_commit: 9322e59 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/phosphonormalizer_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/phosphonormalizer_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/phosphonormalizer_1.10.0.tgz vignettes: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.pdf, vignettes/phosphonormalizer/inst/doc/vignette.html vignetteTitles: phosphonormalizer: Phosphoproteomics Normalization, Pairwise normalization of phosphoproteomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.R, vignettes/phosphonormalizer/inst/doc/vignette.R dependencyCount: 7 Package: PhyloProfile Version: 1.0.7 Depends: R (>= 3.6.0) Imports: ape, bioDist, BiocStyle, Biostrings, colourpicker, data.table, DT, energy, ExperimentHub, ggplot2, gridExtra, pbapply, RColorBrewer, shiny, shinyBS, shinyjs, OmaDB, plyr, xml2, zoo Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 2939da8959637566d74b60064f41eb56 NeedsCompilation: no Title: PhyloProfile Description: PhyloProfile is a tool for exploring complex phylogenetic profiles. Phylogenetic profiles, presence/absence patterns of genes over a set of species, are commonly used to trace the functional and evolutionary history of genes across species and time. With PhyloProfile we can enrich regular phylogenetic profiles with further data like sequence/structure similarity, to make phylogenetic profiling more meaningful. Besides the interactive visualisation powered by R-Shiny, the package offers a set of further analysis features to gain insights like the gene age estimation or core gene identification. biocViews: Software, Visualization, DataRepresentation, MultipleComparison, FunctionalPrediction Author: Vinh Tran [aut, cre], Bastian Greshake Tzovaras [aut], Ingo Ebersberger [aut], Carla Mölbert [ctb] Maintainer: Vinh Tran URL: https://github.com/BIONF/PhyloProfile/ VignetteBuilder: knitr BugReports: https://github.com/BIONF/PhyloProfile/issues git_url: https://git.bioconductor.org/packages/PhyloProfile git_branch: RELEASE_3_10 git_last_commit: b2c5af6 git_last_commit_date: 2020-03-14 Date/Publication: 2020-03-14 source.ver: src/contrib/PhyloProfile_1.0.7.tar.gz win.binary.ver: bin/windows/contrib/3.6/PhyloProfile_1.0.7.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PhyloProfile_1.0.7.tgz vignettes: vignettes/PhyloProfile/inst/doc/PhyloProfile-vignette.html vignetteTitles: PhyloProfile hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhyloProfile/inst/doc/PhyloProfile-vignette.R dependencyCount: 140 Package: phyloseq Version: 1.30.0 Depends: R (>= 3.3.0) Imports: ade4 (>= 1.7.4), ape (>= 5.0), Biobase (>= 2.36.2), BiocGenerics (>= 0.22.0), biomformat (>= 1.0.0), Biostrings (>= 2.40.0), cluster (>= 2.0.4), data.table (>= 1.10.4), foreach (>= 1.4.3), ggplot2 (>= 2.1.0), igraph (>= 1.0.1), methods (>= 3.3.0), multtest (>= 2.28.0), plyr (>= 1.8.3), reshape2 (>= 1.4.1), scales (>= 0.4.0), vegan (>= 2.5) Suggests: BiocStyle (>= 2.4), DESeq2 (>= 1.16.1), genefilter (>= 1.58), knitr (>= 1.16), magrittr (>= 1.5), metagenomeSeq (>= 1.14), rmarkdown (>= 1.6), testthat (>= 1.0.2) Enhances: doParallel (>= 1.0.10) License: AGPL-3 MD5sum: 53e6ba7e3de2b9afd48f1e4d2a88d1a2 NeedsCompilation: no Title: Handling and analysis of high-throughput microbiome census data Description: phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data. biocViews: ImmunoOncology, Sequencing, Microbiome, Metagenomics, Clustering, Classification, MultipleComparison, GeneticVariability Author: Paul J. McMurdie , Susan Holmes , with contributions from Gregory Jordan and Scott Chamberlain Maintainer: Paul J. McMurdie URL: http://dx.plos.org/10.1371/journal.pone.0061217 VignetteBuilder: knitr BugReports: https://github.com/joey711/phyloseq/issues git_url: https://git.bioconductor.org/packages/phyloseq git_branch: RELEASE_3_10 git_last_commit: 2d3021b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/phyloseq_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/phyloseq_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/phyloseq_1.30.0.tgz vignettes: vignettes/phyloseq/inst/doc/phyloseq-analysis.html, vignettes/phyloseq/inst/doc/phyloseq-basics.html, vignettes/phyloseq/inst/doc/phyloseq-FAQ.html, vignettes/phyloseq/inst/doc/phyloseq-mixture-models.html vignetteTitles: analysis vignette, phyloseq basics vignette, phyloseq Frequently Asked Questions (FAQ), phyloseq and DESeq2 on Colorectal Cancer Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phyloseq/inst/doc/phyloseq-analysis.R, vignettes/phyloseq/inst/doc/phyloseq-basics.R, vignettes/phyloseq/inst/doc/phyloseq-FAQ.R, vignettes/phyloseq/inst/doc/phyloseq-mixture-models.R dependsOnMe: microbiome, SIAMCAT importsMe: metavizr, PathoStat, PERFect, RCM, RPA suggestsMe: decontam, metagenomeFeatures, MMUPHin, philr dependencyCount: 85 Package: Pi Version: 1.14.0 Depends: XGR, igraph, dnet, ggplot2, graphics Imports: Matrix, ggbio, GenomicRanges, GenomeInfoDb, supraHex, scales, grDevices, ggrepel, ROCR, randomForest, glmnet, Gviz, lattice, caret, plot3D, stats Suggests: foreach, doParallel, BiocStyle, knitr, rmarkdown, png, GGally, gridExtra, ExpressionAtlas, ggforce, fgsea, pathview, tidyr, dplyr License: GPL-3 MD5sum: 5968955f11ace55ac098c02fd69aa37d NeedsCompilation: no Title: Leveraging Genetic Evidence to Prioritise Drug Targets at the Gene and Pathway Level Description: Priority index or Pi is developed as a genomic-led target prioritisation system, with the focus on leveraging human genetic data to prioritise potential drug targets at the gene, pathway and network level. The long term goal is to use such information to enhance early-stage target validation. Based on evidence of disease association from genome-wide association studies (GWAS), this prioritisation system is able to generate evidence to support identification of the specific modulated genes (seed genes) that are responsible for the genetic association signal by utilising knowledge of linkage disequilibrium (co-inherited genetic variants), distance of associated variants from the gene, evidence of independent genetic association with gene expression in disease-relevant tissues, cell types and states, and evidence of physical interactions between disease-associated genetic variants and gene promoters based on genome-wide capture HiC-generated promoter interactomes in primary blood cell types. Seed genes are scored in an integrative way, quantifying the genetic influence. Scored seed genes are subsequently used as baits to rank seed genes plus additional (non-seed) genes; this is achieved by iteratively exploring the global connectivity of a gene interaction network. Genes with the highest priority are further used to identify/prioritise pathways that are significantly enriched with highly prioritised genes. Prioritised genes are also used to identify a gene network interconnecting highly prioritised genes and a minimal number of less prioritised genes (which act as linkers bringing together highly prioritised genes). biocViews: Software, Genetics, GraphAndNetwork, Pathways, GeneExpression, GeneTarget, GenomeWideAssociation, LinkageDisequilibrium, Network, HiC Author: Hai Fang, the ULTRA-DD Consortium, Julian C Knight Maintainer: Hai Fang URL: http://pi314.r-forge.r-project.org VignetteBuilder: knitr BugReports: https://github.com/hfang-bristol/Pi/issues git_url: https://git.bioconductor.org/packages/Pi git_branch: RELEASE_3_10 git_last_commit: 76882e6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Pi_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Pi_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Pi_1.14.0.tgz vignettes: vignettes/Pi/inst/doc/Pi_vignettes.html vignetteTitles: Pi User Manual (R/Bioconductor package) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pi/inst/doc/Pi_vignettes.R dependencyCount: 189 Package: piano Version: 2.2.0 Depends: R (>= 3.5) Imports: BiocGenerics, Biobase, gplots, igraph, relations, marray, fgsea, shiny, DT, htmlwidgets, shinyjs, shinydashboard, visNetwork, scales, grDevices, graphics, stats, utils, methods Suggests: yeast2.db, rsbml, plotrix, limma, affy, plier, affyPLM, gtools, biomaRt, snowfall, AnnotationDbi, knitr, rmarkdown, BiocStyle License: GPL (>=2) MD5sum: b1e3039e9e541787c06e3fdcbdebb9c4 NeedsCompilation: no Title: Platform for integrative analysis of omics data Description: Piano performs gene set analysis using various statistical methods, from different gene level statistics and a wide range of gene-set collections. Furthermore, the Piano package contains functions for combining the results of multiple runs of gene set analyses. biocViews: Microarray, Preprocessing, QualityControl, DifferentialExpression, Visualization, GeneExpression, GeneSetEnrichment, Pathways Author: Leif Varemo Wigge and Intawat Nookaew Maintainer: Leif Varemo Wigge URL: http://www.sysbio.se/piano VignetteBuilder: knitr BugReports: https://github.com/varemo/piano/issues git_url: https://git.bioconductor.org/packages/piano git_branch: RELEASE_3_10 git_last_commit: c818080 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/piano_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/piano_2.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/piano_2.2.0.tgz vignettes: vignettes/piano/inst/doc/piano-vignette.pdf, vignettes/piano/inst/doc/Running_gene-set_analysis_with_piano.html vignetteTitles: Piano - Platform for Integrative Analysis of Omics data, Running gene-set anaysis with piano hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/piano/inst/doc/piano-vignette.R, vignettes/piano/inst/doc/Running_gene-set_analysis_with_piano.R importsMe: PharmacoGx dependencyCount: 99 Package: pickgene Version: 1.58.0 Imports: graphics, grDevices, MASS, stats, utils License: GPL (>= 2) MD5sum: d51b2e453bb443734dc928738567d605 NeedsCompilation: no Title: Adaptive Gene Picking for Microarray Expression Data Analysis Description: Functions to Analyze Microarray (Gene Expression) Data. biocViews: Microarray, DifferentialExpression Author: Brian S. Yandell Maintainer: Brian S. Yandell URL: http://www.stat.wisc.edu/~yandell/statgen git_url: https://git.bioconductor.org/packages/pickgene git_branch: RELEASE_3_10 git_last_commit: b9e1122 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pickgene_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pickgene_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pickgene_1.58.0.tgz vignettes: vignettes/pickgene/inst/doc/pickgene.pdf vignetteTitles: Adaptive Gene Picking for Microarray Expression Data Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: PICS Version: 2.30.0 Depends: R (>= 2.14.0), BiocGenerics (>= 0.1.3) Imports: methods, stats4, IRanges, GenomicRanges, graphics, grDevices, stats, Rsamtools, GenomicAlignments, S4Vectors Suggests: ShortRead, rtracklayer, parallel License: Artistic-2.0 MD5sum: 22f137ae331b1592b9c36f5958b32d73 NeedsCompilation: yes Title: Probabilistic inference of ChIP-seq Description: Probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach. biocViews: Clustering, Visualization, Sequencing, ChIPSeq Author: Xuekui Zhang , Raphael Gottardo Maintainer: Renan Sauteraud SystemRequirements: GSL (GNU Scientific Library) git_url: https://git.bioconductor.org/packages/PICS git_branch: RELEASE_3_10 git_last_commit: a78c196 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PICS_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PICS_2.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PICS_2.30.0.tgz vignettes: vignettes/PICS/inst/doc/PICS.pdf vignetteTitles: The PICS users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PICS/inst/doc/PICS.R importsMe: PING dependencyCount: 36 Package: Pigengene Version: 1.12.0 Depends: R (>= 3.5.0), graph Imports: bnlearn (>= 4.4.1), C50 (>= 0.1.2), MASS, matrixStats, partykit, Rgraphviz, WGCNA, GO.db, impute, preprocessCore, grDevices, graphics, stats, utils, parallel, pheatmap (>= 1.0.8), dplyr, gdata Suggests: org.Hs.eg.db (>= 3.7.0), org.Mm.eg.db (>= 3.7.0), biomaRt (>= 2.30.0), knitr, BiocStyle, AnnotationDbi, energy License: GPL (>=2) MD5sum: 978702b3fddfcac134b94d2477207ba3 NeedsCompilation: no Title: Infers biological signatures from gene expression data Description: Pigengene package provides an efficient way to infer biological signatures from gene expression profiles. The signatures are independent from the underlying platform, e.g., the input can be microarray or RNA Seq data. It can even infer the signatures using data from one platform, and evaluate them on the other. Pigengene identifies the modules (clusters) of highly coexpressed genes using coexpression network analysis, summarizes the biological information of each module in an eigengene, learns a Bayesian network that models the probabilistic dependencies between modules, and builds a decision tree based on the expression of eigengenes. biocViews: GeneExpression, RNASeq, NetworkInference, Network, GraphAndNetwork, BiomedicalInformatics, SystemsBiology, Transcriptomics, Classification, Clustering, DecisionTree, DimensionReduction, PrincipalComponent, Microarray, Normalization, ImmunoOncology Author: Habil Zare, Amir Foroushani, Rupesh Agrahari, and Meghan Short Maintainer: Habil Zare VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Pigengene git_branch: RELEASE_3_10 git_last_commit: ec1df6b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Pigengene_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Pigengene_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Pigengene_1.12.0.tgz vignettes: vignettes/Pigengene/inst/doc/Pigengene_inference.pdf vignetteTitles: Pigengene: Computing and using eigengenes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pigengene/inst/doc/Pigengene_inference.R dependencyCount: 125 Package: PING Version: 2.30.0 Depends: R(>= 2.15.0), chipseq, IRanges, GenomicRanges Imports: methods, PICS, graphics, grDevices, stats, Gviz, fda, BSgenome, stats4, BiocGenerics, IRanges, S4Vectors Suggests: parallel, ShortRead, rtracklayer License: Artistic-2.0 MD5sum: a7cb8c54597d0602b4a5a84cd93d9cda NeedsCompilation: yes Title: Probabilistic inference for Nucleosome Positioning with MNase-based or Sonicated Short-read Data Description: Probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach. biocViews: Clustering, StatisticalMethod, Visualization, Sequencing Author: Xuekui Zhang , Raphael Gottardo , Sangsoon Woo Maintainer: Renan Sauteraud git_url: https://git.bioconductor.org/packages/PING git_branch: RELEASE_3_10 git_last_commit: 36595f1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PING_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PING_2.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PING_2.30.0.tgz vignettes: vignettes/PING/inst/doc/PING-PE.pdf, vignettes/PING/inst/doc/PING.pdf vignetteTitles: Using PING with paired-end sequencing data, The PING users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PING/inst/doc/PING-PE.R, vignettes/PING/inst/doc/PING.R dependencyCount: 148 Package: pint Version: 1.36.0 Depends: mvtnorm, methods, graphics, Matrix, dmt License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: 4c057dbcaf72343ae1bbd604058d86e1 NeedsCompilation: no Title: Pairwise INTegration of functional genomics data Description: Pairwise data integration for functional genomics, including tools for DNA/RNA/miRNA dependency screens. biocViews: aCGH, GeneExpression, Genetics, DifferentialExpression, Microarray Author: Olli-Pekka Huovilainen and Leo Lahti Maintainer: Olli-Pekka Huovilainen URL: https://github.com/antagomir/pint BugReports: https://github.com/antagomir/pint/issues git_url: https://git.bioconductor.org/packages/pint git_branch: RELEASE_3_10 git_last_commit: b1056b4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pint_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pint_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pint_1.36.0.tgz vignettes: vignettes/pint/inst/doc/depsearch.pdf vignetteTitles: pint hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pint/inst/doc/depsearch.R dependencyCount: 11 Package: pipeFrame Version: 1.2.2 Imports: BSgenome, digest, visNetwork, magrittr, methods, Biostrings, GenomeInfoDb, parallel, stats, utils Suggests: BiocManager, knitr, rtracklayer, testthat License: GPL-3 MD5sum: 544d2cd811eda5864ff75e77f763433e NeedsCompilation: no Title: Pipeline framework for bioinformatics in R Description: pipeFrame is an R package for building a componentized bioinformatics pipeline. Each step in this pipeline is wrapped in the framework, so the connection among steps is created seamlessly and automatically. Users could focus more on fine-tuning arguments rather than spending a lot of time on transforming file format, passing task outputs to task inputs or installing the dependencies. Componentized step elements can be customized into other new pipelines flexibly as well. This pipeline can be split into several important functional steps, so it is much easier for users to understand the complex arguments from each step rather than parameter combination from the whole pipeline. At the same time, componentized pipeline can restart at the breakpoint and avoid rerunning the whole pipeline, which may save a lot of time for users on pipeline tuning or such issues as power off or process other interrupts. biocViews: Software, Infrastructure, WorkflowStep Author: Zheng Wei, Shining Ma Maintainer: Zheng Wei URL: https://github.com/wzthu/pipeFrame VignetteBuilder: knitr BugReports: https://github.com/wzthu/pipeFrame/issues git_url: https://git.bioconductor.org/packages/pipeFrame git_branch: RELEASE_3_10 git_last_commit: 407f7f3 git_last_commit_date: 2019-11-06 Date/Publication: 2019-11-06 source.ver: src/contrib/pipeFrame_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/pipeFrame_1.2.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pipeFrame_1.2.2.tgz vignettes: vignettes/pipeFrame/inst/doc/pipeFrame.html vignetteTitles: An Introduction to pipeFrame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pipeFrame/inst/doc/pipeFrame.R dependsOnMe: enrichTF dependencyCount: 48 Package: pkgDepTools Version: 1.52.0 Depends: methods, graph, RBGL Imports: graph, RBGL Suggests: Biobase, Rgraphviz, RCurl, BiocManager License: GPL-2 Archs: i386, x64 MD5sum: 24f37cd3d6e63a9439bbacddca29fbe0 NeedsCompilation: no Title: Package Dependency Tools Description: This package provides tools for computing and analyzing dependency relationships among R packages. It provides tools for building a graph-based representation of the dependencies among all packages in a list of CRAN-style package repositories. There are also utilities for computing installation order of a given package. If the RCurl package is available, an estimate of the download size required to install a given package and its dependencies can be obtained. biocViews: Infrastructure, GraphAndNetwork Author: Seth Falcon [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/pkgDepTools git_branch: RELEASE_3_10 git_last_commit: c8d6f92 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pkgDepTools_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pkgDepTools_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pkgDepTools_1.52.0.tgz vignettes: vignettes/pkgDepTools/inst/doc/pkgDepTools.pdf vignetteTitles: How to Use pkgDepTools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pkgDepTools/inst/doc/pkgDepTools.R dependencyCount: 10 Package: plethy Version: 1.24.0 Depends: R (>= 3.1.0), methods, DBI (>= 0.5-1), RSQLite (>= 1.1), BiocGenerics, S4Vectors Imports: Streamer, IRanges, reshape2, plyr, RColorBrewer,ggplot2, Biobase Suggests: RUnit, BiocStyle License: GPL-3 MD5sum: 0edfb1bb0306f94d99c8369356bc93a5 NeedsCompilation: no Title: R framework for exploration and analysis of respirometry data Description: This package provides the infrastructure and tools to import, query and perform basic analysis of whole body plethysmography and metabolism data. Currently support is limited to data derived from Buxco respirometry instruments as exported by their FinePointe software. biocViews: DataImport, biocViews, Infastructure, DataRepresentation,TimeCourse Author: Daniel Bottomly [aut, cre], Marty Ferris [ctb], Beth Wilmot [aut], Shannon McWeeney [aut] Maintainer: Daniel Bottomly git_url: https://git.bioconductor.org/packages/plethy git_branch: RELEASE_3_10 git_last_commit: 6ba93fb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/plethy_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/plethy_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/plethy_1.24.0.tgz vignettes: vignettes/plethy/inst/doc/plethy.pdf vignetteTitles: plethy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plethy/inst/doc/plethy.R dependencyCount: 75 Package: plgem Version: 1.58.0 Depends: R (>= 2.10) Imports: utils, Biobase (>= 2.5.5), MASS License: GPL-2 MD5sum: 95fc832c24dc9b29839aefc71bd648a0 NeedsCompilation: no Title: Detect differential expression in microarray and proteomics datasets with the Power Law Global Error Model (PLGEM) Description: The Power Law Global Error Model (PLGEM) has been shown to faithfully model the variance-versus-mean dependence that exists in a variety of genome-wide datasets, including microarray and proteomics data. The use of PLGEM has been shown to improve the detection of differentially expressed genes or proteins in these datasets. biocViews: ImmunoOncology, Microarray, DifferentialExpression, Proteomics, GeneExpression, MassSpectrometry Author: Mattia Pelizzola and Norman Pavelka Maintainer: Norman Pavelka URL: http://www.genopolis.it git_url: https://git.bioconductor.org/packages/plgem git_branch: RELEASE_3_10 git_last_commit: 7dc81e6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/plgem_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/plgem_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/plgem_1.58.0.tgz vignettes: vignettes/plgem/inst/doc/plgem.pdf vignetteTitles: An introduction to PLGEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plgem/inst/doc/plgem.R importsMe: INSPEcT dependencyCount: 9 Package: plier Version: 1.56.0 Depends: R (>= 2.0), methods Imports: affy, Biobase, methods License: GPL (>= 2) MD5sum: f3f492827482204d074e74a13e35cb12 NeedsCompilation: yes Title: Implements the Affymetrix PLIER algorithm Description: The PLIER (Probe Logarithmic Error Intensity Estimate) method produces an improved signal by accounting for experimentally observed patterns in probe behavior and handling error at the appropriately at low and high signal values. biocViews: Software Author: Affymetrix Inc., Crispin J Miller, PICR Maintainer: Crispin Miller git_url: https://git.bioconductor.org/packages/plier git_branch: RELEASE_3_10 git_last_commit: 777ca86 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/plier_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/plier_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/plier_1.56.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: piano dependencyCount: 13 Package: plotGrouper Version: 1.4.0 Depends: R (>= 3.5) Imports: ggplot2 (>= 3.0.0), dplyr (>= 0.7.6), tidyr (>= 0.2.0), tibble (>= 1.4.2), stringr (>= 1.3.1), readr (>= 1.1.1), readxl (>= 1.1.0), scales (>= 1.0.0), stats, grid, gridExtra (>= 2.3), egg (>= 0.4.0), gtable (>= 0.2.0), ggpubr (>= 0.1.8), shiny (>= 1.1.0), shinythemes (>= 1.1.1), colourpicker (>= 1.0), magrittr (>= 1.5), Hmisc (>= 4.1.1), rlang (>= 0.2.2) Suggests: knitr, htmltools, BiocStyle, rmarkdown, testthat License: GPL-3 MD5sum: bed34e69c2d3998e8cfe51c9b8e5a152 NeedsCompilation: no Title: Shiny app GUI wrapper for ggplot with built-in statistical analysis Description: A shiny app-based GUI wrapper for ggplot with built-in statistical analysis. Import data from file and use dropdown menus and checkboxes to specify the plotting variables, graph type, and look of your plots. Once created, plots can be saved independently or stored in a report that can be saved as a pdf. If new data are added to the file, the report can be refreshed to include new data. Statistical tests can be selected and added to the graphs. Analysis of flow cytometry data is especially integrated with plotGrouper. Count data can be transformed to return the absolute number of cells in a sample (this feature requires inclusion of the number of beads per sample and information about any dilution performed). biocViews: ImmunoOncology, FlowCytometry, GraphAndNetwork, StatisticalMethod, DataImport, GUI, MultipleComparison Author: John D. Gagnon [aut, cre] Maintainer: John D. Gagnon URL: https://jdgagnon.github.io/plotGrouper/ VignetteBuilder: knitr BugReports: https://github.com/jdgagnon/plotGrouper/issues git_url: https://git.bioconductor.org/packages/plotGrouper git_branch: RELEASE_3_10 git_last_commit: ab2e3b3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/plotGrouper_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/plotGrouper_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/plotGrouper_1.4.0.tgz vignettes: vignettes/plotGrouper/inst/doc/plotGrouper-vignette.html vignetteTitles: plotGrouper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plotGrouper/inst/doc/plotGrouper-vignette.R dependencyCount: 113 Package: PLPE Version: 1.46.0 Depends: R (>= 2.6.2), Biobase (>= 2.5.5), LPE, MASS, methods License: GPL (>= 2) MD5sum: 8b0ae21698455a6a19a6342bfd3f34a4 NeedsCompilation: no Title: Local Pooled Error Test for Differential Expression with Paired High-throughput Data Description: This package performs tests for paired high-throughput data. biocViews: Proteomics, Microarray, DifferentialExpression Author: HyungJun Cho and Jae K. Lee Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ git_url: https://git.bioconductor.org/packages/PLPE git_branch: RELEASE_3_10 git_last_commit: 1af6e72 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PLPE_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PLPE_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PLPE_1.46.0.tgz vignettes: vignettes/PLPE/inst/doc/PLPE.pdf vignetteTitles: PLPE Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PLPE/inst/doc/PLPE.R dependencyCount: 10 Package: plrs Version: 1.26.0 Depends: R (>= 2.10), Biobase Imports: BiocGenerics, CGHbase, graphics, grDevices, ic.infer, marray, methods, quadprog, Rcsdp, stats, stats4, utils Suggests: mvtnorm, methods License: GPL (>=2.0) Archs: i386, x64 MD5sum: c3a076d5ffda0c4cd535144d6a442658 NeedsCompilation: no Title: Piecewise Linear Regression Splines (PLRS) for the association between DNA copy number and gene expression Description: The present package implements a flexible framework for modeling the relationship between DNA copy number and gene expression data using Piecewise Linear Regression Splines (PLRS). biocViews: Regression Author: Gwenael G.R. Leday Maintainer: Gwenael G.R. Leday to git_url: https://git.bioconductor.org/packages/plrs git_branch: RELEASE_3_10 git_last_commit: bdadf4a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/plrs_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/plrs_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/plrs_1.26.0.tgz vignettes: vignettes/plrs/inst/doc/plrs_vignette.pdf vignetteTitles: plrs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plrs/inst/doc/plrs_vignette.R dependencyCount: 20 Package: plw Version: 1.46.0 Depends: R (>= 2.10), affy (>= 1.23.4) Imports: MASS, affy, graphics, splines, stats Suggests: limma License: GPL-2 MD5sum: ada50270c4ffb76fc0b188bb32bcfbfd NeedsCompilation: yes Title: Probe level Locally moderated Weighted t-tests. Description: Probe level Locally moderated Weighted median-t (PLW) and Locally Moderated Weighted-t (LMW). biocViews: Microarray, OneChannel, TwoChannel, DifferentialExpression Author: Magnus Astrand Maintainer: Magnus Astrand git_url: https://git.bioconductor.org/packages/plw git_branch: RELEASE_3_10 git_last_commit: 3b35b57 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/plw_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/plw_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/plw_1.46.0.tgz vignettes: vignettes/plw/inst/doc/HowToPLW.pdf vignetteTitles: HowTo plw hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plw/inst/doc/HowToPLW.R dependencyCount: 15 Package: plyranges Version: 1.6.10 Depends: R (>= 3.5), BiocGenerics, IRanges (>= 2.12.0), GenomicRanges (>= 1.28.4) Imports: methods, dplyr, rlang (>= 0.2.0), magrittr, tidyselect (>= 1.0.0), rtracklayer, GenomicAlignments, GenomeInfoDb, Rsamtools, S4Vectors (>= 0.23.10), utils Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 2.1.0), HelloRanges, HelloRangesData, BSgenome.Hsapiens.UCSC.hg19, pasillaBamSubset, covr, ggplot2 License: Artistic-2.0 MD5sum: 24540ed1d3b6d3923e695c5a3e2d3e1e NeedsCompilation: no Title: A fluent interface for manipulating GenomicRanges Description: A dplyr-like interface for interacting with the common Bioconductor classes Ranges and GenomicRanges. By providing a grammatical and consistent way of manipulating these classes their accessiblity for new Bioconductor users is hopefully increased. biocViews: Infrastructure, DataRepresentation, WorkflowStep, Coverage Author: Stuart Lee [aut, cre], Michael Lawrence [aut, ctb], Dianne Cook [aut, ctb] Maintainer: Stuart Lee VignetteBuilder: knitr BugReports: https://github.com/sa-lee/plyranges git_url: https://git.bioconductor.org/packages/plyranges git_branch: RELEASE_3_10 git_last_commit: b8eae6a git_last_commit_date: 2020-02-17 Date/Publication: 2020-02-17 source.ver: src/contrib/plyranges_1.6.10.tar.gz win.binary.ver: bin/windows/contrib/3.6/plyranges_1.6.10.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/plyranges_1.6.10.tgz vignettes: vignettes/plyranges/inst/doc/an-introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plyranges/inst/doc/an-introduction.R importsMe: BUSpaRse, methylCC suggestsMe: StructuralVariantAnnotation dependencyCount: 59 Package: pmm Version: 1.18.0 Depends: R (>= 2.10) Imports: lme4, splines License: GPL-3 MD5sum: c906b3644fb7287801679e90d344cb19 NeedsCompilation: no Title: Parallel Mixed Model Description: The Parallel Mixed Model (PMM) approach is suitable for hit selection and cross-comparison of RNAi screens generated in experiments that are performed in parallel under several conditions. For example, we could think of the measurements or readouts from cells under RNAi knock-down, which are infected with several pathogens or which are grown from different cell lines. biocViews: SystemsBiology, Regression Author: Anna Drewek Maintainer: Anna Drewek git_url: https://git.bioconductor.org/packages/pmm git_branch: RELEASE_3_10 git_last_commit: 0b632b9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pmm_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pmm_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pmm_1.18.0.tgz vignettes: vignettes/pmm/inst/doc/pmm-package.pdf vignetteTitles: User manual for R-Package PMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pmm/inst/doc/pmm-package.R dependencyCount: 19 Package: podkat Version: 1.18.0 Depends: R (>= 3.2.0), methods, Rsamtools (>= 1.99.1), GenomicRanges Imports: Rcpp (>= 0.11.1), parallel, stats, graphics, grDevices, utils, Biobase, BiocGenerics, Matrix, GenomeInfoDb, IRanges, Biostrings, BSgenome (>= 1.32.0) LinkingTo: Rcpp, Rhtslib (>= 1.15.3) Suggests: BSgenome.Hsapiens.UCSC.hg38.masked, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Mmusculus.UCSC.mm10.masked, GWASTools (>= 1.13.24), VariantAnnotation, SummarizedExperiment, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: 90f2ffab8f5b87c8de51f9e88a07cc04 NeedsCompilation: yes Title: Position-Dependent Kernel Association Test Description: This package provides an association test that is capable of dealing with very rare and even private variants. This is accomplished by a kernel-based approach that takes the positions of the variants into account. The test can be used for pre-processed matrix data, but also directly for variant data stored in VCF files. Association testing can be performed whole-genome, whole-exome, or restricted to pre-defined regions of interest. The test is complemented by tools for analyzing and visualizing the results. biocViews: Genetics, WholeGenome, Annotation, VariantAnnotation, Sequencing, DataImport Author: Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/podkat/ https://github.com/UBod/podkat SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/podkat git_branch: RELEASE_3_10 git_last_commit: 3db8817 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/podkat_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/podkat_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/podkat_1.18.0.tgz vignettes: vignettes/podkat/inst/doc/podkat.pdf vignetteTitles: PODKAT - An R Package for Association Testing Involving Rare and Private Variants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/podkat/inst/doc/podkat.R dependencyCount: 40 Package: pogos Version: 1.6.0 Depends: R (>= 3.5.0), rjson (>= 0.2.15), httr (>= 1.3.1) Imports: methods, S4Vectors, utils, shiny, ontoProc, ggplot2, graphics Suggests: knitr, DT, ontologyPlot, testthat License: Artistic-2.0 MD5sum: 25b70edbbd17ee14eb830321213948be NeedsCompilation: no Title: PharmacOGenomics Ontology Support Description: Provide simple utilities for querying bhklab PharmacoDB, modeling API outputs, and integrating to cell and compound ontologies. biocViews: Pharmacogenomics, PooledScreens, ImmunoOncology Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pogos git_branch: RELEASE_3_10 git_last_commit: ab557ba git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pogos_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pogos_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pogos_1.6.0.tgz vignettes: vignettes/pogos/inst/doc/pogos.html vignetteTitles: pogos -- simple interface to bhklab PharmacoDB with emphasis on ontology hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pogos/inst/doc/pogos.R suggestsMe: BiocOncoTK dependencyCount: 94 Package: polyester Version: 1.22.0 Depends: R (>= 3.0.0) Imports: Biostrings (>= 2.32.0), IRanges, S4Vectors, logspline, limma, zlibbioc Suggests: knitr, ballgown License: Artistic-2.0 MD5sum: 5e0994b553fc20800ce17f90784c8324 NeedsCompilation: no Title: Simulate RNA-seq reads Description: This package can be used to simulate RNA-seq reads from differential expression experiments with replicates. The reads can then be aligned and used to perform comparisons of methods for differential expression. biocViews: Sequencing, DifferentialExpression Author: Alyssa C. Frazee, Andrew E. Jaffe, Rory Kirchner, Jeffrey T. Leek Maintainer: Jack Fu , Jeff Leek VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/polyester git_branch: RELEASE_3_10 git_last_commit: 3c4f995 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/polyester_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/polyester_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/polyester_1.22.0.tgz vignettes: vignettes/polyester/inst/doc/polyester.html vignetteTitles: The Polyester package for simulating RNA-seq reads hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/polyester/inst/doc/polyester.R dependencyCount: 15 Package: Polyfit Version: 1.20.0 Depends: DESeq Suggests: BiocStyle License: GPL (>= 3) Archs: i386, x64 MD5sum: 9989671fce5540b4e968e315b444305c NeedsCompilation: no Title: Add-on to DESeq to improve p-values and q-values Description: Polyfit is an add-on to the packages DESeq which ensures the p-value distribution is uniform over the interval [0, 1] for data satisfying the null hypothesis of no differential expression, and uses an adpated Storey-Tibshiran method to calculate q-values. biocViews: ImmunoOncology, DifferentialExpression, Sequencing, RNASeq, GeneExpression Author: Conrad Burden Maintainer: Conrad Burden git_url: https://git.bioconductor.org/packages/Polyfit git_branch: RELEASE_3_10 git_last_commit: 50f07f1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Polyfit_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Polyfit_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Polyfit_1.20.0.tgz vignettes: vignettes/Polyfit/inst/doc/polyfit.pdf vignetteTitles: Polyfit hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Polyfit/inst/doc/polyfit.R dependencyCount: 43 Package: POST Version: 1.10.0 Depends: R (>= 3.4.0) Imports: stats, CompQuadForm, Matrix, survival, Biobase, GSEABase License: GPL (>= 2) MD5sum: aa1a22d4713e0f4921568db304c8ad2b NeedsCompilation: no Title: Projection onto Orthogonal Space Testing for High Dimensional Data Description: Perform orthogonal projection of high dimensional data of a set, and statistical modeling of phenotye with projected vectors as predictor. biocViews: Microarray, GeneExpression Author: Xueyuan Cao and Stanley.pounds Maintainer: Xueyuan Cao git_url: https://git.bioconductor.org/packages/POST git_branch: RELEASE_3_10 git_last_commit: 410833b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/POST_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/POST_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/POST_1.10.0.tgz vignettes: vignettes/POST/inst/doc/POST.pdf vignetteTitles: An introduction to POST hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/POST/inst/doc/POST.R dependencyCount: 40 Package: PoTRA Version: 1.2.0 Depends: R (>= 3.6.0), stats, BiocGenerics, org.Hs.eg.db, igraph, graph, graphite Suggests: BiocStyle, knitr, rmarkdown, colr, metap, repmis License: LGPL MD5sum: 357f05eb0d5584eb761cd11edc08ee7e NeedsCompilation: no Title: PoTRA: Pathways of Topological Rank Analysis Description: The PoTRA analysis is based on topological ranks of genes in biological pathways. PoTRA can be used to detect pathways involved in disease (Li, Liu & Dinu, 2018). We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fishers Exact test to determine if the number of hub genes in each pathway is altered from normal to cancer (Li, Liu & Dinu, 2018). Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer (Li, Liu & Dinu, 2018). Hence, we use the Kolmogorov–Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes (Li, Liu & Dinu, 2018). PoTRA can be used with the KEGG, Biocarta, Reactome, NCI, SMPDB and PharmGKB databases from the devel graphite library. biocViews: GraphAndNetwork, StatisticalMethod, GeneExpression, DifferentialExpression, Pathways, Reactome, Network, KEGG, BioCarta Author: Chaoxing Li, Li Liu and Valentin Dinu Maintainer: Valentin Dinu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PoTRA git_branch: RELEASE_3_10 git_last_commit: 1ba3937 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PoTRA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PoTRA_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PoTRA_1.2.0.tgz vignettes: vignettes/PoTRA/inst/doc/PoTRA.html vignetteTitles: Pathways of Topological Rank Analysis (PoTRA) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PoTRA/inst/doc/PoTRA.R dependencyCount: 47 Package: PowerExplorer Version: 1.6.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: DESeq2, ROTS, vsn, stats, utils, methods, gridExtra, MASS, data.table, ggplot2, Biobase, S4Vectors, BiocParallel Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: 853baea64376beb8558ef4721a7e0048 NeedsCompilation: no Title: Power Estimation Tool for RNA-Seq and proteomics data Description: Estimate and predict power among groups and multiple sample sizes with simulated data, the simulations are operated based on distribution parameters estimated from the provided input dataset. biocViews: ImmunoOncology, RNASeq, Proteomics, DifferentialExpression, MultipleComparison, Sequencing, Coverage, ChIPSeq Author: Xu Qiao [aut, cre], Laura Elo [cph] Maintainer: Xu Qiao URL: https://gitlab.utu.fi/CompBioMedNGSTools/PowerExplorer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PowerExplorer git_branch: RELEASE_3_10 git_last_commit: 728f062 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PowerExplorer_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PowerExplorer_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PowerExplorer_1.6.0.tgz vignettes: vignettes/PowerExplorer/inst/doc/PowerExplore_vignette.pdf vignetteTitles: PowerExplorer Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PowerExplorer/inst/doc/PowerExplore_vignette.R dependencyCount: 128 Package: powerTCR Version: 1.6.0 Imports: cubature, doParallel, evmix, foreach, magrittr, methods, parallel, purrr, stats, tcR, truncdist, vegan, VGAM Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 778b4adc0f5419272d4c31f5cbd8a8b4 NeedsCompilation: no Title: Model-Based Comparative Analysis of the TCR Repertoire Description: This package provides a model for the clone size distribution of the TCR repertoire. Further, it permits comparative analysis of TCR repertoire libraries based on theoretical model fits. biocViews: Software, Clustering, BiomedicalInformatics Author: Hillary Koch Maintainer: Hillary Koch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/powerTCR git_branch: RELEASE_3_10 git_last_commit: b8cd57a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/powerTCR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/powerTCR_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/powerTCR_1.6.0.tgz vignettes: vignettes/powerTCR/inst/doc/powerTCR.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/powerTCR/inst/doc/powerTCR.R dependencyCount: 84 Package: PPInfer Version: 1.12.0 Depends: biomaRt, fgsea, kernlab, ggplot2, igraph, STRINGdb, yeastExpData License: Artistic-2.0 MD5sum: f98e299b33324779ed8506016a7d38b7 NeedsCompilation: no Title: Inferring functionally related proteins using protein interaction networks Description: Interactions between proteins occur in many, if not most, biological processes. Most proteins perform their functions in networks associated with other proteins and other biomolecules. This fact has motivated the development of a variety of experimental methods for the identification of protein interactions. This variety has in turn ushered in the development of numerous different computational approaches for modeling and predicting protein interactions. Sometimes an experiment is aimed at identifying proteins closely related to some interesting proteins. A network based statistical learning method is used to infer the putative functions of proteins from the known functions of its neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. biocViews: Software, StatisticalMethod, Network, GraphAndNetwork, GeneSetEnrichment, NetworkEnrichment, Pathways Author: Dongmin Jung, Xijin Ge Maintainer: Dongmin Jung git_url: https://git.bioconductor.org/packages/PPInfer git_branch: RELEASE_3_10 git_last_commit: c6987c6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PPInfer_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PPInfer_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PPInfer_1.12.0.tgz vignettes: vignettes/PPInfer/inst/doc/PPInfer.pdf vignetteTitles: User manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PPInfer/inst/doc/PPInfer.R dependsOnMe: gsean dependencyCount: 118 Package: ppiStats Version: 1.52.0 Depends: ScISI (>= 1.13.2), lattice, ppiData (>= 0.1.19) Imports: Biobase, Category, graph, graphics, grDevices, lattice, methods, RColorBrewer, stats Suggests: yeastExpData, xtable License: Artistic-2.0 MD5sum: 445702debf2fe294a746f19fa966ed91 NeedsCompilation: no Title: Protein-Protein Interaction Statistical Package Description: Tools for the analysis of protein interaction data. biocViews: Proteomics, GraphAndNetwork, Network, NetworkInference Author: T. Chiang and D. Scholtens with contributions from W. Huber and L. Wang Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/ppiStats git_branch: RELEASE_3_10 git_last_commit: 60e3628 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ppiStats_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ppiStats_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ppiStats_1.52.0.tgz vignettes: vignettes/ppiStats/inst/doc/ppiStats.pdf vignetteTitles: ppiStats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ppiStats/inst/doc/ppiStats.R dependsOnMe: PCpheno suggestsMe: BiocCaseStudies, RpsiXML dependencyCount: 51 Package: pqsfinder Version: 2.2.0 Depends: Biostrings Imports: Rcpp (>= 0.12.3), GenomicRanges, IRanges, S4Vectors, methods LinkingTo: Rcpp, BH (>= 1.69.0) Suggests: BiocStyle, knitr, Gviz, rtracklayer, ggplot2, BSgenome.Hsapiens.UCSC.hg38, testthat, stringr, stringi License: BSD_2_clause + file LICENSE MD5sum: 6b62057e16c34475e67fd2ad12d736b3 NeedsCompilation: yes Title: Identification of potential quadruplex forming sequences Description: Pqsfinder detects DNA and RNA sequence patterns that are likely to fold into an intramolecular G-quadruplex (G4). Unlike many other approaches, pqsfinder is able to detect G4s folded from imperfect G-runs containing bulges or mismatches or G4s having long loops. Pqsfinder also assigns an integer score to each hit that was fitted on G4 sequencing data and corresponds to expected stability of the folded G4. biocViews: MotifDiscovery, SequenceMatching, GeneRegulation Author: Jiri Hon, Dominika Labudova, Matej Lexa and Tomas Martinek Maintainer: Jiri Hon URL: https://pqsfinder.fi.muni.cz SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pqsfinder git_branch: RELEASE_3_10 git_last_commit: ce4c161 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pqsfinder_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pqsfinder_2.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pqsfinder_2.2.0.tgz vignettes: vignettes/pqsfinder/inst/doc/pqsfinder.html vignetteTitles: pqsfinder: User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pqsfinder/inst/doc/pqsfinder.R dependencyCount: 19 Package: prada Version: 1.62.0 Depends: R (>= 2.10), Biobase, RColorBrewer, grid, methods, rrcov Imports: Biobase, BiocGenerics, graphics, grDevices, grid, MASS, methods, RColorBrewer, rrcov, stats4, utils Suggests: cellHTS2, tcltk License: LGPL MD5sum: f2ff5e23f63e8c4816b9e55b47f15a9c NeedsCompilation: yes Title: Data analysis for cell-based functional assays Description: Tools for analysing and navigating data from high-throughput phenotyping experiments based on cellular assays and fluorescent detection (flow cytometry (FACS), high-content screening microscopy). biocViews: ImmunoOncology, CellBasedAssays, Visualization Author: Florian Hahne , Wolfgang Huber , Markus Ruschhaupt, Joern Toedling , Joseph Barry Maintainer: Florian Hahne git_url: https://git.bioconductor.org/packages/prada git_branch: RELEASE_3_10 git_last_commit: 13df8db git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/prada_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/prada_1.62.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/prada_1.62.0.tgz vignettes: vignettes/prada/inst/doc/norm2.pdf, vignettes/prada/inst/doc/prada2cellHTS.pdf vignetteTitles: Removal of contaminants from FACS data, Combining prada output and cellHTS2 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/prada/inst/doc/norm2.R, vignettes/prada/inst/doc/prada2cellHTS.R dependsOnMe: RNAither importsMe: cellHTS2 dependencyCount: 18 Package: pram Version: 1.2.0 Depends: R (>= 3.6) Imports: methods, BiocParallel, tools, utils, data.table (>= 1.11.8), GenomicAlignments (>= 1.16.0), rtracklayer (>= 1.40.6), BiocGenerics (>= 0.26.0), GenomeInfoDb (>= 1.16.0), GenomicRanges (>= 1.32.0), IRanges (>= 2.14.12), Rsamtools (>= 1.32.3), S4Vectors (>= 0.18.3) Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL (>= 3) MD5sum: e99ea542fefaccbd9f813cd477a138b9 NeedsCompilation: no Title: Pooling RNA-seq datasets for assembling transcript models Description: Publicly available RNA-seq data is routinely used for retrospective analysis to elucidate new biology. Novel transcript discovery enabled by large collections of RNA-seq datasets has emerged as one of such analysis. To increase the power of transcript discovery from large collections of RNA-seq datasets, we developed a new R package named Pooling RNA-seq and Assembling Models (PRAM), which builds transcript models in intergenic regions from pooled RNA-seq datasets. This package includes functions for defining intergenic regions, extracting and pooling related RNA-seq alignments, predicting, selected, and evaluating transcript models. biocViews: Software, Technology, Sequencing, RNASeq Author: Peng Liu [aut, cre], Colin N. Dewey [aut], Sündüz Keleş [aut] Maintainer: Peng Liu URL: https://github.com/pliu55/pram VignetteBuilder: knitr BugReports: https://github.com/pliu55/pram/issues git_url: https://git.bioconductor.org/packages/pram git_branch: RELEASE_3_10 git_last_commit: cc8b027 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pram_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pram_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pram_1.2.0.tgz vignettes: vignettes/pram/inst/doc/pram.pdf vignetteTitles: Pooling RNA-seq and Assembling Models hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pram/inst/doc/pram.R dependencyCount: 39 Package: prebs Version: 1.26.0 Depends: R (>= 2.14.0), GenomicAlignments, affy, RPA Imports: parallel, methods, stats, GenomicRanges (>= 1.13.3), IRanges, Biobase, GenomeInfoDb, S4Vectors Suggests: prebsdata, hgu133plus2cdf, hgu133plus2probe License: Artistic-2.0 Archs: i386, x64 MD5sum: 3e1f14a1d9f7b6198743e3d1dbfe76f5 NeedsCompilation: no Title: Probe region expression estimation for RNA-seq data for improved microarray comparability Description: The prebs package aims at making RNA-sequencing (RNA-seq) data more comparable to microarray data. The comparability is achieved by summarizing sequencing-based expressions of probe regions using a modified version of RMA algorithm. The pipeline takes mapped reads in BAM format as an input and produces either gene expressions or original microarray probe set expressions as an output. biocViews: ImmunoOncology, Microarray, RNASeq, Sequencing, GeneExpression, Preprocessing Author: Karolis Uziela and Antti Honkela Maintainer: Karolis Uziela git_url: https://git.bioconductor.org/packages/prebs git_branch: RELEASE_3_10 git_last_commit: 14f3b0e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/prebs_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/prebs_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/prebs_1.26.0.tgz vignettes: vignettes/prebs/inst/doc/prebs.pdf vignetteTitles: prebs User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/prebs/inst/doc/prebs.R dependencyCount: 109 Package: PrecisionTrialDrawer Version: 1.2.1 Depends: R (>= 3.6) Imports: graphics, grDevices, stats, utils, methods, cgdsr, parallel, stringr, reshape2, data.table, RColorBrewer, BiocParallel, magrittr, biomaRt, XML, httr, jsonlite, ggplot2, ggrepel, grid, S4Vectors, IRanges, GenomicRanges, LowMACAAnnotation, googleVis, shiny, shinyBS, DT, brglm, matrixStats Suggests: BiocStyle, knitr, rmarkdown, dplyr License: GPL-3 Archs: i386, x64 MD5sum: e459d72ab46b21a83ea7244a2703e1f5 NeedsCompilation: no Title: A Tool to Analyze and Design NGS Based Custom Gene Panels Description: A suite of methods to design umbrella and basket trials for precision oncology. biocViews: SomaticMutation, WholeGenome, Sequencing, DataImport, GeneExpression Author: Giorgio Melloni, Alessandro Guida, Luca Mazzarella Maintainer: Giorgio Melloni VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PrecisionTrialDrawer git_branch: RELEASE_3_10 git_last_commit: 26f6231 git_last_commit_date: 2020-01-06 Date/Publication: 2020-01-06 source.ver: src/contrib/PrecisionTrialDrawer_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/PrecisionTrialDrawer_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PrecisionTrialDrawer_1.2.1.tgz vignettes: vignettes/PrecisionTrialDrawer/inst/doc/PrecisionTrialDrawer.html vignetteTitles: Bioconductor style for HTML documents hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PrecisionTrialDrawer/inst/doc/PrecisionTrialDrawer.R dependencyCount: 127 Package: PREDA Version: 1.32.0 Depends: R (>= 2.9.0), Biobase, lokern (>= 1.0.9), multtest, stats, methods, annotate Suggests: quantsmooth, qvalue, limma, caTools, affy, PREDAsampledata Enhances: Rmpi, rsprng License: GPL-2 MD5sum: 6f7c0cccf05b0093b9170c639ed67687 NeedsCompilation: no Title: Position Related Data Analysis Description: Package for the position related analysis of quantitative functional genomics data. biocViews: Software, CopyNumberVariation, GeneExpression, Genetics Author: Francesco Ferrari Maintainer: Francesco Ferrari git_url: https://git.bioconductor.org/packages/PREDA git_branch: RELEASE_3_10 git_last_commit: a05299a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PREDA_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PREDA_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PREDA_1.32.0.tgz vignettes: vignettes/PREDA/inst/doc/PREDAclasses.pdf, vignettes/PREDA/inst/doc/PREDAtutorial.pdf vignetteTitles: PREDA S4-classes, PREDA tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PREDA/inst/doc/PREDAtutorial.R dependencyCount: 41 Package: predictionet Version: 1.32.0 Depends: igraph, catnet Imports: penalized, RBGL, MASS Suggests: network, minet, knitr License: Artistic-2.0 MD5sum: 5449281479965907a013c965a6e5f289 NeedsCompilation: yes Title: Inference for predictive networks designed for (but not limited to) genomic data Description: This package contains a set of functions related to network inference combining genomic data and prior information extracted from biomedical literature and structured biological databases. The main function is able to generate networks using Bayesian or regression-based inference methods; while the former is limited to < 100 of variables, the latter may infer networks with hundreds of variables. Several statistics at the edge and node levels have been implemented (edge stability, predictive ability of each node, ...) in order to help the user to focus on high quality subnetworks. Ultimately, this package is used in the 'Predictive Networks' web application developed by the Dana-Farber Cancer Institute in collaboration with Entagen. biocViews: GraphAndNetwork, NetworkInference Author: Benjamin Haibe-Kains, Catharina Olsen, Gianluca Bontempi, John Quackenbush Maintainer: Benjamin Haibe-Kains , Catharina Olsen URL: http://compbio.dfci.harvard.edu, http://www.ulb.ac.be/di/mlg git_url: https://git.bioconductor.org/packages/predictionet git_branch: RELEASE_3_10 git_last_commit: 80338f0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/predictionet_1.32.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/predictionet_1.32.0.tgz vignettes: vignettes/predictionet/inst/doc/predictionet.pdf vignetteTitles: predictionet hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/predictionet/inst/doc/predictionet.R dependencyCount: 24 Package: preprocessCore Version: 1.48.0 Imports: stats License: LGPL (>= 2) MD5sum: 243e54533ed3425ecf08f727b217517a NeedsCompilation: yes Title: A collection of pre-processing functions Description: A library of core preprocessing routines. biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/preprocessCore git_url: https://git.bioconductor.org/packages/preprocessCore git_branch: RELEASE_3_10 git_last_commit: 84da519 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/preprocessCore_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/preprocessCore_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/preprocessCore_1.48.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPLM, cqn, crlmm, RefPlus importsMe: affy, bnbc, cn.farms, EMDomics, ExiMiR, fastLiquidAssociation, frma, frmaTools, hipathia, iCheck, ImmuneSpaceR, InPAS, lumi, MADSEQ, MBCB, MBQN, MEDIPS, mimager, minfi, MSnbase, MSstats, NormalyzerDE, oligo, PECA, Pigengene, proBatch, qPLEXanalyzer, sesame, soGGi, waveTiling, yarn suggestsMe: multiClust linksToMe: affy, affyPLM, crlmm, oligo dependencyCount: 1 Package: primirTSS Version: 1.4.0 Depends: R (>= 3.5.0) Imports: GenomicRanges (>= 1.32.2), S4Vectors (>= 0.18.2), rtracklayer (>= 1.40.3), dplyr (>= 0.7.6), stringr (>= 1.3.1), tidyr (>= 0.8.1), Biostrings (>= 2.48.0), purrr (>= 0.2.5), BSgenome.Hsapiens.UCSC.hg38 (>= 1.4.1), phastCons100way.UCSC.hg38 (>= 3.7.1), GenomicScores (>= 1.4.1), shiny (>= 1.0.5), Gviz (>= 1.24.0), BiocGenerics (>= 0.26.0), IRanges (>= 2.14.10), TFBSTools (>= 1.18.0), JASPAR2018 (>= 1.1.1), tibble (>= 1.4.2), R.utils (>= 2.6.0), stats, utils Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 853917985bfe78a491dc8c3da72d10b1 NeedsCompilation: no Title: Prediction of pri-miRNA Transcription Start Site Description: A fast, convenient tool to identify the TSSs of miRNAs by integrating the data of H3K4me3 and Pol II as well as combining the conservation level and sequence feature, provided within both command-line and graphical interfaces, which achieves a better performance than the previous non-cell-specific methods on miRNA TSSs. biocViews: ImmunoOncology, Sequencing, RNASeq, Genetics, Preprocessing, Transcription, GeneRegulation Author: Pumin Li [aut, cre], Qi Xu [aut] Maintainer: Pumin Li URL: https://github.com/ipumin/primirTSS VignetteBuilder: knitr BugReports: http://github.com/ipumin/primirTSS/issues git_url: https://git.bioconductor.org/packages/primirTSS git_branch: RELEASE_3_10 git_last_commit: eef6d15 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/primirTSS_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/primirTSS_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/primirTSS_1.4.0.tgz vignettes: vignettes/primirTSS/inst/doc/primirTSS.html vignetteTitles: primirTSS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/primirTSS/inst/doc/primirTSS.R dependencyCount: 178 Package: PrInCE Version: 1.2.0 Depends: R (>= 3.6.0) Imports: purrr (>= 0.2.4), dplyr (>= 0.7.4), tidyr (>= 0.8.99), forecast (>= 8.2), progress (>= 1.1.2), Hmisc (>= 4.0), naivebayes (>= 0.9.1), robustbase (>= 0.92-7), ranger (>= 0.8.0), LiblineaR (>= 2.10-8), speedglm (>= 0.3-2), tester (>= 0.1.7), magrittr (>= 1.5), Biobase (>= 2.40.0), MSnbase (>= 2.8.3), stats, utils, methods, Rdpack (>= 0.7) Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: 509a2c99288acfb2e924b6a2eb895305 NeedsCompilation: no Title: Predicting Interactomes from Co-Elution Description: PrInCE (Predicting Interactomes from Co-Elution) uses a naive Bayes classifier trained on dataset-derived features to recover protein-protein interactions from co-elution chromatogram profiles. This package contains the R implementation of PrInCE. biocViews: Proteomics, SystemsBiology, NetworkInference Author: Michael Skinnider [aut, trl, cre], R. Greg Stacey [ctb], Nichollas Scott [ctb], Anders Kristensen [ctb], Leonard Foster [aut, led] Maintainer: Michael Skinnider VignetteBuilder: knitr BugReports: https://github.com/fosterlab/PrInCE/issues git_url: https://git.bioconductor.org/packages/PrInCE git_branch: RELEASE_3_10 git_last_commit: 4fdbb74 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PrInCE_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PrInCE_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PrInCE_1.2.0.tgz vignettes: vignettes/PrInCE/inst/doc/intro-to-prince.html vignetteTitles: Interactome reconstruction from co-elution data with PrInCE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PrInCE/inst/doc/intro-to-prince.R dependencyCount: 148 Package: Prize Version: 1.16.0 Imports: diagram, stringr, ggplot2, reshape2, grDevices, matrixcalc, stats, gplots, methods, utils, graphics Suggests: RUnit, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: 398e56c783a4d92c0465761d1221877e NeedsCompilation: no Title: Prize: an R package for prioritization estimation based on analytic hierarchy process Description: The high throughput studies often produce large amounts of numerous genes and proteins of interest. While it is difficult to study and validate all of them. Analytic Hierarchy Process (AHP) offers a novel approach to narrowing down long lists of candidates by prioritizing them based on how well they meet the research goal. AHP is a mathematical technique for organizing and analyzing complex decisions where multiple criteria are involved. The technique structures problems into a hierarchy of elements, and helps to specify numerical weights representing the relative importance of each element. Numerical weight or priority derived from each element allows users to find alternatives that best suit their goal and their understanding of the problem. biocViews: ImmunoOncology, Software, MultipleComparison, GeneExpression, CellBiology, RNASeq Author: Daryanaz Dargahi Maintainer: Daryanaz Dargahi git_url: https://git.bioconductor.org/packages/Prize git_branch: RELEASE_3_10 git_last_commit: 76ab2f2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Prize_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Prize_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Prize_1.16.0.tgz vignettes: vignettes/Prize/inst/doc/Prize.pdf vignetteTitles: Prize: an R package for prioritization estimation based on analytic hierarchy process hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Prize/inst/doc/Prize.R dependencyCount: 67 Package: proBAMr Version: 1.20.0 Depends: R (>= 3.0.1), IRanges, AnnotationDbi Imports: GenomicRanges, Biostrings, GenomicFeatures, rtracklayer Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: f737b9d70ff023b3dc112d8231d08586 NeedsCompilation: no Title: Generating SAM file for PSMs in shotgun proteomics data Description: Mapping PSMs back to genome. The package builds SAM file from shotgun proteomics data The package also provides function to prepare annotation from GTF file. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Software, Visualization Author: Xiaojing Wang Maintainer: Xiaojing Wang git_url: https://git.bioconductor.org/packages/proBAMr git_branch: RELEASE_3_10 git_last_commit: 4566fc2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/proBAMr_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/proBAMr_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/proBAMr_1.20.0.tgz vignettes: vignettes/proBAMr/inst/doc/proBAMr.pdf vignetteTitles: Introduction to proBAMr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proBAMr/inst/doc/proBAMr.R dependencyCount: 83 Package: proBatch Version: 1.2.0 Depends: R (>= 3.6) Imports: Biobase, corrplot, dplyr, data.table, ggfortify, ggplot2, grDevices, lazyeval, lubridate, magrittr, pheatmap, preprocessCore, purrr, pvca, RColorBrewer, reshape2, rlang, scales, stats, sva, tidyr, tibble, tools, utils, viridis, wesanderson, WGCNA Suggests: knitr, rmarkdown, devtools, ggpubr, gtable, gridExtra, roxygen2, testthat (>= 2.1.0), spelling License: GPL-3 MD5sum: 33d0ac55e4cf59f48046dcdd52f3daf3 NeedsCompilation: no Title: Tools for Diagnostics and Corrections of Batch Effects in Proteomics Description: These tools facilitate batch effects analysis and correction in high-throughput experiments. It was developed primarily for mass-spectrometry proteomics (DIA/SWATH), but could also be applicable to most omic data with minor adaptations. The package contains functions for diagnostics (proteome/genome-wide and feature-level), correction (normalization and batch effects correction) and quality control. Non-linear fitting based approaches were also included to deal with complex, mass spectrometry-specific signal drifts. biocViews: BatchEffect, Normalization, Preprocessing, Software, MassSpectrometry,Proteomics, QualityControl Author: Jelena Cuklina , Chloe H. Lee , Patrick Pedrioli Maintainer: Jelena Cuklina URL: https://github.com/symbioticMe/proBatch VignetteBuilder: knitr BugReports: https://github.com/symbioticMe/proBatch/issues git_url: https://git.bioconductor.org/packages/proBatch git_branch: RELEASE_3_10 git_last_commit: 022d628 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/proBatch_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/proBatch_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/proBatch_1.2.0.tgz vignettes: vignettes/proBatch/inst/doc/proBatch.pdf vignetteTitles: proBatch package overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proBatch/inst/doc/proBatch.R dependencyCount: 147 Package: PROcess Version: 1.62.0 Depends: Icens Imports: graphics, grDevices, Icens, stats, utils License: Artistic-2.0 MD5sum: 48541bdc59f082e72cd10b343587b231 NeedsCompilation: no Title: Ciphergen SELDI-TOF Processing Description: A package for processing protein mass spectrometry data. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Xiaochun Li Maintainer: Xiaochun Li git_url: https://git.bioconductor.org/packages/PROcess git_branch: RELEASE_3_10 git_last_commit: 99139ef git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PROcess_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PROcess_1.62.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PROcess_1.62.0.tgz vignettes: vignettes/PROcess/inst/doc/howtoprocess.pdf vignetteTitles: HOWTO PROcess hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROcess/inst/doc/howtoprocess.R dependencyCount: 11 Package: procoil Version: 2.14.0 Depends: R (>= 3.3.0), kebabs Imports: methods, stats, graphics, S4Vectors, Biostrings, utils Suggests: knitr License: GPL (>= 2) MD5sum: 989face25af00193a11edf1da570abf5 NeedsCompilation: no Title: Prediction of Oligomerization of Coiled Coil Proteins Description: The package allows for predicting whether a coiled coil sequence (amino acid sequence plus heptad register) is more likely to form a dimer or more likely to form a trimer. Additionally to the prediction itself, a prediction profile is computed which allows for determining the strengths to which the individual residues are indicative for either class. Prediction profiles can also be visualized as curves or heatmaps. biocViews: Proteomics, Classification, SupportVectorMachine Author: Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/procoil/ https://github.com/UBod/procoil VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/procoil git_branch: RELEASE_3_10 git_last_commit: 613498a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/procoil_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/procoil_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/procoil_2.14.0.tgz vignettes: vignettes/procoil/inst/doc/procoil.pdf vignetteTitles: PrOCoil - A Web Service and an R Package for Predicting the Oligomerization of Coiled-Coil Proteins hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/procoil/inst/doc/procoil.R dependencyCount: 24 Package: proDA Version: 1.0.0 Imports: stats, utils, methods, BiocGenerics, SummarizedExperiment, S4Vectors, extraDistr Suggests: testthat (>= 2.1.0), MSnbase, dplyr, stringr, readr, tidyr, tibble, limma, DEP, numDeriv, pheatmap, knitr, rmarkdown License: GPL-3 MD5sum: 202bfc44a883039796ce4264037e726b NeedsCompilation: no Title: Differential Abundance Analysis of Label-Free Mass Spectrometry Data Description: Account for missing values in label-free mass spectrometry data without imputation. The package implements a probabilistic dropout model that ensures that the information from observed and missing values are properly combined. It adds empirical Bayesian priors to increase power to detect differentially abundant proteins. biocViews: Proteomics, MassSpectrometry, DifferentialExpression, Bayesian, Regression, Software, Normalization, QualityControl Author: Constantin Ahlmann-Eltze [aut, cre] (), Simon Anders [ths] () Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/proDA VignetteBuilder: knitr BugReports: https://github.com/const-ae/proDA/issues git_url: https://git.bioconductor.org/packages/proDA git_branch: RELEASE_3_10 git_last_commit: 12c65fd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/proDA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/proDA_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/proDA_1.0.0.tgz vignettes: vignettes/proDA/inst/doc/data-import.html, vignettes/proDA/inst/doc/Introduction.html vignetteTitles: Data Import, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proDA/inst/doc/data-import.R, vignettes/proDA/inst/doc/Introduction.R dependencyCount: 34 Package: proFIA Version: 1.12.0 Depends: R (>= 2.5.0), xcms Imports: stats, graphics, utils, grDevices, methods, pracma, Biobase, minpack.lm, BiocParallel, missForest, ropls Suggests: BiocGenerics, plasFIA, knitr, License: CeCILL MD5sum: 0d30a869ae836571659737602b77b5dc NeedsCompilation: yes Title: Preprocessing of FIA-HRMS data Description: Flow Injection Analysis coupled to High-Resolution Mass Spectrometry is a promising approach for high-throughput metabolomics. FIA- HRMS data, however, cannot be pre-processed with current software tools which rely on liquid chromatography separation, or handle low resolution data only. Here we present the proFIA package, which implements a new methodology to pre-process FIA-HRMS raw data (netCDF, mzData, mzXML, and mzML) including noise modelling and injection peak reconstruction, and generate the peak table. The workflow includes noise modelling, band detection and filtering then signal matching and missing value imputation. The peak table can then be exported as a .tsv file for further analysis. Visualisations to assess the quality of the data and of the signal made are easely produced. biocViews: MassSpectrometry, Metabolomics, Lipidomics, Preprocessing, PeakDetection, Proteomics Author: Alexis Delabriere and Etienne Thevenot. Maintainer: Alexis Delabriere VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/proFIA git_branch: RELEASE_3_10 git_last_commit: a45a537 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/proFIA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/proFIA_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/proFIA_1.12.0.tgz vignettes: vignettes/proFIA/inst/doc/proFIA-vignette.html vignetteTitles: processing FIA-HRMS data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proFIA/inst/doc/proFIA-vignette.R dependencyCount: 116 Package: profileplyr Version: 1.2.0 Depends: R (>= 3.6), BiocGenerics, SummarizedExperiment Imports: GenomicRanges, stats, soGGi, methods, utils, S4Vectors, R.utils, dplyr, magrittr, tidyr, IRanges, rjson, ChIPseeker,GenomicFeatures,TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene,TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene,org.Hs.eg.db,org.Mm.eg.db,rGREAT, pheatmap, EnrichedHeatmap, ComplexHeatmap, grid, circlize, BiocParallel, rtracklayer, GenomeInfoDb Suggests: BiocStyle, testthat, knitr, rmarkdown, png, Rsamtools, ggplot2 License: GPL (>= 3) MD5sum: 388e8146c2579bf05a2d739c85728846 NeedsCompilation: no Title: Visualization and annotation of read signal over genomic ranges with profileplyr Description: Quick and straightforward visualization of read signal over genomic intervals is key for generating hypotheses from sequencing data sets (e.g. ChIP-seq, ATAC-seq, bisulfite/methyl-seq). Many tools both inside and outside of R and Bioconductor are available to explore these types of data, and they typically start with a bigWig or BAM file and end with some representation of the signal (e.g. heatmap). profileplyr leverages many Bioconductor tools to allow for both flexibility and additional functionality in workflows that end with visualization of the read signal. biocViews: ChIPSeq, DataImport, Sequencing, ChipOnChip, Coverage Author: Tom Carroll and Doug Barrows Maintainer: Tom Carroll , Doug Barrows VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/profileplyr git_branch: RELEASE_3_10 git_last_commit: dbd5d98 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/profileplyr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/profileplyr_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/profileplyr_1.2.0.tgz vignettes: vignettes/profileplyr/inst/doc/profileplyr.html vignetteTitles: Visualization and annotation of read signal over genomic ranges with profileplyr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/profileplyr/inst/doc/profileplyr.R dependencyCount: 181 Package: profileScoreDist Version: 1.14.0 Depends: R(>= 3.3) Imports: Rcpp, BiocGenerics, methods, graphics LinkingTo: Rcpp Suggests: BiocStyle, knitr, MotifDb License: MIT + file LICENSE MD5sum: 03a4c190384716ad607f6eb897ec81cd NeedsCompilation: yes Title: Profile score distributions Description: Regularization and score distributions for position count matrices. biocViews: Software, GeneRegulation, StatisticalMethod Author: Paal O. Westermark Maintainer: Paal O. Westermark VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/profileScoreDist git_branch: RELEASE_3_10 git_last_commit: 33080de git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/profileScoreDist_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/profileScoreDist_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/profileScoreDist_1.14.0.tgz vignettes: vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.pdf vignetteTitles: Using profileScoreDist hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.R dependencyCount: 7 Package: progeny Version: 1.8.0 Depends: R (>= 3.4.0) Imports: Biobase Suggests: airway, biomaRt, BiocFileCache, broom, DESeq2, dplyr, knitr, readr, readxl License: Apache License (== 2.0) | file LICENSE Archs: i386, x64 MD5sum: be12e60471b38dca4c3691963e519746 NeedsCompilation: no Title: Pathway RespOnsive GENes for activity inference from gene expression Description: This package provides a function to infer pathway activity from gene expression using PROGENy. It contains the linear model we inferred in the publication "Perturbation-response genes reveal signaling footprints in cancer gene expression". biocViews: SystemsBiology, GeneExpression, FunctionalPrediction, GeneRegulation Author: Michael Schubert Maintainer: Michael Schubert URL: https://github.com/saezlab/progeny VignetteBuilder: knitr BugReports: https://github.com/saezlab/progeny/issues git_url: https://git.bioconductor.org/packages/progeny git_branch: RELEASE_3_10 git_last_commit: d567e5a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/progeny_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/progeny_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/progeny_1.8.0.tgz vignettes: vignettes/progeny/inst/doc/progeny.html vignetteTitles: narray Usage Examples hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/progeny/inst/doc/progeny.R dependencyCount: 7 Package: projectR Version: 1.2.0 Imports: methods, cluster, stats, limma, CoGAPS, NMF, ROCR Suggests: BiocStyle, gridExtra, grid, testthat, devtools, knitr, rmarkdown, ComplexHeatmap, viridis, ggplot2 License: GPL (==2) MD5sum: 405918c5a350590bd819dea5ed2771a4 NeedsCompilation: no Title: Functions for the projection of weights from PCA, CoGAPS, NMF, correlation, and clustering Description: Functions for the projection of data into the spaces defined by PCA, CoGAPS, NMF, correlation, and clustering. biocViews: FunctionalPrediction, GeneRegulation, BiologicalQuestion, Software Author: Gaurav Sharma, Genevieve Stein-O'Brien Maintainer: Genevieve Stein-O'Brien URL: https://github.com/genesofeve/projectR/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/projectR/ git_url: https://git.bioconductor.org/packages/projectR git_branch: RELEASE_3_10 git_last_commit: 0e1bca2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/projectR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/projectR_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/projectR_1.2.0.tgz vignettes: vignettes/projectR/inst/doc/projectR.pdf vignetteTitles: projectR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/projectR/inst/doc/projectR.R dependencyCount: 105 Package: pRoloc Version: 1.26.0 Depends: R (>= 2.15), MSnbase (>= 1.19.20), MLInterfaces (>= 1.37.1), methods, Rcpp (>= 0.10.3), BiocParallel Imports: Biobase, mclust (>= 4.3), caret, e1071, sampling, class, kernlab, lattice, nnet, randomForest, proxy, FNN, hexbin, BiocGenerics, stats, dendextend, RColorBrewer, scales, MASS, knitr, mvtnorm, LaplacesDemon, coda, mixtools, gtools, plyr, ggplot2, biomaRt, utils, grDevices, graphics LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, rmarkdown, pRolocdata (>= 1.9.4), roxygen2, synapter, xtable, rgl, BiocStyle (>= 2.5.19), hpar (>= 1.15.3), dplyr, GO.db, AnnotationDbi, Rtsne (>= 0.13), nipals, reshape License: GPL-2 Archs: i386, x64 MD5sum: fd4dd3ebcee06afa0be12273c73a7f5a NeedsCompilation: yes Title: A unifying bioinformatics framework for spatial proteomics Description: The pRoloc package implements machine learning and visualisation methods for the analysis and interogation of quantitiative mass spectrometry data to reliably infer protein sub-cellular localisation. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Classification, Clustering, QualityControl Author: Laurent Gatto, Oliver Crook and Lisa M. Breckels with contributions from Thomas Burger and Samuel Wieczorek Maintainer: Laurent Gatto URL: https://github.com/lgatto/pRoloc VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/lgatto/pRoloc/issues git_url: https://git.bioconductor.org/packages/pRoloc git_branch: RELEASE_3_10 git_last_commit: c5fe2c7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pRoloc_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pRoloc_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pRoloc_1.26.0.tgz vignettes: vignettes/pRoloc/inst/doc/v01-pRoloc-tutorial.html, vignettes/pRoloc/inst/doc/v02-pRoloc-ml.html, vignettes/pRoloc/inst/doc/v03-pRoloc-bayesian.html, vignettes/pRoloc/inst/doc/v04-pRoloc-goannotations.html, vignettes/pRoloc/inst/doc/v05-pRoloc-transfer-learning.html vignetteTitles: Using pRoloc for spatial proteomics data analysis, Machine learning techniques available in pRoloc, Bayesian spatial proteomics with pRoloc, Annotating spatial proteomics data, A transfer learning algorithm for spatial proteomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRoloc/inst/doc/v01-pRoloc-tutorial.R, vignettes/pRoloc/inst/doc/v02-pRoloc-ml.R, vignettes/pRoloc/inst/doc/v03-pRoloc-bayesian.R, vignettes/pRoloc/inst/doc/v04-pRoloc-goannotations.R, vignettes/pRoloc/inst/doc/v05-pRoloc-transfer-learning.R dependsOnMe: pRolocGUI suggestsMe: MSnbase dependencyCount: 195 Package: pRolocGUI Version: 1.20.0 Depends: methods, R (>= 3.1.0), pRoloc (>= 1.11.1), Biobase, MSnbase (>= 2.1.11) Imports: shiny (>= 0.9.1), scales, dplyr, DT (>= 0.1.40), graphics, utils, ggplot2 Suggests: pRolocdata, knitr, BiocStyle (>= 2.5.19), rmarkdown License: GPL-2 MD5sum: a7dd43d7c0268f3e449960d9963dfcb2 NeedsCompilation: no Title: Interactive visualisation of spatial proteomics data Description: The package pRolocGUI comprises functions to interactively visualise organelle (spatial) proteomics data on the basis of pRoloc, pRolocdata and shiny. biocViews: Proteomics, Visualization, GUI Author: Lisa M Breckels, Thomas Naake and Laurent Gatto Maintainer: Laurent Gatto , Lisa M Breckels URL: http://ComputationalProteomicsUnit.github.io/pRolocGUI/ VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/ComputationalProteomicsUnit/pRolocGUI/issues git_url: https://git.bioconductor.org/packages/pRolocGUI git_branch: RELEASE_3_10 git_last_commit: 270e9aa git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pRolocGUI_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pRolocGUI_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pRolocGUI_1.20.0.tgz vignettes: vignettes/pRolocGUI/inst/doc/pRolocGUI.html vignetteTitles: pRolocGUI - Interactive visualisation of spatial proteomics data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRolocGUI/inst/doc/pRolocGUI.R dependencyCount: 197 Package: PROMISE Version: 1.38.0 Depends: R (>= 3.1.0), Biobase, GSEABase Imports: Biobase, GSEABase, stats License: GPL (>= 2) MD5sum: 98ff0eb24312e6c68416c4ddf6367830 NeedsCompilation: no Title: PRojection Onto the Most Interesting Statistical Evidence Description: A general tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables as described in Pounds et. al. (2009) Bioinformatics 25: 2013-2019 biocViews: Microarray, OneChannel, MultipleComparison, GeneExpression Author: Stan Pounds , Xueyuan Cao Maintainer: Stan Pounds , Xueyuan Cao git_url: https://git.bioconductor.org/packages/PROMISE git_branch: RELEASE_3_10 git_last_commit: 9fc5b19 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PROMISE_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PROMISE_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PROMISE_1.38.0.tgz vignettes: vignettes/PROMISE/inst/doc/PROMISE.pdf vignetteTitles: An introduction to PROMISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROMISE/inst/doc/PROMISE.R dependsOnMe: CCPROMISE dependencyCount: 33 Package: PROPER Version: 1.18.0 Depends: R (>= 3.3) Imports: edgeR Suggests: BiocStyle,DESeq,DSS,knitr License: GPL Archs: i386, x64 MD5sum: 427c120f2b6a1d7e03c5eda8ecb66da6 NeedsCompilation: no Title: PROspective Power Evaluation for RNAseq Description: This package provide simulation based methods for evaluating the statistical power in differential expression analysis from RNA-seq data. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression Author: Hao Wu Maintainer: Hao Wu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PROPER git_branch: RELEASE_3_10 git_last_commit: c44000f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PROPER_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PROPER_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PROPER_1.18.0.tgz vignettes: vignettes/PROPER/inst/doc/PROPER.pdf vignetteTitles: Power and Sample size analysis for gene expression from RNA-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROPER/inst/doc/PROPER.R dependencyCount: 11 Package: PROPS Version: 1.8.0 Imports: bnlearn, reshape2, sva, stats, utils, Biobase Suggests: knitr, rmarkdown License: GPL-2 MD5sum: aa98669a49f6f212b58d3f58257c1160 NeedsCompilation: no Title: PRObabilistic Pathway Score (PROPS) Description: This package calculates probabilistic pathway scores using gene expression data. Gene expression values are aggregated into pathway-based scores using Bayesian network representations of biological pathways. biocViews: Classification, Bayesian, GeneExpression Author: Lichy Han Maintainer: Lichy Han VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PROPS git_branch: RELEASE_3_10 git_last_commit: 2b15e41 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PROPS_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PROPS_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PROPS_1.8.0.tgz vignettes: vignettes/PROPS/inst/doc/props.html vignetteTitles: PRObabilistic Pathway Scores (PROPS) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROPS/inst/doc/props.R dependencyCount: 56 Package: Prostar Version: 1.18.6 Depends: R (>= 3.6.1) Imports: DAPAR (>= 1.18.1), DAPARdata (>= 1.16.0), rhandsontable, data.table, shinyjs, DT, shiny, shinyBS, shinyAce, highcharter (>= 0.7.0), htmlwidgets, webshot, R.utils, shinythemes, XML,later, rclipboard, shinycssloaders, future, promises, colourpicker, BiocManager, shinyTree, shinyWidgets, sass, tibble Suggests: BiocStyle, testthat License: Artistic-2.0 MD5sum: 712d8eff89de57027550482a1b41802e NeedsCompilation: no Title: Provides a GUI for DAPAR Description: This package provides a GUI interface for DAPAR. biocViews: Proteomics, MassSpectrometry, Normalization, Preprocessing, ImmunoOncology, R.utils, GO, GUI Author: Samuel Wieczorek [cre,aut], Florence Combes [aut], Thomas Burger [aut] Maintainer: Samuel Wieczorek git_url: https://git.bioconductor.org/packages/Prostar git_branch: RELEASE_3_10 git_last_commit: 08c092f git_last_commit_date: 2020-01-23 Date/Publication: 2020-01-23 source.ver: src/contrib/Prostar_1.18.6.tar.gz win.binary.ver: bin/windows/contrib/3.6/Prostar_1.18.6.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Prostar_1.18.6.tgz vignettes: vignettes/Prostar/inst/doc/Prostar_UserManual.pdf vignetteTitles: Prostar user manual hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Prostar/inst/doc/Prostar_UserManual.R Package: proteinProfiles Version: 1.26.0 Depends: R (>= 2.15.2) Imports: graphics, stats Suggests: testthat License: GPL-3 MD5sum: 45988d56bbcd69da9e2dba3564c79e88 NeedsCompilation: no Title: Protein Profiling Description: Significance assessment for distance measures of time-course protein profiles Author: Julian Gehring Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/proteinProfiles git_branch: RELEASE_3_10 git_last_commit: 80bc1bf git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/proteinProfiles_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/proteinProfiles_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/proteinProfiles_1.26.0.tgz vignettes: vignettes/proteinProfiles/inst/doc/proteinProfiles.pdf vignetteTitles: The proteinProfiles package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proteinProfiles/inst/doc/proteinProfiles.R dependencyCount: 2 Package: ProteomicsAnnotationHubData Version: 1.16.0 Depends: AnnotationHub (>= 2.1.45), AnnotationHubData, Imports: mzR (>= 2.3.2), MSnbase, Biostrings, GenomeInfoDb, utils, Biobase, BiocManager, RCurl Suggests: knitr, BiocStyle, rmarkdown, testthat License: Artistic-2.0 MD5sum: 11cfcbe84fb9c73aaa7ef71d23fc6065 NeedsCompilation: no Title: Transform public proteomics data resources into Bioconductor Data Structures Description: These recipes convert a variety and a growing number of public proteomics data sets into easily-used standard Bioconductor data structures. biocViews: DataImport, Proteomics Author: Gatto Laurent [aut, cre], Sonali Arora [aut] Maintainer: Laurent Gatto URL: https://github.com/lgatto/ProteomicsAnnotationHubData VignetteBuilder: knitr BugReports: https://github.com/lgatto/ProteomicsAnnotationHubData/issues git_url: https://git.bioconductor.org/packages/ProteomicsAnnotationHubData git_branch: RELEASE_3_10 git_last_commit: e5904a7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ProteomicsAnnotationHubData_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ProteomicsAnnotationHubData_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ProteomicsAnnotationHubData_1.16.0.tgz vignettes: vignettes/ProteomicsAnnotationHubData/inst/doc/ProteomicsAnnotationHubData.html vignetteTitles: Proteomics Data in Annotation Hub hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteomicsAnnotationHubData/inst/doc/ProteomicsAnnotationHubData.R dependencyCount: 151 Package: ProteoMM Version: 1.4.0 Depends: R (>= 3.5) Imports: gdata, biomaRt, ggplot2, ggrepel, gtools, stats, matrixStats, graphics Suggests: BiocStyle, knitr, rmarkdown License: MIT MD5sum: 6ce483c4668a131d26abdee5430447f3 NeedsCompilation: no Title: Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform Description: ProteoMM is a statistical method to perform model-based peptide-level differential expression analysis of single or multiple datasets. For multiple datasets ProteoMM produces a single fold change and p-value for each protein across multiple datasets. ProteoMM provides functionality for normalization, missing value imputation and differential expression. Model-based peptide-level imputation and differential expression analysis component of package follows the analysis described in “A statistical framework for protein quantitation in bottom-up MS based proteomics" (Karpievitch et al. Bioinformatics 2009). EigenMS normalisation is implemented as described in "Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition." (Karpievitch et al. Bioinformatics 2009). biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Normalization, DifferentialExpression Author: Yuliya V Karpievitch, Tim Stuart and Sufyaan Mohamed Maintainer: Yuliya V Karpievitch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ProteoMM git_branch: RELEASE_3_10 git_last_commit: c91e53f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ProteoMM_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ProteoMM_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ProteoMM_1.4.0.tgz vignettes: vignettes/ProteoMM/inst/doc/ProteoMM_vignette.html vignetteTitles: Multi-Dataset Model-based Differential Expression Proteomics Platform hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteoMM/inst/doc/ProteoMM_vignette.R dependencyCount: 92 Package: proteoQC Version: 1.21.0 Depends: R (>= 3.0.0), XML, VennDiagram, MSnbase Imports: rTANDEM, plyr, seqinr, Nozzle.R1, ggplot2, reshape2, parallel, rpx, tidyr, dplyr, plotly, rmarkdown, Suggests: RforProteomics, knitr, BiocStyle, R.utils, RUnit,BiocGenerics License: LGPL-2 MD5sum: 92692e323c1b0d3b36606b83f40a7f5c NeedsCompilation: no Title: An R package for proteomics data quality control Description: This package creates an HTML format QC report for MS/MS-based proteomics data. The report is intended to allow the user to quickly assess the quality of proteomics data. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, QualityControl, Visualization, ReportWriting Author: Bo Wen , Laurent Gatto Maintainer: Bo Wen URL: https://github.com/wenbostar/proteoQC VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/proteoQC git_branch: master git_last_commit: f5542d9 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-02 source.ver: src/contrib/proteoQC_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/proteoQC_1.21.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/proteoQC_1.21.0.tgz vignettes: vignettes/proteoQC/inst/doc/proteoQC.html vignetteTitles: 00 proteoQC introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proteoQC/inst/doc/proteoQC.R dependencyCount: 133 Package: ProtGenerics Version: 1.18.0 Depends: methods License: Artistic-2.0 MD5sum: d02c7a57034ac815d4e806bfb11e778f NeedsCompilation: no Title: S4 generic functions for Bioconductor proteomics infrastructure Description: S4 generic functions needed by Bioconductor proteomics packages. biocViews: Infrastructure, Proteomics, MassSpectrometry Author: Laurent Gatto Maintainer: Laurent Gatto URL: https://github.com/lgatto/ProtGenerics git_url: https://git.bioconductor.org/packages/ProtGenerics git_branch: RELEASE_3_10 git_last_commit: ff9705b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ProtGenerics_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ProtGenerics_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ProtGenerics_1.18.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Cardinal, MSnbase, tofsims, topdownr importsMe: ensembldb, matter, MSGFplus, MSnID, mzID, mzR, xcms dependencyCount: 1 Package: PSEA Version: 1.20.0 Imports: Biobase, MASS Suggests: BiocStyle License: Artistic-2.0 MD5sum: 68a18ede8dd379789547fde4a94f507b NeedsCompilation: no Title: Population-Specific Expression Analysis. Description: Deconvolution of gene expression data by Population-Specific Expression Analysis (PSEA). biocViews: Software Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/PSEA git_branch: RELEASE_3_10 git_last_commit: e6221fe git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PSEA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PSEA_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PSEA_1.20.0.tgz vignettes: vignettes/PSEA/inst/doc/PSEA_RNAmixtures.pdf, vignettes/PSEA/inst/doc/PSEA.pdf vignetteTitles: PSEA: Deconvolution of RNA mixtures in Nature Methods paper, PSEA: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSEA/inst/doc/PSEA_RNAmixtures.R, vignettes/PSEA/inst/doc/PSEA.R dependencyCount: 9 Package: psichomics Version: 1.12.1 Depends: R (>= 3.6), shiny (>= 1.0.3), shinyBS Imports: AnnotationDbi, AnnotationHub, BiocFileCache, cluster, colourpicker, data.table, digest, dplyr, DT (>= 0.2), edgeR, fastICA, fastmatch, ggplot2, ggrepel, graphics, grDevices, highcharter (>= 0.5.0), htmltools, httr, jsonlite, limma, miscTools, pairsD3, plyr, Rcpp (>= 0.12.14), recount, R.utils, reshape2, shinyjs, stringr, stats, SummarizedExperiment, survival, tools, utils, XML, xtable, methods, org.Hs.eg.db LinkingTo: Rcpp Suggests: testthat, knitr, parallel, devtools, rmarkdown, gplots, covr, car, rstudioapi, spelling License: MIT + file LICENSE MD5sum: c9c735507c48b5aefefd465c2ecea321 NeedsCompilation: yes Title: Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation Description: Interactive R package with an intuitive Shiny-based graphical interface for alternative splicing quantification and integrative analyses of alternative splicing and gene expression based on The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), Sequence Read Archive (SRA) and user-provided data. The tool interactively performs survival, dimensionality reduction and median- and variance-based differential splicing and gene expression analyses that benefit from the incorporation of clinical and molecular sample-associated features (such as tumour stage or survival). Interactive visual access to genomic mapping and functional annotation of selected alternative splicing events is also included. biocViews: Sequencing, RNASeq, AlternativeSplicing, DifferentialSplicing, Transcription, GUI, PrincipalComponent, Survival, BiomedicalInformatics, Transcriptomics, ImmunoOncology, Visualization, MultipleComparison, GeneExpression, DifferentialExpression Author: Nuno Saraiva-Agostinho [aut, cre] (), Nuno Luís Barbosa-Morais [aut, led, ths] (), André Falcão [ths], Lina Gallego Paez [ctb], Marie Bordone [ctb], Teresa Maia [ctb], Mariana Ferreira [ctb], Ana Carolina Leote [ctb], Bernardo de Almeida [ctb] Maintainer: Nuno Saraiva-Agostinho URL: https://nuno-agostinho.github.io/psichomics/ VignetteBuilder: knitr BugReports: https://github.com/nuno-agostinho/psichomics/issues git_url: https://git.bioconductor.org/packages/psichomics git_branch: RELEASE_3_10 git_last_commit: fac011f git_last_commit_date: 2020-01-29 Date/Publication: 2020-01-30 source.ver: src/contrib/psichomics_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/psichomics_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/psichomics_1.12.1.tgz vignettes: vignettes/psichomics/inst/doc/AS_events_preparation.html, vignettes/psichomics/inst/doc/CLI_tutorial.html, vignettes/psichomics/inst/doc/custom_data.html, vignettes/psichomics/inst/doc/GUI_tutorial.html vignetteTitles: Preparing alternative splicing annotations, Case study: command-line interface (CLI), SRA and user-provided RNA-seq data analysis, Case study: visual interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/psichomics/inst/doc/AS_events_preparation.R, vignettes/psichomics/inst/doc/CLI_tutorial.R, vignettes/psichomics/inst/doc/custom_data.R, vignettes/psichomics/inst/doc/GUI_tutorial.R dependencyCount: 197 Package: PSICQUIC Version: 1.24.0 Depends: R (>= 3.2.2), methods, IRanges, biomaRt (>= 2.34.1), BiocGenerics, httr, plyr Imports: RCurl Suggests: org.Hs.eg.db License: Apache License 2.0 MD5sum: eb14a0471a3ed4af48cc9007fce72962 NeedsCompilation: no Title: Proteomics Standard Initiative Common QUery InterfaCe Description: PSICQUIC is a project within the HUPO Proteomics Standard Initiative (HUPO-PSI). It standardises programmatic access to molecular interaction databases. biocViews: DataImport, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon git_url: https://git.bioconductor.org/packages/PSICQUIC git_branch: RELEASE_3_10 git_last_commit: 9eb3444 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PSICQUIC_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PSICQUIC_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PSICQUIC_1.24.0.tgz vignettes: vignettes/PSICQUIC/inst/doc/PSICQUIC.pdf vignetteTitles: PSICQUIC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSICQUIC/inst/doc/PSICQUIC.R dependsOnMe: RefNet dependencyCount: 61 Package: psygenet2r Version: 1.18.0 Depends: R (>= 3.4) Imports: stringr, RCurl, igraph, ggplot2, reshape2, grid, parallel, biomaRt, BgeeDB, topGO, BiocManager, Biobase, labeling, GO.db Suggests: testthat, knitr License: MIT + file LICENSE MD5sum: bb1f52192af0fc5d0f6aba3acf6499e3 NeedsCompilation: no Title: psygenet2r - An R package for querying PsyGeNET and to perform comorbidity studies in psychiatric disorders Description: Package to retrieve data from PsyGeNET database (www.psygenet.org) and to perform comorbidity studies with PsyGeNET's and user's data. biocViews: Software, BiomedicalInformatics, Genetics, Infrastructure, DataImport, DataRepresentation Author: Alba Gutierrez-Sacristan [aut, cre], Carles Hernandez-Ferrer [aut], Jaun R. Gonzalez [aut], Laura I. Furlong [aut] Maintainer: Alba Gutierrez-Sacristan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/psygenet2r git_branch: RELEASE_3_10 git_last_commit: f1f26c7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/psygenet2r_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/psygenet2r_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/psygenet2r_1.18.0.tgz vignettes: vignettes/psygenet2r/inst/doc/case_study.html, vignettes/psygenet2r/inst/doc/general_overview.html vignetteTitles: psygenet2r: Case study on GWAS on bipolar disorder, psygenet2r: An R package for querying PsyGeNET and to perform comorbidity studies in psychiatric disorders hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/psygenet2r/inst/doc/case_study.R, vignettes/psygenet2r/inst/doc/general_overview.R dependencyCount: 102 Package: pulsedSilac Version: 1.0.1 Depends: R (>= 3.6.0) Imports: robustbase, methods, R.utils, taRifx, S4Vectors, SummarizedExperiment, ggplot2, ggridges, stats, utils, UpSetR, cowplot, grid, MuMIn Suggests: testthat (>= 2.1.0), knitr, rmarkdown, gridExtra License: GPL-3 Archs: i386, x64 MD5sum: 29285b7b0943714571bce480b82d9876 NeedsCompilation: no Title: Analysis of pulsed-SILAC quantitative proteomics data Description: This package provides several tools for pulsed-SILAC data analysis. Functions are provided to organize the data, calculate isotope ratios, isotope fractions, model protein turnover, compare turnover models, estimate cell growth and estimate isotope recycling. Several visualization tools are also included to do basic data exploration, quality control, condition comparison, individual model inspection and model comparison. biocViews: Proteomics Author: Marc Pagès-Gallego, Tobias B. Dansen Maintainer: Marc Pagès-Gallego VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pulsedSilac git_branch: RELEASE_3_10 git_last_commit: 43c451a git_last_commit_date: 2020-03-17 Date/Publication: 2020-03-17 source.ver: src/contrib/pulsedSilac_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/pulsedSilac_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pulsedSilac_1.0.1.tgz vignettes: vignettes/pulsedSilac/inst/doc/pulsedsilac.html vignetteTitles: Pulsed-SILAC data analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pulsedSilac/inst/doc/pulsedsilac.R dependencyCount: 92 Package: puma Version: 3.28.0 Depends: R (>= 3.2.0), oligo (>= 1.32.0),graphics,grDevices, methods, stats, utils, mclust, oligoClasses Imports: Biobase (>= 2.5.5), affy (>= 1.46.0), affyio, oligoClasses Suggests: pumadata, affydata, snow, limma, ROCR,annotate License: LGPL MD5sum: 9c6a05fbf44f68a0933792089df740c4 NeedsCompilation: yes Title: Propagating Uncertainty in Microarray Analysis(including Affymetrix tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) Description: Most analyses of Affymetrix GeneChip data (including tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) are based on point estimates of expression levels and ignore the uncertainty of such estimates. By propagating uncertainty to downstream analyses we can improve results from microarray analyses. For the first time, the puma package makes a suite of uncertainty propagation methods available to a general audience. In additon to calculte gene expression from Affymetrix 3' arrays, puma also provides methods to process exon arrays and produces gene and isoform expression for alternative splicing study. puma also offers improvements in terms of scope and speed of execution over previously available uncertainty propagation methods. Included are summarisation, differential expression detection, clustering and PCA methods, together with useful plotting functions. biocViews: Microarray, OneChannel, Preprocessing, DifferentialExpression, Clustering, ExonArray, GeneExpression, mRNAMicroarray, ChipOnChip, AlternativeSplicing, DifferentialSplicing, Bayesian, TwoChannel, DataImport, HTA2.0 Author: Richard D. Pearson, Xuejun Liu, Magnus Rattray, Marta Milo, Neil D. Lawrence, Guido Sanguinetti, Li Zhang Maintainer: Xuejun Liu URL: http://umber.sbs.man.ac.uk/resources/puma git_url: https://git.bioconductor.org/packages/puma git_branch: RELEASE_3_10 git_last_commit: 2b8a766 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/puma_3.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/puma_3.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/puma_3.28.0.tgz vignettes: vignettes/puma/inst/doc/puma.pdf vignetteTitles: puma User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/puma/inst/doc/puma.R suggestsMe: tigre dependencyCount: 60 Package: PureCN Version: 1.16.0 Depends: R (>= 3.3), DNAcopy, VariantAnnotation (>= 1.14.1) Imports: GenomicRanges (>= 1.20.3), IRanges (>= 2.2.1), RColorBrewer, S4Vectors, data.table, grDevices, graphics, stats, utils, SummarizedExperiment, GenomeInfoDb, GenomicFeatures, Rsamtools, Biostrings, BiocGenerics, rtracklayer, ggplot2, gridExtra, futile.logger, VGAM, tools, methods, rhdf5, Matrix Suggests: BiocParallel, BiocStyle, PSCBS, TxDb.Hsapiens.UCSC.hg19.knownGene, copynumber, covr, knitr, optparse, org.Hs.eg.db, rmarkdown, testthat License: Artistic-2.0 MD5sum: 3de7c62e901d4a442798e19d3bd0de85 NeedsCompilation: no Title: Copy number calling and SNV classification using targeted short read sequencing Description: This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples. biocViews: CopyNumberVariation, Software, Sequencing, VariantAnnotation, VariantDetection, Coverage, ImmunoOncology Author: Markus Riester [aut, cre] (), Angad P. Singh [aut] Maintainer: Markus Riester URL: https://github.com/lima1/PureCN VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PureCN git_branch: RELEASE_3_10 git_last_commit: 2182067 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PureCN_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PureCN_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PureCN_1.16.0.tgz vignettes: vignettes/PureCN/inst/doc/PureCN.pdf, vignettes/PureCN/inst/doc/Quick.html vignetteTitles: Overview of the PureCN R package, Best practices,, quick start and command line usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PureCN/inst/doc/PureCN.R, vignettes/PureCN/inst/doc/Quick.R dependencyCount: 118 Package: pvac Version: 1.34.0 Depends: R (>= 2.8.0) Imports: affy (>= 1.20.0), stats, Biobase Suggests: pbapply, affydata, ALLMLL, genefilter License: LGPL (>= 2.0) MD5sum: c770ffbbbd4d5d0f42c3e2595de1b6f5 NeedsCompilation: no Title: PCA-based gene filtering for Affymetrix arrays Description: The package contains the function for filtering genes by the proportion of variation accounted for by the first principal component (PVAC). biocViews: Microarray, OneChannel, QualityControl Author: Jun Lu and Pierre R. Bushel Maintainer: Jun Lu , Pierre R. Bushel git_url: https://git.bioconductor.org/packages/pvac git_branch: RELEASE_3_10 git_last_commit: 4a60725 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pvac_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pvac_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pvac_1.34.0.tgz vignettes: vignettes/pvac/inst/doc/pvac.pdf vignetteTitles: PCA-based gene filtering for Affymetrix GeneChips hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pvac/inst/doc/pvac.R dependencyCount: 13 Package: pvca Version: 1.26.0 Depends: R (>= 2.15.1) Imports: Matrix, Biobase, vsn, stats, lme4 Suggests: golubEsets License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 4b9686d9e870d510242b4d94d04a84f0 NeedsCompilation: no Title: Principal Variance Component Analysis (PVCA) Description: This package contains the function to assess the batch sourcs by fitting all "sources" as random effects including two-way interaction terms in the Mixed Model(depends on lme4 package) to selected principal components, which were obtained from the original data correlation matrix. This package accompanies the book "Batch Effects and Noise in Microarray Experiements, chapter 12. biocViews: Microarray, BatchEffect Author: Pierre Bushel Maintainer: Jianying LI git_url: https://git.bioconductor.org/packages/pvca git_branch: RELEASE_3_10 git_last_commit: 205d884 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pvca_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pvca_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pvca_1.26.0.tgz vignettes: vignettes/pvca/inst/doc/pvca.pdf vignetteTitles: Batch effect estimation in Microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pvca/inst/doc/pvca.R importsMe: proBatch dependencyCount: 70 Package: Pviz Version: 1.20.0 Depends: R(>= 3.0.0), Gviz(>= 1.7.10) Imports: biovizBase, Biostrings, GenomicRanges, IRanges, data.table, methods Suggests: knitr, pepDat License: Artistic-2.0 MD5sum: a86a6c274407a67ebcd1762d10e8c23e NeedsCompilation: no Title: Peptide Annotation and Data Visualization using Gviz Description: Pviz adapts the Gviz package for protein sequences and data. biocViews: Visualization, Proteomics, Microarray Author: Renan Sauteraud, Mike Jiang, Raphael Gottardo Maintainer: Renan Sauteraud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Pviz git_branch: RELEASE_3_10 git_last_commit: a4577dd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Pviz_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Pviz_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Pviz_1.20.0.tgz vignettes: vignettes/Pviz/inst/doc/Pviz.pdf vignetteTitles: The Pviz users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pviz/inst/doc/Pviz.R importsMe: Pbase suggestsMe: pepStat dependencyCount: 143 Package: PWMEnrich Version: 4.22.0 Depends: methods, grid, BiocGenerics, Biostrings, Imports: seqLogo, gdata, evd Suggests: MotifDb, BSgenome.Dmelanogaster.UCSC.dm3, PWMEnrich.Dmelanogaster.background, testthat, gtools, parallel, PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background, BiocStyle, knitr License: LGPL (>= 2) MD5sum: 2e4767f3433884c0ee195a8842c5d4e4 NeedsCompilation: no Title: PWM enrichment analysis Description: A toolkit of high-level functions for DNA motif scanning and enrichment analysis built upon Biostrings. The main functionality is PWM enrichment analysis of already known PWMs (e.g. from databases such as MotifDb), but the package also implements high-level functions for PWM scanning and visualisation. The package does not perform "de novo" motif discovery, but is instead focused on using motifs that are either experimentally derived or computationally constructed by other tools. biocViews: MotifAnnotation, SequenceMatching, Software Author: Robert Stojnic, Diego Diez Maintainer: Robert Stojnic VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PWMEnrich git_branch: RELEASE_3_10 git_last_commit: de4c88d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/PWMEnrich_4.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/PWMEnrich_4.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/PWMEnrich_4.22.0.tgz vignettes: vignettes/PWMEnrich/inst/doc/PWMEnrich.pdf vignetteTitles: Overview of the 'PWMEnrich' package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PWMEnrich/inst/doc/PWMEnrich.R suggestsMe: rTRM dependencyCount: 18 Package: pwOmics Version: 1.18.0 Depends: R (>= 3.2) Imports: data.table, rBiopaxParser, igraph, STRINGdb, graphics, gplots, Biobase, BiocGenerics, AnnotationDbi, biomaRt, AnnotationHub, GenomicRanges, graph, grDevices, stats, utils Suggests: ebdbNet, longitudinal, Mfuzz License: GPL (>= 2) MD5sum: de41da7ece27e6f1bf1ade53d8f80dd9 NeedsCompilation: no Title: Pathway-based data integration of omics data Description: pwOmics performs pathway-based level-specific data comparison of matching omics data sets based on pre-analysed user-specified lists of differential genes/transcripts and phosphoproteins. A separate downstream analysis of phosphoproteomic data including pathway identification, transcription factor identification and target gene identification is opposed to the upstream analysis starting with gene or transcript information as basis for identification of upstream transcription factors and potential proteomic regulators. The cross-platform comparative analysis allows for comprehensive analysis of single time point experiments and time-series experiments by providing static and dynamic analysis tools for data integration. In addition, it provides functions to identify individual signaling axes based on data integration. biocViews: SystemsBiology, Transcription, GeneTarget, GeneSignaling Author: Astrid Wachter Maintainer: Maren Sitte git_url: https://git.bioconductor.org/packages/pwOmics git_branch: RELEASE_3_10 git_last_commit: cdbf5ed git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pwOmics_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pwOmics_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pwOmics_1.18.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 100 Package: pwrEWAS Version: 1.0.0 Depends: shinyBS, foreach Imports: doParallel, abind, truncnorm, CpGassoc, shiny, ggplot2, parallel, shinyWidgets, BiocManager, doSNOW, limma, genefilter, stats, grDevices, methods, utils, graphics, pwrEWAS.data Suggests: knitr, RUnit, BiocGenerics, rmarkdown License: Artistic-2.0 MD5sum: c4efa4c0935791d793269fe7a834d777 NeedsCompilation: no Title: A user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS) Description: pwrEWAS is a user-friendly tool to assists researchers in the design and planning of EWAS to help circumvent under- and overpowered studies. biocViews: DNAMethylation, Microarray, DifferentialMethylation, TissueMicroarray Author: Stefan Graw Maintainer: Stefan Graw VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pwrEWAS git_branch: RELEASE_3_10 git_last_commit: 71a03f6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/pwrEWAS_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/pwrEWAS_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/pwrEWAS_1.0.0.tgz vignettes: vignettes/pwrEWAS/inst/doc/pwrEWAS.pdf vignetteTitles: pwrEWAS User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pwrEWAS/inst/doc/pwrEWAS.R dependencyCount: 115 Package: qckitfastq Version: 1.2.0 Imports: magrittr, ggplot2, dplyr, seqTools, zlibbioc, data.table, reshape2, grDevices, graphics, stats, utils, Rcpp, rlang, RSeqAn LinkingTo: Rcpp, RSeqAn Suggests: knitr, rmarkdown, kableExtra, testthat License: Artistic-2.0 MD5sum: 2b5de2f896d803dab9a5acdcefb631f9 NeedsCompilation: yes Title: FASTQ Quality Control Description: Assessment of FASTQ file format with multiple metrics including quality score, sequence content, overrepresented sequence and Kmers. biocViews: Software,QualityControl,Sequencing Author: Wenyue Xing [aut], August Guang [aut, cre] Maintainer: August Guang SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qckitfastq git_branch: RELEASE_3_10 git_last_commit: 23592da git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/qckitfastq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/qckitfastq_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/qckitfastq_1.2.0.tgz vignettes: vignettes/qckitfastq/inst/doc/vignette-qckitfastq.pdf vignetteTitles: Quality control analysis and visualization using qckitfastq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qckitfastq/inst/doc/vignette-qckitfastq.R dependencyCount: 67 Package: qcmetrics Version: 1.24.0 Depends: R (>= 3.3) Imports: Biobase, methods, knitr, tools, Nozzle.R1, xtable, pander, S4Vectors Suggests: affy, MSnbase, ggplot2, lattice, yaqcaffy, MAQCsubsetAFX, RforProteomics, AnnotationDbi, mzR, hgu133plus2cdf, BiocStyle License: GPL-2 MD5sum: 6a16244cb357da1a059e41aed816b831 NeedsCompilation: no Title: A Framework for Quality Control Description: The package provides a framework for generic quality control of data. It permits to create, manage and visualise individual or sets of quality control metrics and generate quality control reports in various formats. biocViews: ImmunoOncology, Software, QualityControl, Proteomics, Microarray, MassSpectrometry, Visualization, ReportWriting Author: Laurent Gatto [aut, cre] Maintainer: Laurent Gatto URL: https://github.com/lgatto/qcmetrics VignetteBuilder: knitr BugReports: https://github.com/lgatto/qcmetrics/issues git_url: https://git.bioconductor.org/packages/qcmetrics git_branch: RELEASE_3_10 git_last_commit: 667c93e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/qcmetrics_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/qcmetrics_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/qcmetrics_1.24.0.tgz vignettes: vignettes/qcmetrics/inst/doc/qcmetrics.pdf, vignettes/qcmetrics/inst/doc/vig-index.html vignetteTitles: The 'qcmetrics' infrastructure for quality control and reporting, Index file for the qcmetrics package vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qcmetrics/inst/doc/qcmetrics.R, vignettes/qcmetrics/inst/doc/vig-index.R importsMe: MSstatsQC dependencyCount: 27 Package: QDNAseq Version: 1.22.0 Depends: R (>= 3.1.0) Imports: graphics, methods, stats, utils, Biobase (>= 2.18.0), CGHbase (>= 1.18.0), CGHcall (>= 2.18.0), DNAcopy (>= 1.32.0), GenomicRanges (>= 1.20), IRanges (>= 2.2), matrixStats (>= 0.54.0), R.utils (>= 2.9.0), Rsamtools (>= 1.20), future (>= 1.14.0), future.apply (>= 1.3.0) Suggests: BiocStyle (>= 1.8.0), BSgenome (>= 1.38.0), digest (>= 0.6.20), GenomeInfoDb (>= 1.6.0), R.cache (>= 0.13.0), QDNAseq.hg19, QDNAseq.mm10 License: GPL MD5sum: 943f9a7d7a167ce58587d660907dbe80 NeedsCompilation: no Title: Quantitative DNA Sequencing for Chromosomal Aberrations Description: Quantitative DNA sequencing for chromosomal aberrations. The genome is divided into non-overlapping fixed-sized bins, number of sequence reads in each counted, adjusted with a simultaneous two-dimensional loess correction for sequence mappability and GC content, and filtered to remove spurious regions in the genome. Downstream steps of segmentation and calling are also implemented via packages DNAcopy and CGHcall, respectively. biocViews: CopyNumberVariation, DNASeq, Genetics, GenomeAnnotation, Preprocessing, QualityControl, Sequencing Author: Ilari Scheinin [aut], Daoud Sie [aut, cre], Henrik Bengtsson [aut], Erik van Dijk [ctb] Maintainer: Daoud Sie URL: https://github.com/ccagc/QDNAseq BugReports: https://github.com/ccagc/QDNAseq/issues git_url: https://git.bioconductor.org/packages/QDNAseq git_branch: RELEASE_3_10 git_last_commit: 679f388 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/QDNAseq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/QDNAseq_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/QDNAseq_1.22.0.tgz vignettes: vignettes/QDNAseq/inst/doc/QDNAseq.pdf vignetteTitles: Introduction to QDNAseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QDNAseq/inst/doc/QDNAseq.R dependsOnMe: GeneBreak importsMe: ACE, biscuiteer, HiCcompare dependencyCount: 46 Package: qpcrNorm Version: 1.44.0 Depends: methods, Biobase, limma, affy License: LGPL (>= 2) Archs: i386, x64 MD5sum: 05066141b568f87e72cdeb78552af4e7 NeedsCompilation: no Title: Data-driven normalization strategies for high-throughput qPCR data. Description: The package contains functions to perform normalization of high-throughput qPCR data. Basic functions for processing raw Ct data plus functions to generate diagnostic plots are also available. biocViews: Preprocessing, GeneExpression Author: Jessica Mar Maintainer: Jessica Mar git_url: https://git.bioconductor.org/packages/qpcrNorm git_branch: RELEASE_3_10 git_last_commit: ce440de git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/qpcrNorm_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/qpcrNorm_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/qpcrNorm_1.44.0.tgz vignettes: vignettes/qpcrNorm/inst/doc/qpcrNorm.pdf vignetteTitles: qPCR Normalization Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qpcrNorm/inst/doc/qpcrNorm.R suggestsMe: EasyqpcR dependencyCount: 14 Package: qpgraph Version: 2.20.0 Depends: R (>= 3.4) Imports: methods, parallel, Matrix (>= 1.0), grid, annotate, graph (>= 1.45.1), Biobase, S4Vectors, BiocParallel, AnnotationDbi, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, mvtnorm, qtl, Rgraphviz Suggests: RUnit, BiocGenerics, BiocStyle, genefilter, org.EcK12.eg.db, rlecuyer, snow, Category, GOstats License: GPL (>= 2) MD5sum: 4452c36a6c35bb53792b4290ca7ff9e9 NeedsCompilation: yes Title: Estimation of genetic and molecular regulatory networks from high-throughput genomics data Description: Estimate gene and eQTL networks from high-throughput expression and genotyping assays. biocViews: Microarray, GeneExpression, Transcription, Pathways, NetworkInference, GraphAndNetwork, GeneRegulation, Genetics, GeneticVariability, SNP, Software Author: Robert Castelo [aut, cre], Alberto Roverato [aut] Maintainer: Robert Castelo URL: https://github.com/rcastelo/qpgraph BugReports: https://github.com/rcastelo/rcastelo/issues git_url: https://git.bioconductor.org/packages/qpgraph git_branch: RELEASE_3_10 git_last_commit: 2b0cdad git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/qpgraph_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/qpgraph_2.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/qpgraph_2.20.0.tgz vignettes: vignettes/qpgraph/inst/doc/BasicUsersGuide.pdf, vignettes/qpgraph/inst/doc/eQTLnetworks.pdf, vignettes/qpgraph/inst/doc/qpgraphSimulate.pdf, vignettes/qpgraph/inst/doc/qpTxRegNet.pdf vignetteTitles: BasicUsersGuide.pdf, Estimate eQTL networks using qpgraph, Simulating molecular regulatory networks using qpgraph, Reverse-engineer transcriptional regulatory networks using qpgraph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qpgraph/inst/doc/eQTLnetworks.R, vignettes/qpgraph/inst/doc/qpgraphSimulate.R, vignettes/qpgraph/inst/doc/qpTxRegNet.R importsMe: clipper dependencyCount: 89 Package: qPLEXanalyzer Version: 1.4.0 Depends: R (>= 3.6), Biobase, statmod, MSnbase Imports: tidyr, preprocessCore, limma, ggplot2, RColorBrewer, stats, utils, Biostrings, GenomicRanges, IRanges, graphics, BiocGenerics, stringr, purrr, tibble, ggdendro, grDevices, dplyr, magrittr, assertthat Suggests: UniProt.ws, knitr, qPLEXdata License: GPL-2 MD5sum: b6ea7f33154c6c5349d4f9d92a0c2ab8 NeedsCompilation: no Title: Tools for qPLEX-RIME data analysis Description: Tools for quantitative proteomics data analysis generated from qPLEX-RIME method. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Normalization, Preprocessing, QualityControl, DataImport Author: Matthew Eldridge [aut], Kamal Kishore [aut], Ashley Sawle [aut, cre] Maintainer: Ashley Sawle VignetteBuilder: knitr BugReports: https://github.com/crukci-bioinformatics/qPLEXanalyzer/issues git_url: https://git.bioconductor.org/packages/qPLEXanalyzer git_branch: RELEASE_3_10 git_last_commit: dd19a73 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/qPLEXanalyzer_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/qPLEXanalyzer_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/qPLEXanalyzer_1.4.0.tgz vignettes: vignettes/qPLEXanalyzer/inst/doc/qPLEXanalyzer.pdf vignetteTitles: qPLEXanalyzer.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qPLEXanalyzer/inst/doc/qPLEXanalyzer.R dependencyCount: 105 Package: qrqc Version: 1.40.0 Depends: reshape, ggplot2, Biostrings, biovizBase, brew, xtable, testthat Imports: reshape, ggplot2, Biostrings, biovizBase, graphics, methods, plyr, stats LinkingTo: Rhtslib (>= 1.15.3) License: GPL (>=2) MD5sum: 972fe4cf1eff43358f35b9b4904866e4 NeedsCompilation: yes Title: Quick Read Quality Control Description: Quickly scans reads and gathers statistics on base and quality frequencies, read length, k-mers by position, and frequent sequences. Produces graphical output of statistics for use in quality control pipelines, and an optional HTML quality report. S4 SequenceSummary objects allow specific tests and functionality to be written around the data collected. biocViews: Sequencing, QualityControl, DataImport, Preprocessing, Visualization Author: Vince Buffalo Maintainer: Vince Buffalo URL: http://github.com/vsbuffalo/qrqc SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/qrqc git_branch: RELEASE_3_10 git_last_commit: a7fa1d3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/qrqc_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/qrqc_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/qrqc_1.40.0.tgz vignettes: vignettes/qrqc/inst/doc/qrqc.pdf vignetteTitles: Using the qrqc package to gather information about sequence qualities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qrqc/inst/doc/qrqc.R dependencyCount: 146 Package: qsea Version: 1.12.0 Depends: R (>= 3.5) Imports: Biostrings, graphics, gtools, methods, stats, utils, HMMcopy, rtracklayer, BSgenome, GenomicRanges, Rsamtools, IRanges, limma, GenomeInfoDb, BiocGenerics, grDevices, zoo, BiocParallel Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, testthat, BiocStyle, knitr, rmarkdown, BiocManager License: GPL (>=2) Archs: i386, x64 MD5sum: 41c7607bea8d7c440c893a58fb681d6f NeedsCompilation: yes Title: IP-seq data analysis and vizualization Description: qsea (quantitative sequencing enrichment analysis) was developed as the successor of the MEDIPS package for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, qsea provides several functionalities for the analysis of other kinds of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential enrichment between groups of samples. biocViews: Sequencing, DNAMethylation, CpGIsland, ChIPSeq, Preprocessing, Normalization, QualityControl, Visualization, CopyNumberVariation, ChipOnChip, DifferentialMethylation Author: Matthias Lienhard, Lukas Chavez, Ralf Herwig Maintainer: Matthias Lienhard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qsea git_branch: RELEASE_3_10 git_last_commit: c6cbd48 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/qsea_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/qsea_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/qsea_1.12.0.tgz vignettes: vignettes/qsea/inst/doc/qsea_tutorial.html vignetteTitles: qsea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsea/inst/doc/qsea_tutorial.R dependencyCount: 44 Package: qsmooth Version: 1.2.0 Depends: R (>= 3.6.0) Imports: SummarizedExperiment, utils, sva, stats, methods, graphics Suggests: bodymapRat, quantro, knitr, rmarkdown, BiocStyle, testthat License: CC BY 4.0 MD5sum: 8757740b2ae6a040fe154c9aad5effc5 NeedsCompilation: no Title: Smooth quantile normalization Description: Smooth quantile normalization is a generalization of quantile normalization, which is average of the two types of assumptions about the data generation process: quantile normalization and quantile normalization between groups. biocViews: Normalization, Preprocessing, MultipleComparison, Microarray, Sequencing, RNASeq, BatchEffect Author: Stephanie C. Hicks [aut, cre] (), Kwame Okrah [aut], Hector Corrada Bravo [aut] (), Rafael Irizarry [aut] () Maintainer: Stephanie C. Hicks VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qsmooth git_branch: RELEASE_3_10 git_last_commit: 8d78176 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/qsmooth_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/qsmooth_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/qsmooth_1.2.0.tgz vignettes: vignettes/qsmooth/inst/doc/qsmooth.html vignetteTitles: The qsmooth user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsmooth/inst/doc/qsmooth.R dependencyCount: 57 Package: QSutils Version: 1.4.0 Depends: R (>= 3.5), Biostrings, BiocGenerics,methods Imports: ape, stats, psych Suggests: BiocStyle, knitr, rmarkdown, ggplot2 License: file LICENSE Archs: i386, x64 MD5sum: c86ffc8081899834abf7251e9570685e NeedsCompilation: no Title: Quasispecies Diversity Description: Set of utility functions for viral quasispecies analysis with NGS data. Most functions are equally useful for metagenomic studies. There are three main types: (1) data manipulation and exploration—functions useful for converting reads to haplotypes and frequencies, repairing reads, intersecting strand haplotypes, and visualizing haplotype alignments. (2) diversity indices—functions to compute diversity and entropy, in which incidence, abundance, and functional indices are considered. (3) data simulation—functions useful for generating random viral quasispecies data. biocViews: Software, Genetics, DNASeq, GeneticVariability, Sequencing, Alignment, SequenceMatching, DataImport Author: Mercedes Guerrero-Murillo and Josep Gregori i Font Maintainer: Mercedes Guerrero-Murillo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QSutils git_branch: RELEASE_3_10 git_last_commit: f3692f2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/QSutils_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/QSutils_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/QSutils_1.4.0.tgz vignettes: vignettes/QSutils/inst/doc/QSUtils-Alignment.html, vignettes/QSutils/inst/doc/QSutils-Diversity.html, vignettes/QSutils/inst/doc/QSutils-Simulation.html vignetteTitles: QSUtils-Alignment, QSutils-Diversity, QSutils-Simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/QSutils/inst/doc/QSUtils-Alignment.R, vignettes/QSutils/inst/doc/QSutils-Diversity.R, vignettes/QSutils/inst/doc/QSutils-Simulation.R dependencyCount: 21 Package: Qtlizer Version: 1.0.0 Depends: R (>= 3.6.0) Imports: httr, curl, GenomicRanges, stringi Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 7fd678781a5cb573e4cc50b12ec45f1c NeedsCompilation: no Title: Qtlizer: comprehensive QTL annotation of GWAS results Description: This R package provides access to the Qtlizer web server. Qtlizer annotates lists of common small variants (mainly SNPs) and genes in humans with associated changes in gene expression using the most comprehensive database of published quantitative trait loci (QTLs). biocViews: GenomeWideAssociation, SNP, Genetics, LinkageDisequilibrium Author: Matthias Munz [aut, cre], Julia Remes [aut] Maintainer: Matthias Munz VignetteBuilder: knitr BugReports: https://github.com/matmu/Qtlizer/issues git_url: https://git.bioconductor.org/packages/Qtlizer git_branch: RELEASE_3_10 git_last_commit: 01f78ba git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Qtlizer_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Qtlizer_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Qtlizer_1.0.0.tgz vignettes: vignettes/Qtlizer/inst/doc/Qtlizer.html vignetteTitles: Qtlizer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Qtlizer/inst/doc/Qtlizer.R dependencyCount: 26 Package: QUALIFIER Version: 1.29.1 Depends: R (>= 2.14.0),flowCore,flowViz,ncdfFlow,flowWorkspace, data.table,reshape Imports: MASS,hwriter,lattice,stats4,flowCore,flowViz,methods,flowWorkspace,latticeExtra,grDevices,tools, Biobase,XML,grid Suggests: RSVGTipsDevice, knitr License: Artistic-2.0 MD5sum: 6fd639300180d2f5cd470dff1bb8b1d0 NeedsCompilation: no Title: Quality Control of Gated Flow Cytometry Experiments Description: Provides quality control and quality assessment tools for gated flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays Author: Mike Jiang,Greg Finak,Raphael Gottardo Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QUALIFIER git_branch: master git_last_commit: c7eb3e5 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-02 source.ver: src/contrib/QUALIFIER_1.29.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/QUALIFIER_1.29.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/QUALIFIER_1.29.1.tgz vignettes: vignettes/QUALIFIER/inst/doc/QUALIFIER.html vignetteTitles: Quick plot for cytometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QUALIFIER/inst/doc/QUALIFIER.R dependencyCount: 71 Package: quantro Version: 1.20.0 Depends: R (>= 3.1.3) Imports: Biobase, minfi, doParallel, foreach, iterators, ggplot2, methods, RColorBrewer Suggests: knitr, RUnit, BiocGenerics, BiocStyle License: GPL (>=3) MD5sum: 4fd8e72b18e275aec2ff8ec28e1918bb NeedsCompilation: no Title: A test for when to use quantile normalization Description: A data-driven test for the assumptions of quantile normalization using raw data such as objects that inherit eSets (e.g. ExpressionSet, MethylSet). Group level information about each sample (such as Tumor / Normal status) must also be provided because the test assesses if there are global differences in the distributions between the user-defined groups. biocViews: Normalization, Preprocessing, MultipleComparison, Microarray, Sequencing Author: Stephanie Hicks [aut, cre] (), Rafael Irizarry [aut] () Maintainer: Stephanie Hicks VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/quantro git_branch: RELEASE_3_10 git_last_commit: 1d70da9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/quantro_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/quantro_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/quantro_1.20.0.tgz vignettes: vignettes/quantro/inst/doc/quantro.html vignetteTitles: The quantro user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantro/inst/doc/quantro.R importsMe: yarn suggestsMe: qsmooth dependencyCount: 146 Package: quantsmooth Version: 1.52.0 Depends: R(>= 2.10.0), quantreg, grid License: GPL-2 MD5sum: 7850e792837c1a236f0e4286e7501076 NeedsCompilation: no Title: Quantile smoothing and genomic visualization of array data Description: Implements quantile smoothing as introduced in: Quantile smoothing of array CGH data; Eilers PH, de Menezes RX; Bioinformatics. 2005 Apr 1;21(7):1146-53. biocViews: Visualization, CopyNumberVariation Author: Jan Oosting, Paul Eilers, Renee Menezes Maintainer: Jan Oosting git_url: https://git.bioconductor.org/packages/quantsmooth git_branch: RELEASE_3_10 git_last_commit: 78d969a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/quantsmooth_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/quantsmooth_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/quantsmooth_1.52.0.tgz vignettes: vignettes/quantsmooth/inst/doc/quantsmooth.pdf vignetteTitles: quantsmooth hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantsmooth/inst/doc/quantsmooth.R dependsOnMe: beadarraySNP importsMe: GWASTools, SIM suggestsMe: PREDA dependencyCount: 11 Package: QuartPAC Version: 1.18.0 Depends: iPAC, GraphPAC, SpacePAC, data.table Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: f439b5478c65af581db59ac2c599b2c1 NeedsCompilation: no Title: Identification of mutational clusters in protein quaternary structures. Description: Identifies clustering of somatic mutations in proteins over the entire quaternary structure. biocViews: Clustering, Proteomics, SomaticMutation Author: Gregory Ryslik, Yuwei Cheng, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/QuartPAC git_branch: RELEASE_3_10 git_last_commit: c5fcfd7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/QuartPAC_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/QuartPAC_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/QuartPAC_1.18.0.tgz vignettes: vignettes/QuartPAC/inst/doc/QuartPAC.pdf vignetteTitles: SpacePAC: Identifying mutational clusters in 3D protein space using simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuartPAC/inst/doc/QuartPAC.R dependencyCount: 37 Package: QuasR Version: 1.26.0 Depends: R (>= 3.6), parallel, GenomicRanges (>= 1.13.3), Rbowtie Imports: methods, grDevices, graphics, utils, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, BiocManager, Biobase, Biostrings, BSgenome, Rsamtools (>= 1.99.1), GenomicFeatures (>= 1.17.13), ShortRead (>= 1.19.1), GenomicAlignments, BiocParallel, GenomeInfoDb, rtracklayer, GenomicFiles, Rhisat2, AnnotationDbi LinkingTo: Rhtslib (>= 1.15.3) Suggests: Gviz, BiocStyle, knitr, rmarkdown, covr, testthat License: GPL-2 MD5sum: 66215a04a176f9b59b38dbde6d90caa9 NeedsCompilation: yes Title: Quantify and Annotate Short Reads in R Description: This package provides a framework for the quantification and analysis of Short Reads. It covers a complete workflow starting from raw sequence reads, over creation of alignments and quality control plots, to the quantification of genomic regions of interest. biocViews: Genetics, Preprocessing, Sequencing, ChIPSeq, RNASeq, MethylSeq, Coverage, Alignment, QualityControl, ImmunoOncology Author: Anita Lerch, Charlotte Soneson, Dimos Gaiditzis and Michael Stadler Maintainer: Michael Stadler SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QuasR git_branch: RELEASE_3_10 git_last_commit: 666a27b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/QuasR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/QuasR_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/QuasR_1.26.0.tgz vignettes: vignettes/QuasR/inst/doc/QuasR.html vignetteTitles: An introduction to QuasR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuasR/inst/doc/QuasR.R dependencyCount: 98 Package: QuaternaryProd Version: 1.20.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.3), dplyr, yaml (>= 2.1.18) LinkingTo: Rcpp Suggests: knitr License: GPL (>=3) MD5sum: d07df71d35602c129726bb79f1687b26 NeedsCompilation: yes Title: Computes the Quaternary Dot Product Scoring Statistic for Signed and Unsigned Causal Graphs Description: QuaternaryProd is an R package that performs causal reasoning on biological networks, including publicly available networks such as STRINGdb. QuaternaryProd is an open-source alternative to commercial products such as Inginuity Pathway Analysis. For a given a set of differentially expressed genes, QuaternaryProd computes the significance of upstream regulators in the network by performing causal reasoning using the Quaternary Dot Product Scoring Statistic (Quaternary Statistic), Ternary Dot product Scoring Statistic (Ternary Statistic) and Fisher's exact test (Enrichment test). The Quaternary Statistic handles signed, unsigned and ambiguous edges in the network. Ambiguity arises when the direction of causality is unknown, or when the source node (e.g., a protein) has edges with conflicting signs for the same target gene. On the other hand, the Ternary Statistic provides causal reasoning using the signed and unambiguous edges only. The Vignette provides more details on the Quaternary Statistic and illustrates an example of how to perform causal reasoning using STRINGdb. biocViews: GraphAndNetwork, GeneExpression, Transcription Author: Carl Tony Fakhry [cre, aut], Ping Chen [ths], Kourosh Zarringhalam [aut, ths] Maintainer: Carl Tony Fakhry VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QuaternaryProd git_branch: RELEASE_3_10 git_last_commit: 3fcf14a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/QuaternaryProd_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/QuaternaryProd_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/QuaternaryProd_1.20.0.tgz vignettes: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.pdf vignetteTitles: QuaternaryProdVignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.R dependencyCount: 27 Package: QUBIC Version: 1.14.0 Depends: R (>= 3.1), biclust Imports: Rcpp (>= 0.11.0), methods, Matrix LinkingTo: Rcpp, RcppArmadillo Suggests: QUBICdata, qgraph, fields, knitr, rmarkdown Enhances: RColorBrewer License: CC BY-NC-ND 4.0 + file LICENSE MD5sum: 44f72ca28dd1c8edf0143745137c7278 NeedsCompilation: yes Title: An R package for qualitative biclustering in support of gene co-expression analyses Description: The core function of this R package is to provide the implementation of the well-cited and well-reviewed QUBIC algorithm, aiming to deliver an effective and efficient biclustering capability. This package also includes the following related functions: (i) a qualitative representation of the input gene expression data, through a well-designed discretization way considering the underlying data property, which can be directly used in other biclustering programs; (ii) visualization of identified biclusters using heatmap in support of overall expression pattern analysis; (iii) bicluster-based co-expression network elucidation and visualization, where different correlation coefficient scores between a pair of genes are provided; and (iv) a generalize output format of biclusters and corresponding network can be freely downloaded so that a user can easily do following comprehensive functional enrichment analysis (e.g. DAVID) and advanced network visualization (e.g. Cytoscape). biocViews: StatisticalMethod, Microarray, DifferentialExpression, MultipleComparison, Clustering, Visualization, GeneExpression, Network Author: Yu Zhang [aut, cre], Qin Ma [aut] Maintainer: Yu Zhang URL: http://github.com/zy26/QUBIC SystemRequirements: C++11, Rtools (>= 3.1) VignetteBuilder: knitr BugReports: http://github.com/zy26/QUBIC/issues git_url: https://git.bioconductor.org/packages/QUBIC git_branch: RELEASE_3_10 git_last_commit: 6ff739f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/QUBIC_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/QUBIC_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/QUBIC_1.14.0.tgz vignettes: vignettes/QUBIC/inst/doc/qubic_vignette.pdf vignetteTitles: QUBIC Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/QUBIC/inst/doc/qubic_vignette.R suggestsMe: runibic dependencyCount: 69 Package: qusage Version: 2.20.0 Depends: R (>= 2.10), limma (>= 3.14), methods Imports: utils, Biobase, nlme, emmeans License: GPL (>= 2) Archs: x64 MD5sum: 3ecf34ce7f562f93ba781adca1c67ae6 NeedsCompilation: no Title: qusage: Quantitative Set Analysis for Gene Expression Description: This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies. qusage accounts for inter-gene correlations using the Variance Inflation Factor technique proposed by Wu et al. (Nucleic Acids Res, 2012). In addition, rather than simply evaluating the deviation from a null hypothesis with a single number (a P value), qusage quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. Finally, while qusage is compatible with individual gene statistics from existing methods (e.g., LIMMA), a Welch-based method is implemented that is shown to improve specificity. For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu) biocViews: GeneSetEnrichment, Microarray, RNASeq, Software, ImmunoOncology Author: Christopher Bolen and Gur Yaari, with contributions from Juilee Thakar, Hailong Meng, Jacob Turner, Derek Blankenship, and Steven Kleinstein Maintainer: Christopher Bolen URL: http://clip.med.yale.edu/qusage git_url: https://git.bioconductor.org/packages/qusage git_branch: RELEASE_3_10 git_last_commit: 81a68e6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/qusage_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/qusage_2.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/qusage_2.20.0.tgz vignettes: vignettes/qusage/inst/doc/qusage.pdf vignetteTitles: Running qusage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qusage/inst/doc/qusage.R suggestsMe: SigCheck dependencyCount: 19 Package: qvalue Version: 2.18.0 Depends: R(>= 2.10) Imports: splines, ggplot2, grid, reshape2 Suggests: knitr License: LGPL Archs: i386, x64 MD5sum: 9ece11e073a69fb6633042ec67aca2ac NeedsCompilation: no Title: Q-value estimation for false discovery rate control Description: This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Various plots are automatically generated, allowing one to make sensible significance cut-offs. Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining. biocViews: MultipleComparisons Author: John D. Storey [aut, cre], Andrew J. Bass [aut], Alan Dabney [aut], David Robinson [aut], Gregory Warnes [ctb] Maintainer: John D. Storey , Andrew J. Bass URL: http://github.com/jdstorey/qvalue VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qvalue git_branch: RELEASE_3_10 git_last_commit: 605a5bb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/qvalue_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/qvalue_2.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/qvalue_2.18.0.tgz vignettes: vignettes/qvalue/inst/doc/qvalue.pdf vignetteTitles: qvalue Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qvalue/inst/doc/qvalue.R dependsOnMe: anota, CancerMutationAnalysis, DEGseq, DrugVsDisease, metaseqR, r3Cseq, SSPA, webbioc importsMe: Anaquin, anota, clusterProfiler, derfinder, DOSE, edge, epihet, erccdashboard, EventPointer, fishpond, methylKit, msmsTests, MWASTools, netresponse, normr, OPWeight, PAST, RNAsense, Rnits, SDAMS, sights, signatureSearch, sRAP, subSeq, synapter, trigger, webbioc suggestsMe: biobroom, LBE, maanova, PREDA, RnBeads, SummarizedBenchmark, swfdr dependencyCount: 58 Package: R3CPET Version: 1.18.0 Depends: R (>= 3.2), Rcpp (>= 0.10.4), methods Imports: methods, parallel, clues, ggplot2, pheatmap, clValid, igraph, data.table, reshape2, Hmisc, RCurl, BiocGenerics, S4Vectors, IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8), ggbio LinkingTo: Rcpp Suggests: BiocStyle, knitr, TxDb.Hsapiens.UCSC.hg19.knownGene, biovizBase, biomaRt, AnnotationDbi, org.Hs.eg.db, shiny, ChIPpeakAnno License: GPL (>=2) Archs: i386, x64 MD5sum: df7bff3d99fbc4da7accf1424e443829 NeedsCompilation: yes Title: 3CPET: Finding Co-factor Complexes in Chia-PET experiment using a Hierarchical Dirichlet Process Description: The package provides a method to infer the set of proteins that are more probably to work together to maintain chormatin interaction given a ChIA-PET experiment results. biocViews: NetworkInference, GenePrediction, Bayesian, GraphAndNetwork, Network, GeneExpression, HiC Author: Djekidel MN, Yang Chen et al. Maintainer: Mohamed Nadhir Djekidel VignetteBuilder: knitr BugReports: https://github.com/sirusb/R3CPET/issues git_url: https://git.bioconductor.org/packages/R3CPET git_branch: RELEASE_3_10 git_last_commit: 5fe842c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/R3CPET_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/R3CPET_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/R3CPET_1.18.0.tgz vignettes: vignettes/R3CPET/inst/doc/R3CPET.pdf vignetteTitles: 3CPET: Finding Co-factor Complexes maintaining Chia-PET interactions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R3CPET/inst/doc/R3CPET.R dependencyCount: 156 Package: r3Cseq Version: 1.32.0 Depends: GenomicRanges, Rsamtools, rtracklayer, VGAM, qvalue Imports: methods, GenomeInfoDb, IRanges, Biostrings, data.table, sqldf, RColorBrewer Suggests: BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Rnorvegicus.UCSC.rn5.masked License: GPL-3 MD5sum: 2a74920e5eb79644bc121582903cd3cc NeedsCompilation: no Title: Analysis of Chromosome Conformation Capture and Next-generation Sequencing (3C-seq) Description: This package is used for the analysis of long-range chromatin interactions from 3C-seq assay. biocViews: Preprocessing, Sequencing Author: Supat Thongjuea, MRC WIMM Centre for Computational Biology, Weatherall Institute of Molecular Medicine, University of Oxford, UK Maintainer: Supat Thongjuea or URL: http://r3cseq.genereg.net,https://github.com/supatt-lab/r3Cseq/ git_url: https://git.bioconductor.org/packages/r3Cseq git_branch: RELEASE_3_10 git_last_commit: 7a896ef git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/r3Cseq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/r3Cseq_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/r3Cseq_1.32.0.tgz vignettes: vignettes/r3Cseq/inst/doc/r3Cseq.pdf vignetteTitles: r3Cseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/r3Cseq/inst/doc/r3Cseq.R dependencyCount: 101 Package: R453Plus1Toolbox Version: 1.36.0 Depends: R (>= 2.12.0), methods, VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6), Biobase Imports: utils, grDevices, graphics, stats, tools, xtable, R2HTML, TeachingDemos, BiocGenerics, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), XVector, GenomicRanges (>= 1.31.8), SummarizedExperiment, biomaRt, BSgenome (>= 1.47.3), Rsamtools, ShortRead (>= 1.37.1) Suggests: rtracklayer, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Scerevisiae.UCSC.sacCer2 License: LGPL-3 MD5sum: 8826df9bf747dc28675faef0a1114c9e NeedsCompilation: yes Title: A package for importing and analyzing data from Roche's Genome Sequencer System Description: The R453Plus1 Toolbox comprises useful functions for the analysis of data generated by Roche's 454 sequencing platform. It adds functions for quality assurance as well as for annotation and visualization of detected variants, complementing the software tools shipped by Roche with their product. Further, a pipeline for the detection of structural variants is provided. biocViews: Sequencing, Infrastructure, DataImport, DataRepresentation, Visualization, QualityControl, ReportWriting Author: Hans-Ulrich Klein, Christoph Bartenhagen, Christian Ruckert Maintainer: Hans-Ulrich Klein git_url: https://git.bioconductor.org/packages/R453Plus1Toolbox git_branch: RELEASE_3_10 git_last_commit: e9bb707 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/R453Plus1Toolbox_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/R453Plus1Toolbox_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/R453Plus1Toolbox_1.36.0.tgz vignettes: vignettes/R453Plus1Toolbox/inst/doc/vignette.pdf vignetteTitles: A package for importing and analyzing data from Roche's Genome Sequencer System hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R453Plus1Toolbox/inst/doc/vignette.R dependencyCount: 94 Package: R4RNA Version: 1.14.0 Depends: R (>= 3.2.0), Biostrings (>= 2.38.0) License: GPL-3 Archs: i386, x64 MD5sum: 3e9efcd6cf947f252217fd7baad9c5bd NeedsCompilation: no Title: An R package for RNA visualization and analysis Description: A package for RNA basepair analysis, including the visualization of basepairs as arc diagrams for easy comparison and annotation of sequence and structure. Arc diagrams can additionally be projected onto multiple sequence alignments to assess basepair conservation and covariation, with numerical methods for computing statistics for each. biocViews: Alignment, MultipleSequenceAlignment, Preprocessing, Visualization, DataImport, DataRepresentation, MultipleComparison Author: Daniel Lai, Irmtraud Meyer Maintainer: Daniel Lai URL: http://www.e-rna.org/r-chie/ git_url: https://git.bioconductor.org/packages/R4RNA git_branch: RELEASE_3_10 git_last_commit: c4dcc01 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/R4RNA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/R4RNA_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/R4RNA_1.14.0.tgz vignettes: vignettes/R4RNA/inst/doc/R4RNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R4RNA/inst/doc/R4RNA.R dependencyCount: 12 Package: RaggedExperiment Version: 1.10.0 Depends: R (>= 3.6.0), GenomicRanges (>= 1.37.17) Imports: BiocGenerics, GenomeInfoDb, IRanges, methods, S4Vectors, stats, SummarizedExperiment Suggests: BiocStyle, knitr, testthat, MultiAssayExperiment License: Artistic-2.0 MD5sum: 19aa05469fab8d9093cd44ec7c813f6b NeedsCompilation: no Title: Representation of Sparse Experiments and Assays Across Samples Description: This package provides a flexible representation of copy number, mutation, and other data that fit into the ragged array schema for genomic location data. The basic representation of such data provides a rectangular flat table interface to the user with range information in the rows and samples/specimen in the columns. biocViews: Infrastructure, DataRepresentation Author: Martin Morgan [aut, cre], Marcel Ramos [aut] Maintainer: Martin Morgan VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/RaggedExperiment/issues git_url: https://git.bioconductor.org/packages/RaggedExperiment git_branch: RELEASE_3_10 git_last_commit: 79c03c8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RaggedExperiment_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RaggedExperiment_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RaggedExperiment_1.10.0.tgz vignettes: vignettes/RaggedExperiment/inst/doc/RaggedExperiment.html vignetteTitles: RaggedExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RaggedExperiment/inst/doc/RaggedExperiment.R dependsOnMe: CNVRanger, TxRegInfra importsMe: omicsPrint, RTCGAToolbox, TCGAutils suggestsMe: MultiAssayExperiment, MultiDataSet dependencyCount: 32 Package: rain Version: 1.20.0 Depends: R (>= 2.10), gmp, multtest Suggests: lattice, BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: ee3c46c701640e4990ba4b5b2ee57012 NeedsCompilation: no Title: Rhythmicity Analysis Incorporating Non-parametric Methods Description: This package uses non-parametric methods to detect rhythms in time series. It deals with outliers, missing values and is optimized for time series comprising 10-100 measurements. As it does not assume expect any distinct waveform it is optimal or detecting oscillating behavior (e.g. circadian or cell cycle) in e.g. genome- or proteome-wide biological measurements such as: micro arrays, proteome mass spectrometry, or metabolome measurements. biocViews: TimeCourse, Genetics, SystemsBiology, Proteomics, Microarray, MultipleComparison Author: Paul F. Thaben, Pål O. Westermark Maintainer: Paul F. Thaben git_url: https://git.bioconductor.org/packages/rain git_branch: RELEASE_3_10 git_last_commit: 8a69999 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rain_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rain_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rain_1.20.0.tgz vignettes: vignettes/rain/inst/doc/rain.pdf vignetteTitles: Rain Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rain/inst/doc/rain.R dependencyCount: 17 Package: rama Version: 1.60.0 Depends: R(>= 2.5.0) License: GPL (>= 2) MD5sum: a310ec8af919460ad3b2c39af96da04f NeedsCompilation: yes Title: Robust Analysis of MicroArrays Description: Robust estimation of cDNA microarray intensities with replicates. The package uses a Bayesian hierarchical model for the robust estimation. Outliers are modeled explicitly using a t-distribution, and the model also addresses classical issues such as design effects, normalization, transformation, and nonconstant variance. biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Raphael Gottardo Maintainer: Raphael Gottardo git_url: https://git.bioconductor.org/packages/rama git_branch: RELEASE_3_10 git_last_commit: 6e78e64 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rama_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rama_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rama_1.60.0.tgz vignettes: vignettes/rama/inst/doc/rama.pdf vignetteTitles: rama Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rama/inst/doc/rama.R dependsOnMe: bridge dependencyCount: 0 Package: ramwas Version: 1.10.5 Depends: R (>= 3.3.0), methods, filematrix Imports: graphics, stats, utils, digest, glmnet, KernSmooth, grDevices, GenomicAlignments, Rsamtools, parallel, biomaRt, Biostrings, BiocGenerics Suggests: knitr, rmarkdown, pander, BiocStyle, BSgenome.Ecoli.NCBI.20080805 License: LGPL-3 MD5sum: 5c5da660c971625702c2398050137301 NeedsCompilation: yes Title: Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms Description: A complete toolset for methylome-wide association studies (MWAS). It is specifically designed for data from enrichment based methylation assays, but can be applied to other data as well. The analysis pipeline includes seven steps: (1) scanning aligned reads from BAM files, (2) calculation of quality control measures, (3) creation of methylation score (coverage) matrix, (4) principal component analysis for capturing batch effects and detection of outliers, (5) association analysis with respect to phenotypes of interest while correcting for top PCs and known covariates, (6) annotation of significant findings, and (7) multi-marker analysis (methylation risk score) using elastic net. Additionally, RaMWAS include tools for joint analysis of methlyation and genotype data. This work is published in Bioinformatics, Shabalin et al. (2018) . biocViews: DNAMethylation, Sequencing, QualityControl, Coverage, Preprocessing, Normalization, BatchEffect, PrincipalComponent, DifferentialMethylation, Visualization Author: Andrey A Shabalin [aut, cre] (), Shaunna L Clark [aut], Mohammad W Hattab [aut], Karolina A Aberg [aut], Edwin J C G van den Oord [aut] Maintainer: Andrey A Shabalin URL: https://bioconductor.org/packages/ramwas/ VignetteBuilder: knitr BugReports: https://github.com/andreyshabalin/ramwas/issues git_url: https://git.bioconductor.org/packages/ramwas git_branch: RELEASE_3_10 git_last_commit: b2e2541 git_last_commit_date: 2019-11-27 Date/Publication: 2019-11-27 source.ver: src/contrib/ramwas_1.10.5.tar.gz win.binary.ver: bin/windows/contrib/3.6/ramwas_1.10.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ramwas_1.10.5.tgz vignettes: vignettes/ramwas/inst/doc/RW1_intro.html, vignettes/ramwas/inst/doc/RW2_CpG_sets.html, vignettes/ramwas/inst/doc/RW3_BAM_QCs.html, vignettes/ramwas/inst/doc/RW4_SNPs.html, vignettes/ramwas/inst/doc/RW5a_matrix.html, vignettes/ramwas/inst/doc/RW5c_matrix.html, vignettes/ramwas/inst/doc/RW6_param.html vignetteTitles: 1. Overview, 2. CpG sets, 3. BAM Quality Control Measures, 4. Joint Analysis of Methylation and Genotype Data, 5.a. Analyzing Illumina Methylation Array Data, 5.c. Analyzing data from other sources, 6. RaMWAS parameters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ramwas/inst/doc/RW1_intro.R, vignettes/ramwas/inst/doc/RW2_CpG_sets.R, vignettes/ramwas/inst/doc/RW3_BAM_QCs.R, vignettes/ramwas/inst/doc/RW4_SNPs.R, vignettes/ramwas/inst/doc/RW5a_matrix.R, vignettes/ramwas/inst/doc/RW5c_matrix.R, vignettes/ramwas/inst/doc/RW6_param.R dependencyCount: 88 Package: RandomWalkRestartMH Version: 1.6.0 Depends: R(>= 3.5.0) Imports: igraph, Matrix, dnet, methods Suggests: BiocStyle, testthat License: GPL (>= 2) MD5sum: 094da0564299b9f0e4c762e756c15082 NeedsCompilation: no Title: Random walk with restart on multiplex and heterogeneous Networks Description: This package performs Random Walk with Restart on multiplex and heterogeneous networks. It is described in the following article: "Random Walk With Restart On Multiplex And Heterogeneous Biological Networks". https://www.biorxiv.org/content/early/2017/08/30/134734 . biocViews: GenePrediction, NetworkInference, SomaticMutation, BiomedicalInformatics, MathematicalBiology, SystemsBiology, GraphAndNetwork, Pathways, BioCarta, KEGG, Reactome, Network Author: Alberto Valdeolivas Urbelz Maintainer: Alberto Valdeolivas Urbelz URL: https://www.biorxiv.org/content/early/2017/08/30/134734 git_url: https://git.bioconductor.org/packages/RandomWalkRestartMH git_branch: RELEASE_3_10 git_last_commit: 76232d5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RandomWalkRestartMH_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RandomWalkRestartMH_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RandomWalkRestartMH_1.6.0.tgz vignettes: vignettes/RandomWalkRestartMH/inst/doc/RandomWalkRestartMH1.pdf vignetteTitles: Random Walk with Restart on Multiplex and Heterogeneous Networks hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RandomWalkRestartMH/inst/doc/RandomWalkRestartMH1.R dependencyCount: 24 Package: randPack Version: 1.32.0 Depends: methods Imports: Biobase License: Artistic 2.0 Archs: i386, x64 MD5sum: e80baebf52c7d74b9b02f34cc089a156 NeedsCompilation: no Title: Randomization routines for Clinical Trials Description: A suite of classes and functions for randomizing patients in clinical trials. biocViews: StatisticalMethod Author: Vincent Carey and Robert Gentleman Maintainer: Robert Gentleman git_url: https://git.bioconductor.org/packages/randPack git_branch: RELEASE_3_10 git_last_commit: 97a26b5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/randPack_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/randPack_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/randPack_1.32.0.tgz vignettes: vignettes/randPack/inst/doc/randPack.pdf vignetteTitles: Clinical trial randomization infrastructure hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/randPack/inst/doc/randPack.R dependencyCount: 7 Package: RankProd Version: 3.12.0 Depends: R (>= 3.2.1), stats, methods, Rmpfr, gmp Imports: graphics License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: b68cc58b738a455ca4c29c80d5fb405a NeedsCompilation: no Title: Rank Product method for identifying differentially expressed genes with application in meta-analysis Description: Non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp). The method can combine data sets from different origins (meta-analysis) to increase the power of the identification. biocViews: DifferentialExpression, StatisticalMethod, Software, ResearchField, Metabolomics, Lipidomics, Proteomics, SystemsBiology, GeneExpression, Microarray, GeneSignaling Author: Francesco Del Carratore , Andris Jankevics Fangxin Hong , Ben Wittner , Rainer Breitling , and Florian Battke Maintainer: Francesco Del Carratore git_url: https://git.bioconductor.org/packages/RankProd git_branch: RELEASE_3_10 git_last_commit: d0d94df git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RankProd_3.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RankProd_3.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RankProd_3.12.0.tgz vignettes: vignettes/RankProd/inst/doc/RankProd.pdf vignetteTitles: RankProd Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RankProd/inst/doc/RankProd.R dependsOnMe: RNAither, tRanslatome importsMe: HTSanalyzeR, synlet dependencyCount: 6 Package: RareVariantVis Version: 2.14.0 Depends: BiocGenerics, VariantAnnotation, googleVis, GenomicFeatures Imports: S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, gtools, BSgenome, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19, SummarizedExperiment, GenomicScores Suggests: knitr License: Artistic-2.0 MD5sum: 1da9ff6851b205d1f7fec2d2439fee36 NeedsCompilation: no Title: A suite for analysis of rare genomic variants in whole genome sequencing data Description: Second version of RareVariantVis package aims to provide comprehensive information about rare variants for your genome data. It annotates, filters and presents genomic variants (especially rare ones) in a global, per chromosome way. For discovered rare variants CRISPR guide RNAs are designed, so the user can plan further functional studies. Large structural variants, including copy number variants are also supported. Package accepts variants directly from variant caller - for example GATK or Speedseq. Output of package are lists of variants together with adequate visualization. Visualization of variants is performed in two ways - standard that outputs png figures and interactive that uses JavaScript d3 package. Interactive visualization allows to analyze trio/family data, for example in search for causative variants in rare Mendelian diseases, in point-and-click interface. The package includes homozygous region caller and allows to analyse whole human genomes in less than 30 minutes on a desktop computer. RareVariantVis disclosed novel causes of several rare monogenic disorders, including one with non-coding causative variant - keratolythic winter erythema. biocViews: GenomicVariation, Sequencing, WholeGenome Author: Adam Gudys and Tomasz Stokowy Maintainer: Tomasz Stokowy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RareVariantVis git_branch: RELEASE_3_10 git_last_commit: 837df96 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RareVariantVis_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RareVariantVis_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RareVariantVis_2.14.0.tgz vignettes: vignettes/RareVariantVis/inst/doc/RareVariantsVis.pdf vignetteTitles: RareVariantVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RareVariantVis/inst/doc/RareVariantsVis.R dependencyCount: 104 Package: Rariant Version: 1.22.0 Depends: R (>= 3.0.2), GenomicRanges, VariantAnnotation Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, ggbio, ggplot2, exomeCopy, SomaticSignatures, Rsamtools, shiny, VGAM, dplyr, reshape2 Suggests: h5vcData, testthat, knitr, optparse, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 MD5sum: c0c66863184a316e65fc719169cad695 NeedsCompilation: no Title: Identification and Assessment of Single Nucleotide Variants through Shifts in Non-Consensus Base Call Frequencies Description: The 'Rariant' package identifies single nucleotide variants from sequencing data based on the difference of binomially distributed mismatch rates between matched samples. biocViews: Sequencing, StatisticalMethod, GenomicVariation, SomaticMutation, VariantDetection, Visualization Author: Julian Gehring, Simon Anders, Bernd Klaus Maintainer: Julian Gehring URL: https://github.com/juliangehring/Rariant VignetteBuilder: knitr BugReports: https://support.bioconductor.org git_url: https://git.bioconductor.org/packages/Rariant git_branch: RELEASE_3_10 git_last_commit: 06ddee3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Rariant_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Rariant_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rariant_1.22.0.tgz vignettes: vignettes/Rariant/inst/doc/Rariant-vignette.html vignetteTitles: Rariant hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rariant/inst/doc/Rariant-vignette.R dependencyCount: 173 Package: RbcBook1 Version: 1.54.0 Depends: R (>= 2.10), Biobase, graph, rpart License: Artistic-2.0 MD5sum: 61bce19efc6b776bb955f3833a5a45ba NeedsCompilation: no Title: Support for Springer monograph on Bioconductor Description: tools for building book biocViews: Software Author: Vince Carey and Wolfgang Huber Maintainer: Vince Carey URL: http://www.biostat.harvard.edu/~carey git_url: https://git.bioconductor.org/packages/RbcBook1 git_branch: RELEASE_3_10 git_last_commit: b27b060 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RbcBook1_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RbcBook1_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RbcBook1_1.54.0.tgz vignettes: vignettes/RbcBook1/inst/doc/RbcBook1.pdf vignetteTitles: RbcBook1 Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RbcBook1/inst/doc/RbcBook1.R dependencyCount: 11 Package: RBGL Version: 1.62.1 Depends: graph, methods Imports: methods LinkingTo: BH Suggests: Rgraphviz, XML, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: e76752b41e6c2ed501f6b8d8ff6aaa93 NeedsCompilation: yes Title: An interface to the BOOST graph library Description: A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library. biocViews: GraphAndNetwork, Network Author: Vince Carey , Li Long , R. Gentleman Maintainer: Bioconductor Package Maintainer URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/RBGL git_branch: RELEASE_3_10 git_last_commit: 1615f29 git_last_commit_date: 2019-10-30 Date/Publication: 2019-10-30 source.ver: src/contrib/RBGL_1.62.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/RBGL_1.62.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RBGL_1.62.1.tgz vignettes: vignettes/RBGL/inst/doc/RBGL.pdf vignetteTitles: RBGL Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBGL/inst/doc/RBGL.R dependsOnMe: apComplex, BioNet, CellNOptR, joda, pkgDepTools, RpsiXML importsMe: alpine, BiocPkgTools, biocViews, CAMERA, Category, ChIPpeakAnno, CHRONOS, clipper, CytoML, DEGraph, DEsubs, EventPointer, flowWorkspace, GAPGOM, GeneAnswers, GOSim, GOstats, MIGSA, NCIgraph, nem, OrganismDbi, pkgDepTools, predictionet, RDAVIDWebService, signet, Streamer, ToPASeq, VariantFiltering suggestsMe: BiocCaseStudies, DEGraph, GeneNetworkBuilder, graph, gwascat, KEGGgraph, rBiopaxParser, VariantTools dependencyCount: 9 Package: RBioinf Version: 1.46.0 Depends: graph, methods Suggests: Rgraphviz License: Artistic-2.0 MD5sum: 2df96dc6b04aee3557e7e5e2fcecbc27 NeedsCompilation: yes Title: RBioinf Description: Functions and datasets and examples to accompany the monograph R For Bioinformatics. biocViews: GeneExpression, Microarray, Preprocessing, QualityControl, Classification, Clustering, MultipleComparison, Annotation Author: Robert Gentleman Maintainer: Robert Gentleman git_url: https://git.bioconductor.org/packages/RBioinf git_branch: RELEASE_3_10 git_last_commit: 7a5e7a8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RBioinf_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RBioinf_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RBioinf_1.46.0.tgz vignettes: vignettes/RBioinf/inst/doc/RBioinf.pdf vignetteTitles: RBioinf Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBioinf/inst/doc/RBioinf.R dependencyCount: 8 Package: rBiopaxParser Version: 2.26.0 Depends: R (>= 3.0.0), data.table Imports: XML Suggests: Rgraphviz, RCurl, graph, RUnit, BiocGenerics, nem, RBGL, igraph License: GPL (>= 2) MD5sum: fa746f5e710eca1dfe77181607c1152e NeedsCompilation: no Title: Parses BioPax files and represents them in R Description: Parses BioPAX files and represents them in R, at the moment BioPAX level 2 and level 3 are supported. biocViews: DataRepresentation Author: Frank Kramer Maintainer: Frank Kramer URL: https://github.com/frankkramer-lab/rBiopaxParser git_url: https://git.bioconductor.org/packages/rBiopaxParser git_branch: RELEASE_3_10 git_last_commit: e3452ac git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rBiopaxParser_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rBiopaxParser_2.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rBiopaxParser_2.26.0.tgz vignettes: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.pdf vignetteTitles: rBiopaxParser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.R importsMe: AnnotationHubData, pwOmics suggestsMe: AnnotationHub, NetPathMiner dependencyCount: 4 Package: RBM Version: 1.18.0 Depends: R (>= 3.2.0), limma, marray License: GPL (>= 2) Archs: i386, x64 MD5sum: dffc19e85a866f5bcb9c1dad87c4e789 NeedsCompilation: no Title: RBM: a R package for microarray and RNA-Seq data analysis Description: Use A Resampling-Based Empirical Bayes Approach to Assess Differential Expression in Two-Color Microarrays and RNA-Seq data sets. biocViews: Microarray, DifferentialExpression Author: Dongmei Li and Chin-Yuan Liang Maintainer: Dongmei Li git_url: https://git.bioconductor.org/packages/RBM git_branch: RELEASE_3_10 git_last_commit: a7c5213 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RBM_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RBM_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RBM_1.18.0.tgz vignettes: vignettes/RBM/inst/doc/RBM.pdf vignetteTitles: RBM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBM/inst/doc/RBM.R dependencyCount: 7 Package: Rbowtie Version: 1.26.0 Suggests: parallel, BiocStyle, knitr, rmarkdown License: Artistic-2.0 | file LICENSE Archs: i386, x64 MD5sum: 4463cc9ef76fbcb05546a95dd12e6713 NeedsCompilation: yes Title: R bowtie wrapper Description: This package provides an R wrapper around the popular bowtie short read aligner and around SpliceMap, a de novo splice junction discovery and alignment tool. The package is used by the QuasR bioconductor package. We recommend to use the QuasR package instead of using Rbowtie directly. biocViews: Sequencing, Alignment Author: Florian Hahne, Anita Lerch, Michael B Stadler Maintainer: Michael Stadler SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rbowtie git_branch: RELEASE_3_10 git_last_commit: ce8b181 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Rbowtie_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Rbowtie_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rbowtie_1.26.0.tgz vignettes: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.html vignetteTitles: An introduction to Rbowtie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.R dependsOnMe: QuasR importsMe: MACPET dependencyCount: 0 Package: Rbowtie2 Version: 1.8.0 Depends: R (>= 3.5) Suggests: knitr License: GPL (>= 3) MD5sum: 1e82e9297588240f4209f471d32612e2 NeedsCompilation: yes Title: An R Wrapper for Bowtie2 and AdapterRemoval Description: This package provides an R wrapper of the popular bowtie2 sequencing reads aligner and AdapterRemoval, a convenient tool for rapid adapter trimming, identification, and read merging. biocViews: Sequencing, Alignment, Preprocessing Author: Zheng Wei, Wei Zhang Maintainer: Zheng Wei SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rbowtie2 git_branch: RELEASE_3_10 git_last_commit: 06e43e8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Rbowtie2_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Rbowtie2_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rbowtie2_1.8.0.tgz vignettes: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.html vignetteTitles: An Introduction to Rbowtie2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.R importsMe: esATAC dependencyCount: 0 Package: rbsurv Version: 2.44.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), survival License: GPL (>= 2) MD5sum: 2a41292fc17d8b015eaf42d566e42f47 NeedsCompilation: no Title: Robust likelihood-based survival modeling with microarray data Description: This package selects genes associated with survival. biocViews: Microarray Author: HyungJun Cho , Sukwoo Kim , Soo-heang Eo , Jaewoo Kang Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ git_url: https://git.bioconductor.org/packages/rbsurv git_branch: RELEASE_3_10 git_last_commit: 60b2220 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rbsurv_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rbsurv_2.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rbsurv_2.44.0.tgz vignettes: vignettes/rbsurv/inst/doc/rbsurv.pdf vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rbsurv/inst/doc/rbsurv.R dependencyCount: 13 Package: Rcade Version: 1.28.0 Depends: R (>= 3.5.0), methods, GenomicRanges, Rsamtools, baySeq Imports: utils, grDevices, stats, graphics, rgl, plotrix, S4Vectors (>= 0.23.19), IRanges, GenomeInfoDb, GenomicAlignments Suggests: limma, biomaRt, RUnit, BiocGenerics, BiocStyle License: GPL-2 Archs: x64 MD5sum: 8c40784c729e39ba9ccb939555186317 NeedsCompilation: no Title: R-based analysis of ChIP-seq And Differential Expression - a tool for integrating a count-based ChIP-seq analysis with differential expression summary data Description: Rcade (which stands for "R-based analysis of ChIP-seq And Differential Expression") is a tool for integrating ChIP-seq data with differential expression summary data, through a Bayesian framework. A key application is in identifing the genes targeted by a transcription factor of interest - that is, we collect genes that are associated with a ChIP-seq peak, and differential expression under some perturbation related to that TF. biocViews: DifferentialExpression, GeneExpression, Transcription, ChIPSeq, Sequencing, Genetics Author: Jonathan Cairns Maintainer: Jonathan Cairns git_url: https://git.bioconductor.org/packages/Rcade git_branch: RELEASE_3_10 git_last_commit: 9723b22 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Rcade_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Rcade_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rcade_1.28.0.tgz vignettes: vignettes/Rcade/inst/doc/Rcade.pdf vignetteTitles: Rcade Vignette hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rcade/inst/doc/Rcade.R dependencyCount: 79 Package: RCAS Version: 1.12.0 Depends: R (>= 3.3.0), plotly (>= 4.5.2), DT (>= 0.2), data.table, topGO, motifRG Imports: biomaRt, AnnotationDbi, GenomicRanges, BSgenome.Hsapiens.UCSC.hg19, GenomeInfoDb (>= 1.12.0), Biostrings, rtracklayer, org.Hs.eg.db, GenomicFeatures, rmarkdown (>= 0.9.5), genomation (>= 1.5.5), knitr (>= 1.12.3), BiocGenerics, S4Vectors, stats, plotrix, pbapply, RSQLite, proxy, DBI, pheatmap, ggplot2, cowplot, ggseqlogo, methods, utils Suggests: BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Celegans.UCSC.ce10, BSgenome.Dmelanogaster.UCSC.dm3, org.Mm.eg.db, org.Ce.eg.db, org.Dm.eg.db, testthat, covr License: Artistic-2.0 Archs: x64 MD5sum: cd938309afdc0f712213ec105c5c8ce1 NeedsCompilation: no Title: RNA Centric Annotation System Description: RCAS is an automated system that provides dynamic genome annotations for custom input files that contain transcriptomic regions. Such transcriptomic regions could be, for instance, peak regions detected by CLIP-Seq analysis that detect protein-RNA interactions, RNA modifications (alias the epitranscriptome), CAGE-tag locations, or any other collection of target regions at the level of the transcriptome. RCAS is designed as a reporting tool for the functional analysis of RNA-binding sites detected by high-throughput experiments. It takes as input a BED format file containing the genomic coordinates of the RNA binding sites and a GTF file that contains the genomic annotation features usually provided by publicly available databases such as Ensembl and UCSC. RCAS performs overlap operations between the genomic coordinates of the RNA binding sites and the genomic annotation features and produces in-depth annotation summaries such as the distribution of binding sites with respect to gene features (exons, introns, 5'/3' UTR regions, exon-intron boundaries, promoter regions, and whole transcripts). Moreover, by detecting the collection of targeted transcripts, RCAS can carry out functional annotation tables for enriched gene sets (annotated by the Molecular Signatures Database) and GO terms. As one of the most important questions that arise during protein-RNA interaction analysis; RCAS has a module for detecting sequence motifs enriched in the targeted regions of the transcriptome. A full interactive report in HTML format can be generated that contains interactive figures and tables that are ready for publication purposes. biocViews: Software, GeneTarget, MotifAnnotation, MotifDiscovery, GO, Transcriptomics, GenomeAnnotation, GeneSetEnrichment, Coverage Author: Bora Uyar [aut, cre], Dilmurat Yusuf [aut], Ricardo Wurmus [aut], Altuna Akalin [aut] Maintainer: Bora Uyar SystemRequirements: pandoc (>= 1.12.3) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCAS git_branch: RELEASE_3_10 git_last_commit: 8b40ab4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RCAS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RCAS_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RCAS_1.12.0.tgz vignettes: vignettes/RCAS/inst/doc/RCAS.metaAnalysis.vignette.html, vignettes/RCAS/inst/doc/RCAS.vignette.html vignetteTitles: How to do meta-analysis of multiple samples, Introduction - single sample analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCAS/inst/doc/RCAS.metaAnalysis.vignette.R, vignettes/RCAS/inst/doc/RCAS.vignette.R dependencyCount: 154 Package: RCASPAR Version: 1.32.0 License: GPL (>=3) MD5sum: 4f9a3ff62f52136401f2794801dbc7df NeedsCompilation: no Title: A package for survival time prediction based on a piecewise baseline hazard Cox regression model. Description: The package is the R-version of the C-based software \bold{CASPAR} (Kaderali,2006: \url{http://bioinformatics.oxfordjournals.org/content/22/12/1495}). It is meant to help predict survival times in the presence of high-dimensional explanatory covariates. The model is a piecewise baseline hazard Cox regression model with an Lq-norm based prior that selects for the most important regression coefficients, and in turn the most relevant covariates for survival analysis. It was primarily tried on gene expression and aCGH data, but can be used on any other type of high-dimensional data and in disciplines other than biology and medicine. biocViews: aCGH, GeneExpression, Genetics, Proteomics, Visualization Author: Douaa Mugahid, Lars Kaderali Maintainer: Douaa Mugahid , Lars Kaderali git_url: https://git.bioconductor.org/packages/RCASPAR git_branch: RELEASE_3_10 git_last_commit: 1b76e18 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RCASPAR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RCASPAR_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RCASPAR_1.32.0.tgz vignettes: vignettes/RCASPAR/inst/doc/RCASPAR.pdf vignetteTitles: RCASPAR: Software for high-dimentional-data driven survival time prediction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCASPAR/inst/doc/RCASPAR.R dependencyCount: 0 Package: rcellminer Version: 2.8.0 Depends: R (>= 3.2), Biobase, rcdk, fingerprint, rcellminerData Imports: stringr, gplots, ggplot2, methods, stats, utils, shiny Suggests: knitr, RColorBrewer, sqldf, BiocGenerics, testthat, BiocStyle, jsonlite, d3heatmap, glmnet, foreach, doSNOW, parallel License: LGPL-3 + file LICENSE MD5sum: df63b29253d372a7e060249e69077898 NeedsCompilation: no Title: rcellminer: Molecular Profiles and Drug Response for the NCI-60 Cell Lines Description: The NCI-60 cancer cell line panel has been used over the course of several decades as an anti-cancer drug screen. This panel was developed as part of the Developmental Therapeutics Program (DTP, http://dtp.nci.nih.gov/) of the U.S. National Cancer Institute (NCI). Thousands of compounds have been tested on the NCI-60, which have been extensively characterized by many platforms for gene and protein expression, copy number, mutation, and others (Reinhold, et al., 2012). The purpose of the CellMiner project (http://discover.nci.nih.gov/ cellminer) has been to integrate data from multiple platforms used to analyze the NCI-60 and to provide a powerful suite of tools for exploration of NCI-60 data. biocViews: aCGH, CellBasedAssays, CopyNumberVariation, GeneExpression, Pharmacogenomics, Pharmacogenetics, miRNA, Cheminformatics, Visualization, Software, SystemsBiology Author: Augustin Luna, Vinodh Rajapakse, Fabricio Sousa Maintainer: Augustin Luna , Vinodh Rajapakse , Fathi Elloumi URL: http://discover.nci.nih.gov/cellminer/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rcellminer git_branch: RELEASE_3_10 git_last_commit: 5567b70 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rcellminer_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rcellminer_2.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rcellminer_2.8.0.tgz vignettes: vignettes/rcellminer/inst/doc/rcellminerUsage.html vignetteTitles: Using rcellminer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rcellminer/inst/doc/rcellminerUsage.R dependencyCount: 84 Package: rCGH Version: 1.16.0 Depends: R (>= 3.4),methods,stats,utils,graphics Imports: plyr,DNAcopy,lattice,ggplot2,grid,shiny (>= 0.11.1), limma,affy,mclust,TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db,GenomicFeatures,GenomeInfoDb,GenomicRanges,AnnotationDbi, parallel,IRanges,grDevices,aCGH Suggests: BiocStyle, knitr, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: c57d7fbb031c0c1816e9cae5392e547e NeedsCompilation: no Title: Comprehensive Pipeline for Analyzing and Visualizing Array-Based CGH Data Description: A comprehensive pipeline for analyzing and interactively visualizing genomic profiles generated through commercial or custom aCGH arrays. As inputs, rCGH supports Agilent dual-color Feature Extraction files (.txt), from 44 to 400K, Affymetrix SNP6.0 and cytoScanHD probeset.txt, cychp.txt, and cnchp.txt files exported from ChAS or Affymetrix Power Tools. rCGH also supports custom arrays, provided data complies with the expected format. This package takes over all the steps required for individual genomic profiles analysis, from reading files to profiles segmentation and gene annotations. This package also provides several visualization functions (static or interactive) which facilitate individual profiles interpretation. Input files can be in compressed format, e.g. .bz2 or .gz. biocViews: aCGH,CopyNumberVariation,Preprocessing,FeatureExtraction Author: Frederic Commo [aut, cre] Maintainer: Frederic Commo URL: https://github.com/fredcommo/rCGH VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rCGH git_branch: RELEASE_3_10 git_last_commit: b46fe43 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rCGH_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rCGH_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rCGH_1.16.0.tgz vignettes: vignettes/rCGH/inst/doc/rCGH.pdf vignetteTitles: using rCGH package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rCGH/inst/doc/rCGH.R dependencyCount: 134 Package: RchyOptimyx Version: 2.26.0 Depends: R (>= 2.10) Imports: Rgraphviz, sfsmisc, graphics, methods, graph, grDevices, flowType (>= 2.0.0) Suggests: flowCore License: Artistic-2.0 MD5sum: 5d9c125e899b84be211b567ebf507aeb NeedsCompilation: yes Title: Optimyzed Cellular Hierarchies for Flow Cytometry Description: Constructs a hierarchy of cells using flow cytometry for maximization of an external variable (e.g., a clinical outcome or a cytokine response). biocViews: FlowCytometry Author: Adrin Jalali, Nima Aghaeepour Maintainer: Adrin Jalali , Nima Aghaeepour git_url: https://git.bioconductor.org/packages/RchyOptimyx git_branch: RELEASE_3_10 git_last_commit: 7b26205 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RchyOptimyx_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RchyOptimyx_2.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RchyOptimyx_2.26.0.tgz vignettes: vignettes/RchyOptimyx/inst/doc/RchyOptimyx.pdf vignetteTitles: flowType package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RchyOptimyx/inst/doc/RchyOptimyx.R dependencyCount: 94 Package: RcisTarget Version: 1.6.0 Depends: R (>= 3.4) Imports: AUCell (>= 1.1.6), BiocGenerics, data.table, feather, graphics, GSEABase, methods, R.utils, stats, SummarizedExperiment, utils Suggests: Biobase, BiocStyle, BiocParallel, doParallel, DT, foreach, igraph, knitr, RcisTarget.hg19.motifDBs.cisbpOnly.500bp, rmarkdown, testthat, visNetwork, arrow Enhances: doMC, doRNG, zoo License: GPL-3 MD5sum: a7dbf5fac6074a9218d5b51857686da9 NeedsCompilation: no Title: RcisTarget: Identify transcription factor binding motifs enriched on a gene list Description: RcisTarget identifies transcription factor binding motifs (TFBS) over-represented on a gene list. In a first step, RcisTarget selects DNA motifs that are significantly over-represented in the surroundings of the transcription start site (TSS) of the genes in the gene-set. This is achieved by using a database that contains genome-wide cross-species rankings for each motif. The motifs that are then annotated to TFs and those that have a high Normalized Enrichment Score (NES) are retained. Finally, for each motif and gene-set, RcisTarget predicts the candidate target genes (i.e. genes in the gene-set that are ranked above the leading edge). biocViews: GeneRegulation, MotifAnnotation, Transcriptomics, Transcription, GeneSetEnrichment, GeneTarget Author: Sara Aibar, Gert Hulselmans, Stein Aerts. Laboratory of Computational Biology. VIB-KU Leuven Center for Brain & Disease Research. Leuven, Belgium Maintainer: Sara Aibar URL: http://scenic.aertslab.org VignetteBuilder: knitr BugReports: https://github.com/aertslab/RcisTarget/issues git_url: https://git.bioconductor.org/packages/RcisTarget git_branch: RELEASE_3_10 git_last_commit: b1622e7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RcisTarget_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RcisTarget_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RcisTarget_1.6.0.tgz vignettes: vignettes/RcisTarget/inst/doc/RcisTarget.html vignetteTitles: RcisTarget: Transcription factor binding motif enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RcisTarget/inst/doc/RcisTarget.R dependencyCount: 84 Package: RCM Version: 1.2.0 Imports: RColorBrewer, alabama, edgeR, reshape2, tseries, vegan, VGAM, ggplot2 (>= 2.2.1.9000), nleqslv, phyloseq, tensor, MASS, stats, grDevices, graphics, methods Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: ac95eba6189839698c0ce0f9b41290a8 NeedsCompilation: no Title: Fit row-column association models with the negative binomial distribution for the microbiome Description: Combine ideas of log-linear analysis of contingency table, flexible response function estimation and empirical Bayes dispersion estimation for explorative visualization of microbiome datasets. The package includes unconstrained as well as constrained analysis. biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization Author: Stijn Hawinkel Maintainer: Joris Meys URL: http://users.ugent.be/~shawinke/RCMmanual/ VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/RCM/issues git_url: https://git.bioconductor.org/packages/RCM git_branch: RELEASE_3_10 git_last_commit: ef1fb29 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RCM_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RCM_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RCM_1.2.0.tgz vignettes: vignettes/RCM/inst/doc/RCMvignette.html vignetteTitles: Manual for the RCM pacakage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCM/inst/doc/RCMvignette.R dependencyCount: 101 Package: Rcpi Version: 1.22.0 Depends: R (>= 3.0.2) Imports: stats, utils, methods, RCurl, rjson, foreach, doParallel, Biostrings, GOSemSim, ChemmineR, fmcsR, rcdk (>= 3.3.8) Suggests: knitr, rmarkdown, RUnit, BiocGenerics Enhances: ChemmineOB License: Artistic-2.0 | file LICENSE Archs: i386, x64 MD5sum: 475c6bbd7e31ba96ca5c1c913cf14b03 NeedsCompilation: no Title: Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery Description: Rcpi offers a molecular informatics toolkit with a comprehensive integration of bioinformatics and chemoinformatics tools for drug discovery. biocViews: Software, DataImport, DataRepresentation, FeatureExtraction, Cheminformatics, BiomedicalInformatics, Proteomics, GO, SystemsBiology Author: Nan Xiao [aut, cre], Dong-Sheng Cao [aut], Qing-Song Xu [aut] Maintainer: Nan Xiao URL: https://nanx.me/Rcpi/, https://github.com/nanxstats/Rcpi VignetteBuilder: knitr BugReports: https://github.com/nanxstats/Rcpi/issues git_url: https://git.bioconductor.org/packages/Rcpi git_branch: RELEASE_3_10 git_last_commit: c1ae1aa git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Rcpi_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Rcpi_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rcpi_1.22.0.tgz vignettes: vignettes/Rcpi/inst/doc/Rcpi-quickref.html, vignettes/Rcpi/inst/doc/Rcpi.html vignetteTitles: Rcpi Quick Reference Card, Rcpi: R/Bioconductor Package as an Integrated Informatics Platform for Drug Discovery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rcpi/inst/doc/Rcpi.R dependencyCount: 102 Package: Rcwl Version: 1.2.1 Depends: R (>= 3.6), yaml, methods, S4Vectors Imports: utils, stats, BiocParallel, batchtools, DiagrammeR, shiny, R.utils, codetools Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-2 | file LICENSE MD5sum: e21efac0653a8bee1613f49a3cf41c94 NeedsCompilation: no Title: Wrap Command Tools and Pipelines Using CWL Description: The package can be a simple and user-friendly way to manage command line tools and build data analysis pipelines in R using Common Workflow Language (CWL). biocViews: Software, WorkflowStep, ImmunoOncology Author: Qiang Hu [aut, cre], Qian Liu [aut] Maintainer: Qiang Hu SystemRequirements: python (>= 2.7), cwltool (>= 1.0.2018) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rcwl git_branch: RELEASE_3_10 git_last_commit: fe7536a git_last_commit_date: 2019-10-31 Date/Publication: 2019-11-01 source.ver: src/contrib/Rcwl_1.2.1.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rcwl_1.2.1.tgz vignettes: vignettes/Rcwl/inst/doc/Rcwl.html vignetteTitles: User Guide for Rcwl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rcwl/inst/doc/Rcwl.R dependsOnMe: RcwlPipelines dependencyCount: 106 Package: RcwlPipelines Version: 1.2.0 Depends: R (>= 3.6), Rcwl, BiocFileCache Imports: dplyr, rappdirs, jsonlite, methods, utils, tximport Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: c4b7558678c594a0f981a583f059a4be NeedsCompilation: no Title: Bioinformatics pipelines based on Rcwl Description: A collection of Bioinformatics tools and pipelines based on R and the Common Workflow Language. biocViews: Software, WorkflowStep, Alignment, Preprocessing, QualityControl, DNASeq, RNASeq, DataImport, ImmunoOncology Author: Qiang Hu [aut, cre], Shuang Gao [aut] Maintainer: Qiang Hu SystemRequirements: nodejs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RcwlPipelines git_branch: RELEASE_3_10 git_last_commit: 5c395c4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RcwlPipelines_1.2.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RcwlPipelines_1.2.0.tgz vignettes: vignettes/RcwlPipelines/inst/doc/RcwlPipelines.html vignetteTitles: Rcwl Pipelines hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RcwlPipelines/inst/doc/RcwlPipelines.R dependencyCount: 121 Package: RCy3 Version: 2.6.3 Imports: httr, methods, RJSONIO, XML, utils, BiocGenerics, igraph, stats, graph, R.utils Suggests: RUnit, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE Archs: i386, x64 MD5sum: a830d07984e70cf5f0163b3140465358 NeedsCompilation: no Title: Functions to Access and Control Cytoscape Description: Vizualize, analyze and explore networks using Cytoscape via R. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network Author: Alex Pico [aut, cre] (), Tanja Muetze [aut], Paul Shannon [aut], Ruth Isserlin [ctb], Shraddha Pai [ctb], Julia Gustavsen [ctb], Georgi Kolishovski [ctb] Maintainer: Alex Pico URL: https://github.com/cytoscape/RCy3 SystemRequirements: Cytoscape (>= 3.7.1), CyREST (>= 3.8.0) VignetteBuilder: knitr BugReports: https://github.com/cytoscape/RCy3/issues git_url: https://git.bioconductor.org/packages/RCy3 git_branch: RELEASE_3_10 git_last_commit: 5c4d0da git_last_commit_date: 2020-01-12 Date/Publication: 2020-01-12 source.ver: src/contrib/RCy3_2.6.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/RCy3_2.6.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RCy3_2.6.3.tgz vignettes: vignettes/RCy3/inst/doc/Cancer-networks-and-data.html, vignettes/RCy3/inst/doc/Custom-Graphics.html, vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.html, vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.html, vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.html, vignettes/RCy3/inst/doc/Filtering-Networks.html, vignettes/RCy3/inst/doc/Group-nodes.html, vignettes/RCy3/inst/doc/Identifier-mapping.html, vignettes/RCy3/inst/doc/Importing-data.html, vignettes/RCy3/inst/doc/Network-functions-and-visualization.html, vignettes/RCy3/inst/doc/Overview-of-RCy3.html, vignettes/RCy3/inst/doc/Phylogenetic-trees.html, vignettes/RCy3/inst/doc/Upgrading-existing-scripts.html vignetteTitles: 06. Cancer networks and data ~40 min, 11. Custom Graphics and Labels ~10 min, 03. Cytoscape and graphNEL ~5 min, 02. Cytoscape and igraph ~5 min, 09. Cytoscape and NDEx ~20 min, 12. Filtering Networks ~10 min, 10. Group nodes ~15 min, 07. Identifier mapping ~20 min, 04. Importing data ~5 min, 05. Network functions and visualization ~15 min, 01. Overview of RCy3 ~25 min, 13. Phylogenetic Trees ~3 min, 08. Upgrading existing scripts ~15 min hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCy3/inst/doc/Cancer-networks-and-data.R, vignettes/RCy3/inst/doc/Custom-Graphics.R, vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.R, vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.R, vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.R, vignettes/RCy3/inst/doc/Filtering-Networks.R, vignettes/RCy3/inst/doc/Group-nodes.R, vignettes/RCy3/inst/doc/Identifier-mapping.R, vignettes/RCy3/inst/doc/Importing-data.R, vignettes/RCy3/inst/doc/Network-functions-and-visualization.R, vignettes/RCy3/inst/doc/Overview-of-RCy3.R, vignettes/RCy3/inst/doc/Phylogenetic-trees.R, vignettes/RCy3/inst/doc/Upgrading-existing-scripts.R importsMe: categoryCompare, NCIgraph suggestsMe: graphite, rScudo dependencyCount: 29 Package: RCyjs Version: 2.8.0 Depends: R (>= 3.5.0), BrowserViz (>= 2.7.18), graph (>= 1.56.0) Imports: methods, httpuv (>= 1.5.0), BiocGenerics, base64enc, utils Suggests: RUnit, BiocStyle, RefNet, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 4a91e83d8d3de1b915b5ad79fcc0b1e4 NeedsCompilation: no Title: Display and manipulate graphs in cytoscape.js Description: Interactive viewing and exploration of graphs, connecting R to Cytoscape.js, using websockets. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCyjs git_branch: RELEASE_3_10 git_last_commit: 4687ef0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RCyjs_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RCyjs_2.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RCyjs_2.8.0.tgz vignettes: vignettes/RCyjs/inst/doc/RCyjs.html vignetteTitles: "RCyjs: programmatic access to the web browser-based network viewer,, cytoscape.js" hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCyjs/inst/doc/RCyjs.R dependencyCount: 19 Package: RDAVIDWebService Version: 1.24.0 Depends: R (>= 2.14.1), methods, graph, GOstats, ggplot2 Imports: Category, GO.db, RBGL, rJava Suggests: Rgraphviz License: GPL (>=2) MD5sum: d61c32a5519bf42ee4eea0ac064d2a3c NeedsCompilation: no Title: An R Package for retrieving data from DAVID into R objects using Web Services API. Description: Tools for retrieving data from the Database for Annotation, Visualization and Integrated Discovery (DAVID) using Web Services into R objects. This package offers the main functionalities of DAVID website including: i) user friendly connectivity to upload gene/background list/s, change gene/background position, select current specie/s, select annotations, etc. ii) Reports of the submitted Gene List, Annotation Category Summary, Gene/Term Clusters, Functional Annotation Chart, Functional Annotation Table biocViews: Visualization, DifferentialExpression, GraphAndNetwork Author: Cristobal Fresno and Elmer A. Fernandez Maintainer: Cristobal Fresno URL: http://www.bdmg.com.ar, http://david.abcc.ncifcrf.gov/ git_url: https://git.bioconductor.org/packages/RDAVIDWebService git_branch: RELEASE_3_10 git_last_commit: b718967 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RDAVIDWebService_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RDAVIDWebService_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RDAVIDWebService_1.24.0.tgz vignettes: vignettes/RDAVIDWebService/inst/doc/RDavidWS-vignette.pdf vignetteTitles: RDAVIDWebService: a versatile R interface to DAVID hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RDAVIDWebService/inst/doc/RDavidWS-vignette.R dependsOnMe: CompGO suggestsMe: FGNet, IntramiRExploreR dependencyCount: 85 Package: rDGIdb Version: 1.12.0 Imports: jsonlite,httr,methods,graphics Suggests: BiocStyle,knitr,testthat License: MIT + file LICENSE MD5sum: 19b25cdefc94d4a0a700050d8c530f73 NeedsCompilation: no Title: R Wrapper for DGIdb Description: The rDGIdb package provides a wrapper for the Drug Gene Interaction Database (DGIdb). For simplicity, the wrapper query function and output resembles the user interface and results format provided on the DGIdb website (http://www.dgidb.org/). biocViews: Software,ResearchField,Pharmacogenetics,Pharmacogenomics, FunctionalGenomics,WorkflowStep,Annotation Author: Thomas Thurnherr, Franziska Singer, Daniel J. Stekhoven, and Niko Beerenwinkel Maintainer: Thomas Thurnherr VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rDGIdb git_branch: RELEASE_3_10 git_last_commit: 94aef8a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rDGIdb_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rDGIdb_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rDGIdb_1.12.0.tgz vignettes: vignettes/rDGIdb/inst/doc/vignette.pdf vignetteTitles: Query DGIdb using R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rDGIdb/inst/doc/vignette.R dependencyCount: 11 Package: Rdisop Version: 1.46.0 Depends: R (>= 2.0.0), Rcpp LinkingTo: Rcpp Suggests: RUnit License: GPL-2 MD5sum: 30b3bafe14b0670eaaf555a03ee37079 NeedsCompilation: yes Title: Decomposition of Isotopic Patterns Description: Identification of metabolites using high precision mass spectrometry. MS Peaks are used to derive a ranked list of sum formulae, alternatively for a given sum formula the theoretical isotope distribution can be calculated to search in MS peak lists. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Anton Pervukhin , Steffen Neumann Maintainer: Steffen Neumann URL: https://github.com/sneumann/Rdisop SystemRequirements: None BugReports: https://github.com/sneumann/Rdisop/issues/new git_url: https://git.bioconductor.org/packages/Rdisop git_branch: RELEASE_3_10 git_last_commit: fdd73a9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Rdisop_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Rdisop_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rdisop_1.46.0.tgz vignettes: vignettes/Rdisop/inst/doc/Rdisop.pdf vignetteTitles: Molecule Identification with Rdisop hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE suggestsMe: adductomicsR, MSnbase dependencyCount: 3 Package: RDRToolbox Version: 1.36.0 Depends: R (>= 2.9.0) Imports: graphics, grDevices, methods, stats, MASS, rgl Suggests: golubEsets License: GPL (>= 2) MD5sum: cf96edc2ace7c484a46c3dde163d8494 NeedsCompilation: no Title: A package for nonlinear dimension reduction with Isomap and LLE. Description: A package for nonlinear dimension reduction using the Isomap and LLE algorithm. It also includes a routine for computing the Davis-Bouldin-Index for cluster validation, a plotting tool and a data generator for microarray gene expression data and for the Swiss Roll dataset. biocViews: DimensionReduction, FeatureExtraction, Visualization, Clustering, Microarray Author: Christoph Bartenhagen Maintainer: Christoph Bartenhagen git_url: https://git.bioconductor.org/packages/RDRToolbox git_branch: RELEASE_3_10 git_last_commit: 74a5e90 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RDRToolbox_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RDRToolbox_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RDRToolbox_1.36.0.tgz vignettes: vignettes/RDRToolbox/inst/doc/vignette.pdf vignetteTitles: A package for nonlinear dimension reduction with Isomap and LLE. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RDRToolbox/inst/doc/vignette.R dependencyCount: 45 Package: ReactomeGSA Version: 1.0.0 Imports: jsonlite, httr, progress, ggplot2, methods Suggests: testthat, knitr, rmarkdown, ReactomeGSA.data, Biobase, devtools Enhances: limma, edgeR License: MIT + file LICENSE MD5sum: f51d19cdde1cf8d27b8327accd5ec433 NeedsCompilation: no Title: Client for the Reactome Analysis Service for comparative multi-omics gene set analysis Description: The ReactomeGSA packages uses Reactome's online analysis service to perform a multi-omics gene set analysis. The main advantage of this package is, that the retrieved results can be visualized using REACTOME's powerful webapplication. Since Reactome's analysis service also uses R to perfrom the actual gene set analysis you will get similar results when using the same packages (such as limma and edgeR) locally. Therefore, if you only require a gene set analysis, different packages are more suited. biocViews: GeneSetEnrichment, Proteomics, Transcriptomics, SystemsBiology, GeneExpression, Reactome Author: Johannes Griss [aut, cre] () Maintainer: Johannes Griss URL: https://github.com/reactome/ReactomeGSA VignetteBuilder: knitr BugReports: https://github.com/reactome/ReactomeGSA/issues git_url: https://git.bioconductor.org/packages/ReactomeGSA git_branch: RELEASE_3_10 git_last_commit: 16be774 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ReactomeGSA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ReactomeGSA_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ReactomeGSA_1.0.0.tgz vignettes: vignettes/ReactomeGSA/inst/doc/using-reactomegsa.html vignetteTitles: Using the ReactomeGSA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ReactomeGSA/inst/doc/using-reactomegsa.R dependencyCount: 63 Package: ReactomePA Version: 1.30.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, DOSE (>= 3.5.1), enrichplot, ggplot2, ggraph, reactome.db, igraph, graphite Suggests: BiocStyle, clusterProfiler, knitr, org.Hs.eg.db, prettydoc, testthat License: GPL-2 MD5sum: 068dd0affcfcb6d847f179baa7787c25 NeedsCompilation: no Title: Reactome Pathway Analysis Description: This package provides functions for pathway analysis based on REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization. biocViews: Pathways, Visualization, Annotation, MultipleComparison, GeneSetEnrichment, Reactome Author: Guangchuang Yu [aut, cre], Vladislav Petyuk [ctb] Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/software/ReactomePA VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ReactomePA/issues git_url: https://git.bioconductor.org/packages/ReactomePA git_branch: RELEASE_3_10 git_last_commit: 3c8414c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ReactomePA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ReactomePA_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ReactomePA_1.30.0.tgz vignettes: vignettes/ReactomePA/inst/doc/ReactomePA.html vignetteTitles: An R package for Reactome Pathway Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReactomePA/inst/doc/ReactomePA.R importsMe: bioCancer, epihet, LINC, miRspongeR, scTensor suggestsMe: ChIPseeker, CINdex, clusterProfiler, cola, scGPS dependencyCount: 128 Package: readat Version: 1.11.0 Depends: R (>= 3.4.0) Imports: assertive.base (>= 0.0-7), assertive.files (>= 0.0-2), assertive.numbers (>= 0.0-2), assertive.properties (>= 0.0-4), assertive.sets (>= 0.0-3), assertive.types (>= 0.0-3), Biobase (>= 2.34.0), data.table (>= 1.10.4), dplyr (>= 0.5.0), magrittr (>= 1.5), openxlsx (>= 4.0.17), pathological (>= 0.1-2), reshape2 (>= 1.4.2), stats, stringi (>= 1.1.5), SummarizedExperiment (>= 1.4.0), testthat (>= 1.0.2), tidyr (>= 0.6.2), utils Suggests: knitr, MSnbase, rmarkdown, withr License: GPL-3 Archs: i386, x64 MD5sum: 051747343a79c753e7077c978084dbcd NeedsCompilation: no Title: Functionality to Read and Manipulate SomaLogic ADAT files Description: This package contains functionality to import, transform and annotate data from ADAT files generated by the SomaLogic SOMAscan platform. biocViews: GeneExpression, DataImport, Proteomics, OneChannel, ProprietaryPlatforms Author: Richard Cotton [cre, aut], Aditya Bhagwat [aut] Maintainer: Richard Cotton URL: https://bitbucket.org/graumannlabtools/readat VignetteBuilder: knitr BugReports: https://bitbucket.org/graumannlabtools/readat/issues git_url: https://git.bioconductor.org/packages/readat git_branch: master git_last_commit: d175061 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-02 source.ver: src/contrib/readat_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/readat_1.11.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/readat_1.11.0.tgz vignettes: vignettes/readat/inst/doc/introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/readat/inst/doc/introduction.R dependencyCount: 83 Package: ReadqPCR Version: 1.32.0 Depends: R(>= 2.14.0), Biobase, methods Suggests: qpcR License: LGPL-3 MD5sum: 9b548d753c7800128c2c7ad714f20867 NeedsCompilation: no Title: Read qPCR data Description: The package provides functions to read raw RT-qPCR data of different platforms. biocViews: DataImport, MicrotitrePlateAssay, GeneExpression, qPCR Author: James Perkins, Matthias Kohl, Nor Izayu Abdul Rahman Maintainer: James Perkins URL: http://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.html git_url: https://git.bioconductor.org/packages/ReadqPCR git_branch: RELEASE_3_10 git_last_commit: 7503b70 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ReadqPCR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ReadqPCR_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ReadqPCR_1.32.0.tgz vignettes: vignettes/ReadqPCR/inst/doc/ReadqPCR.pdf vignetteTitles: Functions to load RT-qPCR data into R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReadqPCR/inst/doc/ReadqPCR.R dependsOnMe: NormqPCR dependencyCount: 7 Package: reb Version: 1.64.0 Depends: R (>= 2.0), Biobase, idiogram (>= 1.5.3) License: GPL-2 MD5sum: 1cad7c7e52592f5ae1f48f3d16fb57ae NeedsCompilation: yes Title: Regional Expression Biases Description: A set of functions to dentify regional expression biases biocViews: Microarray, CopyNumberVariation, Visualization Author: Kyle A. Furge and Karl Dykema Maintainer: Karl J. Dykema git_url: https://git.bioconductor.org/packages/reb git_branch: RELEASE_3_10 git_last_commit: 754d9a5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/reb_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/reb_1.64.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/reb_1.64.0.tgz vignettes: vignettes/reb/inst/doc/reb.pdf vignetteTitles: Smoothing of Microarray Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/reb/inst/doc/reb.R dependencyCount: 34 Package: REBET Version: 1.4.0 Depends: ASSET Imports: stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: b67ad744a8897503e0985ba1f5ab5e84 NeedsCompilation: yes Title: The subREgion-based BurdEn Test (REBET) Description: There is an increasing focus to investigate the association between rare variants and diseases. The REBET package implements the subREgion-based BurdEn Test which is a powerful burden test that simultaneously identifies susceptibility loci and sub-regions. biocViews: Software, VariantAnnotation, SNP Author: Bill Wheeler [cre], Bin Zhu [aut], Lisa Mirabello [ctb], Nilanjan Chatterjee [ctb] Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/REBET git_branch: RELEASE_3_10 git_last_commit: bc066d2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/REBET_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/REBET_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/REBET_1.4.0.tgz vignettes: vignettes/REBET/inst/doc/vignette.pdf vignetteTitles: REBET Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REBET/inst/doc/vignette.R dependencyCount: 16 Package: recount Version: 1.12.1 Depends: R (>= 3.3.0), SummarizedExperiment Imports: BiocParallel, derfinder, downloader, GEOquery, GenomeInfoDb, GenomicRanges, IRanges, methods, RCurl, rentrez, rtracklayer (>= 1.35.3), S4Vectors, stats, utils Suggests: AnnotationDbi, BiocManager, BiocStyle (>= 2.5.19), DESeq2, sessioninfo, EnsDb.Hsapiens.v79, GenomicFeatures, knitcitations, knitr (>= 1.6), org.Hs.eg.db, RefManageR, regionReport (>= 1.9.4), rmarkdown (>= 0.9.5), testthat (>= 2.1.0) License: Artistic-2.0 MD5sum: df590fc9b3e304c6ddd8826104388998 NeedsCompilation: no Title: Explore and download data from the recount project Description: Explore and download data from the recount project available at https://jhubiostatistics.shinyapps.io/recount/. Using the recount package you can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level, the raw counts, the phenotype metadata used, the urls to the sample coverage bigWig files or the mean coverage bigWig file for a particular study. The RangedSummarizedExperiment objects can be used by different packages for performing differential expression analysis. Using http://bioconductor.org/packages/derfinder you can perform annotation-agnostic differential expression analyses with the data from the recount project as described at http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html. biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, DataImport, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (), Abhinav Nellore [ctb], Andrew E. Jaffe [ctb] (), Margaret A. Taub [ctb], Kai Kammers [ctb], Shannon E. Ellis [ctb] (), Kasper Daniel Hansen [ctb] (), Ben Langmead [ctb] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/recount VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/recount/ git_url: https://git.bioconductor.org/packages/recount git_branch: RELEASE_3_10 git_last_commit: 188f220 git_last_commit_date: 2019-11-06 Date/Publication: 2019-11-06 source.ver: src/contrib/recount_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/recount_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/recount_1.12.1.tgz vignettes: vignettes/recount/inst/doc/recount-quickstart.html, vignettes/recount/inst/doc/SRP009615-results.html vignetteTitles: recount quick start guide, Basic DESeq2 results exploration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recount/inst/doc/recount-quickstart.R, vignettes/recount/inst/doc/SRP009615-results.R importsMe: psichomics dependencyCount: 157 Package: recoup Version: 1.14.0 Depends: R (>= 2.13.0), GenomicRanges, GenomicAlignments, ggplot2, ComplexHeatmap Imports: BiocGenerics, biomaRt, circlize, graphics, grDevices, methods, rtracklayer, plyr, stats, utils Suggests: grid, GenomeInfoDb, Rsamtools, BiocStyle, knitr, rmarkdown, zoo, RUnit, BiocManager, BSgenome, RSQLite, RMySQL Enhances: parallel License: GPL (>= 3) MD5sum: 99dfd93339a1afba83e4217edf1e401d NeedsCompilation: no Title: An R package for the creation of complex genomic profile plots Description: recoup calculates and plots signal profiles created from short sequence reads derived from Next Generation Sequencing technologies. The profiles provided are either sumarized curve profiles or heatmap profiles. Currently, recoup supports genomic profile plots for reads derived from ChIP-Seq and RNA-Seq experiments. The package uses ggplot2 and ComplexHeatmap graphics facilities for curve and heatmap coverage profiles respectively. biocViews: ImmunoOncology, Software, GeneExpression, Preprocessing, QualityControl, RNASeq, ChIPSeq, Sequencing, Coverage Author: Panagiotis Moulos Maintainer: Panagiotis Moulos URL: https://github.com/pmoulos/recoup VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/recoup git_branch: RELEASE_3_10 git_last_commit: e16d8bf git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/recoup_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/recoup_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/recoup_1.14.0.tgz vignettes: vignettes/recoup/inst/doc/recoup_intro.html vignetteTitles: Introduction to the recoup package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recoup/inst/doc/recoup_intro.R dependencyCount: 119 Package: RedeR Version: 1.34.0 Depends: R (>= 3.3.3), methods Imports: igraph Suggests: pvclust, BiocStyle, knitr, rmarkdown License: GPL (>= 2) Archs: i386, x64 MD5sum: a062b9114e7a1428da1f1b7172afee56 NeedsCompilation: no Title: Interactive visualization and manipulation of nested networks Description: RedeR is an R-based package combined with a stand-alone Java application for interactive visualization and manipulation of modular structures, nested networks and multiple levels of hierarchical associations. biocViews: Infrastructure, GraphAndNetwork, Software, Network, Visualization, DataRepresentation Author: Mauro Castro, Xin Wang, Florian Markowetz Maintainer: Mauro Castro URL: http://genomebiology.com/2012/13/4/R29 SystemRequirements: Java Runtime Environment (>= 6) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RedeR git_branch: RELEASE_3_10 git_last_commit: 0a973e9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RedeR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RedeR_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RedeR_1.34.0.tgz vignettes: vignettes/RedeR/inst/doc/RedeR.html vignetteTitles: "RedeR: hierarchical network representation" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RedeR/inst/doc/RedeR.R importsMe: PANR, RTN, transcriptogramer dependencyCount: 11 Package: REDseq Version: 1.32.0 Depends: R (>= 2.15.0), BiocGenerics (>= 0.1.0), BSgenome.Celegans.UCSC.ce2, multtest, Biostrings, BSgenome, ChIPpeakAnno Imports: BiocGenerics, AnnotationDbi, Biostrings, ChIPpeakAnno, graphics, IRanges (>= 1.13.5), multtest, stats, utils License: GPL (>=2) Archs: i386, x64 MD5sum: f55543fbab9e0bc13c84daf684447339 NeedsCompilation: no Title: Analysis of high-throughput sequencing data processed by restriction enzyme digestion Description: The package includes functions to build restriction enzyme cut site (RECS) map, distribute mapped sequences on the map with five different approaches, find enriched/depleted RECSs for a sample, and identify differentially enriched/depleted RECSs between samples. biocViews: Sequencing, SequenceMatching, Preprocessing Author: Lihua Julie Zhu and Thomas Fazzio Maintainer: Lihua Julie Zhu git_url: https://git.bioconductor.org/packages/REDseq git_branch: RELEASE_3_10 git_last_commit: 7af0556 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/REDseq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/REDseq_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/REDseq_1.32.0.tgz vignettes: vignettes/REDseq/inst/doc/REDseq.pdf vignetteTitles: REDseq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REDseq/inst/doc/REDseq.R dependencyCount: 107 Package: RefNet Version: 1.22.0 Depends: R (>= 2.15.0), methods, IRanges, PSICQUIC, AnnotationHub, RCurl, shiny Imports: BiocGenerics Suggests: RUnit, BiocStyle, org.Hs.eg.db License: Artistic-2.0 MD5sum: 793b4af973cc5999053335c9fcf53eb2 NeedsCompilation: no Title: A queryable collection of molecular interactions, from many sources Description: Obtain molecular interactions with metadata, some archived, some dynamically queried. biocViews: GraphAndNetwork Author: Paul Shannon Maintainer: Paul Shannon git_url: https://git.bioconductor.org/packages/RefNet git_branch: RELEASE_3_10 git_last_commit: e2997ca git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RefNet_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RefNet_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RefNet_1.22.0.tgz vignettes: vignettes/RefNet/inst/doc/RefNet.pdf vignetteTitles: RefNet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RefNet/inst/doc/RefNet.R suggestsMe: RCyjs dependencyCount: 75 Package: RefPlus Version: 1.56.0 Depends: R (>= 2.8.0), Biobase (>= 2.1.0), affy (>= 1.20.0), affyPLM (>= 1.18.0), preprocessCore (>= 1.4.0) Suggests: affydata License: GPL (>= 2) MD5sum: 92694e6cd6017bec39d8b2e8b70b4177 NeedsCompilation: no Title: A function set for the Extrapolation Strategy (RMA+) and Extrapolation Averaging (RMA++) methods. Description: The package contains functions for pre-processing Affymetrix data using the RMA+ and the RMA++ methods. biocViews: Microarray, OneChannel, Preprocessing Author: Kai-Ming Chang , Chris Harbron , Marie C South Maintainer: Kai-Ming Chang git_url: https://git.bioconductor.org/packages/RefPlus git_branch: RELEASE_3_10 git_last_commit: cc05d05 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RefPlus_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RefPlus_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RefPlus_1.56.0.tgz vignettes: vignettes/RefPlus/inst/doc/RefPlus.pdf vignetteTitles: RefPlus Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RefPlus/inst/doc/RefPlus.R dependencyCount: 21 Package: regioneR Version: 1.18.1 Depends: GenomicRanges Imports: memoise, GenomicRanges, IRanges, BSgenome, Biostrings, rtracklayer, parallel, graphics, stats, utils, methods, GenomeInfoDb, S4Vectors, tools Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19.masked, testthat License: Artistic-2.0 MD5sum: 21c5bf139ce890952dfedf611bdace6c NeedsCompilation: no Title: Association analysis of genomic regions based on permutation tests Description: regioneR offers a statistical framework based on customizable permutation tests to assess the association between genomic region sets and other genomic features. biocViews: Genetics, ChIPSeq, DNASeq, MethylSeq, CopyNumberVariation Author: Anna Diez-Villanueva , Roberto Malinverni and Bernat Gel Maintainer: Bernat Gel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/regioneR git_branch: RELEASE_3_10 git_last_commit: 6c60b03 git_last_commit_date: 2020-01-14 Date/Publication: 2020-01-14 source.ver: src/contrib/regioneR_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/regioneR_1.18.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/regioneR_1.18.1.tgz vignettes: vignettes/regioneR/inst/doc/regioneR.html vignetteTitles: regioneR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regioneR/inst/doc/regioneR.R dependsOnMe: karyoploteR importsMe: annotatr, ChIPpeakAnno, CNVfilteR, CopyNumberPlots, karyoploteR suggestsMe: CNVRanger dependencyCount: 41 Package: regionReport Version: 1.20.0 Depends: R(>= 3.2) Imports: BiocStyle (>= 2.5.19), derfinder (>= 1.1.0), DEFormats, DESeq2, GenomeInfoDb, GenomicRanges, knitcitations (>= 1.0.1), knitr (>= 1.6), knitrBootstrap (>= 0.9.0), methods, RefManageR, rmarkdown (>= 0.9.5), S4Vectors, SummarizedExperiment Suggests: BiocManager, biovizBase, bumphunter (>= 1.7.6), derfinderPlot (>= 1.3.2), sessioninfo, DT, DESeq, edgeR, ggbio (>= 1.13.13), ggplot2, grid, gridExtra, IRanges, mgcv, pasilla, pheatmap, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene, whisker License: Artistic-2.0 MD5sum: 999431968e3efd099f882d082cac83ca NeedsCompilation: no Title: Generate HTML or PDF reports for a set of genomic regions or DESeq2/edgeR results Description: Generate HTML or PDF reports to explore a set of regions such as the results from annotation-agnostic expression analysis of RNA-seq data at base-pair resolution performed by derfinder. You can also create reports for DESeq2 or edgeR results. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization, Transcription, Coverage, ReportWriting, DifferentialMethylation, DifferentialPeakCalling, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (), Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/regionReport VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/regionReport/ git_url: https://git.bioconductor.org/packages/regionReport git_branch: RELEASE_3_10 git_last_commit: a9c205e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/regionReport_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/regionReport_1.20.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/regionReport_1.20.1.tgz vignettes: vignettes/regionReport/inst/doc/bumphunterExample.html, vignettes/regionReport/inst/doc/bumphunterExampleOutput.html, vignettes/regionReport/inst/doc/regionReport.html vignetteTitles: Example report using bumphunter results, Basic genomic regions exploration, Introduction to regionReport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regionReport/inst/doc/bumphunterExample.R, vignettes/regionReport/inst/doc/bumphunterExampleOutput.R, vignettes/regionReport/inst/doc/regionReport.R suggestsMe: recount dependencyCount: 170 Package: regsplice Version: 1.12.6 Imports: glmnet, SummarizedExperiment, S4Vectors, limma, edgeR, stats, pbapply, utils, methods Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: c48f5b9e664a5328795d5cc1db249dc5 NeedsCompilation: no Title: L1-regularization based methods for detection of differential splicing Description: Statistical methods for detection of differential splicing (differential exon usage) in RNA-seq and exon microarray data, using L1-regularization (lasso) to improve power. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Sequencing, RNASeq, Microarray, ExonArray, ExperimentalDesign, Software Author: Lukas M. Weber [aut, cre] Maintainer: Lukas M. Weber URL: https://github.com/lmweber/regsplice VignetteBuilder: knitr BugReports: https://github.com/lmweber/regsplice/issues git_url: https://git.bioconductor.org/packages/regsplice git_branch: RELEASE_3_10 git_last_commit: ebac21b git_last_commit_date: 2019-12-08 Date/Publication: 2019-12-08 source.ver: src/contrib/regsplice_1.12.6.tar.gz win.binary.ver: bin/windows/contrib/3.6/regsplice_1.12.6.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/regsplice_1.12.6.tgz vignettes: vignettes/regsplice/inst/doc/regsplice-workflow.html vignetteTitles: regsplice workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/regsplice/inst/doc/regsplice-workflow.R dependencyCount: 42 Package: REMP Version: 1.10.1 Depends: R (>= 3.6), SummarizedExperiment(>= 1.1.6), minfi (>= 1.22.0) Imports: readr, rtracklayer, graphics, stats, utils, methods, settings, BiocGenerics, S4Vectors, Biostrings, GenomicRanges, IRanges, GenomeInfoDb, BiocParallel, doParallel, parallel, foreach, caret, kernlab, ranger, BSgenome, AnnotationHub, org.Hs.eg.db, impute, iterators Suggests: IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, minfiDataEPIC License: GPL-3 MD5sum: 6aad6412097ad5ab0810a03dd55c13cc NeedsCompilation: no Title: Repetitive Element Methylation Prediction Description: Machine learning-based tools to predict DNA methylation of locus-specific repetitive elements (RE) by learning surrounding genetic and epigenetic information. These tools provide genomewide and single-base resolution of DNA methylation prediction on RE that are difficult to measure using array-based or sequencing-based platforms, which enables epigenome-wide association study (EWAS) and differentially methylated region (DMR) analysis on RE. biocViews: DNAMethylation, Microarray, MethylationArray, Sequencing, GenomeWideAssociation, Epigenetics, Preprocessing, MultiChannel, TwoChannel, DifferentialMethylation, QualityControl, DataImport Author: Yinan Zheng [aut, cre], Lei Liu [aut], Wei Zhang [aut], Warren Kibbe [aut], Lifang Hou [aut, cph] Maintainer: Yinan Zheng URL: https://github.com/YinanZheng/REMP BugReports: https://github.com/YinanZheng/REMP/issues git_url: https://git.bioconductor.org/packages/REMP git_branch: RELEASE_3_10 git_last_commit: e474b6f git_last_commit_date: 2020-04-12 Date/Publication: 2020-04-12 source.ver: src/contrib/REMP_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/REMP_1.10.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/REMP_1.10.1.tgz vignettes: vignettes/REMP/inst/doc/REMP.pdf vignetteTitles: An Introduction to the REMP Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REMP/inst/doc/REMP.R dependencyCount: 183 Package: Repitools Version: 1.32.0 Depends: R (>= 3.0.0), methods, BiocGenerics (>= 0.8.0) Imports: parallel, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, GenomicAlignments, rtracklayer, BSgenome (>= 1.47.3), gplots, grid, MASS, gsmoothr, edgeR (>= 3.4.0), DNAcopy, Ringo, aroma.affymetrix, Rsolnp, cluster Suggests: ShortRead, BSgenome.Hsapiens.UCSC.hg18 License: LGPL (>= 2) MD5sum: 509411082522676bcc453e2d6ff059c1 NeedsCompilation: yes Title: Epigenomic tools Description: Tools for the analysis of enrichment-based epigenomic data. Features include summarization and visualization of epigenomic data across promoters according to gene expression context, finding regions of differential methylation/binding, BayMeth for quantifying methylation etc. biocViews: DNAMethylation, GeneExpression, MethylSeq Author: Mark Robinson , Dario Strbenac , Aaron Statham , Andrea Riebler Maintainer: Mark Robinson git_url: https://git.bioconductor.org/packages/Repitools git_branch: RELEASE_3_10 git_last_commit: 81b4a51 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Repitools_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Repitools_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Repitools_1.32.0.tgz vignettes: vignettes/Repitools/inst/doc/Repitools_vignette.pdf vignetteTitles: Using Repitools for Epigenomic Sequencing Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Repitools/inst/doc/Repitools_vignette.R dependencyCount: 133 Package: ReportingTools Version: 2.26.0 Depends: methods, knitr, utils Imports: Biobase,hwriter,Category,GOstats,limma(>= 3.17.5),lattice,AnnotationDbi,edgeR, annotate,PFAM.db, GSEABase, BiocGenerics(>= 0.1.6), grid, XML, R.utils, DESeq2(>= 1.3.41), ggplot2, ggbio, IRanges Suggests: RUnit, ALL, hgu95av2.db, org.Mm.eg.db, shiny, pasilla, org.Sc.sgd.db License: Artistic-2.0 MD5sum: 3ae06b203e6f183a5501040fad7ed09b NeedsCompilation: no Title: Tools for making reports in various formats Description: The ReportingTools software package enables users to easily display reports of analysis results generated from sources such as microarray and sequencing data. The package allows users to create HTML pages that may be viewed on a web browser such as Safari, or in other formats readable by programs such as Excel. Users can generate tables with sortable and filterable columns, make and display plots, and link table entries to other data sources such as NCBI or larger plots within the HTML page. Using the package, users can also produce a table of contents page to link various reports together for a particular project that can be viewed in a web browser. For more examples, please visit our site: http:// research-pub.gene.com/ReportingTools. biocViews: ImmunoOncology, Software, Visualization, Microarray, RNASeq, GO, DataRepresentation, GeneSetEnrichment Author: Jason A. Hackney, Melanie Huntley, Jessica L. Larson, Christina Chaivorapol, Gabriel Becker, and Josh Kaminker Maintainer: Jason A. Hackney , Gabriel Becker , Jessica L. Larson VignetteBuilder: utils, knitr git_url: https://git.bioconductor.org/packages/ReportingTools git_branch: RELEASE_3_10 git_last_commit: 26d68bd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ReportingTools_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ReportingTools_2.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ReportingTools_2.26.0.tgz vignettes: vignettes/ReportingTools/inst/doc/basicReportingTools.pdf, vignettes/ReportingTools/inst/doc/microarrayAnalysis.pdf, vignettes/ReportingTools/inst/doc/rnaseqAnalysis.pdf, vignettes/ReportingTools/inst/doc/shiny.pdf, vignettes/ReportingTools/inst/doc/knitr.html vignetteTitles: ReportingTools basics, Reporting on microarray differential expression, Reporting on RNA-seq differential expression, ReportingTools shiny, Knitr and ReportingTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReportingTools/inst/doc/basicReportingTools.R, vignettes/ReportingTools/inst/doc/knitr.R, vignettes/ReportingTools/inst/doc/microarrayAnalysis.R, vignettes/ReportingTools/inst/doc/rnaseqAnalysis.R, vignettes/ReportingTools/inst/doc/shiny.R importsMe: affycoretools suggestsMe: cpvSNP, EnrichmentBrowser, GSEABase, npGSEA dependencyCount: 171 Package: RepViz Version: 1.2.0 Depends: R (>= 3.5.1), GenomicRanges (>= 1.30.0), Rsamtools (>= 1.34.1), IRanges (>= 2.14.0), biomaRt (>= 2.36.0), S4Vectors (>= 0.18.0), graphics, grDevices, utils Suggests: knitr, testthat License: GPL-3 MD5sum: 06fec4812af3f96954f2a23f855ecb86 NeedsCompilation: no Title: Replicate oriented Visualization of a genomic region Description: RepViz enables the view of a genomic region in a simple and efficient way. RepViz allows simultaneous viewing of both intra- and intergroup variation in sequencing counts of the studied conditions, as well as their comparison to the output features (e.g. identified peaks) from user selected data analysis methods.The RepViz tool is primarily designed for chromatin data such as ChIP-seq and ATAC-seq, but can also be used with other sequencing data such as RNA-seq, or combinations of different types of genomic data. biocViews: WorkflowStep, Visualization, Sequencing, ChIPSeq, ATACSeq, Software, Coverage, GenomicVariation Author: Thomas Faux, Kalle Rytkönen, Asta Laiho, Laura L. Elo Maintainer: Thomas Faux, Asta Laiho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RepViz git_branch: RELEASE_3_10 git_last_commit: 576593d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RepViz_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RepViz_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RepViz_1.2.0.tgz vignettes: vignettes/RepViz/inst/doc/RepViz.html vignetteTitles: RepViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RepViz/inst/doc/RepViz.R dependencyCount: 74 Package: ReQON Version: 1.32.0 Depends: R (>= 3.0.2), Rsamtools, seqbias Imports: rJava, graphics, stats, utils, grDevices Suggests: BiocStyle License: GPL-2 MD5sum: e0e496cad40a37684891c97021342ad5 NeedsCompilation: no Title: Recalibrating Quality Of Nucleotides Description: Algorithm for recalibrating the base quality scores for aligned sequencing data in BAM format. biocViews: Sequencing, HighThroughputSequencing, Preprocessing, QualityControl Author: Christopher Cabanski, Keary Cavin, Chris Bizon Maintainer: Christopher Cabanski SystemRequirements: Java version >= 1.6 git_url: https://git.bioconductor.org/packages/ReQON git_branch: RELEASE_3_10 git_last_commit: 2d86444 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ReQON_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ReQON_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ReQON_1.32.0.tgz vignettes: vignettes/ReQON/inst/doc/ReQON.pdf vignetteTitles: ReQON Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReQON/inst/doc/ReQON.R dependencyCount: 29 Package: restfulSE Version: 1.8.0 Depends: R (>= 3.6), SummarizedExperiment,DelayedArray Imports: utils, stats, methods, S4Vectors, Biobase,reshape2, AnnotationDbi, DBI, GO.db, rhdf5client, dplyr (>= 0.7.1), magrittr, bigrquery, ExperimentHub, AnnotationHub, rlang Suggests: knitr, testthat, Rtsne, org.Mm.eg.db, org.Hs.eg.db, BiocStyle, restfulSEData License: Artistic-2.0 Archs: i386, x64 MD5sum: 119abb3edf4c3863afa4c92790fedcfb NeedsCompilation: no Title: Access matrix-like HDF5 server content or BigQuery content through a SummarizedExperiment interface Description: This package provides functions and classes to interface with remote data stores by operating on SummarizedExperiment-like objects. biocViews: Infrastructure, SingleCell, Transcriptomics, Sequencing, Coverage Author: Vincent Carey [aut], Shweta Gopaulakrishnan [cre, aut] Maintainer: Shweta Gopaulakrishnan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/restfulSE git_branch: RELEASE_3_10 git_last_commit: 6ab37f9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/restfulSE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/restfulSE_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/restfulSE_1.8.0.tgz vignettes: vignettes/restfulSE/inst/doc/restfulSE.pdf vignetteTitles: restfulSE -- experiments with SE interface to remote HDF5 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/restfulSE/inst/doc/restfulSE.R dependsOnMe: tenXplore suggestsMe: BiocOncoTK, BiocSklearn dependencyCount: 99 Package: rexposome Version: 1.8.0 Depends: R (>= 3.5), Biobase Imports: methods, utils, stats, lsr, FactoMineR, stringr, circlize, corrplot, ggplot2, reshape2, pryr, S4Vectors, imputeLCMD, scatterplot3d, glmnet, gridExtra, grid, Hmisc, gplots, gtools, scales, lme4, grDevices, graphics, ggrepel, mice Suggests: mclust, flexmix, testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 38325afb17f4f7ca426e2ce1333219ee NeedsCompilation: no Title: Exposome exploration and outcome data analysis Description: Package that allows to explore the exposome and to perform association analyses between exposures and health outcomes. biocViews: Software, BiologicalQuestion, Infrastructure, DataImport, DataRepresentation, BiomedicalInformatics, ExperimentalDesign, MultipleComparison, Classification, Clustering Author: Carles Hernandez-Ferrer [aut, cre], Juan R. Gonzalez [aut] Maintainer: Carles Hernandez-Ferrer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rexposome git_branch: RELEASE_3_10 git_last_commit: e7b7b9c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rexposome_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rexposome_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rexposome_1.8.0.tgz vignettes: vignettes/rexposome/inst/doc/exposome_data_analysis.html, vignettes/rexposome/inst/doc/mutiple_imputation_data_analysis.html vignetteTitles: Exposome Data Analysis, Dealing with Multiple Imputations hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rexposome/inst/doc/exposome_data_analysis.R, vignettes/rexposome/inst/doc/mutiple_imputation_data_analysis.R importsMe: omicRexposome dependencyCount: 157 Package: rfPred Version: 1.24.0 Depends: Rsamtools, GenomicRanges, IRanges, data.table, methods, parallel Suggests: BiocStyle License: GPL (>=2 ) MD5sum: 61ce15e67e28f7a99ceb1aa03ee6fe6b NeedsCompilation: yes Title: Assign rfPred functional prediction scores to a missense variants list Description: Based on external numerous data files where rfPred scores are pre-calculated on all genomic positions of the human exome, the package gives rfPred scores to missense variants identified by the chromosome, the position (hg19 version), the referent and alternative nucleotids and the uniprot identifier of the protein. Note that for using the package, the user has to be connected on the Internet or to download the TabixFile and index (approximately 3.3 Go). biocViews: Software, Annotation, Classification Author: Fabienne Jabot-Hanin, Hugo Varet and Jean-Philippe Jais Maintainer: Hugo Varet URL: http://www.sbim.fr/rfPred git_url: https://git.bioconductor.org/packages/rfPred git_branch: RELEASE_3_10 git_last_commit: 88c985b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rfPred_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rfPred_1.23.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rfPred_1.24.0.tgz vignettes: vignettes/rfPred/inst/doc/vignette.pdf vignetteTitles: CalculatingrfPredscoreswithpackagerfPred hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rfPred/inst/doc/vignette.R dependencyCount: 27 Package: rGADEM Version: 2.34.1 Depends: R (>= 2.11.0), Biostrings, IRanges, BSgenome, methods, seqLogo Imports: Biostrings, GenomicRanges, methods, graphics, seqLogo Suggests: BSgenome.Hsapiens.UCSC.hg19, rtracklayer License: Artistic-2.0 MD5sum: b29a287c9cd0d40ed63855cd56eb39e7 NeedsCompilation: yes Title: de novo motif discovery Description: rGADEM is an efficient de novo motif discovery tool for large-scale genomic sequence data. It is an open-source R package, which is based on the GADEM software. biocViews: Microarray, ChIPchip, Sequencing, ChIPSeq, MotifDiscovery Author: Arnaud Droit, Raphael Gottardo, Gordon Robertson and Leiping Li Maintainer: Arnaud Droit git_url: https://git.bioconductor.org/packages/rGADEM git_branch: RELEASE_3_10 git_last_commit: 803317f git_last_commit_date: 2019-12-16 Date/Publication: 2019-12-16 source.ver: src/contrib/rGADEM_2.34.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/rGADEM_2.34.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rGADEM_2.34.1.tgz vignettes: vignettes/rGADEM/inst/doc/rGADEM.pdf vignetteTitles: The rGADEM users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rGADEM/inst/doc/rGADEM.R importsMe: MotIV dependencyCount: 40 Package: RGalaxy Version: 1.30.0 Depends: XML, methods, tools, optparse Imports: BiocGenerics, Biobase, roxygen2 Suggests: RUnit, hgu95av2.db, AnnotationDbi, knitr, formatR, Rserve Enhances: RSclient License: Artistic-2.0 MD5sum: a98c9d78b5c25e94cdd1ca7d22867be7 NeedsCompilation: no Title: Make an R function available in the Galaxy web platform Description: Given an R function and its manual page, make the documented function available in Galaxy. biocViews: Infrastructure Author: Dan Tenenbaum Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RGalaxy git_branch: RELEASE_3_10 git_last_commit: ee04b70 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RGalaxy_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RGalaxy_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RGalaxy_1.30.0.tgz vignettes: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.html vignetteTitles: Introduction to RGalaxy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.R dependencyCount: 47 Package: Rgin Version: 1.6.0 Depends: R (>= 3.5) LinkingTo: RcppEigen (>= 0.3.3.5.0) Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: 74dfa08e0f844022eb98618dfd7275dd NeedsCompilation: yes Title: gin in R Description: C++ implementation of SConES. biocViews: Software, GenomeWideAssociation, SNP, GeneticVariability, Genetics, FeatureExtraction, GraphAndNetwork, Network Author: Hector Climente-Gonzalez [aut, cre], Dominik Gerhard Grimm [aut], Chloe-Agathe Azencott [aut] Maintainer: Hector Climente-Gonzalez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rgin git_branch: RELEASE_3_10 git_last_commit: 8721092 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Rgin_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Rgin_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rgin_1.6.0.tgz vignettes: vignettes/Rgin/inst/doc/Rgin-UsingCppLibraries.html vignetteTitles: Using Rgin C++ libraries hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE linksToMe: martini dependencyCount: 10 Package: RGMQL Version: 1.6.0 Depends: R(>= 3.4.2), RGMQLlib Imports: httr, rJava, GenomicRanges, rtracklayer, data.table, utils, plyr, xml2, methods, S4Vectors, dplyr, stats, glue, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 886770e1cd356a2d1bfd714475f90fd8 NeedsCompilation: no Title: GenoMetric Query Language for R/Bioconductor Description: This package brings the GenoMetric Query Language (GMQL) functionalities into the R environment. GMQL is a high-level, declarative language to manage heterogeneous genomic datasets for biomedical purposes, using simple queries to process genomic regions and their metadata and properties. GMQL adopts algorithms efficiently designed for big data using cloud-computing technologies (like Apache Hadoop and Spark) allowing GMQL to run on modern infrastructures, in order to achieve scalability and high performance. It allows to create, manipulate and extract genomic data from different data sources both locally and remotely. Our RGMQL functions allow complex queries and processing leveraging on the R idiomatic paradigm. The RGMQL package also provides a rich set of ancillary classes that allow sophisticated input/output management and sorting, such as: ASC, DESC, BAG, MIN, MAX, SUM, AVG, MEDIAN, STD, Q1, Q2, Q3 (and many others). Note that many RGMQL functions are not directly executed in R environment, but are deferred until real execution is issued. biocViews: Software, Infrastructure, DataImport, Network, ImmunoOncology, SingleCell Author: Simone Pallotta, Marco Masseroli Maintainer: Simone Pallotta URL: http://www.bioinformatics.deib.polimi.it/genomic_computing/GMQL/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RGMQL git_branch: RELEASE_3_10 git_last_commit: 8b679b5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RGMQL_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RGMQL_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RGMQL_1.6.0.tgz vignettes: vignettes/RGMQL/inst/doc/RGMQL-vignette.pdf vignetteTitles: RGMQL: GenoMetric Query Language for R/Bioconductor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGMQL/inst/doc/RGMQL-vignette.R dependencyCount: 71 Package: RGraph2js Version: 1.14.0 Imports: utils, whisker, rjson, digest, graph Suggests: RUnit, BiocStyle, BiocGenerics, xtable, sna License: GPL-2 Archs: i386, x64 MD5sum: 07620d04f503777a69241cdef406dba9 NeedsCompilation: no Title: Convert a Graph into a D3js Script Description: Generator of web pages which display interactive network/graph visualizations with D3js, jQuery and Raphael. biocViews: Visualization, Network, GraphAndNetwork, ThirdPartyClient Author: Stephane Cano [aut, cre], Sylvain Gubian [aut], Florian Martin [aut] Maintainer: Stephane Cano SystemRequirements: jQuery, jQueryUI, qTip2, D3js and Raphael are required Javascript libraries made available via the online CDNJS service (http://cdnjs.cloudflare.com). git_url: https://git.bioconductor.org/packages/RGraph2js git_branch: RELEASE_3_10 git_last_commit: 75b929b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RGraph2js_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RGraph2js_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RGraph2js_1.14.0.tgz vignettes: vignettes/RGraph2js/inst/doc/RGraph2js.pdf vignetteTitles: RGraph2js hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGraph2js/inst/doc/RGraph2js.R dependencyCount: 11 Package: Rgraphviz Version: 2.30.0 Depends: R (>= 2.6.0), methods, utils, graph, grid Imports: stats4, graphics, grDevices Suggests: RUnit, BiocGenerics, XML License: EPL MD5sum: de619ba8e1c382d060c9ab2ebde50d04 NeedsCompilation: yes Title: Provides plotting capabilities for R graph objects Description: Interfaces R with the AT and T graphviz library for plotting R graph objects from the graph package. biocViews: GraphAndNetwork, Visualization Author: Kasper Daniel Hansen [cre, aut], Jeff Gentry [aut], Li Long [aut], Robert Gentleman [aut], Seth Falcon [aut], Florian Hahne [aut], Deepayan Sarkar [aut] Maintainer: Kasper Daniel Hansen SystemRequirements: optionally Graphviz (>= 2.16) git_url: https://git.bioconductor.org/packages/Rgraphviz git_branch: RELEASE_3_10 git_last_commit: 947bd09 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Rgraphviz_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Rgraphviz_2.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rgraphviz_2.30.0.tgz vignettes: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.pdf, vignettes/Rgraphviz/inst/doc/Rgraphviz.pdf vignetteTitles: A New Interface to Plot Graphs Using Rgraphviz, How To Plot A Graph Using Rgraphviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.R, vignettes/Rgraphviz/inst/doc/Rgraphviz.R dependsOnMe: biocGraph, BioMVCClass, CellNOptR, flowCL, GOFunction, MineICA, netresponse, paircompviz, pathRender, ROntoTools, SplicingGraphs, TDARACNE importsMe: apComplex, biocGraph, BiocOncoTK, chimeraviz, CompGO, CytoML, DEGraph, EnrichmentBrowser, flowWorkspace, GeneNetworkBuilder, GOFunction, GOstats, hyperdraw, MIGSA, mirIntegrator, mnem, nem, OncoSimulR, ontoProc, paircompviz, pathview, Pigengene, qpgraph, RchyOptimyx, SplicingGraphs, trackViewer, TRONCO suggestsMe: altcdfenvs, annotate, BiocCaseStudies, Category, CNORfeeder, CNORfuzzy, DEGraph, flowCore, geneplotter, GlobalAncova, globaltest, GSEABase, KEGGgraph, MLP, NCIgraph, pcaGoPromoter, pkgDepTools, RBGL, RBioinf, rBiopaxParser, RDAVIDWebService, Rtreemix, safe, SPIA, SRAdb, Streamer, topGO, ViSEAGO, vtpnet dependencyCount: 10 Package: rGREAT Version: 1.18.0 Depends: R (>= 3.1.2), GenomicRanges, IRanges, methods Imports: rjson, GetoptLong (>= 0.0.9), RCurl, utils, stats Suggests: testthat (>= 0.3), knitr, circlize (>= 0.4.8), rmarkdown License: MIT + file LICENSE Archs: i386, x64 MD5sum: 6f6e9503eacd7a6d60ad0125f0db50d9 NeedsCompilation: no Title: Client for GREAT Analysis Description: This package makes GREAT (Genomic Regions Enrichment of Annotations Tool) analysis automatic by constructing a HTTP POST request according to user's input and automatically retrieving results from GREAT web server. biocViews: GeneSetEnrichment, GO, Pathways, Software, Sequencing, WholeGenome, GenomeAnnotation, Coverage Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/rGREAT, http://great.stanford.edu/public/html/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rGREAT git_branch: RELEASE_3_10 git_last_commit: 7349794 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rGREAT_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rGREAT_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rGREAT_1.18.0.tgz vignettes: vignettes/rGREAT/inst/doc/rGREAT.html vignetteTitles: Analyze with GREAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rGREAT/inst/doc/rGREAT.R dependencyCount: 19 Package: RGSEA Version: 1.20.0 Depends: R(>= 2.10.0) Imports: BiocGenerics Suggests: BiocStyle, GEOquery, knitr, RUnit License: GPL(>=3) MD5sum: 2b0760a9a4efda361940958fd2fcb95e NeedsCompilation: no Title: Random Gene Set Enrichment Analysis Description: Combining bootstrap aggregating and Gene set enrichment analysis (GSEA), RGSEA is a classfication algorithm with high robustness and no over-fitting problem. It performs well especially for the data generated from different exprements. biocViews: GeneSetEnrichment, StatisticalMethod, Classification Author: Chengcheng Ma Maintainer: Chengcheng Ma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RGSEA git_branch: RELEASE_3_10 git_last_commit: a03a1cc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RGSEA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RGSEA_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RGSEA_1.20.0.tgz vignettes: vignettes/RGSEA/inst/doc/RGSEA.pdf vignetteTitles: Introduction to RGSEA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGSEA/inst/doc/RGSEA.R dependencyCount: 6 Package: rgsepd Version: 1.18.0 Depends: R (>= 3.5.0), DESeq2, goseq (>= 1.28) Imports: gplots, biomaRt, org.Hs.eg.db, GO.db, SummarizedExperiment, hash, AnnotationDbi Suggests: boot, tools, BiocGenerics, knitr, xtable License: GPL-3 MD5sum: 2eb29fb723c976c03e1e73281d933bb4 NeedsCompilation: no Title: Gene Set Enrichment / Projection Displays Description: R/GSEPD is a bioinformatics package for R to help disambiguate transcriptome samples (a matrix of RNA-Seq counts at transcript IDs) by automating differential expression (with DESeq2), then gene set enrichment (with GOSeq), and finally a N-dimensional projection to quantify in which ways each sample is like either treatment group. biocViews: ImmunoOncology, Software, DifferentialExpression, GeneSetEnrichment, RNASeq Author: Karl Stamm Maintainer: Karl Stamm VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rgsepd git_branch: RELEASE_3_10 git_last_commit: cfc3cc7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rgsepd_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rgsepd_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rgsepd_1.18.0.tgz vignettes: vignettes/rgsepd/inst/doc/rgsepd.pdf vignetteTitles: An Introduction to the rgsepd package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rgsepd/inst/doc/rgsepd.R dependencyCount: 152 Package: rhdf5 Version: 2.30.1 Depends: R (>= 3.5.0), methods Imports: Rhdf5lib (>= 1.3.2) LinkingTo: Rhdf5lib Suggests: bit64, BiocStyle, knitr, rmarkdown, testthat, microbenchmark, dplyr, ggplot2 License: Artistic-2.0 Archs: i386, x64 MD5sum: 26c5e7873905fda4f3afa0ca681e21fc NeedsCompilation: yes Title: R Interface to HDF5 Description: This package provides an interface between HDF5 and R. HDF5's main features are the ability to store and access very large and/or complex datasets and a wide variety of metadata on mass storage (disk) through a completely portable file format. The rhdf5 package is thus suited for the exchange of large and/or complex datasets between R and other software package, and for letting R applications work on datasets that are larger than the available RAM. biocViews: Infrastructure, DataImport Author: Bernd Fischer [aut], Gregoire Pau [aut], Mike Smith [aut, cre], Martin Morgan [ctb], Daniel van Twisk [ctb] Maintainer: Mike Smith URL: https://github.com/grimbough/rhdf5 SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/grimbough/rhdf5/issues git_url: https://git.bioconductor.org/packages/rhdf5 git_branch: RELEASE_3_10 git_last_commit: 137dc19 git_last_commit_date: 2019-11-25 Date/Publication: 2019-11-26 source.ver: src/contrib/rhdf5_2.30.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/rhdf5_2.30.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rhdf5_2.30.1.tgz vignettes: vignettes/rhdf5/inst/doc/practical_tips.html, vignettes/rhdf5/inst/doc/rhdf5.html vignetteTitles: rhdf5 Practical Tips, rhdf5 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5/inst/doc/practical_tips.R, vignettes/rhdf5/inst/doc/rhdf5.R dependsOnMe: GenoGAM, GSCA, HDF5Array, HiCBricks, LoomExperiment importsMe: biomformat, bsseq, CoGAPS, CopyNumberPlots, cTRAP, diffHic, DropletUtils, EventPointer, gep2pep, h5vc, HiCcompare, IONiseR, KnowSeq, MOFA, phantasus, PureCN, scone, signatureSearch, slinky suggestsMe: edgeR, slalom, SummarizedExperiment, tximport dependencyCount: 2 Package: rhdf5client Version: 1.8.0 Depends: R (>= 3.6), methods, DelayedArray Imports: S4Vectors, httr, R6, rjson, utils Suggests: knitr, testthat, BiocStyle, DT, reticulate License: Artistic-2.0 MD5sum: 51d60eb4753c4625d83aecdcf8c3c622 NeedsCompilation: yes Title: Access HDF5 content from h5serv Description: Provides functionality for reading data from h5serv server from within R. biocViews: DataImport, Software Author: Samuela Pollack [aut], Shweta Gopaulakrishnan [aut], Vincent Carey [cre, aut] Maintainer: Vincent Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rhdf5client git_branch: RELEASE_3_10 git_last_commit: 875b34e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rhdf5client_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rhdf5client_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rhdf5client_1.8.0.tgz vignettes: vignettes/rhdf5client/inst/doc/delayed-array.html vignetteTitles: HSDSArray DelayedArray backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5client/inst/doc/delayed-array.R importsMe: restfulSE dependencyCount: 32 Package: Rhdf5lib Version: 1.8.0 Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 9c56283a0d64a4717025770593e99c2d NeedsCompilation: yes Title: hdf5 library as an R package Description: Provides C and C++ hdf5 libraries. biocViews: Infrastructure Author: Mike Smith Maintainer: Mike Smith URL: https://github.com/grimbough/Rhdf5lib SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/grimbough/Rhdf5lib git_url: https://git.bioconductor.org/packages/Rhdf5lib git_branch: RELEASE_3_10 git_last_commit: 16d91c4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Rhdf5lib_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Rhdf5lib_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rhdf5lib_1.8.0.tgz vignettes: vignettes/Rhdf5lib/inst/doc/downloadHDF5.html, vignettes/Rhdf5lib/inst/doc/Rhdf5lib.html vignetteTitles: Creating this HDF5 distribution, Linking to Rhdf5lib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhdf5lib/inst/doc/downloadHDF5.R, vignettes/Rhdf5lib/inst/doc/Rhdf5lib.R importsMe: rhdf5 suggestsMe: mbkmeans linksToMe: DropletUtils, HDF5Array, mbkmeans, mzR, ncdfFlow, rhdf5 dependencyCount: 0 Package: Rhisat2 Version: 1.2.0 Depends: R (>= 3.6) Imports: GenomicFeatures, SGSeq, GenomicRanges, methods, utils Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: c6c04d3916ab0b74598cc9ce7f6d5032 NeedsCompilation: yes Title: R Wrapper for HISAT2 Aligner Description: An R interface to the HISAT2 spliced short-read aligner by Kim et al. (2015). The package contains wrapper functions to create a genome index and to perform the read alignment to the generated index. biocViews: Alignment, Sequencing, SplicedAlignment Author: Charlotte Soneson [aut, cre] () Maintainer: Charlotte Soneson URL: https://github.com/fmicompbio/Rhisat2 SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/Rhisat2/issues git_url: https://git.bioconductor.org/packages/Rhisat2 git_branch: RELEASE_3_10 git_last_commit: 458e19e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Rhisat2_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Rhisat2_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rhisat2_1.2.0.tgz vignettes: vignettes/Rhisat2/inst/doc/Rhisat2.html vignetteTitles: Rhisat2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rhisat2/inst/doc/Rhisat2.R importsMe: QuasR dependencyCount: 86 Package: Rhtslib Version: 1.18.1 Imports: zlibbioc LinkingTo: zlibbioc Suggests: BiocStyle, knitr License: LGPL (>= 2) Archs: i386, x64 MD5sum: 3dd8796283f9fc9503a8172acfca22b6 NeedsCompilation: yes Title: HTSlib high-throughput sequencing library as an R package Description: This package provides version 1.7 of the 'HTSlib' C library for high-throughput sequence analysis. The package is primarily useful to developers of other R packages who wish to make use of HTSlib. Motivation and instructions for use of this package are in the vignette, vignette(package="Rhtslib", "Rhtslib"). biocViews: DataImport, Sequencing Author: Nathaniel Hayden [led, aut], Martin Morgan [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/Rhtslib, http://www.htslib.org/ SystemRequirements: libbz2 & liblzma & libcurl (with header files), GNU make VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Rhtslib git_url: https://git.bioconductor.org/packages/Rhtslib git_branch: RELEASE_3_10 git_last_commit: 751a2eb git_last_commit_date: 2020-01-27 Date/Publication: 2020-01-29 source.ver: src/contrib/Rhtslib_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/Rhtslib_1.18.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rhtslib_1.18.1.tgz vignettes: vignettes/Rhtslib/inst/doc/Rhtslib.html vignetteTitles: Motivation and use of Rhtslib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhtslib/inst/doc/Rhtslib.R dependsOnMe: deepSNV importsMe: deepSNV, diffHic, scPipe linksToMe: ArrayExpressHTS, bamsignals, BitSeq, csaw, deepSNV, DiffBind, diffHic, h5vc, methylKit, podkat, qrqc, QuasR, Rsamtools, scPipe, seqbias, TransView, VariantAnnotation dependencyCount: 1 Package: RiboProfiling Version: 1.16.0 Depends: R (>= 3.2.2), Biostrings Imports: BiocGenerics, GenomeInfoDb, GenomicRanges, IRanges, reshape2, GenomicFeatures, grid, plyr, S4Vectors, GenomicAlignments, ggplot2, ggbio, Rsamtools, rtracklayer, data.table, sqldf Suggests: knitr, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, testthat, SummarizedExperiment License: GPL-3 Archs: i386, x64 MD5sum: 3842a829de9f644b3e5e7c167af05e6a NeedsCompilation: no Title: Ribosome Profiling Data Analysis: from BAM to Data Representation and Interpretation Description: Starting with a BAM file, this package provides the necessary functions for quality assessment, read start position recalibration, the counting of reads on CDS, 3'UTR, and 5'UTR, plotting of count data: pairs, log fold-change, codon frequency and coverage assessment, principal component analysis on codon coverage. biocViews: RiboSeq, Sequencing, Coverage, Alignment, QualityControl, Software, PrincipalComponent Author: Alexandra Popa Maintainer: A. Popa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RiboProfiling git_branch: RELEASE_3_10 git_last_commit: eaed267 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RiboProfiling_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RiboProfiling_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RiboProfiling_1.16.0.tgz vignettes: vignettes/RiboProfiling/inst/doc/RiboProfiling.pdf vignetteTitles: Analysing Ribo-Seq data with the "RiboProfiling" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RiboProfiling/inst/doc/RiboProfiling.R dependencyCount: 155 Package: riboSeqR Version: 1.20.0 Depends: R (>= 3.0.2), methods, GenomicRanges, abind Imports: Rsamtools, IRanges, baySeq, GenomeInfoDb, seqLogo Suggests: BiocStyle, RUnit, BiocGenerics License: GPL-3 Archs: i386, x64 MD5sum: f0c447503a1510f06ec18bc7299b0546 NeedsCompilation: no Title: Analysis of sequencing data from ribosome profiling experiments Description: Plotting functions, frameshift detection and parsing of sequencing data from ribosome profiling experiments. biocViews: Sequencing,Genetics,Visualization,RiboSeq Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/riboSeqR git_branch: RELEASE_3_10 git_last_commit: c4b01c3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/riboSeqR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/riboSeqR_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/riboSeqR_1.20.0.tgz vignettes: vignettes/riboSeqR/inst/doc/riboSeqR.pdf vignetteTitles: riboSeqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/riboSeqR/inst/doc/riboSeqR.R dependencyCount: 36 Package: RImmPort Version: 1.14.0 Imports: plyr, dplyr, DBI, data.table, reshape2, methods, sqldf, tools, utils, RSQLite Suggests: knitr License: GPL-3 Archs: x64 MD5sum: d7b680cbed7c22b3fd93518d6059f7ca NeedsCompilation: no Title: RImmPort: Enabling Ready-for-analysis Immunology Research Data Description: The RImmPort package simplifies access to ImmPort data for analysis in the R environment. It provides a standards-based interface to the ImmPort study data that is in a proprietary format. biocViews: BiomedicalInformatics, DataImport, DataRepresentation Author: Ravi Shankar Maintainer: Zicheng Hu , Ravi Shankar URL: http://bioconductor.org/packages/RImmPort/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RImmPort git_branch: RELEASE_3_10 git_last_commit: e5dbf01 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RImmPort_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RImmPort_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RImmPort_1.14.0.tgz vignettes: vignettes/RImmPort/inst/doc/RImmPort_Article.pdf, vignettes/RImmPort/inst/doc/RImmPort_QuickStart.pdf vignetteTitles: RImmPort: Enabling ready-for-analysis immunology research data, RImmPort: Quick Start Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RImmPort/inst/doc/RImmPort_Article.R, vignettes/RImmPort/inst/doc/RImmPort_QuickStart.R dependencyCount: 43 Package: Ringo Version: 1.50.0 Depends: methods, Biobase (>= 1.14.1), RColorBrewer, limma, Matrix, grid, lattice Imports: BiocGenerics (>= 0.1.11), genefilter, limma, vsn, stats4 Suggests: rtracklayer (>= 1.3.1), mclust, topGO (>= 1.15.0) License: Artistic-2.0 Archs: i386, x64 MD5sum: 7de3ba1e49b48aec93a5ed2884a5202c NeedsCompilation: yes Title: R Investigation of ChIP-chip Oligoarrays Description: The package Ringo facilitates the primary analysis of ChIP-chip data. The main functionalities of the package are data read-in, quality assessment, data visualisation and identification of genomic regions showing enrichment in ChIP-chip. The package has functions to deal with two-color oligonucleotide microarrays from NimbleGen used in ChIP-chip projects, but also contains more general functions for ChIP-chip data analysis, given that the data is supplied as RGList (raw) or ExpressionSet (pre- processed). The package employs functions from various other packages of the Bioconductor project and provides additional ChIP-chip-specific and NimbleGen-specific functionalities. biocViews: Microarray,TwoChannel,DataImport,QualityControl,Preprocessing Author: Joern Toedling, Oleg Sklyar, Tammo Krueger, Matt Ritchie, Wolfgang Huber Maintainer: J. Toedling git_url: https://git.bioconductor.org/packages/Ringo git_branch: RELEASE_3_10 git_last_commit: 8b72eec git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Ringo_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Ringo_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Ringo_1.50.0.tgz vignettes: vignettes/Ringo/inst/doc/Ringo.pdf vignetteTitles: R Investigation of NimbleGen Oligoarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Ringo/inst/doc/Ringo.R dependsOnMe: SimBindProfiles, Starr importsMe: Repitools dependencyCount: 83 Package: RIPSeeker Version: 1.26.0 Depends: R (>= 2.15), methods, S4Vectors (>= 0.9.25), IRanges, GenomicRanges, SummarizedExperiment, Rsamtools, GenomicAlignments, rtracklayer Suggests: biomaRt, ChIPpeakAnno, parallel, GenomicFeatures License: GPL-2 MD5sum: 030b01b7c754d7f2929f4e9db785d566 NeedsCompilation: no Title: RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments Description: Infer and discriminate RIP peaks from RIP-seq alignments using two-state HMM with negative binomial emission probability. While RIPSeeker is specifically tailored for RIP-seq data analysis, it also provides a suite of bioinformatics tools integrated within this self-contained software package comprehensively addressing issues ranging from post-alignments processing to visualization and annotation. biocViews: Sequencing, RIPSeq Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/software.html git_url: https://git.bioconductor.org/packages/RIPSeeker git_branch: RELEASE_3_10 git_last_commit: 0051eea git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RIPSeeker_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RIPSeeker_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RIPSeeker_1.26.0.tgz vignettes: vignettes/RIPSeeker/inst/doc/RIPSeeker.pdf vignetteTitles: RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RIPSeeker/inst/doc/RIPSeeker.R dependencyCount: 38 Package: Risa Version: 1.28.0 Depends: R (>= 2.0.9), Biobase (>= 2.4.0), methods, Rcpp (>= 0.9.13), biocViews, affy Imports: xcms Suggests: faahKO (>= 1.2.11) License: LGPL MD5sum: ba6db70fb09e4108c1f3a97e4436eba1 NeedsCompilation: no Title: Converting experimental metadata from ISA-tab into Bioconductor data structures Description: The Investigation / Study / Assay (ISA) tab-delimited format is a general purpose framework with which to collect and communicate complex metadata (i.e. sample characteristics, technologies used, type of measurements made) from experiments employing a combination of technologies, spanning from traditional approaches to high-throughput techniques. Risa allows to access metadata/data in ISA-Tab format and build Bioconductor data structures. Currently, data generated from microarray, flow cytometry and metabolomics-based (i.e. mass spectrometry) assays are supported. The package is extendable and efforts are undergoing to support metadata associated to proteomics assays. biocViews: Annotation, DataImport, MassSpectrometry Author: Alejandra Gonzalez-Beltran, Audrey Kauffmann, Steffen Neumann, Gabriella Rustici, ISA Team Maintainer: Alejandra Gonzalez-Beltran URL: http://www.isa-tools.org/ BugReports: https://github.com/ISA-tools/Risa/issues git_url: https://git.bioconductor.org/packages/Risa git_branch: RELEASE_3_10 git_last_commit: 4432469 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Risa_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Risa_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Risa_1.28.0.tgz vignettes: vignettes/Risa/inst/doc/Risa.pdf vignetteTitles: Risa: converts experimental metadata from ISA-tab into Bioconductor data structures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Risa/inst/doc/Risa.R dependencyCount: 103 Package: RITAN Version: 1.10.0 Depends: R (>= 3.4), Imports: graphics, stats, utils, grid, gridExtra, reshape2, gplots, ggplot2, plotrix, RColorBrewer, STRINGdb, MCL, linkcomm, dynamicTreeCut, sqldf, gsubfn, hash, png, sqldf, igraph, BgeeDB, knitr, RITANdata Suggests: rmarkdown License: file LICENSE MD5sum: bd1519b88c3832458c70be38977f6f06 NeedsCompilation: no Title: Rapid Integration of Term Annotation and Network resources Description: Tools for comprehensive gene set enrichment and extraction of multi-resource high confidence subnetworks. RITAN facilitates bioinformatic tasks for enabling network biology research. biocViews: QualityControl, Network, NetworkEnrichment, NetworkInference, GeneSetEnrichment, FunctionalGenomics Author: Michael Zimmermann Maintainer: Michael Zimmermann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RITAN git_branch: RELEASE_3_10 git_last_commit: 905d244 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RITAN_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RITAN_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RITAN_1.10.0.tgz vignettes: vignettes/RITAN/inst/doc/choosing_resources.html, vignettes/RITAN/inst/doc/enrichment.html, vignettes/RITAN/inst/doc/multi_tissue_analysis.html, vignettes/RITAN/inst/doc/resource_relationships.html, vignettes/RITAN/inst/doc/subnetworks.html vignetteTitles: Choosing Resources, Enrichment Vignette, Multi-Tissue Analysis, Relationships Among Resources, Network Biology Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RITAN/inst/doc/choosing_resources.R, vignettes/RITAN/inst/doc/enrichment.R, vignettes/RITAN/inst/doc/multi_tissue_analysis.R, vignettes/RITAN/inst/doc/resource_relationships.R, vignettes/RITAN/inst/doc/subnetworks.R dependencyCount: 112 Package: RIVER Version: 1.10.0 Depends: R (>= 3.3.2) Imports: glmnet, pROC, ggplot2, graphics, stats, Biobase, methods, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools License: GPL (>= 2) Archs: i386, x64 MD5sum: 04c9abc7f3573851f692316b006598a0 NeedsCompilation: no Title: R package for RIVER (RNA-Informed Variant Effect on Regulation) Description: An implementation of a probabilistic modeling framework that jointly analyzes personal genome and transcriptome data to estimate the probability that a variant has regulatory impact in that individual. It is based on a generative model that assumes that genomic annotations, such as the location of a variant with respect to regulatory elements, determine the prior probability that variant is a functional regulatory variant, which is an unobserved variable. The functional regulatory variant status then influences whether nearby genes are likely to display outlier levels of gene expression in that person. See the RIVER website for more information, documentation and examples. biocViews: GeneExpression, GeneticVariability, SNP, Transcription, FunctionalPrediction, GeneRegulation, GenomicVariation, BiomedicalInformatics, FunctionalGenomics, Genetics, SystemsBiology, Transcriptomics, Bayesian, Clustering, TranscriptomeVariant, Regression Author: Yungil Kim [aut, cre], Alexis Battle [aut] Maintainer: Yungil Kim URL: https://github.com/ipw012/RIVER VignetteBuilder: knitr BugReports: https://github.com/ipw012/RIVER/issues git_url: https://git.bioconductor.org/packages/RIVER git_branch: RELEASE_3_10 git_last_commit: dcabbe6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RIVER_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RIVER_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RIVER_1.10.0.tgz vignettes: vignettes/RIVER/inst/doc/RIVER.html vignetteTitles: RIVER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RIVER/inst/doc/RIVER.R dependencyCount: 64 Package: RJMCMCNucleosomes Version: 1.10.0 Depends: R (>= 3.4), IRanges, GenomicRanges Imports: Rcpp (>= 0.12.5), consensusSeekeR, BiocGenerics, GenomeInfoDb, S4Vectors (>= 0.23.10), BiocParallel, stats, graphics, methods, grDevices LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, nucleoSim, RUnit License: Artistic-2.0 MD5sum: c59ed9a04a450ca85a2cd94a96d39bcf NeedsCompilation: yes Title: Bayesian hierarchical model for genome-wide nucleosome positioning with high-throughput short-read data (MNase-Seq) Description: This package does nucleosome positioning using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling. biocViews: BiologicalQuestion, ChIPSeq, NucleosomePositioning, Software, StatisticalMethod, Bayesian, Sequencing, Coverage Author: Pascal Belleau [aut], Rawane Samb [aut], Astrid Deschênes [cre, aut], Khader Khadraoui [aut], Lajmi Lakhal-Chaieb [aut], Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes SystemRequirements: Rcpp VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes/issues git_url: https://git.bioconductor.org/packages/RJMCMCNucleosomes git_branch: RELEASE_3_10 git_last_commit: 85d3a46 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RJMCMCNucleosomes_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RJMCMCNucleosomes_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RJMCMCNucleosomes_1.10.0.tgz vignettes: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.html vignetteTitles: Nucleosome Positioning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.R dependencyCount: 44 Package: RLMM Version: 1.48.0 Depends: R (>= 2.1.0) Imports: graphics, grDevices, MASS, stats, utils License: LGPL (>= 2) MD5sum: be36a5d8018d389f078fdb3076cece86 NeedsCompilation: no Title: A Genotype Calling Algorithm for Affymetrix SNP Arrays Description: A classification algorithm, based on a multi-chip, multi-SNP approach for Affymetrix SNP arrays. Using a large training sample where the genotype labels are known, this aglorithm will obtain more accurate classification results on new data. RLMM is based on a robust, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variation is removed through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as thousands other SNPs for accurate classification. NOTE: 100K-Xba only at for now. biocViews: Microarray, OneChannel, SNP, GeneticVariability Author: Nusrat Rabbee , Gary Wong Maintainer: Nusrat Rabbee URL: http://www.stat.berkeley.edu/users/nrabbee/RLMM SystemRequirements: Internal files Xba.CQV, Xba.regions (or other regions file) git_url: https://git.bioconductor.org/packages/RLMM git_branch: RELEASE_3_10 git_last_commit: 789655c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RLMM_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RLMM_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RLMM_1.48.0.tgz vignettes: vignettes/RLMM/inst/doc/RLMM.pdf vignetteTitles: RLMM Doc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RLMM/inst/doc/RLMM.R dependencyCount: 6 Package: Rmagpie Version: 1.42.0 Depends: R (>= 2.6.1), Biobase (>= 2.5.5) Imports: Biobase (>= 2.5.5), e1071, graphics, grDevices, kernlab, methods, pamr, stats, utils Suggests: xtable License: GPL (>= 3) MD5sum: 501645901da8f0e8eb93917454187e15 NeedsCompilation: no Title: MicroArray Gene-expression-based Program In Error rate estimation Description: Microarray Classification is designed for both biologists and statisticians. It offers the ability to train a classifier on a labelled microarray dataset and to then use that classifier to predict the class of new observations. A range of modern classifiers are available, including support vector machines (SVMs), nearest shrunken centroids (NSCs)... Advanced methods are provided to estimate the predictive error rate and to report the subset of genes which appear essential in discriminating between classes. biocViews: Microarray, Classification Author: Camille Maumet , with contributions from C. Ambroise J. Zhu Maintainer: Camille Maumet URL: http://www.bioconductor.org/ git_url: https://git.bioconductor.org/packages/Rmagpie git_branch: RELEASE_3_10 git_last_commit: e53e505 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Rmagpie_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Rmagpie_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rmagpie_1.42.0.tgz vignettes: vignettes/Rmagpie/inst/doc/Magpie_examples.pdf vignetteTitles: Rmagpie Examples hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rmagpie/inst/doc/Magpie_examples.R dependencyCount: 19 Package: RMassBank Version: 2.14.1 Depends: Rcpp Imports: XML,RCurl,rjson,S4Vectors,digest, rcdk,yaml,mzR,methods,Biobase,MSnbase,httr Suggests: gplots,RMassBankData, xcms (>= 1.37.1), CAMERA, RUnit, enviPat License: Artistic-2.0 MD5sum: d2c06db13247013607796151daf83fed NeedsCompilation: no Title: Workflow to process tandem MS files and build MassBank records Description: Workflow to process tandem MS files and build MassBank records. Functions include automated extraction of tandem MS spectra, formula assignment to tandem MS fragments, recalibration of tandem MS spectra with assigned fragments, spectrum cleanup, automated retrieval of compound information from Internet databases, and export to MassBank records. biocViews: ImmunoOncology, Bioinformatics, MassSpectrometry, Metabolomics, Software Author: Michael Stravs, Emma Schymanski, Steffen Neumann, Erik Mueller, with contributions from Tobias Schulze Maintainer: RMassBank at Eawag SystemRequirements: OpenBabel git_url: https://git.bioconductor.org/packages/RMassBank git_branch: RELEASE_3_10 git_last_commit: cc98f8e git_last_commit_date: 2019-11-18 Date/Publication: 2019-11-18 source.ver: src/contrib/RMassBank_2.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/RMassBank_2.14.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RMassBank_2.14.1.tgz vignettes: vignettes/RMassBank/inst/doc/RMassBank.pdf, vignettes/RMassBank/inst/doc/RMassBankNonstandard.pdf, vignettes/RMassBank/inst/doc/RMassBankXCMS.pdf vignetteTitles: RMassBank walkthrough, RMassBank non-standard usage, RMassBank using XCMS walkthrough hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RMassBank/inst/doc/RMassBank.R, vignettes/RMassBank/inst/doc/RMassBankNonstandard.R, vignettes/RMassBank/inst/doc/RMassBankXCMS.R dependencyCount: 106 Package: rmelting Version: 1.2.0 Depends: R (>= 3.6) Imports: Rdpack, rJava (>= 0.5-0) Suggests: readxl, knitr, rmarkdown, reshape2, pander, testthat License: GPL-2 | GPL-3 Archs: i386, x64 MD5sum: 00208c533d0b09783191137c79ba0d13 NeedsCompilation: no Title: R Interface to MELTING 5 Description: R interface to the MELTING 5 program () to compute melting temperatures of nucleic acid duplexes along with other thermodynamic parameters. biocViews: BiomedicalInformatics, Cheminformatics, Author: J. Aravind [aut, cre] (), G. K. Krishna [aut], Bob Rudis [ctb] (melting5jars), Nicolas Le Novère [ctb] (MELTING 5 Java Library), Marine Dumousseau [ctb] (MELTING 5 Java Library), William John Gowers [ctb] (MELTING 5 Java Library) Maintainer: J. Aravind URL: https://github.com/aravind-j/rmelting, https://aravind-j.github.io/PGRdup/ SystemRequirements: Java VignetteBuilder: knitr BugReports: https://github.com/aravind-j/rmelting/issues git_url: https://git.bioconductor.org/packages/rmelting git_branch: RELEASE_3_10 git_last_commit: a7c397d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rmelting_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rmelting_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rmelting_1.2.0.tgz vignettes: vignettes/rmelting/inst/doc/Tutorial.pdf vignetteTitles: Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 13 Package: RmiR Version: 1.42.0 Depends: R (>= 2.7.0), RmiR.Hs.miRNA, RSVGTipsDevice Imports: DBI, methods, stats Suggests: hgug4112a.db,org.Hs.eg.db License: Artistic-2.0 MD5sum: c889c80547f88f5e0a7f5889fd2447dc NeedsCompilation: no Title: Package to work with miRNAs and miRNA targets with R Description: Useful functions to merge microRNA and respective targets using differents databases biocViews: Software,GeneExpression,Microarray,TimeCourse,Visualization Author: Francesco Favero Maintainer: Francesco Favero git_url: https://git.bioconductor.org/packages/RmiR git_branch: RELEASE_3_10 git_last_commit: 2525ee6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RmiR_1.42.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RmiR_1.42.0.tgz vignettes: vignettes/RmiR/inst/doc/RmiR.pdf vignetteTitles: RmiR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RmiR/inst/doc/RmiR.R dependencyCount: 28 Package: Rmmquant Version: 1.4.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.12.8), methods, S4Vectors, GenomicRanges, SummarizedExperiment, devtools, TBX20BamSubset, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, DESeq2, BiocStyle LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: a68c7bbb0cde127ef5acf2aaac905955 NeedsCompilation: yes Title: RNA-Seq multi-mapping Reads Quantification Tool Description: RNA-Seq is currently used routinely, and it provides accurate information on gene transcription. However, the method cannot accurately estimate duplicated genes expression. Several strategies have been previously used, but all of them provide biased results. With Rmmquant, if a read maps at different positions, the tool detects that the corresponding genes are duplicated; it merges the genes and creates a merged gene. The counts of ambiguous reads is then based on the input genes and the merged genes. Rmmquant is a drop-in replacement of the widely used tools findOverlaps and featureCounts that handles multi-mapping reads in an unabiased way. biocViews: GeneExpression, Transcription Author: Zytnicki Matthias [aut, cre] Maintainer: Zytnicki Matthias SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rmmquant git_branch: RELEASE_3_10 git_last_commit: cebe4af git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Rmmquant_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Rmmquant_1.4.0.zip vignettes: vignettes/Rmmquant/inst/doc/Rmmquant.html vignetteTitles: The Rmmquant package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rmmquant/inst/doc/Rmmquant.R dependencyCount: 174 Package: RNAdecay Version: 1.6.0 Depends: R (>= 3.5) Imports: stats, grDevices, grid, ggplot2, gplots, utils, TMB, nloptr Suggests: parallel, knitr, reshape2, rmarkdown License: GPL-2 MD5sum: ec185baad2837ba15e5572adf7ea4636 NeedsCompilation: yes Title: Maximum Likelihood Decay Modeling of RNA Degradation Data Description: RNA degradation is monitored through measurement of RNA abundance after inhibiting RNA synthesis. This package has functions and example scripts to facilitate (1) data normalization, (2) data modeling using constant decay rate or time-dependent decay rate models, (3) the evaluation of treatment or genotype effects, and (4) plotting of the data and models. Data Normalization: functions and scripts make easy the normalization to the initial (T0) RNA abundance, as well as a method to correct for artificial inflation of Reads per Million (RPM) abundance in global assessments as the total size of the RNA pool decreases. Modeling: Normalized data is then modeled using maximum likelihood to fit parameters. For making treatment or genotype comparisons (up to four), the modeling step models all possible treatment effects on each gene by repeating the modeling with constraints on the model parameters (i.e., the decay rate of treatments A and B are modeled once with them being equal and again allowing them to both vary independently). Model Selection: The AICc value is calculated for each model, and the model with the lowest AICc is chosen. Modeling results of selected models are then compiled into a single data frame. Graphical Plotting: a function is provided to easily visualize the data and the selected model using ggplot2 package functions. biocViews: ImmunoOncology, Software, GeneExpression, GeneRegulation, DifferentialExpression, Transcription, Transcriptomics, TimeCourse, Regression, RNASeq, Normalization, WorkflowStep Author: Reed Sorenson [aut, cre], Katrina Johnson [aut], Frederick Adler [aut], Leslie Sieburth [aut] Maintainer: Reed Sorenson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RNAdecay git_branch: RELEASE_3_10 git_last_commit: 32e147e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RNAdecay_1.6.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RNAdecay_1.6.0.tgz vignettes: vignettes/RNAdecay/inst/doc/RNAdecay_workflow.html vignetteTitles: RNAdecay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAdecay/inst/doc/RNAdecay_workflow.R dependencyCount: 63 Package: RNAinteract Version: 1.34.0 Depends: R (>= 2.12.0), abind, locfit, Biobase Imports: RColorBrewer, ICS, ICSNP, cellHTS2, geneplotter, gplots, grid, hwriter, lattice, latticeExtra, limma, methods, splots (>= 1.13.12) License: Artistic-2.0 MD5sum: 4bbb61057ec4a6710b3283027a1a7235 NeedsCompilation: no Title: Estimate Pairwise Interactions from multidimensional features Description: RNAinteract estimates genetic interactions from multi-dimensional read-outs like features extracted from images. The screen is assumed to be performed in multi-well plates or similar designs. Starting from a list of features (e.g. cell number, area, fluorescence intensity) per well, genetic interactions are estimated. The packages provides functions for reporting interacting gene pairs, plotting heatmaps and double RNAi plots. An HTML report can be written for quality control and analysis. biocViews: ImmunoOncology, CellBasedAssays, QualityControl, Preprocessing, Visualization Author: Bernd Fischer Maintainer: Bernd Fischer git_url: https://git.bioconductor.org/packages/RNAinteract git_branch: RELEASE_3_10 git_last_commit: 4ea90c8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RNAinteract_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RNAinteract_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RNAinteract_1.34.0.tgz vignettes: vignettes/RNAinteract/inst/doc/RNAinteract.pdf vignetteTitles: RNAinteract hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAinteract/inst/doc/RNAinteract.R dependencyCount: 113 Package: RNAither Version: 2.34.0 Depends: R (>= 2.10), topGO, RankProd, prada Imports: geneplotter, limma, biomaRt, car, splots, methods License: Artistic-2.0 MD5sum: be29b5f304687d67719be4bc1642bf6f NeedsCompilation: no Title: Statistical analysis of high-throughput RNAi screens Description: RNAither analyzes cell-based RNAi screens, and includes quality assessment, customizable normalization and statistical tests, leading to lists of significant genes and biological processes. biocViews: CellBasedAssays, QualityControl, Preprocessing, Visualization, Annotation, GO, ImmunoOncology Author: Nora Rieber and Lars Kaderali, University of Heidelberg, Viroquant Research Group Modeling, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany Maintainer: Lars Kaderali git_url: https://git.bioconductor.org/packages/RNAither git_branch: RELEASE_3_10 git_last_commit: 8ce4f87 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RNAither_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RNAither_2.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RNAither_2.34.0.tgz vignettes: vignettes/RNAither/inst/doc/vignetteRNAither.pdf vignetteTitles: RNAither,, an automated pipeline for the statistical analysis of high-throughput RNAi screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAither/inst/doc/vignetteRNAither.R dependencyCount: 114 Package: RNAmodR Version: 1.0.2 Depends: R (>= 3.6), IRanges (>= 2.19.7), S4Vectors (>= 0.23.18), GenomicRanges, Modstrings Imports: methods, stats, graphics, grDevices, grid, matrixStats, assertive, BiocGenerics, XVector, Biostrings, BiocParallel, GenomicFeatures, GenomicAlignments, GenomeInfoDb, rtracklayer, Rsamtools, BSgenome, RColorBrewer, colorRamps, biovizBase, ggplot2, Gviz, reshape2, stringi, stringr, ROCR Suggests: BiocStyle, knitr, rmarkdown, testthat, RNAmodR.Data License: Artistic-2.0 MD5sum: 92673f9960f77533240c4aa1e769f840 NeedsCompilation: no Title: Detection of post-transcriptional modifications in high throughput sequencing data Description: RNAmodR provides classes and workflows for loading/aggregation data from high througput sequencing aimed at detecting post-transcriptional modifications through analysis of specific patterns. In addition, utilities are provided to validate and visualize the results. The RNAmodR package provides a core functionality from which specific analysis strategies can be easily implemented as a seperate package. biocViews: Software, Infrastructure, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR/issues git_url: https://git.bioconductor.org/packages/RNAmodR git_branch: RELEASE_3_10 git_last_commit: 73dfc12 git_last_commit_date: 2020-01-12 Date/Publication: 2020-01-12 source.ver: src/contrib/RNAmodR_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/RNAmodR_1.0.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RNAmodR_1.0.2.tgz vignettes: vignettes/RNAmodR/inst/doc/RNAmodR.creation.html, vignettes/RNAmodR/inst/doc/RNAmodR.html vignetteTitles: RNAmodR - creating new classes for a new detection strategy, RNAmodR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR/inst/doc/RNAmodR.creation.R, vignettes/RNAmodR/inst/doc/RNAmodR.R dependsOnMe: RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq dependencyCount: 170 Package: RNAmodR.AlkAnilineSeq Version: 1.0.0 Depends: R (>= 3.6), RNAmodR Imports: methods, S4Vectors, IRanges, BiocGenerics, GenomicRanges, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, Biostrings, RNAmodR.Data License: Artistic-2.0 Archs: i386, x64 MD5sum: 99075ed93497709905b16161fb5baa39 NeedsCompilation: no Title: Detection of m7G, m3C and D modification by AlkAnilineSeq Description: RNAmodR.AlkAnilineSeq implements the detection of m7G, m3C and D modifications on RNA from experimental data generated with the AlkAnilineSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data. biocViews: Software, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.AlkAnilineSeq VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.AlkAnilineSeq/issues git_url: https://git.bioconductor.org/packages/RNAmodR.AlkAnilineSeq git_branch: RELEASE_3_10 git_last_commit: d60d6ed git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RNAmodR.AlkAnilineSeq_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RNAmodR.AlkAnilineSeq_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RNAmodR.AlkAnilineSeq_1.0.0.tgz vignettes: vignettes/RNAmodR.AlkAnilineSeq/inst/doc/RNAmodR.AlkAnilineSeq.html vignetteTitles: RNAmodR.AlkAnilineSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR.AlkAnilineSeq/inst/doc/RNAmodR.AlkAnilineSeq.R suggestsMe: RNAmodR.ML dependencyCount: 171 Package: RNAmodR.ML Version: 1.0.0 Depends: R (>= 3.6), RNAmodR Imports: methods, assertive, BiocGenerics, S4Vectors, IRanges, GenomicRanges, stats, ranger Suggests: BiocStyle, knitr, rmarkdown, testthat, RNAmodR.Data, RNAmodR.AlkAnilineSeq, GenomicFeatures, Rsamtools, rtracklayer, keras License: Artistic-2.0 MD5sum: 094e60f36d19d517d4e3f838d0eda0db NeedsCompilation: no Title: Detecting patterns of post-transcriptional modifications using machine learning Description: RNAmodR.ML extend the functionality of the RNAmodR package and classical detection strategies towards detection through machine learning models. RNAmodR.ML provides classes, functions and an example workflow to establish a detection stratedy, which can be packaged. biocViews: Software, Infrastructure, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.ML VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.ML/issues git_url: https://git.bioconductor.org/packages/RNAmodR.ML git_branch: RELEASE_3_10 git_last_commit: 16ca853 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RNAmodR.ML_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RNAmodR.ML_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RNAmodR.ML_1.0.0.tgz vignettes: vignettes/RNAmodR.ML/inst/doc/RNAmodR.ML.html vignetteTitles: RNAmodR.ML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR.ML/inst/doc/RNAmodR.ML.R dependencyCount: 173 Package: RNAmodR.RiboMethSeq Version: 1.0.0 Depends: R (>= 3.6), RNAmodR Imports: methods, S4Vectors, BiocGenerics, IRanges, GenomicRanges, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, RNAmodR.Data License: Artistic-2.0 MD5sum: bf63a0fb4a8472f7db2298501cea998b NeedsCompilation: no Title: Detection of 2'-O methylations by RiboMethSeq Description: RNAmodR.RiboMethSeq implements the detection of 2'-O methylations on RNA from experimental data generated with the RiboMethSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data. biocViews: Software, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.RiboMethSeq VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.RiboMethSeq/issues git_url: https://git.bioconductor.org/packages/RNAmodR.RiboMethSeq git_branch: RELEASE_3_10 git_last_commit: e6b2c4d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RNAmodR.RiboMethSeq_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RNAmodR.RiboMethSeq_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RNAmodR.RiboMethSeq_1.0.0.tgz vignettes: vignettes/RNAmodR.RiboMethSeq/inst/doc/RNAmodR.RiboMethSeq.html vignetteTitles: RNAmodR.RiboMethSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR.RiboMethSeq/inst/doc/RNAmodR.RiboMethSeq.R dependencyCount: 171 Package: RNAprobR Version: 1.18.0 Depends: R (>= 3.1.1), GenomicFeatures(>= 1.16.3), plyr(>= 1.8.1), BiocGenerics(>= 0.10.0) Imports: Biostrings(>= 2.32.1), GenomicRanges(>= 1.16.4), IRanges(>= 2.10.5), Rsamtools(>= 1.16.1), rtracklayer(>= 1.24.2), GenomicAlignments(>= 1.5.12), S4Vectors(>= 0.14.7), graphics, stats, utils Suggests: BiocStyle License: GPL (>=2) MD5sum: 04fe4bb4856dec2ac40a90fb5f695579 NeedsCompilation: no Title: An R package for analysis of massive parallel sequencing based RNA structure probing data Description: This package facilitates analysis of Next Generation Sequencing data for which positional information with a single nucleotide resolution is a key. It allows for applying different types of relevant normalizations, data visualization and export in a table or UCSC compatible bedgraph file. biocViews: Coverage, Normalization, Sequencing, GenomeAnnotation Author: Lukasz Jan Kielpinski [aut], Nikos Sidiropoulos [cre, aut], Jeppe Vinther [aut] Maintainer: Nikos Sidiropoulos git_url: https://git.bioconductor.org/packages/RNAprobR git_branch: RELEASE_3_10 git_last_commit: 35a155a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RNAprobR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RNAprobR_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RNAprobR_1.18.0.tgz vignettes: vignettes/RNAprobR/inst/doc/RNAprobR.pdf vignetteTitles: RNAprobR: An R package for analysis of the massive parallel sequencing based methods of RNA structure probing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAprobR/inst/doc/RNAprobR.R dependencyCount: 84 Package: RNAsense Version: 1.0.0 Depends: R (>= 3.6) Imports: ggplot2, parallel, NBPSeq, qvalue, SummarizedExperiment, stats, utils, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 9b5ebc5d1e621ed4face4f8cdbb1dc18 NeedsCompilation: no Title: Analysis of Time-Resolved RNA-Seq Data Description: RNA-sense tool compares RNA-seq time curves in two experimental conditions, i.e. wild-type and mutant, and works in three steps. At Step 1, it builds expression profile for each transcript in one condition (i.e. wild-type) and tests if the transcript abundance grows or decays significantly. Dynamic transcripts are then sorted to non-overlapping groups (time profiles) by the time point of switch up or down. At Step 2, RNA-sense outputs the groups of differentially expressed transcripts, which are up- or downregulated in the mutant compared to the wild-type at each time point. At Step 3, Correlations (Fisher's exact test) between the outputs of Step 1 (switch up- and switch down- time profile groups) and the outputs of Step2 (differentially expressed transcript groups) are calculated. The results of the correlation analysis are printed as two-dimensional color plot, with time profiles and differential expression groups at y- and x-axis, respectively, and facilitates the biological interpretation of the data. biocViews: RNASeq, GeneExpression, DifferentialExpression Author: Marcus Rosenblatt [cre], Gao Meijang [aut], Helge Hass [aut], Daria Onichtchouk [aut] Maintainer: Marcus Rosenblatt VignetteBuilder: knitr BugReports: https://github.com/marcusrosenblatt/RNAsense git_url: https://git.bioconductor.org/packages/RNAsense git_branch: RELEASE_3_10 git_last_commit: dcdd4f1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RNAsense_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RNAsense_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RNAsense_1.0.0.tgz vignettes: vignettes/RNAsense/inst/doc/example.html vignetteTitles: Put the title of your vignette here hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAsense/inst/doc/example.R dependencyCount: 83 Package: rnaseqcomp Version: 1.16.0 Depends: R (>= 3.2.0) Imports: RColorBrewer, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 1ad799545ef103c355375c47898335a9 NeedsCompilation: no Title: Benchmarks for RNA-seq Quantification Pipelines Description: Several quantitative and visualized benchmarks for RNA-seq quantification pipelines. Two-condition quantifications for genes, transcripts, junctions or exons by each pipeline with necessary meta information should be organized into numeric matrices in order to proceed the evaluation. biocViews: RNASeq, Visualization, QualityControl Author: Mingxiang Teng and Rafael A. Irizarry Maintainer: Mingxiang Teng URL: https://github.com/tengmx/rnaseqcomp VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rnaseqcomp git_branch: RELEASE_3_10 git_last_commit: a9367d4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rnaseqcomp_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rnaseqcomp_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rnaseqcomp_1.16.0.tgz vignettes: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.pdf vignetteTitles: The rnaseqcomp user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.R suggestsMe: SummarizedBenchmark dependencyCount: 2 Package: rnaSeqMap Version: 2.44.0 Depends: R (>= 2.11.0), methods, Biobase, Rsamtools, GenomicAlignments Imports: GenomicRanges , IRanges, edgeR, DESeq, DBI License: GPL-2 MD5sum: 90e49fd31c876e1f13978295bffb1416 NeedsCompilation: yes Title: rnaSeq secondary analyses Description: The rnaSeqMap library provides classes and functions to analyze the RNA-sequencing data using the coverage profiles in multiple samples at a time biocViews: ImmunoOncology, Annotation, ReportWriting, Transcription, GeneExpression, DifferentialExpression, Sequencing, RNASeq, SAGE, Visualization Author: Anna Lesniewska ; Michal Okoniewski Maintainer: Michal Okoniewski git_url: https://git.bioconductor.org/packages/rnaSeqMap git_branch: RELEASE_3_10 git_last_commit: 746db82 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rnaSeqMap_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rnaSeqMap_2.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rnaSeqMap_2.44.0.tgz vignettes: vignettes/rnaSeqMap/inst/doc/rnaSeqMap.pdf vignetteTitles: rnaSeqMap primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaSeqMap/inst/doc/rnaSeqMap.R dependencyCount: 64 Package: RNASeqPower Version: 1.26.0 License: LGPL (>=2) MD5sum: 43fcb9fc337d49fe94a448e691d89324 NeedsCompilation: no Title: Sample size for RNAseq studies Description: RNA-seq, sample size biocViews: ImmunoOncology, RNASeq Author: Terry M Therneau [aut, cre], Hart Stephen [ctb] Maintainer: Terry M Therneau git_url: https://git.bioconductor.org/packages/RNASeqPower git_branch: RELEASE_3_10 git_last_commit: d8568b6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RNASeqPower_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RNASeqPower_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RNASeqPower_1.26.0.tgz vignettes: vignettes/RNASeqPower/inst/doc/samplesize.pdf vignetteTitles: RNAseq samplesize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNASeqPower/inst/doc/samplesize.R dependencyCount: 0 Package: RNASeqR Version: 1.4.0 Depends: R(>= 3.5.0), ggplot2, pathview, edgeR, methods Imports: Rsamtools, tools, reticulate, ballgown, gridExtra, rafalib, FactoMineR, factoextra, corrplot, PerformanceAnalytics, reshape2, DESeq2, systemPipeR, systemPipeRdata, clusterProfiler, org.Hs.eg.db, org.Sc.sgd.db, stringr, pheatmap, grDevices, graphics, stats, utils, DOSE, Biostrings, parallel Suggests: knitr, png, grid, RNASeqRData License: Artistic-2.0 MD5sum: 0199c3314bacde786d8f7b4aab860a6f NeedsCompilation: no Title: RNASeqR: an R package for automated two-group RNA-Seq analysis workflow Description: This R package is designed for case-control RNA-Seq analysis (two-group). There are six steps: "RNASeqRParam S4 Object Creation", "Environment Setup", "Quality Assessment", "Reads Alignment & Quantification", "Gene-level Differential Analyses" and "Functional Analyses". Each step corresponds to a function in this package. After running functions in order, a basic RNASeq analysis would be done easily. biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, GeneExpression, GeneSetEnrichment, Alignment, QualityControl, DifferentialExpression, FunctionalPrediction, ExperimentalDesign, GO, KEGG, Visualization, Normalization, Pathways, Clustering, ImmunoOncology Author: Kuan-Hao Chao Maintainer: Kuan-Hao Chao URL: https://github.com/HowardChao/RNASeqR SystemRequirements: RNASeqR only support Linux and macOS. Window is not supported. Python2 is highly recommended. If your machine is Python3, make sure '2to3' command is available. VignetteBuilder: knitr BugReports: https://github.com/HowardChao/RNASeqR/issues git_url: https://git.bioconductor.org/packages/RNASeqR git_branch: RELEASE_3_10 git_last_commit: 74ac5ec git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RNASeqR_1.4.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RNASeqR_1.4.0.tgz vignettes: vignettes/RNASeqR/inst/doc/RNASeqR.html vignetteTitles: RNA-Seq analysis based on one independent variable hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNASeqR/inst/doc/RNASeqR.R dependencyCount: 243 Package: RnaSeqSampleSize Version: 1.17.0 Depends: R (>= 2.10), RnaSeqSampleSizeData Imports: biomaRt,edgeR,heatmap3,matlab,KEGGREST,Rcpp (>= 0.11.2) LinkingTo: Rcpp Suggests: BiocStyle, knitr License: GPL (>= 2) MD5sum: e763581def20707512926731ada15953 NeedsCompilation: yes Title: RnaSeqSampleSize Description: RnaSeqSampleSize package provides a sample size calculation method based on negative binomial model and the exact test for assessing differential expression analysis of RNA-seq data biocViews: ImmunoOncology, ExperimentalDesign, Sequencing, RNASeq, GeneExpression, DifferentialExpression Author: Shilin Zhao, Chung-I Li, Yan Guo, Quanhu Sheng, Yu Shyr Maintainer: Shilin Zhao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RnaSeqSampleSize git_branch: master git_last_commit: 5f49d32 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-02 source.ver: src/contrib/RnaSeqSampleSize_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RnaSeqSampleSize_1.17.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RnaSeqSampleSize_1.17.0.tgz vignettes: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.pdf vignetteTitles: RnaSeqSampleSize: Sample size estimation by real data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.R dependencyCount: 72 Package: RnBeads Version: 2.4.0 Depends: R (>= 3.0.0), BiocGenerics, S4Vectors (>= 0.9.25), GenomicRanges, MASS, cluster, ff, fields, ggplot2 (>= 0.9.2), gplots, gridExtra, limma, matrixStats, methods, illuminaio, methylumi, plyr Imports: IRanges Suggests: Category, GOstats, Gviz, IlluminaHumanMethylation450kmanifest, RPMM, RefFreeEWAS, RnBeads.hg19, XML, annotate, biomaRt, foreach, doParallel, ggbio, isva, mclust, mgcv, minfi, nlme, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, quadprog, rtracklayer, qvalue, sva, wateRmelon, wordcloud, qvalue, argparse, glmnet, GLAD, IlluminaHumanMethylation450kanno.ilmn12.hg19, scales, missMethyl, impute, shiny, shinyjs, plotrix, hexbin, RUnit, MethylSeekR License: GPL-3 MD5sum: 5a1762bd599e9a9cdb8869fea2fd760f NeedsCompilation: no Title: RnBeads Description: RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale. biocViews: DNAMethylation, MethylationArray, MethylSeq, Epigenetics, QualityControl, Preprocessing, BatchEffect, DifferentialMethylation, Sequencing, CpGIsland, ImmunoOncology, TwoChannel, DataImport Author: Yassen Assenov [aut], Pavlo Lutsik [aut], Michael Scherer [aut], Fabian Mueller [aut, cre] Maintainer: Fabian Mueller git_url: https://git.bioconductor.org/packages/RnBeads git_branch: RELEASE_3_10 git_last_commit: 855fc99 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RnBeads_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RnBeads_2.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RnBeads_2.4.0.tgz vignettes: vignettes/RnBeads/inst/doc/RnBeads_Annotations.pdf, vignettes/RnBeads/inst/doc/RnBeads.pdf vignetteTitles: RnBeads Annotation, Comprehensive DNA Methylation Analysis with RnBeads hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RnBeads/inst/doc/RnBeads_Annotations.R, vignettes/RnBeads/inst/doc/RnBeads.R dependencyCount: 162 Package: Rnits Version: 1.20.0 Depends: R (>= 3.6.0), Biobase, ggplot2, limma, methods Imports: affy, boot, impute, splines, graphics, qvalue, reshape2 Suggests: BiocStyle, knitr, GEOquery, stringr License: GPL-3 Archs: i386, x64 MD5sum: 2b16b2621dbdd7aebfaaa18102ca6b4f NeedsCompilation: no Title: R Normalization and Inference of Time Series data Description: R/Bioconductor package for normalization, curve registration and inference in time course gene expression data. biocViews: GeneExpression, Microarray, TimeCourse, DifferentialExpression, Normalization Author: Dipen P. Sangurdekar Maintainer: Dipen P. Sangurdekar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rnits git_branch: RELEASE_3_10 git_last_commit: cb1abf4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Rnits_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Rnits_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rnits_1.20.0.tgz vignettes: vignettes/Rnits/inst/doc/Rnits-vignette.pdf vignetteTitles: R/Bioconductor package for normalization and differential expression inference in time series gene expression microarray data. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rnits/inst/doc/Rnits-vignette.R dependencyCount: 70 Package: roar Version: 1.22.0 Depends: R (>= 3.0.1) Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, GenomicAlignments (>= 0.99.4), rtracklayer, GenomeInfoDb Suggests: RNAseqData.HNRNPC.bam.chr14, testthat License: GPL-3 MD5sum: 848d0082e13711f37afa043360f10618 NeedsCompilation: no Title: Identify differential APA usage from RNA-seq alignments Description: Identify preferential usage of APA sites, comparing two biological conditions, starting from known alternative sites and alignments obtained from standard RNA-seq experiments. biocViews: Sequencing, HighThroughputSequencing, RNAseq, Transcription Author: Elena Grassi Maintainer: Elena Grassi URL: https://github.com/vodkatad/roar/ git_url: https://git.bioconductor.org/packages/roar git_branch: RELEASE_3_10 git_last_commit: b0295cb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/roar_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/roar_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/roar_1.22.0.tgz vignettes: vignettes/roar/inst/doc/roar.pdf vignetteTitles: Identify differential APA usage from RNA-seq alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/roar/inst/doc/roar.R importsMe: XBSeq dependencyCount: 38 Package: ROC Version: 1.62.0 Depends: R (>= 1.9.0), utils, methods Imports: knitr Suggests: Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 5b28401ba4783184338cc56a55e68d2e NeedsCompilation: yes Title: utilities for ROC, with microarray focus Description: Provide utilities for ROC, with microarray focus. biocViews: DifferentialExpression Author: Vince Carey , Henning Redestig for C++ language enhancements Maintainer: Vince Carey URL: http://www.bioconductor.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ROC git_branch: RELEASE_3_10 git_last_commit: 60250fd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ROC_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ROC_1.62.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ROC_1.62.0.tgz vignettes: vignettes/ROC/inst/doc/ROCnotes.html vignetteTitles: Notes on ROC package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: TCC, wateRmelon importsMe: clst suggestsMe: genefilter, MCRestimate dependencyCount: 15 Package: Roleswitch Version: 1.24.0 Depends: R (>= 2.10), pracma, reshape, plotrix, microRNA, biomaRt, Biostrings, Biobase, DBI Suggests: ggplot2 License: GPL-2 MD5sum: ef2d7427ea318f60bdb11c9e01c41c7b NeedsCompilation: no Title: Infer miRNA-mRNA interactions using paired expression data from a single sample Description: Infer Probabilities of MiRNA-mRNA Interaction Signature (ProMISe) using paired expression data from a single sample. Roleswitch operates in two phases by inferring the probability of mRNA (miRNA) being the targets ("targets") of miRNA (mRNA), taking into account the expression of all of the mRNAs (miRNAs) due to their potential competition for the same miRNA (mRNA). Due to dynamic miRNA repression in the cell, Roleswitch assumes that the total transcribed mRNA levels are higher than the observed (equilibrium) mRNA levels and iteratively updates the total transcription of each mRNA targets based on the above inference. NB: in the paper, we used ProMISe as both the model name and inferred score name. biocViews: miRNA Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/roleswitch.html git_url: https://git.bioconductor.org/packages/Roleswitch git_branch: RELEASE_3_10 git_last_commit: dfb087a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Roleswitch_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Roleswitch_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Roleswitch_1.24.0.tgz vignettes: vignettes/Roleswitch/inst/doc/Roleswitch.pdf vignetteTitles: Roleswitch hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Roleswitch/inst/doc/Roleswitch.R importsMe: miRLAB dependencyCount: 66 Package: rols Version: 2.14.0 Depends: methods Imports: httr, progress, jsonlite, utils, Biobase, BiocGenerics (>= 0.23.1) Suggests: GO.db, knitr (>= 1.1.0), BiocStyle (>= 2.5.19), testthat, lubridate, DT, rmarkdown, License: GPL-2 MD5sum: 79c2471c6607349fecc005faaa88809f NeedsCompilation: no Title: An R interface to the Ontology Lookup Service Description: The rols package is an interface to the Ontology Lookup Service (OLS) to access and query hundred of ontolgies directly from R. biocViews: ImmunoOncology, Software, Annotation, MassSpectrometry, GO Author: Laurent Gatto [aut, cre], Tiage Chedraoui Silva [ctb] Maintainer: Laurent Gatto URL: http://lgatto.github.com/rols/ VignetteBuilder: knitr BugReports: https://github.com/lgatto/rols/issues git_url: https://git.bioconductor.org/packages/rols git_branch: RELEASE_3_10 git_last_commit: d505b18 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rols_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rols_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rols_2.14.0.tgz vignettes: vignettes/rols/inst/doc/rols.html vignetteTitles: An R interface to the Ontology Lookup Service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rols/inst/doc/rols.R suggestsMe: MSnbase dependencyCount: 27 Package: ROntoTools Version: 2.14.0 Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz Suggests: RUnit, BiocGenerics License: CC BY-NC-ND 4.0 + file LICENSE MD5sum: 6d6542362afbcbca9e6e1354c8a400f6 NeedsCompilation: no Title: R Onto-Tools suite Description: Suite of tools for functional analysis. biocViews: NetworkAnalysis, Microarray, GraphsAndNetworks Author: Calin Voichita and Sahar Ansari and Sorin Draghici Maintainer: Calin Voichita git_url: https://git.bioconductor.org/packages/ROntoTools git_branch: RELEASE_3_10 git_last_commit: bb84c5d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ROntoTools_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ROntoTools_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ROntoTools_2.14.0.tgz vignettes: vignettes/ROntoTools/inst/doc/rontotools.pdf vignetteTitles: ROntoTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ROntoTools/inst/doc/rontotools.R dependsOnMe: BLMA dependencyCount: 32 Package: ropls Version: 1.18.8 Depends: Biobase Imports: graphics, grDevices, methods, MultiDataSet, stats Suggests: BiocGenerics, BiocStyle, knitr, multtest, omicade4, rmarkdown, testthat License: CeCILL Archs: i386, x64 MD5sum: 0f2dd40be29a743227c3e84dfb50b889 NeedsCompilation: no Title: PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data Description: Latent variable modeling with Principal Component Analysis(PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. Successful applications of these chemometrics techniques include spectroscopic data such as Raman spectroscopy, nuclear magnetic resonance (NMR), mass spectrometry (MS) in metabolomics and proteomics, but also transcriptomics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients). The package can be accessed via a user interface on the Workflow4Metabolomics.org online resource for computational metabolomics (built upon the Galaxy environment). biocViews: Regression, Classification, PrincipalComponent, Transcriptomics, Proteomics, Metabolomics, Lipidomics, MassSpectrometry, ImmunoOncology Author: Etienne A. Thevenot Maintainer: Etienne A. Thevenot URL: http://dx.doi.org/10.1021/acs.jproteome.5b00354 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ropls git_branch: RELEASE_3_10 git_last_commit: 6ba4e6d git_last_commit_date: 2020-01-15 Date/Publication: 2020-01-15 source.ver: src/contrib/ropls_1.18.8.tar.gz win.binary.ver: bin/windows/contrib/3.6/ropls_1.18.8.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ropls_1.18.8.tgz vignettes: vignettes/ropls/inst/doc/ropls-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ropls/inst/doc/ropls-vignette.R dependsOnMe: biosigner importsMe: ASICS, lipidr, proFIA dependencyCount: 82 Package: ROTS Version: 1.14.0 Depends: R (>= 3.3) Imports: Rcpp, stats, Biobase, methods LinkingTo: Rcpp Suggests: testthat License: GPL (>= 2) MD5sum: 22f7eaff52537da6393fb861df5ee442 NeedsCompilation: yes Title: Reproducibility-Optimized Test Statistic Description: Calculates the Reproducibility-Optimized Test Statistic (ROTS) for differential testing in omics data. biocViews: Software, GeneExpression, DifferentialExpression, Microarray, RNASeq, Proteomics, ImmunoOncology Author: Fatemeh Seyednasrollah, Tomi Suomi, Laura L. Elo Maintainer: Tomi Suomi git_url: https://git.bioconductor.org/packages/ROTS git_branch: RELEASE_3_10 git_last_commit: ee51edf git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ROTS_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ROTS_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ROTS_1.14.0.tgz vignettes: vignettes/ROTS/inst/doc/ROTS.pdf vignetteTitles: ROTS: Reproducibility Optimized Test Statistic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROTS/inst/doc/ROTS.R importsMe: PECA, PowerExplorer dependencyCount: 8 Package: RPA Version: 1.42.0 Depends: R (>= 3.1.1), affy, BiocGenerics, methods Imports: phyloseq Suggests: affydata, knitr, parallel License: BSD_2_clause + file LICENSE MD5sum: 38f01be7cb4104887e1d72cc2ed95f36 NeedsCompilation: no Title: RPA: Robust Probabilistic Averaging for probe-level analysis Description: Probabilistic analysis of probe reliability and differential gene expression on short oligonucleotide arrays. biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Leo Lahti [aut, cre] Maintainer: Leo Lahti URL: https://github.com/antagomir/RPA VignetteBuilder: knitr BugReports: https://github.com/antagomir/RPA git_url: https://git.bioconductor.org/packages/RPA git_branch: RELEASE_3_10 git_last_commit: dfec2ce git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RPA_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RPA_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RPA_1.42.0.tgz vignettes: vignettes/RPA/inst/doc/RPA.html vignetteTitles: RPA R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: prebs dependencyCount: 90 Package: RProtoBufLib Version: 1.8.0 License: BSD_3_clause MD5sum: 383768dc0eacc94a6600b265f13d2164 NeedsCompilation: yes Title: C++ headers and static libraries of Protocol buffers Description: This package provides the headers and static library of Protocol buffers 2.6.0 for other R packages to compile and link against. biocViews: Infrastructure Author: Mike Jiang Maintainer: Mike Jiang SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/RProtoBufLib git_branch: RELEASE_3_10 git_last_commit: 8dcdaae git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RProtoBufLib_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RProtoBufLib_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RProtoBufLib_1.8.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE linksToMe: CytoML, flowWorkspace dependencyCount: 0 Package: RpsiXML Version: 2.28.0 Depends: methods, annotate (>= 1.21.0), graph (>= 1.21.0), Biobase, RBGL (>= 1.17.0), XML (>= 2.4.0), hypergraph (>= 1.15.2), AnnotationDbi Suggests: org.Hs.eg.db, org.Mm.eg.db, org.Dm.eg.db, org.Rn.eg.db, org.Sc.sgd.db,hom.Hs.inp.db, hom.Mm.inp.db, hom.Dm.inp.db, hom.Rn.inp.db, hom.Sc.inp.db,Rgraphviz, ppiStats, ScISI License: LGPL-3 MD5sum: ee2a63111e804ab84f5fc710ca2b52e7 NeedsCompilation: no Title: R interface to PSI-MI 2.5 files Description: Queries, data structure and interface to visualization of interaction datasets. This package inplements the PSI-MI 2.5 standard and supports up to now 8 databases. Further databases supporting PSI-MI 2.5 standard will be added continuously. biocViews: Infrastructure, Proteomics Author: Jitao David Zhang, Stefan Wiemann, Marc Carlson, with contributions from Tony Chiang Maintainer: Jitao David Zhang URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/RpsiXML git_branch: RELEASE_3_10 git_last_commit: 91db8ee git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RpsiXML_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RpsiXML_2.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RpsiXML_2.28.0.tgz vignettes: vignettes/RpsiXML/inst/doc/RpsiXML.pdf, vignettes/RpsiXML/inst/doc/RpsiXMLApp.pdf vignetteTitles: Reading PSI-25 XML files, Application Examples of RpsiXML package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RpsiXML/inst/doc/RpsiXML.R, vignettes/RpsiXML/inst/doc/RpsiXMLApp.R dependsOnMe: ScISI importsMe: ScISI dependencyCount: 34 Package: rpx Version: 1.22.0 Depends: methods Imports: xml2, RCurl, utils Suggests: MSnbase, Biostrings, BiocStyle, testthat, knitr License: GPL-2 Archs: i386, x64 MD5sum: 73f0699f7bfe7d68130d0e7ac9af1ae5 NeedsCompilation: no Title: R Interface to the ProteomeXchange Repository Description: The rpx package implements an interface to proteomics data submitted to the ProteomeXchange consortium. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, DataImport, ThirdPartyClient Author: Laurent Gatto Maintainer: Laurent Gatto URL: https://github.com/lgatto/rpx VignetteBuilder: knitr BugReports: https://github.com/lgatto/rpx/issues git_url: https://git.bioconductor.org/packages/rpx git_branch: RELEASE_3_10 git_last_commit: 30bed1b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rpx_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rpx_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rpx_1.22.0.tgz vignettes: vignettes/rpx/inst/doc/rpx.html vignetteTitles: An R interface to the ProteomeXchange repository hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rpx/inst/doc/rpx.R importsMe: MBQN, proteoQC suggestsMe: MSnbase dependencyCount: 5 Package: Rqc Version: 1.20.0 Depends: BiocParallel, ShortRead, ggplot2 Imports: BiocGenerics (>= 0.25.1), Biostrings, IRanges, methods, S4Vectors, knitr (>= 1.7), BiocStyle, plyr, markdown, grid, reshape2, Rcpp (>= 0.11.6), biovizBase, shiny, Rsamtools, GenomicAlignments, GenomicFiles LinkingTo: Rcpp Suggests: testthat License: GPL (>= 2) MD5sum: eb8a15465ae50d331a7a10758be2c0a8 NeedsCompilation: yes Title: Quality Control Tool for High-Throughput Sequencing Data Description: Rqc is an optimised tool designed for quality control and assessment of high-throughput sequencing data. It performs parallel processing of entire files and produces a report which contains a set of high-resolution graphics. biocViews: Sequencing, QualityControl, DataImport Author: Welliton Souza, Benilton Carvalho Maintainer: Welliton Souza URL: https://github.com/labbcb/Rqc VignetteBuilder: knitr BugReports: https://github.com/labbcb/Rqc/issues git_url: https://git.bioconductor.org/packages/Rqc git_branch: RELEASE_3_10 git_last_commit: dd3ec8b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Rqc_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Rqc_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rqc_1.20.0.tgz vignettes: vignettes/Rqc/inst/doc/Rqc.html vignetteTitles: Using Rqc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rqc/inst/doc/Rqc.R dependencyCount: 159 Package: rqt Version: 1.12.0 Depends: R (>= 3.4), SummarizedExperiment Imports: stats,Matrix,ropls,methods,car,RUnit,metap,CompQuadForm,glmnet,utils,pls Suggests: BiocStyle, knitr, rmarkdown License: GPL Archs: i386, x64 MD5sum: 626011fe29a7be27c5f72ee18dd8d483 NeedsCompilation: no Title: rqt: utilities for gene-level meta-analysis Description: Despite the recent advances of modern GWAS methods, it still remains an important problem of addressing calculation an effect size and corresponding p-value for the whole gene rather than for single variant. The R- package rqt offers gene-level GWAS meta-analysis. For more information, see: "Gene-set association tests for next-generation sequencing data" by Lee et al (2016), Bioinformatics, 32(17), i611-i619, . biocViews: GenomeWideAssociation, Regression, Survival, PrincipalComponent, StatisticalMethod, Sequencing Author: I. Y. Zhbannikov, K. G. Arbeev, A. I. Yashin. Maintainer: Ilya Y. Zhbannikov URL: https://github.com/izhbannikov/rqt VignetteBuilder: knitr BugReports: https://github.com/izhbannikov/rqt/issues git_url: https://git.bioconductor.org/packages/rqt git_branch: RELEASE_3_10 git_last_commit: 49586e4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rqt_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rqt_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rqt_1.12.0.tgz vignettes: vignettes/rqt/inst/doc/rqt-vignette.html vignetteTitles: Tutorial for rqt package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rqt/inst/doc/rqt-vignette.R dependencyCount: 143 Package: rqubic Version: 1.32.0 Imports: methods, Biobase, BiocGenerics, biclust Suggests: RColorBrewer License: GPL-2 MD5sum: 89aa7859402845a0419baf51bf079d29 NeedsCompilation: yes Title: Qualitative biclustering algorithm for expression data analysis in R Description: This package implements the QUBIC algorithm introduced by Li et al. for the qualitative biclustering with gene expression data. biocViews: Microarray, Clustering Author: Jitao David Zhang, with inputs from Laura Badi and Martin Ebeling Maintainer: Jitao David Zhang git_url: https://git.bioconductor.org/packages/rqubic git_branch: RELEASE_3_10 git_last_commit: 5eeae51 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rqubic_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rqubic_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rqubic_1.32.0.tgz vignettes: vignettes/rqubic/inst/doc/rqubic.pdf vignetteTitles: Qualitative Biclustering with Bioconductor Package rqubic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rqubic/inst/doc/rqubic.R importsMe: miRSM dependencyCount: 70 Package: rRDP Version: 1.20.0 Depends: Biostrings (>= 2.26.2) Suggests: rRDPData License: GPL-2 | file LICENSE MD5sum: c8d7d5e1d0b5df5775b681eaa247a81e NeedsCompilation: no Title: Interface to the RDP Classifier Description: Seamlessly interfaces RDP classifier (version 2.9). biocViews: Genetics, Sequencing, Infrastructure, Classification, Microbiome, ImmunoOncology Author: Michael Hahsler, Anurag Nagar Maintainer: Michael Hahsler SystemRequirements: Java git_url: https://git.bioconductor.org/packages/rRDP git_branch: RELEASE_3_10 git_last_commit: ad150a8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rRDP_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rRDP_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rRDP_1.20.0.tgz vignettes: vignettes/rRDP/inst/doc/rRDP.pdf vignetteTitles: rRDP: Interface to the RDP Classifier hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rRDP/inst/doc/rRDP.R dependencyCount: 12 Package: RRHO Version: 1.26.0 Depends: R (>= 2.10), grid Imports: VennDiagram Suggests: lattice License: GPL-2 Archs: i386, x64 MD5sum: c87fd5dd9746ac2b9bcb743df7435e4f NeedsCompilation: no Title: Inference on agreement between ordered lists Description: The package is aimed at inference on the amount of agreement in two sorted lists using the Rank-Rank Hypergeometric Overlap test. biocViews: Genetics, SequenceMatching, Microarray, Transcription Author: Jonathan Rosenblatt and Jason Stein Maintainer: Jonathan Rosenblatt git_url: https://git.bioconductor.org/packages/RRHO git_branch: RELEASE_3_10 git_last_commit: 2313afd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RRHO_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RRHO_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RRHO_1.26.0.tgz vignettes: vignettes/RRHO/inst/doc/RRHO.pdf vignetteTitles: RRHO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RRHO/inst/doc/RRHO.R dependencyCount: 7 Package: Rsamtools Version: 2.2.3 Depends: methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.31.8), Biostrings (>= 2.47.6) Imports: utils, BiocGenerics (>= 0.25.1), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), XVector (>= 0.19.7), zlibbioc, bitops, BiocParallel LinkingTo: Rhtslib (>= 1.17.7), S4Vectors, IRanges, XVector, Biostrings Suggests: GenomicAlignments, ShortRead (>= 1.19.10), GenomicFeatures, TxDb.Dmelanogaster.UCSC.dm3.ensGene, KEGG.db, TxDb.Hsapiens.UCSC.hg18.knownGene, RNAseqData.HNRNPC.bam.chr14, BSgenome.Hsapiens.UCSC.hg19, RUnit, BiocStyle License: Artistic-2.0 | file LICENSE MD5sum: 6a6dac898ab4664214d346be0c755ec5 NeedsCompilation: yes Title: Binary alignment (BAM), FASTA, variant call (BCF), and tabix file import Description: This package provides an interface to the 'samtools', 'bcftools', and 'tabix' utilities for manipulating SAM (Sequence Alignment / Map), FASTA, binary variant call (BCF) and compressed indexed tab-delimited (tabix) files. biocViews: DataImport, Sequencing, Coverage, Alignment, QualityControl Author: Martin Morgan, Herv\'e Pag\`es, Valerie Obenchain, Nathaniel Hayden Maintainer: Bioconductor Package Maintainer URL: http://bioconductor.org/packages/Rsamtools SystemRequirements: GNU make Video: https://www.youtube.com/watch?v=Rfon-DQYbWA&list=UUqaMSQd_h-2EDGsU6WDiX0Q git_url: https://git.bioconductor.org/packages/Rsamtools git_branch: RELEASE_3_10 git_last_commit: 073d892 git_last_commit_date: 2020-02-22 Date/Publication: 2020-02-23 source.ver: src/contrib/Rsamtools_2.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/Rsamtools_2.2.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rsamtools_2.2.3.tgz vignettes: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.pdf, vignettes/Rsamtools/inst/doc/Rsamtools-UsingCLibraries.pdf vignetteTitles: An introduction to Rsamtools, Using samtools C libraries hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.R, vignettes/Rsamtools/inst/doc/Rsamtools-UsingCLibraries.R dependsOnMe: ArrayExpressHTS, BitSeq, chimera, CODEX, contiBAIT, CoverageView, esATAC, exomeCopy, GenomicAlignments, GenomicFiles, girafe, gmapR, HelloRanges, IntEREst, MEDIPS, methylPipe, MMDiff2, podkat, r3Cseq, Rcade, RepViz, ReQON, rfPred, RIPSeeker, rnaSeqMap, SGSeq, ShortRead, SICtools, SNPhood, systemPipeR, TarSeqQC, TEQC, VariantAnnotation, wavClusteR importsMe: AllelicImbalance, alpine, AneuFinder, annmap, AnnotationHubData, appreci8R, ArrayExpressHTS, ASpediaFI, ASpli, ATACseqQC, BadRegionFinder, BBCAnalyzer, biovizBase, biscuiteer, breakpointR, BSgenome, CAGEr, casper, cellbaseR, CexoR, chimeraviz, ChIPexoQual, ChIPpeakAnno, ChIPQC, ChIPSeqSpike, chromstaR, chromVAR, cn.mops, CNVfilteR, CNVPanelizer, CNVrd2, compEpiTools, consensusDE, CopyNumberPlots, CopywriteR, CrispRVariants, csaw, customProDB, derfinder, DEXSeq, DiffBind, diffHic, easyRNASeq, EDASeq, ensembldb, epigenomix, eudysbiome, FourCSeq, FunChIP, FunciSNP, gcapc, GeneGeneInteR, GenoGAM, genomation, GenomicAlignments, GenomicInteractions, GenVisR, ggbio, GGtools, GOTHiC, GreyListChIP, GUIDEseq, Gviz, gwascat, h5vc, HTSeqGenie, icetea, IMAS, INSPEcT, karyoploteR, ldblock, MACPET, MADSEQ, MDTS, metagene, metagene2, methylKit, MMAPPR2, mosaics, motifmatchr, msgbsR, MTseeker, NADfinder, ngsReports, nucleR, ORFik, panelcn.mops, PGA, PICS, plyranges, pram, PureCN, QDNAseq, qsea, QuasR, R453Plus1Toolbox, ramwas, Rariant, Repitools, RiboProfiling, riboSeqR, RNAmodR, RNAprobR, RNASeqR, Rqc, rtracklayer, scruff, segmentSeq, seqplots, seqsetvis, soGGi, SplicingGraphs, srnadiff, strandCheckR, TCseq, TFutils, tracktables, trackViewer, transcriptR, tRNAscanImport, TSRchitect, TVTB, VariantFiltering, VariantTools, VCFArray suggestsMe: AnnotationHub, APAlyzer, bamsignals, BaseSpaceR, BiocGenerics, BiocParallel, biomvRCNS, Chicago, epivizrChart, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, gQTLstats, IRanges, metaseqR, omicsPrint, profileplyr, recoup, RNAmodR.ML, SeqArray, seqbias, SigFuge, similaRpeak, Streamer, TFutils dependencyCount: 25 Package: rsbml Version: 2.44.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils Imports: BiocGenerics, graph, utils License: Artistic-2.0 Archs: i386, x64 MD5sum: 92a38a0be748f0ed7dfb6342301dcff2 NeedsCompilation: yes Title: R support for SBML, using libsbml Description: Links R to libsbml for SBML parsing, validating output, provides an S4 SBML DOM, converts SBML to R graph objects. Optionally links to the SBML ODE Solver Library (SOSLib) for simulating models. biocViews: GraphAndNetwork, Pathways, Network Author: Michael Lawrence Maintainer: Michael Lawrence URL: http://www.sbml.org SystemRequirements: libsbml (==5.10.2) git_url: https://git.bioconductor.org/packages/rsbml git_branch: RELEASE_3_10 git_last_commit: 6e45be1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rsbml_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rsbml_2.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rsbml_2.44.0.tgz vignettes: vignettes/rsbml/inst/doc/quick-start.pdf vignetteTitles: Quick start for rsbml hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rsbml/inst/doc/quick-start.R dependsOnMe: BiGGR suggestsMe: piano, SBMLR dependencyCount: 8 Package: rScudo Version: 1.2.0 Depends: R (>= 3.6) Imports: methods, stats, igraph, stringr, grDevices, Biobase, S4Vectors, SummarizedExperiment, BiocGenerics Suggests: testthat, BiocStyle, knitr, rmarkdown, ALL, RCy3, caret, e1071, parallel, doParallel License: GPL-3 MD5sum: ddf6c0573187b986219a36a0c8892955 NeedsCompilation: no Title: Signature-based Clustering for Diagnostic Purposes Description: SCUDO (Signature-based Clustering for Diagnostic Purposes) is a rank-based method for the analysis of gene expression profiles for diagnostic and classification purposes. It is based on the identification of sample-specific gene signatures composed of the most up- and down-regulated genes for that sample. Starting from gene expression data, functions in this package identify sample-specific gene signatures and use them to build a graph of samples. In this graph samples are joined by edges if they have a similar expression profile, according to a pre-computed similarity matrix. The similarity between the expression profiles of two samples is computed using a method similar to GSEA. The graph of samples can then be used to perform community clustering or to perform supervised classification of samples in a testing set. biocViews: GeneExpression, DifferentialExpression, BiomedicalInformatics, Classification, Clustering, GraphAndNetwork, Network, Proteomics, Transcriptomics, SystemsBiology, FeatureExtraction Author: Matteo Ciciani [aut, cre], Thomas Cantore [aut], Enrica Colasurdo [ctb], Mario Lauria [ctb] Maintainer: Matteo Ciciani URL: https://github.com/Matteo-Ciciani/scudo VignetteBuilder: knitr BugReports: https://github.com/Matteo-Ciciani/scudo/issues git_url: https://git.bioconductor.org/packages/rScudo git_branch: RELEASE_3_10 git_last_commit: dc9f0db git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rScudo_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rScudo_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rScudo_1.2.0.tgz vignettes: vignettes/rScudo/inst/doc/rScudo-vignette.pdf vignetteTitles: Signature-based Clustering for Diagnostic Purposes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rScudo/inst/doc/rScudo-vignette.R dependencyCount: 38 Package: RSeqAn Version: 1.6.0 Imports: Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: 6e26c43d876e39b42f38fb53244b449d NeedsCompilation: yes Title: R SeqAn Description: Headers and some wrapper functions from the SeqAn C++ library for ease of usage in R. biocViews: Infrastructure, Software Author: August Guang [aut, cre] Maintainer: August Guang VignetteBuilder: knitr BugReports: https://github.com/compbiocore/RSeqAn/issues git_url: https://git.bioconductor.org/packages/RSeqAn git_branch: RELEASE_3_10 git_last_commit: de2b9d9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RSeqAn_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RSeqAn_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RSeqAn_1.6.0.tgz vignettes: vignettes/RSeqAn/inst/doc/first_example.html vignetteTitles: Introduction to Using RSeqAn hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RSeqAn/inst/doc/first_example.R importsMe: qckitfastq linksToMe: qckitfastq dependencyCount: 3 Package: Rsubread Version: 2.0.1 Imports: grDevices, stats, utils License: GPL-3 Archs: i386, x64 MD5sum: 8afb5100b52d8416f421e2ae2c4161c0 NeedsCompilation: yes Title: Subread Sequence Alignment and Counting for R Description: Alignment, quantification and analysis of second and third generation sequencing data. Includes functionality for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery. Can be applied to all major sequencing techologies and to both short and long sequence reads. biocViews: Sequencing, Alignment, SequenceMatching, RNASeq, ChIPSeq, SingleCell, GeneExpression, GeneRegulation, Genetics, ImmunoOncology, SNP, GeneticVariability, Preprocessing, QualityControl, GenomeAnnotation, GeneFusionDetection, IndelDetection, VariantAnnotation, VariantDetection, MultipleSequenceAlignment Author: Wei Shi, Yang Liao and Gordon K Smyth with contributions from Jenny Dai Maintainer: Wei Shi , Yang Liao and Gordon K Smyth URL: http://bioconductor.org/packages/Rsubread git_url: https://git.bioconductor.org/packages/Rsubread git_branch: RELEASE_3_10 git_last_commit: c205772 git_last_commit_date: 2020-01-27 Date/Publication: 2020-01-27 source.ver: src/contrib/Rsubread_2.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/Rsubread_2.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rsubread_2.0.1.tgz vignettes: vignettes/Rsubread/inst/doc/Rsubread.pdf, vignettes/Rsubread/inst/doc/SubreadUsersGuide.pdf vignetteTitles: Rsubread Vignette, SubreadUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rsubread/inst/doc/Rsubread.R dependsOnMe: ExCluster importsMe: APAlyzer, dupRadar suggestsMe: icetea, scPipe, scruff, singleCellTK dependencyCount: 3 Package: RSVSim Version: 1.26.0 Depends: R (>= 3.0.0), Biostrings, GenomicRanges Imports: methods, IRanges, ShortRead Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, MASS, rtracklayer License: LGPL-3 Archs: i386, x64 MD5sum: 171b33e22f5054afb09d7dac1cf68c13 NeedsCompilation: no Title: RSVSim: an R/Bioconductor package for the simulation of structural variations Description: RSVSim is a package for the simulation of deletions, insertions, inversion, tandem-duplications and translocations of various sizes in any genome available as FASTA-file or BSgenome data package. SV breakpoints can be placed uniformly accross the whole genome, with a bias towards repeat regions and regions of high homology (for hg19) or at user-supplied coordinates. biocViews: Sequencing Author: Christoph Bartenhagen Maintainer: Christoph Bartenhagen git_url: https://git.bioconductor.org/packages/RSVSim git_branch: RELEASE_3_10 git_last_commit: b036186 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RSVSim_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RSVSim_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RSVSim_1.26.0.tgz vignettes: vignettes/RSVSim/inst/doc/vignette.pdf vignetteTitles: RSVSim: an R/Bioconductor package for the simulation of structural variations hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RSVSim/inst/doc/vignette.R dependencyCount: 42 Package: rTANDEM Version: 1.26.0 Depends: XML, Rcpp, data.table (>= 1.8.8) Imports: methods LinkingTo: Rcpp Suggests: biomaRt License: Artistic-1.0 | file LICENSE MD5sum: 573c8fe9051d5a225a60865fbb28abe0 NeedsCompilation: yes Title: Interfaces the tandem protein identification algorithm in R Description: This package interfaces the tandem protein identification algorithm in R. Identification can be launched in the X!Tandem style, by using as sole parameter the path to a parameter file. But rTANDEM aslo provides extended syntax and functions to streamline launching analyses, as well as function to convert results, parameters and taxonomy to/from R. A related package, shinyTANDEM, provides visualization interface for result objects. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Frederic Fournier , Charles Joly Beauparlant , Rene Paradis , Arnaud Droit Maintainer: Frederic Fournier SystemRequirements: rTANDEM uses expat and pthread libraries. See the README file for details. git_url: https://git.bioconductor.org/packages/rTANDEM git_branch: RELEASE_3_10 git_last_commit: 5103055 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rTANDEM_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rTANDEM_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rTANDEM_1.26.0.tgz vignettes: vignettes/rTANDEM/inst/doc/rTANDEM.pdf vignetteTitles: The rTANDEM users guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rTANDEM/inst/doc/rTANDEM.R dependsOnMe: PGA, shinyTANDEM importsMe: proteoQC dependencyCount: 5 Package: RTCA Version: 1.38.0 Depends: methods,stats,graphics,Biobase,RColorBrewer, gtools Suggests: xtable License: LGPL-3 MD5sum: d9bd56a1db07deb1030059fb36e399b6 NeedsCompilation: no Title: Open-source toolkit to analyse data from xCELLigence System (RTCA) Description: Import, analyze and visualize data from Roche(R) xCELLigence RTCA systems. The package imports real-time cell electrical impedance data into R. As an alternative to commercial software shipped along the system, the Bioconductor package RTCA provides several unique transformation (normalization) strategies and various visualization tools. biocViews: ImmunoOncology, CellBasedAssays, Infrastructure, Visualization, TimeCourse Author: Jitao David Zhang Maintainer: Jitao David Zhang URL: http://code.google.com/p/xcelligence/,http://www.xcelligence.roche.com/,http://www.nextbiomotif.com/Home/scientific-programming git_url: https://git.bioconductor.org/packages/RTCA git_branch: RELEASE_3_10 git_last_commit: da8ffe5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RTCA_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RTCA_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RTCA_1.38.0.tgz vignettes: vignettes/RTCA/inst/doc/aboutRTCA.pdf, vignettes/RTCA/inst/doc/RTCAtransformation.pdf vignetteTitles: Introduction to Data Analysis of the Roche xCELLigence System with RTCA Package, RTCAtransformation: Discussion of transformation methods of RTCA data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTCA/inst/doc/aboutRTCA.R, vignettes/RTCA/inst/doc/RTCAtransformation.R dependencyCount: 9 Package: RTCGA Version: 1.16.0 Depends: R (>= 3.3.0) Imports: XML, assertthat, stringi, rvest, data.table, xml2, dplyr, purrr, survival, survminer, ggplot2, ggthemes, viridis, knitr, scales Suggests: devtools, testthat, pander, Biobase, GenomicRanges, IRanges, S4Vectors, RTCGA.rnaseq, RTCGA.clinical, RTCGA.mutations, RTCGA.RPPA, RTCGA.mRNA, RTCGA.miRNASeq, RTCGA.methylation, RTCGA.CNV, RTCGA.PANCAN12, magrittr, tidyr License: GPL-2 Archs: i386, x64 MD5sum: fe505ecc8f9699c5765abef812fd6f4d NeedsCompilation: no Title: The Cancer Genome Atlas Data Integration Description: The Cancer Genome Atlas (TCGA) Data Portal provides a platform for researchers to search, download, and analyze data sets generated by TCGA. It contains clinical information, genomic characterization data, and high level sequence analysis of the tumor genomes. The key is to understand genomics to improve cancer care. RTCGA package offers download and integration of the variety and volume of TCGA data using patient barcode key, what enables easier data possession. This may have an benefcial infuence on impact on development of science and improvement of patients' treatment. Furthermore, RTCGA package transforms TCGA data to tidy form which is convenient to use. biocViews: ImmunoOncology, Software, DataImport, DataRepresentation, Preprocessing, RNASeq Author: Marcin Kosinski , Przemyslaw Biecek Maintainer: Marcin Kosinski URL: https://rtcga.github.io/RTCGA VignetteBuilder: knitr BugReports: https://github.com/RTCGA/RTCGA/issues git_url: https://git.bioconductor.org/packages/RTCGA git_branch: RELEASE_3_10 git_last_commit: 1ec6cf6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RTCGA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RTCGA_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RTCGA_1.16.0.tgz vignettes: vignettes/RTCGA/inst/doc/RTCGA_Workflow.html vignetteTitles: Integrating TCGA Data - RTCGA Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 102 Package: RTCGAToolbox Version: 2.16.3 Depends: R (>= 3.5.0) Imports: BiocGenerics, data.table, DelayedArray, GenomicRanges, GenomeInfoDb, httr, IRanges, limma, methods, RaggedExperiment, RCircos, RCurl, RJSONIO, S4Vectors (>= 0.23.10), stats, stringr, SummarizedExperiment, survival, TCGAutils, XML Suggests: BiocStyle, Homo.sapiens, knitr, readr, rmarkdown License: file LICENSE MD5sum: fd84c3f6c03ab888dc027a9004df44e6 NeedsCompilation: no Title: A new tool for exporting TCGA Firehose data Description: Managing data from large scale projects such as The Cancer Genome Atlas (TCGA) for further analysis is an important and time consuming step for research projects. Several efforts, such as Firehose project, make TCGA pre-processed data publicly available via web services and data portals but it requires managing, downloading and preparing the data for following steps. We developed an open source and extensible R based data client for Firehose pre-processed data and demonstrated its use with sample case studies. Results showed that RTCGAToolbox could improve data management for researchers who are interested with TCGA data. In addition, it can be integrated with other analysis pipelines for following data analysis. biocViews: DifferentialExpression, GeneExpression, Sequencing Author: Mehmet Samur [aut], Marcel Ramos [aut, cre], Ludwig Geistlinger [ctb] Maintainer: Marcel Ramos URL: http://mksamur.github.io/RTCGAToolbox/ VignetteBuilder: knitr BugReports: https://github.com/mksamur/RTCGAToolbox/issues git_url: https://git.bioconductor.org/packages/RTCGAToolbox git_branch: RELEASE_3_10 git_last_commit: 093edfc git_last_commit_date: 2020-04-09 Date/Publication: 2020-04-09 source.ver: src/contrib/RTCGAToolbox_2.16.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/RTCGAToolbox_2.16.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RTCGAToolbox_2.16.3.tgz vignettes: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.html vignetteTitles: RTCGAToolbox Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.R importsMe: CVE suggestsMe: TCGAutils dependencyCount: 99 Package: RTN Version: 2.10.1 Depends: R (>= 3.5.0), methods Imports: RedeR, minet, viper, mixtools, snow, limma, data.table, IRanges, igraph, S4Vectors, SummarizedExperiment Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 4a22018e77be4cd80def09eafe23f45b NeedsCompilation: no Title: RTN: Reconstruction of Transcriptional regulatory Networks and analysis of regulons Description: A transcriptional regulatory network (TRN) consists of a collection of transcription factors (TFs) and the regulated target genes. TFs are regulators that recognize specific DNA sequences and guide the expression of the genome, either activating or repressing the expression the target genes. The set of genes controlled by the same TF forms a regulon. This package provides classes and methods for the reconstruction of TRNs and analysis of regulons. biocViews: Transcription, Network, NetworkInference, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork, GeneSetEnrichment, GeneticVariability Author: Clarice Groeneveld [ctb], Gordon Robertson [ctb], Xin Wang [aut], Michael Fletcher [aut], Florian Markowetz [aut], Kerstin Meyer [aut], and Mauro Castro [aut] Maintainer: Mauro Castro URL: http://dx.doi.org/10.1038/ncomms3464 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTN git_branch: RELEASE_3_10 git_last_commit: 27b801c git_last_commit_date: 2019-11-16 Date/Publication: 2019-11-16 source.ver: src/contrib/RTN_2.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/RTN_2.10.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RTN_2.10.1.tgz vignettes: vignettes/RTN/inst/doc/RTN.html vignetteTitles: "RTN: reconstruction of transcriptional regulatory networks and analysis of regulons."" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTN/inst/doc/RTN.R dependsOnMe: RTNduals, RTNsurvival suggestsMe: geneplast dependencyCount: 50 Package: RTNduals Version: 1.10.0 Depends: R(>= 3.5), RTN(>= 2.6.3), methods Imports: graphics, grDevices, stats, utils Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 3d02e8265526e990b204b455a9ac5ea8 NeedsCompilation: no Title: Analysis of co-regulation and inference of 'dual regulons' Description: RTNduals is a tool that searches for possible co-regulatory loops between regulon pairs generated by the RTN package. It compares the shared targets in order to infer 'dual regulons', a new concept that tests whether regulators can co-operate or compete in influencing targets. biocViews: GeneRegulation, GeneExpression, NetworkEnrichment, NetworkInference, GraphAndNetwork Author: Vinicius S. Chagas, Clarice S. Groeneveld, Gordon Robertson, Kerstin B. Meyer, Mauro A. A. Castro Maintainer: Mauro Castro , Clarice Groeneveld VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTNduals git_branch: RELEASE_3_10 git_last_commit: 83480dc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RTNduals_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RTNduals_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RTNduals_1.10.0.tgz vignettes: vignettes/RTNduals/inst/doc/RTNduals.html vignetteTitles: "RTNduals: analysis of co-regulation and inference of dual regulons." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTNduals/inst/doc/RTNduals.R dependsOnMe: RTNsurvival dependencyCount: 51 Package: RTNsurvival Version: 1.10.0 Depends: R(>= 3.5), RTN(>= 2.6.3), RTNduals(>= 1.6.1), methods Imports: survival, RColorBrewer, grDevices, graphics, stats, utils, scales, data.table, egg, ggplot2, pheatmap, dunn.test Suggests: Fletcher2013b, knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: c2a48cc127b6423e3a95f989117a1438 NeedsCompilation: no Title: Survival analysis using transcriptional networks inferred by the RTN package Description: RTNsurvival is a tool for integrating regulons generated by the RTN package with survival information. For a given regulon, the 2-tailed GSEA approach computes a differential Enrichment Score (dES) for each individual sample, and the dES distribution of all samples is then used to assess the survival statistics for the cohort. There are two main survival analysis workflows: a Cox Proportional Hazards approach used to model regulons as predictors of survival time, and a Kaplan-Meier analysis assessing the stratification of a cohort based on the regulon activity. All plots can be fine-tuned to the user's specifications. biocViews: NetworkEnrichment, Survival, GeneRegulation, GeneSetEnrichment, NetworkInference, GraphAndNetwork Author: Clarice S. Groeneveld, Vinicius S. Chagas, Mauro A. A. Castro Maintainer: Clarice Groeneveld , Mauro A. A. Castro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTNsurvival git_branch: RELEASE_3_10 git_last_commit: cbc46a0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RTNsurvival_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RTNsurvival_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RTNsurvival_1.10.0.tgz vignettes: vignettes/RTNsurvival/inst/doc/RTNsurvival.html vignetteTitles: "RTNsurvival: multivariate survival analysis using transcriptional networks and regulons." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTNsurvival/inst/doc/RTNsurvival.R dependencyCount: 97 Package: RTopper Version: 1.32.0 Depends: R (>= 2.11.0), Biobase Imports: limma, multtest Suggests: limma, org.Hs.eg.db, KEGG.db, GO.db License: GPL (>= 3) MD5sum: 7ec635114c55836d4b0ceaa16ad95230 NeedsCompilation: no Title: This package is designed to perform Gene Set Analysis across multiple genomic platforms Description: the RTopper package is designed to perform and integrate gene set enrichment results across multiple genomic platforms. biocViews: Microarray Author: Luigi Marchionni , Svitlana Tyekucheva Maintainer: Luigi Marchionni git_url: https://git.bioconductor.org/packages/RTopper git_branch: RELEASE_3_10 git_last_commit: 5c564e2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RTopper_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RTopper_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RTopper_1.32.0.tgz vignettes: vignettes/RTopper/inst/doc/RTopper.pdf vignetteTitles: RTopper user's manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RTopper/inst/doc/RTopper.R dependencyCount: 17 Package: rtracklayer Version: 1.46.0 Depends: R (>= 3.3), methods, GenomicRanges (>= 1.37.2) Imports: XML (>= 1.98-0), BiocGenerics (>= 0.25.1), S4Vectors (>= 0.23.18), IRanges (>= 2.13.13), XVector (>= 0.19.7), GenomeInfoDb (>= 1.15.2), Biostrings (>= 2.47.6), zlibbioc, RCurl (>= 1.4-2), Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6), tools LinkingTo: S4Vectors, IRanges, XVector Suggests: BSgenome (>= 1.33.4), humanStemCell, microRNA (>= 1.1.1), genefilter, limma, org.Hs.eg.db, hgu133plus2.db, GenomicFeatures, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit License: Artistic-2.0 + file LICENSE MD5sum: b78bb0152cb6a62c7a8ca4489537dd24 NeedsCompilation: yes Title: R interface to genome annotation files and the UCSC genome browser Description: Extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/import tracks to/from the supported browsers, as well as query and modify the browser state, such as the current viewport. biocViews: Annotation,Visualization,DataImport Author: Michael Lawrence, Vince Carey, Robert Gentleman Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/rtracklayer git_branch: RELEASE_3_10 git_last_commit: e9404ff git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rtracklayer_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rtracklayer_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rtracklayer_1.46.0.tgz vignettes: vignettes/rtracklayer/inst/doc/rtracklayer.pdf vignetteTitles: rtracklayer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/rtracklayer/inst/doc/rtracklayer.R dependsOnMe: BSgenome, CAGEfightR, ChIPSeqSpike, CoverageView, cummeRbund, ExCluster, geneXtendeR, GenomicFiles, groHMM, HelloRanges, LoomExperiment, MethylSeekR, r3Cseq, RIPSeeker, StructuralVariantAnnotation importsMe: ALPS, AnnotationHubData, annotatr, ASpediaFI, ATACseqQC, ballgown, BiocSet, biscuiteer, BiSeq, branchpointer, BSgenome, CAGEr, casper, CexoR, chipenrich, ChIPpeakAnno, ChIPseeker, ChromHeatMap, chromswitch, circRNAprofiler, CNEr, coMET, CompGO, consensusSeekeR, contiBAIT, conumee, customProDB, DeepBlueR, derfinder, DEScan2, diffHic, diffloop, DMCFB, DMCHMM, dmrseq, ELMER, ENCODExplorer, enrichTF, ensembldb, erma, esATAC, fcScan, FourCSeq, FunciSNP, genbankr, geneAttribution, genomation, GenomicFeatures, GenomicInteractions, genotypeeval, ggbio, GGtools, gmapR, GOTHiC, gQTLBase, GreyListChIP, Gviz, gwascat, hiAnnotator, HiTC, HTSeqGenie, icetea, igvR, IsoformSwitchAnalyzeR, karyoploteR, MACPET, MADSEQ, maser, MEDIPS, metagene, metagene2, methyAnalysis, methylKit, motifbreakR, MotifDb, MTseeker, NADfinder, nanotatoR, normr, OMICsPCA, ORFik, PAST, Pbase, PGA, plyranges, pram, primirTSS, proBAMr, profileplyr, PureCN, qsea, QuasR, RCAS, recount, recoup, regioneR, REMP, Repitools, RGMQL, RiboProfiling, RNAmodR, RNAprobR, roar, scPipe, scruff, seqCAT, seqplots, seqsetvis, sevenC, SGSeq, SigsPack, soGGi, srnadiff, TFBSTools, trackViewer, transcriptR, tRNAscanImport, TSRchitect, VariantAnnotation, VariantTools, wavClusteR, wiggleplotr suggestsMe: alpine, AnnotationHub, BiocFileCache, biovizBase, bsseq, cicero, CINdex, compEpiTools, CrispRVariants, epivizrChart, epivizrData, geneXtendeR, GenomicAlignments, GenomicRanges, goseq, InPAS, interactiveDisplay, metaseqR, methylumi, miRBaseConverter, MotIV, MutationalPatterns, NarrowPeaks, OrganismDbi, PICS, PING, pipeFrame, pqsfinder, R453Plus1Toolbox, rGADEM, Ringo, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RnBeads, RSVSim, signeR, similaRpeak, TCGAutils, triplex, tRNAdbImport, TVTB dependencyCount: 37 Package: Rtreemix Version: 1.48.0 Depends: R (>= 2.5.0) Imports: methods, graph, Biobase, Hmisc Suggests: Rgraphviz License: LGPL MD5sum: 1a23d717d7592684d8686d1c14d465e4 NeedsCompilation: yes Title: Rtreemix: Mutagenetic trees mixture models. Description: Rtreemix is a package that offers an environment for estimating the mutagenetic trees mixture models from cross-sectional data and using them for various predictions. It includes functions for fitting the trees mixture models, likelihood computations, model comparisons, waiting time estimations, stability analysis, etc. biocViews: StatisticalMethod Author: Jasmina Bogojeska Maintainer: Jasmina Bogojeska git_url: https://git.bioconductor.org/packages/Rtreemix git_branch: RELEASE_3_10 git_last_commit: adae72d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Rtreemix_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Rtreemix_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rtreemix_1.48.0.tgz vignettes: vignettes/Rtreemix/inst/doc/Rtreemix.pdf vignetteTitles: Rtreemix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rtreemix/inst/doc/Rtreemix.R dependencyCount: 87 Package: rTRM Version: 1.24.0 Depends: R (>= 2.10), igraph (>= 1.0) Imports: AnnotationDbi, DBI, RSQLite Suggests: RUnit, BiocGenerics, MotifDb, graph, PWMEnrich, biomaRt, knitr, Biostrings, BSgenome.Mmusculus.UCSC.mm8.masked, org.Hs.eg.db, org.Mm.eg.db, ggplot2 License: GPL-3 MD5sum: f155d74efbb6754568391730250775ac NeedsCompilation: no Title: Identification of transcriptional regulatory modules from PPI networks Description: rTRM identifies transcriptional regulatory modules (TRMs) from protein-protein interaction networks. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/rTRM VignetteBuilder: knitr BugReports: https://github.com/ddiez/rTRM/issues git_url: https://git.bioconductor.org/packages/rTRM git_branch: RELEASE_3_10 git_last_commit: fa52516 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rTRM_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rTRM_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rTRM_1.24.0.tgz vignettes: vignettes/rTRM/inst/doc/rTRM_Introduction.pdf vignetteTitles: Introduction to rTRM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRM/inst/doc/rTRM_Introduction.R importsMe: rTRMui dependencyCount: 32 Package: rTRMui Version: 1.24.0 Imports: shiny (>= 0.9), rTRM, MotifDb, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: ac0a078379924ed68672c6b5250a4954 NeedsCompilation: no Title: A shiny user interface for rTRM Description: This package provides a web interface to compute transcriptional regulatory modules with rTRM. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork, GUI Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/rTRMui BugReports: https://github.com/ddiez/rTRMui/issues git_url: https://git.bioconductor.org/packages/rTRMui git_branch: RELEASE_3_10 git_last_commit: 41dc349 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/rTRMui_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/rTRMui_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rTRMui_1.24.0.tgz vignettes: vignettes/rTRMui/inst/doc/rTRMui.pdf vignetteTitles: Introduction to rTRMui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRMui/inst/doc/rTRMui.R dependencyCount: 73 Package: runibic Version: 1.8.0 Depends: R (>= 3.4.0), biclust, SummarizedExperiment Imports: Rcpp (>= 0.12.12), testthat, methods LinkingTo: Rcpp Suggests: knitr, rmarkdown, GEOquery, affy, airway, QUBIC License: MIT + file LICENSE Archs: i386, x64 MD5sum: 3bea7f2a2afbf48aabd7f9c31dd6e98b NeedsCompilation: yes Title: runibic: row-based biclustering algorithm for analysis of gene expression data in R Description: This package implements UbiBic algorithm in R. This biclustering algorithm for analysis of gene expression data was introduced by Zhenjia Wang et al. in 2016. It is currently considered the most promising biclustering method for identification of meaningful structures in complex and noisy data. biocViews: Microarray, Clustering, GeneExpression, Sequencing, Coverage Author: Patryk Orzechowski, Artur Pańszczyk Maintainer: Patryk Orzechowski URL: http://github.com/athril/runibic SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: http://github.com/athril/runibic/issues git_url: https://git.bioconductor.org/packages/runibic git_branch: RELEASE_3_10 git_last_commit: 45815d8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/runibic_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/runibic_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/runibic_1.8.0.tgz vignettes: vignettes/runibic/inst/doc/runibic.html vignetteTitles: runibic: UniBic in R Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: miRSM dependencyCount: 88 Package: RUVcorr Version: 1.18.0 Imports: corrplot, MASS, stats, lattice, grDevices, gridExtra, snowfall, psych, BiocParallel, grid, bladderbatch, reshape2 Suggests: knitr, BiocStyle, hgu133a2.db License: GPL-2 Archs: i386, x64 MD5sum: 6f2c5811ca3e64783f090dca1ca36cc6 NeedsCompilation: no Title: Removal of unwanted variation for gene-gene correlations and related analysis Description: RUVcorr allows to apply global removal of unwanted variation (ridged version of RUV) to real and simulated gene expression data. biocViews: GeneExpression, Normalization Author: Saskia Freytag Maintainer: Saskia Freytag VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RUVcorr git_branch: RELEASE_3_10 git_last_commit: 730fdd3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RUVcorr_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RUVcorr_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RUVcorr_1.18.0.tgz vignettes: vignettes/RUVcorr/inst/doc/RUVcorrVignetteNew.pdf vignetteTitles: RUVcorr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVcorr/inst/doc/RUVcorrVignetteNew.R dependencyCount: 34 Package: RUVnormalize Version: 1.20.1 Depends: R (>= 2.10.0) Imports: RUVnormalizeData, Biobase Enhances: spams License: GPL-3 MD5sum: 9827aee2addb698867184ec3292a7ba6 NeedsCompilation: no Title: RUV for normalization of expression array data Description: RUVnormalize is meant to remove unwanted variation from gene expression data when the factor of interest is not defined, e.g., to clean up a dataset for general use or to do any kind of unsupervised analysis. biocViews: StatisticalMethod, Normalization Author: Laurent Jacob Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/RUVnormalize git_branch: RELEASE_3_10 git_last_commit: c900c6b git_last_commit_date: 2019-11-06 Date/Publication: 2019-11-06 source.ver: src/contrib/RUVnormalize_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/RUVnormalize_1.20.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RUVnormalize_1.20.1.tgz vignettes: vignettes/RUVnormalize/inst/doc/RUVnormalize.pdf vignetteTitles: RUVnormalize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVnormalize/inst/doc/RUVnormalize.R dependencyCount: 8 Package: RUVSeq Version: 1.20.0 Depends: Biobase, EDASeq (>= 1.99.1), edgeR Imports: methods, MASS Suggests: BiocStyle, knitr, RColorBrewer, zebrafishRNASeq, DESeq2 License: Artistic-2.0 MD5sum: 4aac4f58919108f989ecf21caf9fca87 NeedsCompilation: no Title: Remove Unwanted Variation from RNA-Seq Data Description: This package implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples. biocViews: ImmunoOncology, DifferentialExpression, Preprocessing, RNASeq, Software Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Lorena Pantano [ctb], Kamil Slowikowski [ctb] Maintainer: Davide Risso URL: https://github.com/drisso/RUVSeq VignetteBuilder: knitr BugReports: https://github.com/drisso/RUVSeq/issues git_url: https://git.bioconductor.org/packages/RUVSeq git_branch: RELEASE_3_10 git_last_commit: e211923 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RUVSeq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RUVSeq_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RUVSeq_1.20.0.tgz vignettes: vignettes/RUVSeq/inst/doc/RUVSeq.pdf vignetteTitles: RUVSeq: Remove Unwanted Variation from RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVSeq/inst/doc/RUVSeq.R importsMe: consensusDE, scone suggestsMe: DEScan2 dependencyCount: 106 Package: RVS Version: 1.8.0 Depends: R (>= 3.5.0) Imports: GENLIB, gRain, snpStats, kinship2, methods, stats, utils Suggests: knitr, testthat, rmarkdown, BiocStyle, VariantAnnotation License: GPL-2 Archs: i386, x64 MD5sum: 30551dcf54a6a0e3bbe64a8b350a3a67 NeedsCompilation: no Title: Computes estimates of the probability of related individuals sharing a rare variant Description: Rare Variant Sharing (RVS) implements tests of association and linkage between rare genetic variant genotypes and a dichotomous phenotype, e.g. a disease status, in family samples. The tests are based on probabilities of rare variant sharing by relatives under the null hypothesis of absence of linkage and association between the rare variants and the phenotype and apply to single variants or multiple variants in a region (e.g. gene-based test). biocViews: ImmunoOncology, Genetics, GenomeWideAssociation, VariantDetection, ExomeSeq, WholeGenome Author: Alexandre Bureau, Ingo Ruczinski, Samuel Younkin, Thomas Sherman Maintainer: Thomas Sherman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RVS git_branch: RELEASE_3_10 git_last_commit: bcbaf2b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/RVS_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/RVS_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/RVS_1.8.0.tgz vignettes: vignettes/RVS/inst/doc/RVS.html vignetteTitles: The RVS Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RVS/inst/doc/RVS.R dependencyCount: 35 Package: rWikiPathways Version: 1.6.1 Imports: caTools, httr, rjson, utils, XML Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 16ed8da73032d819ff5420532f73e372 NeedsCompilation: no Title: rWikiPathways - R client library for the WikiPathways API Description: Use this package to interface with the WikiPathways API. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network, Metabolomics Author: Egon Willighagen [aut, cre] (), Alex Pico [aut] () Maintainer: Egon Willighagen URL: https://github.com/wikipathways/rwikipathways VignetteBuilder: knitr BugReports: https://github.com/wikipathways/rwikipathways/issues git_url: https://git.bioconductor.org/packages/rWikiPathways git_branch: RELEASE_3_10 git_last_commit: a022537 git_last_commit_date: 2019-11-20 Date/Publication: 2019-11-21 source.ver: src/contrib/rWikiPathways_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/rWikiPathways_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/rWikiPathways_1.6.1.tgz vignettes: vignettes/rWikiPathways/inst/doc/Overview.html, vignettes/rWikiPathways/inst/doc/Pathway-Analysis.html, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.html, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.html vignetteTitles: 1. Overview, 4. Pathway Analysis, 2. rWikiPathways and BridgeDbR, 3. rWikiPathways and RCy3 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rWikiPathways/inst/doc/Overview.R, vignettes/rWikiPathways/inst/doc/Pathway-Analysis.R, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.R, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.R suggestsMe: TRONCO dependencyCount: 15 Package: S4Vectors Version: 0.24.4 Depends: R (>= 3.3.0), methods, utils, stats, stats4, BiocGenerics (>= 0.31.1) Suggests: IRanges, GenomicRanges, SummarizedExperiment, Matrix, DelayedArray, ShortRead, graph, data.table, RUnit, BiocStyle License: Artistic-2.0 MD5sum: 4849cd70eef86f1be04b3b28162469be NeedsCompilation: yes Title: Foundation of vector-like and list-like containers in Bioconductor Description: The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages). biocViews: Infrastructure, DataRepresentation Author: H. Pagès, M. Lawrence and P. Aboyoun Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/S4Vectors git_branch: RELEASE_3_10 git_last_commit: 0287f96 git_last_commit_date: 2020-04-03 Date/Publication: 2020-04-09 source.ver: src/contrib/S4Vectors_0.24.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/S4Vectors_0.24.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/S4Vectors_0.24.4.tgz vignettes: vignettes/S4Vectors/inst/doc/RleTricks.pdf, vignettes/S4Vectors/inst/doc/S4QuickOverview.pdf, vignettes/S4Vectors/inst/doc/S4VectorsOverview.pdf vignetteTitles: Rle Tips and Tricks, A quick overview of the S4 class system, An Overview of the S4Vectors package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/S4Vectors/inst/doc/RleTricks.R, vignettes/S4Vectors/inst/doc/S4QuickOverview.R, vignettes/S4Vectors/inst/doc/S4VectorsOverview.R dependsOnMe: altcdfenvs, AnnotationHubData, ATACseqQC, Biostrings, BiSeq, BSgenome, bumphunter, Cardinal, CellMapper, CexoR, chimeraviz, ChIPpeakAnno, chipseq, ChIPseqR, ClassifyR, CODEX, coseq, CSAR, DelayedArray, DelayedDataFrame, DESeq2, DEXSeq, DirichletMultinomial, DMCFB, DMCHMM, DMRcaller, epigenomix, epihet, ExperimentHubData, ExpressionAtlas, fCCAC, GA4GHclient, GenoGAM, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicScores, GenomicTuples, girafe, groHMM, Gviz, HelloRanges, InPAS, InTAD, IntEREst, IRanges, LoomExperiment, MotifDb, MSnbase, MTseeker, NADfinder, NBAMSeq, OTUbase, plethy, Rcwl, RepViz, RIPSeeker, RNAmodR, RnBeads, segmentSeq, SQLDataFrame, Structstrings, SummarizedBenchmark, TimeSeriesExperiment, topdownr, TreeSummarizedExperiment, triplex, VariantExperiment, VariantTools, vulcan, XVector importsMe: adaptest, affycoretools, ALDEx2, AllelicImbalance, alpine, amplican, anamiR, AneuFinder, animalcules, AnnotationDbi, AnnotationForge, AnnotationHub, annotatr, appreci8R, ArrayTV, ASpediaFI, ASpli, AUCell, BadRegionFinder, ballgown, BASiCS, batchelor, BiocNeighbors, BiocOncoTK, BiocSingular, biotmle, biovizBase, biscuiteer, BiSeq, BitSeq, bnbc, BPRMeth, branchpointer, breakpointR, BSgenome, bsseq, BUSpaRse, CAGEfightR, CAGEr, casper, CATALYST, ccfindR, celaref, celda, CHETAH, ChIC, chipenrich, ChIPexoQual, ChIPQC, ChIPseeker, ChIPSeqSpike, chromstaR, chromswitch, chromVAR, cicero, circRNAprofiler, cleaver, CluMSID, clusterExperiment, cn.mops, CNEr, CNVPanelizer, CNVRanger, COCOA, CoGAPS, coMET, compEpiTools, consensusDE, consensusSeekeR, contiBAIT, copynumber, CopywriteR, CoverageView, CRISPRseek, CrispRVariants, csaw, CTDquerier, cummeRbund, customProDB, cydar, DChIPRep, debrowser, DECIPHER, decompTumor2Sig, DEFormats, DEGreport, DelayedMatrixStats, derfinder, derfinderHelper, derfinderPlot, DEScan2, DEWSeq, DiffBind, diffcyt, diffHic, diffloop, DiscoRhythm, DMRcate, dmrseq, DOSE, doseR, DRIMSeq, DropletUtils, easyRNASeq, eegc, ELMER, ENCODExplorer, EnrichmentBrowser, enrichTF, ensembldb, ensemblVEP, epivizr, epivizrData, epivizrStandalone, erma, esATAC, EventPointer, ExperimentHub, FastqCleaner, fastseg, FindMyFriends, fishpond, FunciSNP, GA4GHshiny, gcapc, GDSArray, genbankr, GeneRegionScan, GENESIS, genomation, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicFiles, GenomicInteractions, GenomicOZone, genoset, GGBase, ggbio, GGtools, Glimma, gmapR, GOpro, GOTHiC, gQTLBase, gQTLstats, GRmetrics, GSEABenchmarkeR, GUIDEseq, gwascat, h5vc, HCABrowser, HCAExplorer, HDF5Array, HiCBricks, HiCcompare, HiLDA, hipathia, hmdbQuery, HTSeqGenie, HumanTranscriptomeCompendium, icetea, ideal, IMAS, ImpulseDE2, INSPEcT, InteractionSet, InterMineR, iSEE, isomiRs, IVAS, ivygapSE, JunctionSeq, karyoploteR, kebabs, lionessR, lipidr, loci2path, LOLA, M3D, MACPET, MADSEQ, martini, MAST, mCSEA, MEAL, meshr, metagenomeFeatures, MetCirc, MethCP, methInheritSim, methylCC, methylInheritance, methylKit, methylPipe, methylumi, methyvim, mimager, minfi, MinimumDistance, MIRA, MiRaGE, missRows, MMAPPR2, MMDiff2, Modstrings, mosaics, MOSim, motifbreakR, motifmatchr, MotIV, mpra, msa, msgbsR, MultiAssayExperiment, MultiDataSet, muscat, MutationalPatterns, mygene, myvariant, NarrowPeaks, nucleoSim, nucleR, oligoClasses, ontoProc, openPrimeR, ORFik, Organism.dplyr, OrganismDbi, OUTRIDER, PAIRADISE, panelcn.mops, PAST, PathwaySplice, Pbase, pcaExplorer, pdInfoBuilder, PGA, phemd, PICS, PING, plyranges, pogos, polyester, PowerExplorer, pqsfinder, pram, prebs, PrecisionTrialDrawer, primirTSS, procoil, proDA, profileplyr, pulsedSilac, PureCN, qcmetrics, qpgraph, QuasR, R3CPET, R453Plus1Toolbox, RaggedExperiment, RareVariantVis, Rariant, Rcade, RCAS, recount, regioneR, regionReport, regsplice, REMP, Repitools, restfulSE, rexposome, RGMQL, rhdf5client, RiboProfiling, RJMCMCNucleosomes, Rmmquant, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RNAprobR, roar, Rqc, Rsamtools, rScudo, RTCGAToolbox, RTN, rtracklayer, SC3, scater, scDD, scds, scmap, scMerge, SCnorm, scPipe, scran, scruff, scTensor, scTGIF, SeqArray, seqCAT, seqplots, seqsetvis, SeqSQC, SeqVarTools, sesame, sevenbridges, sevenC, SGSeq, ShortRead, SingleCellExperiment, singleCellTK, SingleR, singscore, skewr, SMITE, SNPchip, SNPhood, soGGi, SomaticSignatures, Spaniel, splatter, SplicingGraphs, SPLINTER, sRACIPE, srnadiff, STAN, strandCheckR, SummarizedExperiment, TarSeqQC, TCGAbiolinks, TCGAutils, TFBSTools, TFHAZ, TnT, trackViewer, tradeSeq, transcriptR, TransView, Trendy, tRNA, tRNAdbImport, tRNAscanImport, TSRchitect, TVTB, twoddpcr, tximeta, TxRegInfra, Ularcirc, universalmotif, VanillaICE, VariantAnnotation, VariantFiltering, VCFArray, wavClusteR, wiggleplotr, XCIR, xcms, XVector, yamss suggestsMe: BiocGenerics, epivizrChart, globalSeq, GWASTools, RTCGA, StructuralVariantAnnotation, TFEA.ChIP, TFutils linksToMe: Biostrings, CNEr, DECIPHER, DelayedArray, GenomicAlignments, GenomicRanges, HDF5Array, IRanges, kebabs, MatrixRider, Rsamtools, rtracklayer, ShortRead, Structstrings, triplex, VariantAnnotation, VariantFiltering, XVector dependencyCount: 7 Package: safe Version: 3.26.0 Depends: R (>= 2.4.0), AnnotationDbi, Biobase, methods, SparseM Suggests: GO.db, PFAM.db, reactome.db, hgu133a.db, breastCancerUPP, survival, foreach, doRNG, Rgraphviz, GOstats License: GPL (>= 2) MD5sum: e55c2f643bcb4615a8f25b63abb8cdde NeedsCompilation: no Title: Significance Analysis of Function and Expression Description: SAFE is a resampling-based method for testing functional categories in gene expression experiments. SAFE can be applied to 2-sample and multi-class comparisons, or simple linear regressions. Other experimental designs can also be accommodated through user-defined functions. biocViews: DifferentialExpression, Pathways, GeneSetEnrichment, StatisticalMethod, Software Author: William T. Barry Maintainer: William T. Barry git_url: https://git.bioconductor.org/packages/safe git_branch: RELEASE_3_10 git_last_commit: b22e93c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/safe_3.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/safe_3.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/safe_3.26.0.tgz vignettes: vignettes/safe/inst/doc/SAFEmanual3.pdf vignetteTitles: SAFE manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/safe/inst/doc/SAFEmanual3.R importsMe: EGSEA, EnrichmentBrowser dependencyCount: 27 Package: sagenhaft Version: 1.56.0 Depends: R (>= 2.10), SparseM (>= 0.73), methods Imports: graphics, methods, SparseM, stats, utils License: GPL (>= 2) MD5sum: f5f62ae0ac7167868219834f394716fa NeedsCompilation: no Title: Collection of functions for reading and comparing SAGE libraries Description: This package implements several functions useful for analysis of gene expression data by sequencing tags as done in SAGE (Serial Analysis of Gene Expressen) data, i.e. extraction of a SAGE library from sequence files, sequence error correction, library comparison. Sequencing error correction is implementing using an Expectation Maximization Algorithm based on a Mixture Model of tag counts. biocViews: SAGE Author: Tim Beissbarth , with contributions from Gordon Smyth and Lavinia Hyde . Maintainer: Tim Beissbarth URL: http://tagcalling.mbgproject.org git_url: https://git.bioconductor.org/packages/sagenhaft git_branch: RELEASE_3_10 git_last_commit: c49299a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sagenhaft_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sagenhaft_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sagenhaft_1.56.0.tgz vignettes: vignettes/sagenhaft/inst/doc/SAGEnhaft.pdf vignetteTitles: SAGEnhaft hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sagenhaft/inst/doc/SAGEnhaft.R dependencyCount: 5 Package: SAGx Version: 1.60.0 Depends: R (>= 2.5.0), stats, multtest, methods Imports: Biobase, stats4 Suggests: KEGG.db, hu6800.db, MASS License: GPL-3 MD5sum: c65de3cd3ea03e1b7bbc854e586e7065 NeedsCompilation: yes Title: Statistical Analysis of the GeneChip Description: A package for retrieval, preparation and analysis of data from the Affymetrix GeneChip. In particular the issue of identifying differentially expressed genes is addressed. biocViews: Microarray, OneChannel, Preprocessing, DataImport, DifferentialExpression, Clustering, MultipleComparison, GeneExpression, GeneSetEnrichment, Pathways, Regression, KEGG Author: Per Broberg Maintainer: Per Broberg, URL: http://home.swipnet.se/pibroberg/expression_hemsida1.html git_url: https://git.bioconductor.org/packages/SAGx git_branch: RELEASE_3_10 git_last_commit: 4878e17 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SAGx_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SAGx_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SAGx_1.60.0.tgz vignettes: vignettes/SAGx/inst/doc/samroc-ex.pdf vignetteTitles: samroc - example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SAGx/inst/doc/samroc-ex.R dependencyCount: 16 Package: SAIGEgds Version: 1.0.2 Depends: R (>= 3.5.0), gdsfmt (>= 1.20.0), SeqArray (>= 1.24.1), Rcpp Imports: methods, stats, utils, RcppParallel, SPAtest (>= 3.0.0) LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: parallel, crayon, RUnit, knitr, BiocGenerics, SNPRelate License: GPL-3 Archs: i386, x64 MD5sum: f19e0bbe0ceb2746bd1c2848d65e3272 NeedsCompilation: yes Title: Scalable Implementation of Generalized mixed models using GDS files in Phenome-Wide Association Studies Description: Scalable implementation of generalized mixed models with highly optimized C++ implementation and integration with Genomic Data Structure (GDS) files. It is designed for single variant tests in large-scale phenome-wide association studies (PheWAS) with millions of variants and samples, controlling for sample structure and case-control imbalance. The implementation is based on the original SAIGE R package (v0.29.4.4). SAIGEgds also implements some of the SPAtest functions in C to speed up the calculation of Saddlepoint approximation. Benchmarks show that SAIGEgds is 5 to 6 times faster than the original SAIGE R package. biocViews: Software, Genetics, StatisticalMethod Author: Xiuwen Zheng [aut, cre] (), Wei Zhou [ctb] (the original author of the SAIGE R package), J. Wade Davis [ctb] Maintainer: Xiuwen Zheng URL: https://github.com/AbbVie-ComputationalGenomics/SAIGEgds SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SAIGEgds git_branch: RELEASE_3_10 git_last_commit: 39ef568 git_last_commit_date: 2020-04-08 Date/Publication: 2020-04-09 source.ver: src/contrib/SAIGEgds_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/SAIGEgds_1.0.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SAIGEgds_1.0.2.tgz vignettes: vignettes/SAIGEgds/inst/doc/SAIGEgds.html vignetteTitles: SAIGEgds Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SAIGEgds/inst/doc/SAIGEgds.R dependencyCount: 23 Package: samExploreR Version: 1.10.0 Depends: ggplot2,Rsubread,RNAseqData.HNRNPC.bam.chr14,edgeR,R (>= 3.4.0) Imports: grDevices, stats, graphics Suggests: BiocStyle,RUnit,BiocGenerics,Matrix License: GPL-3 MD5sum: a0a467a8c2e6577dc2fa0aa112081b5a NeedsCompilation: no Title: samExploreR package: high-performance read summarisation to count vectors with avaliability of sequencing depth reduction simulation Description: This R package is designed for subsampling procedure to simulate sequencing experiments with reduced sequencing depth. This package can be used to anlayze data generated from all major sequencing platforms such as Illumina GA, HiSeq, MiSeq, Roche GS-FLX, ABI SOLiD and LifeTech Ion PGM Proton sequencers. It supports multiple operating systems incluidng Linux, Mac OS X, FreeBSD and Solaris. Was developed with usage of Rsubread. biocViews: ImmunoOncology, Sequencing, SequenceMatching, RNASeq, ChIPSeq, DNASeq, WholeGenome, GeneTarget, Alignment, GeneExpression, GeneticVariability, GeneRegulation, Preprocessing, GenomeAnnotation, Software Author: Alexey Stupnikov, Shailesh Tripathi and Frank Emmert-Streib Maintainer: shailesh tripathi git_url: https://git.bioconductor.org/packages/samExploreR git_branch: RELEASE_3_10 git_last_commit: 7a79a2e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/samExploreR_1.10.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/samExploreR_1.10.0.tgz vignettes: vignettes/samExploreR/inst/doc/Manual.pdf vignetteTitles: samExploreR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/samExploreR/inst/doc/Manual.R dependencyCount: 59 Package: sampleClassifier Version: 1.10.0 Depends: R (>= 3.4), MGFM, MGFR, annotate Imports: e1071, ggplot2, stats, utils Suggests: sampleClassifierData, BiocStyle, hgu133a.db, hgu133plus2.db License: Artistic-2.0 MD5sum: 9e8cacad200b1a57f4df3fd69df9399c NeedsCompilation: no Title: Sample Classifier Description: The package is designed to classify gene expression profiles. biocViews: ImmunoOncology, Classification, Microarray, RNASeq, GeneExpression Author: Khadija El-Amrani Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/sampleClassifier git_branch: RELEASE_3_10 git_last_commit: f0cb265 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sampleClassifier_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sampleClassifier_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sampleClassifier_1.10.0.tgz vignettes: vignettes/sampleClassifier/inst/doc/sampleClassifier.pdf vignetteTitles: sampleClassifier Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sampleClassifier/inst/doc/sampleClassifier.R dependencyCount: 96 Package: SamSPECTRAL Version: 1.40.0 Depends: R (>= 3.3.3) Imports: methods License: GPL (>= 2) MD5sum: 382aa2b8d4d433c2643f9ed20792c46f NeedsCompilation: yes Title: Identifies cell population in flow cytometry data. Description: Samples large data such that spectral clustering is possible while preserving density information in edge weights. More specifically, given a matrix of coordinates as input, SamSPECTRAL first builds the communities to sample the data points. Then, it builds a graph and after weighting the edges by conductance computation, the graph is passed to a classic spectral clustering algorithm to find the spectral clusters. The last stage of SamSPECTRAL is to combine the spectral clusters. The resulting "connected components" estimate biological cell populations in the data. See the vignette for more details on how to use this package, some illustrations, and simple examples. biocViews: FlowCytometry, CellBiology, Clustering, Cancer, FlowCytometry, StemCells, HIV, ImmunoOncology Author: Habil Zare and Parisa Shooshtari Maintainer: Habil git_url: https://git.bioconductor.org/packages/SamSPECTRAL git_branch: RELEASE_3_10 git_last_commit: ca93b20 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SamSPECTRAL_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SamSPECTRAL_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SamSPECTRAL_1.40.0.tgz vignettes: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.pdf vignetteTitles: A modified spectral clustering method for clustering Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.R importsMe: ddPCRclust dependencyCount: 1 Package: sangerseqR Version: 1.22.0 Depends: R (>= 3.0.2), Biostrings Imports: methods, shiny Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-2 Archs: i386, x64 MD5sum: 2ae81e6297fe48f5405cc16f70ab512d NeedsCompilation: no Title: Tools for Sanger Sequencing Data in R Description: This package contains several tools for analyzing Sanger Sequencing data files in R, including reading .scf and .ab1 files, making basecalls and plotting chromatograms. biocViews: Sequencing, SNP, Visualization Author: Jonathon T. Hill, Bradley Demarest Maintainer: Jonathon Hill VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sangerseqR git_branch: RELEASE_3_10 git_last_commit: 13d2e13 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sangerseqR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sangerseqR_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sangerseqR_1.22.0.tgz vignettes: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.pdf vignetteTitles: sangerseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.R suggestsMe: CrispRVariants dependencyCount: 31 Package: SANTA Version: 2.24.0 Depends: R (>= 2.14), igraph Imports: Matrix, snow Suggests: RUnit, BiocGenerics, knitr, knitcitations, formatR, org.Sc.sgd.db, BioNet, DLBCL, msm License: GPL (>= 2) Archs: i386, x64 MD5sum: 03ff5bf9f194cfb0c7cda1e0613266bc NeedsCompilation: yes Title: Spatial Analysis of Network Associations Description: This package provides methods for measuring the strength of association between a network and a phenotype. It does this by measuring clustering of the phenotype across the network (Knet). Vertices can also be individually ranked by their strength of association with high-weight vertices (Knode). biocViews: Network, NetworkEnrichment, Clustering Author: Alex J. Cornish and Florian Markowetz Maintainer: Alex J. Cornish VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SANTA git_branch: RELEASE_3_10 git_last_commit: 24ff5f6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SANTA_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SANTA_2.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SANTA_2.24.0.tgz vignettes: vignettes/SANTA/inst/doc/SANTA-vignette.html vignetteTitles: Introduction to SANTA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SANTA/inst/doc/SANTA-vignette.R dependencyCount: 12 Package: sapFinder Version: 1.24.0 Depends: R (>= 3.0.0),rTANDEM (>= 1.3.5) Imports: pheatmap,Rcpp (>= 0.10.6),graphics,grDevices,stats, utils LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-2 MD5sum: 57035876c2148c1b8ec6942ef906fb91 NeedsCompilation: yes Title: A package for variant peptides detection and visualization in shotgun proteomics. Description: sapFinder is developed to automate (1) variation-associated database construction, (2) database searching, (3) post-processing, (4) HTML-based report generation in shotgun proteomics. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, SNP, RNASeq, Visualization, ReportWriting Author: Shaohang Xu, Bo Wen Maintainer: Shaohang Xu , Bo Wen git_url: https://git.bioconductor.org/packages/sapFinder git_branch: RELEASE_3_10 git_last_commit: e9a3107 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sapFinder_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sapFinder_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sapFinder_1.24.0.tgz vignettes: vignettes/sapFinder/inst/doc/sapFinder.pdf vignetteTitles: sapFinder Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sapFinder/inst/doc/sapFinder.R dependencyCount: 23 Package: savR Version: 1.24.0 Depends: ggplot2 Imports: methods, reshape2, scales, gridExtra, XML Suggests: Cairo, testthat License: AGPL-3 Archs: i386, x64 MD5sum: 8701209d952d68a3f74de30149dbd651 NeedsCompilation: no Title: Parse and analyze Illumina SAV files Description: Parse Illumina Sequence Analysis Viewer (SAV) files, access data, and generate QC plots. biocViews: Sequencing Author: R. Brent Calder Maintainer: R. Brent Calder URL: https://github.com/bcalder/savR BugReports: https://github.com/bcalder/savR/issues git_url: https://git.bioconductor.org/packages/savR git_branch: RELEASE_3_10 git_last_commit: e9fd16f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/savR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/savR_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/savR_1.24.0.tgz vignettes: vignettes/savR/inst/doc/savR.pdf vignetteTitles: Using savR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/savR/inst/doc/savR.R dependencyCount: 60 Package: SBGNview Version: 1.0.0 Depends: R (>= 3.6), pathview, SBGNview.data Imports: Rdpack, grDevices, methods, stats, utils, xml2, rsvg, igraph, rmarkdown, knitr, SummarizedExperiment, AnnotationDbi Suggests: testthat, gage License: AGPL-3 MD5sum: 25a759dbf1bbac9c84ddd31b313f99a3 NeedsCompilation: no Title: Overlay omics data onto SBGN pathway diagram Description: SBGNview is an R package for visualizing omics data on SBGN pathway maps. Given omics data and a SBGN-ML file with layout information, SBGNview can display omics data as colors on glyphs and output image files. SBGNview provides extensive options to control glyph and edge features (e.g. color, line width etc.). To facilitate pathway based analysis, SBGNview also provides functions to extract molecule sets from SBGN-ML files. SBGNview can map a large collection of gene, protein and compound ID typs to glyphs. biocViews: GeneTarget, Pathways, GraphAndNetwork, Visualization, GeneSetEnrichment, DifferentialExpression, GeneExpression, Microarray, RNASeq, Genetics, Metabolomics, Proteomics, SystemsBiology, Sequencing, GeneTarget Author: Xiaoxi Dong, Weijun Luo Maintainer: Xiaoxi Dong URL: https://github.com/datapplab/SBGNview VignetteBuilder: knitr BugReports: https://github.com/datapplab/SBGNview/issues git_url: https://git.bioconductor.org/packages/SBGNview git_branch: RELEASE_3_10 git_last_commit: 8c59137 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SBGNview_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SBGNview_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SBGNview_1.0.0.tgz vignettes: vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.html, vignettes/SBGNview/inst/doc/SBGNview.Vignette.html vignetteTitles: Pathway analysis using SBGNview gene set, SBGNview functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.R, vignettes/SBGNview/inst/doc/SBGNview.Vignette.R dependencyCount: 84 Package: SBMLR Version: 1.82.0 Depends: XML, deSolve Suggests: rsbml License: GPL-2 Archs: i386, x64 MD5sum: 458168fad2501ad9d9671c40fc74ad6a NeedsCompilation: no Title: SBML-R Interface and Analysis Tools Description: This package contains a systems biology markup language (SBML) interface to R. biocViews: GraphAndNetwork, Pathways, Network Author: Tomas Radivoyevitch, Vishak Venkateswaran Maintainer: Tomas Radivoyevitch URL: http://epbi-radivot.cwru.edu/SBMLR/SBMLR.html git_url: https://git.bioconductor.org/packages/SBMLR git_branch: RELEASE_3_10 git_last_commit: 2a7e710 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SBMLR_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SBMLR_1.82.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SBMLR_1.82.0.tgz vignettes: vignettes/SBMLR/inst/doc/quick-start.pdf vignetteTitles: Quick intro to SBMLR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SBMLR/inst/doc/quick-start.R dependencyCount: 7 Package: SC3 Version: 1.14.0 Depends: R(>= 3.3) Imports: graphics, stats, utils, methods, e1071, parallel, foreach, doParallel, doRNG, shiny, ggplot2, pheatmap (>= 1.0.8), ROCR, robustbase, rrcov, cluster, WriteXLS, Rcpp (>= 0.11.1), SummarizedExperiment, SingleCellExperiment, BiocGenerics, S4Vectors LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, mclust, scater License: GPL-3 Archs: i386, x64 MD5sum: 489416a994453512efbff393456bd0ba NeedsCompilation: yes Title: Single-Cell Consensus Clustering Description: A tool for unsupervised clustering and analysis of single cell RNA-Seq data. biocViews: ImmunoOncology, SingleCell, Software, Classification, Clustering, DimensionReduction, SupportVectorMachine, RNASeq, Visualization, Transcriptomics, DataRepresentation, GUI, DifferentialExpression, Transcription Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/SC3 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/sc3/ git_url: https://git.bioconductor.org/packages/SC3 git_branch: RELEASE_3_10 git_last_commit: d2785b6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SC3_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SC3_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SC3_1.14.0.tgz vignettes: vignettes/SC3/inst/doc/SC3.html vignetteTitles: SC3 package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SC3/inst/doc/SC3.R dependencyCount: 111 Package: Scale4C Version: 1.8.0 Depends: R (>= 3.4), smoothie, GenomicRanges, IRanges, SummarizedExperiment Imports: methods, grDevices, graphics, utils License: LGPL-3 MD5sum: 3b1a8b69c0e9e98cbb12fe22e4e573b6 NeedsCompilation: no Title: Scale4C: an R/Bioconductor package for scale-space transformation of 4C-seq data Description: Scale4C is an R/Bioconductor package for scale-space transformation and visualization of 4C-seq data. The scale-space transformation is a multi-scale visualization technique to transform a 2D signal (e.g. 4C-seq reads on a genomic interval of choice) into a tesselation in the scale space (2D, genomic position x scale factor) by applying different smoothing kernels (Gauss, with increasing sigma). This transformation allows for explorative analysis and comparisons of the data's structure with other samples. biocViews: Visualization, QualityControl, DataImport, Sequencing, Coverage Author: Carolin Walter Maintainer: Carolin Walter git_url: https://git.bioconductor.org/packages/Scale4C git_branch: RELEASE_3_10 git_last_commit: 80d2be2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Scale4C_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Scale4C_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Scale4C_1.8.0.tgz vignettes: vignettes/Scale4C/inst/doc/vignette.pdf vignetteTitles: Scale4C: an R/Bioconductor package for scale-space transformation of 4C-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Scale4C/inst/doc/vignette.R dependencyCount: 33 Package: scAlign Version: 1.2.0 Depends: R (>= 3.5), SingleCellExperiment (>= 1.4), Seurat (>= 2.3.4), tensorflow, purrr, irlba, Rtsne, ggplot2, methods, utils, FNN, PMA Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 2a7beb0cbff455eeab86ce1d44b9c40b NeedsCompilation: no Title: An alignment and integration method for single cell genomics Description: An unsupervised deep learning method for data alignment, integration and estimation of per-cell differences in -omic data (e.g. gene expression) across datasets (conditions, tissues, species). See Johansen and Quon (2019) for more details. biocViews: SingleCell, Transcriptomics, DimensionReduction, NeuralNetwork Author: Nelson Johansen [aut, cre], Gerald Quon [aut] Maintainer: Nelson Johansen URL: https://github.com/quon-titative-biology/scAlign SystemRequirements: python (< 3.7), tensorflow VignetteBuilder: knitr BugReports: https://github.com/quon-titative-biology/scAlign/issues git_url: https://git.bioconductor.org/packages/scAlign git_branch: RELEASE_3_10 git_last_commit: 0254014 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/scAlign_1.2.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scAlign_1.1.0.tgz vignettes: vignettes/scAlign/inst/doc/scAlign.pdf vignetteTitles: alignment_tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scAlign/inst/doc/scAlign.R dependencyCount: 156 Package: SCAN.UPC Version: 2.28.0 Depends: R (>= 2.14.0), Biobase (>= 2.6.0), oligo, Biostrings, GEOquery, affy, affyio, foreach, sva Imports: utils, methods, MASS, tools, IRanges Suggests: pd.hg.u95a License: MIT MD5sum: 56e20ecaad63f06b2afeb79e7c720dca NeedsCompilation: no Title: Single-channel array normalization (SCAN) and Universal exPression Codes (UPC) Description: SCAN is a microarray normalization method to facilitate personalized-medicine workflows. Rather than processing microarray samples as groups, which can introduce biases and present logistical challenges, SCAN normalizes each sample individually by modeling and removing probe- and array-specific background noise using only data from within each array. SCAN can be applied to one-channel (e.g., Affymetrix) or two-channel (e.g., Agilent) microarrays. The Universal exPression Codes (UPC) method is an extension of SCAN that estimates whether a given gene/transcript is active above background levels in a given sample. The UPC method can be applied to one-channel or two-channel microarrays as well as to RNA-Seq read counts. Because UPC values are represented on the same scale and have an identical interpretation for each platform, they can be used for cross-platform data integration. biocViews: ImmunoOncology, Software, Microarray, Preprocessing, RNASeq, TwoChannel, OneChannel Author: Stephen R. Piccolo and Andrea H. Bild and W. Evan Johnson Maintainer: Stephen R. Piccolo URL: http://bioconductor.org, http://jlab.bu.edu/software/scan-upc git_url: https://git.bioconductor.org/packages/SCAN.UPC git_branch: RELEASE_3_10 git_last_commit: fc8f234 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SCAN.UPC_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SCAN.UPC_2.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SCAN.UPC_2.28.0.tgz vignettes: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.pdf vignetteTitles: Primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.R dependencyCount: 97 Package: SCANVIS Version: 1.0.0 Depends: R (>= 3.6) Imports: IRanges,plotrix,RCurl,rtracklayer Suggests: knitr, rmarkdown License: file LICENSE MD5sum: 4d14b1fa26912596b9f0a0a1292d1c75 NeedsCompilation: no Title: SCANVIS - a tool for SCoring, ANnotating and VISualizing splice junctions Description: SCANVIS is a set of annotation-dependent tools for analyzing splice junctions and their read support as predetermined by an alignment tool of choice (for example, STAR aligner). SCANVIS assesses each junction's relative read support (RRS) by relating to the context of local split reads aligning to annotated transcripts. SCANVIS also annotates each splice junction by indicating whether the junction is supported by annotation or not, and if not, what type of junction it is (e.g. exon skipping, alternative 5' or 3' events, Novel Exons). Unannotated junctions are also futher annotated by indicating whether it induces a frame shift or not. SCANVIS includes a visualization function to generate static sashimi-style plots depicting relative read support and number of split reads using arc thickness and arc heights, making it easy for users to spot well-supported junctions. These plots also clearly delineate unannotated junctions from annotated ones using designated color schemes, and users can also highlight splice junctions of choice. Variants and/or a read profile are also incoroporated into the plot if the user supplies variants in bed format and/or the BAM file. One further feature of the visualization function is that users can submit multiple samples of a certain disease or cohort to generate a single plot - this occurs via a "merge" function wherein junction details over multiple samples are merged to generate a single sashimi plot, which is useful when contrasting cohorots (eg. disease vs control). biocViews: Software,ResearchField,Transcriptomics,WorkflowStep,Annotation,Visualization Author: Phaedra Agius Maintainer: Phaedra Agius VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCANVIS git_branch: RELEASE_3_10 git_last_commit: e6d0426 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SCANVIS_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SCANVIS_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SCANVIS_1.0.0.tgz vignettes: vignettes/SCANVIS/inst/doc/runningSCANVIS.pdf vignetteTitles: SCANVIS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SCANVIS/inst/doc/runningSCANVIS.R dependencyCount: 39 Package: scater Version: 1.14.6 Depends: SingleCellExperiment, ggplot2 Imports: BiocGenerics, SummarizedExperiment, Matrix, ggbeeswarm, grid, DelayedArray, DelayedMatrixStats, methods, S4Vectors, stats, utils, viridis, Rcpp, BiocNeighbors, BiocSingular, BiocParallel LinkingTo: Rcpp, beachmat Suggests: BiocStyle, BiocFileCache, biomaRt, beachmat, cowplot, destiny, knitr, scRNAseq, robustbase, rmarkdown, Rtsne, uwot, testthat, pheatmap, Biobase, limma, DropletUtils License: GPL-3 Archs: i386, x64 MD5sum: fde387c247c92c06bbbfd003e5fead55 NeedsCompilation: yes Title: Single-Cell Analysis Toolkit for Gene Expression Data in R Description: A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization. biocViews: ImmunoOncology, SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, DataImport, DataRepresentation, Infrastructure, Coverage Author: Davis McCarthy [aut, cre], Kieran Campbell [aut], Aaron Lun [aut, ctb], Quin Wills [aut], Vladimir Kiselev [ctb] Maintainer: Davis McCarthy URL: http://bioconductor.org/packages/scater/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/scater git_branch: RELEASE_3_10 git_last_commit: ba37f4d git_last_commit_date: 2019-12-13 Date/Publication: 2019-12-16 source.ver: src/contrib/scater_1.14.6.tar.gz win.binary.ver: bin/windows/contrib/3.6/scater_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scater_1.14.6.tgz vignettes: vignettes/scater/inst/doc/overview.html vignetteTitles: Overview of scater functionality hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scater/inst/doc/overview.R dependsOnMe: muscat, netSmooth importsMe: batchelor, CATALYST, CellMixS, scDblFinder, scran, Spaniel, splatter suggestsMe: CellTrails, fcoex, iSEE, M3Drop, MAST, mbkmeans, monocle, SC3, scds, schex, scMerge, SingleR, slalom, SummarizedBenchmark, waddR dependencyCount: 94 Package: scBFA Version: 1.0.0 Depends: R (>= 3.6) Imports: SingleCellExperiment, SummarizedExperiment, Seurat, MASS, zinbwave, stats, copula, ggplot2, DESeq2, utils, grid, methods Suggests: knitr, rmarkdown, testthat, Rtsne License: GPL-3 MD5sum: 903309020f17397dc3fe8c3b00dab44b NeedsCompilation: no Title: A dimensionality reduction tool using gene detection pattern to mitigate noisy expression profile of scRNA-seq Description: This package is designed to model gene detection pattern of scRNA-seq through a binary factor analysis model. This model allows user to pass into a cell level covariate matrix X and gene level covariate matrix Q to account for nuisance variance(e.g batch effect), and it will output a low dimensional embedding matrix for downstream analysis. biocViews: SingleCell, Transcriptomics, DimensionReduction,GeneExpression, ATACSeq, BatchEffect, KEGG, QualityControl Author: Ruoxin Li [aut, cre], Gerald Quon [aut] Maintainer: Ruoxin Li URL: https://github.com/ucdavis/quon-titative-biology/BFA VignetteBuilder: knitr BugReports: https://github.com/ucdavis/quon-titative-biology/BFA/issues git_url: https://git.bioconductor.org/packages/scBFA git_branch: RELEASE_3_10 git_last_commit: c6f50d5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/scBFA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/scBFA_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scBFA_1.0.0.tgz vignettes: vignettes/scBFA/inst/doc/vignette.html vignetteTitles: Gene Detection Analysis for scRNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scBFA/inst/doc/vignette.R dependencyCount: 193 Package: SCBN Version: 1.4.0 Depends: R (>= 3.5.0) Imports: stats Suggests: knitr,rmarkdown License: GPL-2 MD5sum: e42fb495445e25dc9807e828b2f2b497 NeedsCompilation: no Title: A statistical normalization method and differential expression analysis for RNA-seq data between different species Description: This package provides a scale based normalization (SCBN) method to identify genes with differential expression between different species. It takes into account the available knowledge of conserved orthologous genes and the hypothesis testing framework to detect differentially expressed orthologous genes. The method on this package are described in the article 'A statistical normalization method and differential expression analysis for RNA-seq data between different species' by Yan Zhou, Jiadi Zhu, Tiejun Tong, Junhui Wang, Bingqing Lin, Jun Zhang (2018, pending publication). biocViews: DifferentialExpression, GeneExpression, Normalization Author: Yan Zhou Maintainer: Yan Zhou <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCBN git_branch: RELEASE_3_10 git_last_commit: 96f8985 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SCBN_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SCBN_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SCBN_1.4.0.tgz vignettes: vignettes/SCBN/inst/doc/SCBN.html vignetteTitles: SCBN Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCBN/inst/doc/SCBN.R dependencyCount: 1 Package: scDblFinder Version: 1.1.8 Depends: R (>= 3.6) Imports: igraph, Matrix, matrixStats, BiocParallel, BiocNeighbors, SummarizedExperiment, SingleCellExperiment, scran, scater, data.table, dplyr, ggplot2, randomForest, graphics, methods, stats, DelayedArray Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 6f54baa8050bc68ccadc029cd1f4380f NeedsCompilation: no Title: scDblFinder Description: Efficient identification of doublets in single-cell RNAseq directly from counts using overclustering-based generation of artifical doublets. biocViews: Preprocessing, SingleCell, RNASeq Author: Pierre-Luc Germain [cre, aut] () Maintainer: Pierre-Luc Germain URL: https://github.com/plger/scDblFinder VignetteBuilder: knitr BugReports: https://github.com/plger/scDblFinder/issues git_url: https://git.bioconductor.org/packages/scDblFinder git_branch: RELEASE_3_10 git_last_commit: f9806b7 git_last_commit_date: 2020-03-05 Date/Publication: 2020-03-05 source.ver: src/contrib/scDblFinder_1.1.8.tar.gz win.binary.ver: bin/windows/contrib/3.6/scDblFinder_1.1.8.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scDblFinder_1.1.8.tgz vignettes: vignettes/scDblFinder/inst/doc/scDblFinder.html vignetteTitles: scDblFinder hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDblFinder/inst/doc/scDblFinder.R dependencyCount: 109 Package: scDD Version: 1.10.0 Depends: R (>= 3.4) Imports: fields, mclust, BiocParallel, outliers, ggplot2, EBSeq, arm, SingleCellExperiment, SummarizedExperiment, grDevices, graphics, stats, S4Vectors, scran Suggests: BiocStyle, knitr, gridExtra License: GPL-2 Archs: i386, x64 MD5sum: fcf1093e7703d0a4842500b66aea74b6 NeedsCompilation: yes Title: Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions Description: This package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions. biocViews: ImmunoOncology, Bayesian, Clustering, RNASeq, SingleCell, MultipleComparison, Visualization, DifferentialExpression Author: Keegan Korthauer [cre, aut] () Maintainer: Keegan Korthauer URL: https://github.com/kdkorthauer/scDD VignetteBuilder: knitr BugReports: https://github.com/kdkorthauer/scDD/issues git_url: https://git.bioconductor.org/packages/scDD git_branch: RELEASE_3_10 git_last_commit: cec2aa4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/scDD_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/scDD_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scDD_1.10.0.tgz vignettes: vignettes/scDD/inst/doc/scDD.pdf vignetteTitles: scDD Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDD/inst/doc/scDD.R suggestsMe: splatter dependencyCount: 124 Package: scde Version: 2.14.0 Depends: R (>= 3.0.0), flexmix Imports: Rcpp (>= 0.10.4), RcppArmadillo (>= 0.5.400.2.0), mgcv, Rook, rjson, MASS, Cairo, RColorBrewer, edgeR, quantreg, methods, nnet, RMTstat, extRemes, pcaMethods, BiocParallel, parallel LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, cba, fastcluster, WGCNA, GO.db, org.Hs.eg.db, rmarkdown License: GPL-2 MD5sum: d130468976480caaa535ef867db4b055 NeedsCompilation: yes Title: Single Cell Differential Expression Description: The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734). biocViews: ImmunoOncology, RNASeq, StatisticalMethod, DifferentialExpression, Bayesian, Transcription, Software Author: Peter Kharchenko [aut, cre], Jean Fan [aut] Maintainer: Jean Fan URL: http://pklab.med.harvard.edu/scde VignetteBuilder: knitr BugReports: https://github.com/hms-dbmi/scde/issues git_url: https://git.bioconductor.org/packages/scde git_branch: RELEASE_3_10 git_last_commit: b652503 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/scde_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/scde_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scde_2.14.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 45 Package: scds Version: 1.2.0 Depends: R (>= 3.6.0) Imports: Matrix, S4Vectors, SingleCellExperiment, SummarizedExperiment, xgboost, methods, stats, dplyr, pROC Suggests: BiocStyle, knitr, rsvd, Rtsne, scater, cowplot License: MIT + file LICENSE MD5sum: e787a1ebbc90803c9f80e4b958aa3c51 NeedsCompilation: no Title: In-Silico Annotation of Doublets for Single Cell RNA Sequencing Data Description: In single cell RNA sequencing (scRNA-seq) data combinations of cells are sometimes considered a single cell (doublets). The scds package provides methods to annotate doublets in scRNA-seq data computationally. biocViews: SingleCell, RNASeq, QualityControl, Preprocessing, Transcriptomics, GeneExpression, Sequencing, Software, Classification Author: Dennis Kostka [aut, cre], Bais Abha [aut] Maintainer: Dennis Kostka VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scds git_branch: RELEASE_3_10 git_last_commit: d291b1c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/scds_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/scds_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scds_1.2.0.tgz vignettes: vignettes/scds/inst/doc/scds.html vignetteTitles: Introduction to the scds package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scds/inst/doc/scds.R dependencyCount: 59 Package: scFeatureFilter Version: 1.6.0 Depends: R (>= 3.5) Imports: dplyr (>= 0.7.3), ggplot2 (>= 2.1.0), magrittr (>= 1.5), rlang (>= 0.1.2), tibble (>= 1.3.4), stats, methods Suggests: testthat, knitr, rmarkdown, SingleCellExperiment, SummarizedExperiment, scRNAseq, cowplot License: MIT + file LICENSE MD5sum: 67c85a195034dcd56443d96f888af2ba NeedsCompilation: no Title: A correlation-based method for quality filtering of single-cell RNAseq data Description: An R implementation of the correlation-based method developed in the Joshi laboratory to analyse and filter processed single-cell RNAseq data. It returns a filtered version of the data containing only genes expression values unaffected by systematic noise. biocViews: ImmunoOncology, SingleCell, RNASeq, Preprocessing, GeneExpression Author: Angeles Arzalluz-Luque [aut], Guillaume Devailly [aut, cre], Anagha Joshi [aut] Maintainer: Guillaume Devailly VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scFeatureFilter git_branch: RELEASE_3_10 git_last_commit: ee50c8e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/scFeatureFilter_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/scFeatureFilter_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scFeatureFilter_1.6.0.tgz vignettes: vignettes/scFeatureFilter/inst/doc/Introduction.html vignetteTitles: Introduction to scFeatureFilter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scFeatureFilter/inst/doc/Introduction.R dependencyCount: 59 Package: scfind Version: 1.8.0 Depends: R(>= 3.4) Imports: SingleCellExperiment, SummarizedExperiment, methods, stats, bit, dplyr, hash, reshape2, Rcpp(>= 0.12.12) LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: a49088473985b6fbfb107cc84434a4f9 NeedsCompilation: yes Title: A search tool for single cell RNA-seq data by gene lists Description: Recently a very large collection of single-cell RNA-seq (scRNA-seq) datasets has been generated and publicly released. For the collection to be useful, the information must be organized in a way that supports queries that are relevant to researchers. `scfind` builds an index from scRNA-seq datasets which organizes the information in a suitable and compact manner so that the datasets can be very efficiently searched for either cells or cell types in which a given list of genes is expressed. biocViews: ImmunoOncology, SingleCell, Software, RNASeq, Transcriptomics, DataRepresentation, Transcription, Sequencing, GeneExpression Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/scfind VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/scfind/ git_url: https://git.bioconductor.org/packages/scfind git_branch: RELEASE_3_10 git_last_commit: 684ee78 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/scfind_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/scfind_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scfind_1.8.0.tgz vignettes: vignettes/scfind/inst/doc/scfind.html vignetteTitles: `scfind` package vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scfind/inst/doc/scfind.R dependencyCount: 60 Package: scGPS Version: 1.0.0 Depends: R (>= 3.6), SummarizedExperiment, dynamicTreeCut, SingleCellExperiment Imports: glmnet (> 2.0), caret (>= 6.0), ggplot2 (>= 2.2.1), fastcluster, dplyr, Rcpp, RcppArmadillo, RcppParallel, grDevices, graphics, stats, utils, DESeq, locfit LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: Matrix (>= 1.2), testthat, knitr, parallel, rmarkdown, RColorBrewer, ReactomePA, clusterProfiler, cowplot, org.Hs.eg.db, reshape2, xlsx, dendextend, networkD3, Rtsne, BiocParallel, e1071, WGCNA, devtools, DOSE License: GPL-3 Archs: i386, x64 MD5sum: 538cbf78562b1ac4f907d5d8f9c57497 NeedsCompilation: yes Title: A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation) Description: The package implements two main algorithms to answer two key questions: a SCORE (Stable Clustering at Optimal REsolution) to find subpopulations, followed by scGPS to investigate the relationships between subpopulations. biocViews: SingleCell, Clustering, DataImport, Sequencing, Coverage Author: Quan Nguyen [aut, cre], Michael Thompson [aut], Anne Senabouth [aut] Maintainer: Quan Nguyen SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/IMB-Computational-Genomics-Lab/scGPS/issues git_url: https://git.bioconductor.org/packages/scGPS git_branch: RELEASE_3_10 git_last_commit: ccc1da7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/scGPS_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/scGPS_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scGPS_1.0.0.tgz vignettes: vignettes/scGPS/inst/doc/vignette.html vignetteTitles: single cell Global fate Potential of Subpopulations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scGPS/inst/doc/vignette.R dependencyCount: 129 Package: schex Version: 1.0.55 Depends: SingleCellExperiment (>= 1.7.4), Seurat, ggplot2 Imports: hexbin, stats, methods, cluster, dplyr, entropy Suggests: ggrepel, knitr, rmarkdown, testthat (>= 2.1.0), covr, TENxPBMCData, scater, shinydashboard, iSEE, igraph, scran License: GPL-3 MD5sum: 679058276bfa869e835a93572fc46687 NeedsCompilation: no Title: Hexbin plots for single cell omics data Description: Builds hexbin plots for variables and dimension reduction stored in single cell omics data such as SingleCellExperiment and SeuratObject. The ideas used in this package are based on the excellent work of Dan Carr, Nicholas Lewin-Koh, Martin Maechler and Thomas Lumley. biocViews: Software, Sequencing, SingleCell, DimensionReduction, Visualization Author: Saskia Freytag Maintainer: Saskia Freytag URL: https://github.com/SaskiaFreytag/schex VignetteBuilder: knitr BugReports: https://github.com/SaskiaFreytag/schex/issues git_url: https://git.bioconductor.org/packages/schex git_branch: RELEASE_3_10 git_last_commit: ec51a99 git_last_commit_date: 2019-12-18 Date/Publication: 2019-12-19 source.ver: src/contrib/schex_1.0.55.tar.gz win.binary.ver: bin/windows/contrib/3.6/schex_1.0.55.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/schex_1.0.55.tgz vignettes: vignettes/schex/inst/doc/Seurat_schex.html, vignettes/schex/inst/doc/shiny_schex.html, vignettes/schex/inst/doc/using_schex.html vignetteTitles: Seurat_schex, shiny_schhex, using_schex hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/schex/inst/doc/Seurat_schex.R, vignettes/schex/inst/doc/shiny_schex.R, vignettes/schex/inst/doc/using_schex.R dependencyCount: 152 Package: ScISI Version: 1.58.0 Depends: R (>= 2.10), GO.db, RpsiXML, annotate, apComplex Imports: AnnotationDbi, GO.db, RpsiXML, annotate, methods, org.Sc.sgd.db, utils Suggests: ppiData, xtable License: LGPL MD5sum: 217860d1a650a492fc16747f009f7aef NeedsCompilation: no Title: In Silico Interactome Description: Package to create In Silico Interactomes biocViews: GraphAndNetwork, Proteomics, NetworkInference, DecisionTree Author: Tony Chiang Maintainer: Tony Chiang git_url: https://git.bioconductor.org/packages/ScISI git_branch: RELEASE_3_10 git_last_commit: ca82c68 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ScISI_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ScISI_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ScISI_1.58.0.tgz vignettes: vignettes/ScISI/inst/doc/vignette.pdf vignetteTitles: ScISI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ScISI/inst/doc/vignette.R dependsOnMe: PCpheno, ppiStats, SLGI importsMe: PCpheno, SLGI suggestsMe: RpsiXML dependencyCount: 41 Package: scmap Version: 1.8.0 Depends: R(>= 3.4) Imports: Biobase, SingleCellExperiment, SummarizedExperiment, BiocGenerics, S4Vectors, dplyr, reshape2, matrixStats, proxy, utils, googleVis, ggplot2, methods, stats, e1071, randomForest, Rcpp (>= 0.12.12) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: c70dbe7c517c3130bbb196fb5108d86e NeedsCompilation: yes Title: A tool for unsupervised projection of single cell RNA-seq data Description: Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. scmap is a method for projecting cells from a scRNA-seq experiment on to the cell-types or individual cells identified in a different experiment. biocViews: ImmunoOncology, SingleCell, Software, Classification, SupportVectorMachine, RNASeq, Visualization, Transcriptomics, DataRepresentation, Transcription, Sequencing, Preprocessing, GeneExpression, DataImport Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/scmap VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/scmap/ git_url: https://git.bioconductor.org/packages/scmap git_branch: RELEASE_3_10 git_last_commit: 1ff0a4b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/scmap_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/scmap_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scmap_1.8.0.tgz vignettes: vignettes/scmap/inst/doc/scmap.html vignetteTitles: `scmap` package vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scmap/inst/doc/scmap.R dependencyCount: 93 Package: scMerge Version: 1.2.0 Depends: R (>= 3.6.0) Imports: BiocParallel, BiocSingular, cluster, DelayedArray, DelayedMatrixStats, distr, igraph, M3Drop (>= 1.9.4), parallel, pdist, proxy, Rcpp (>= 0.12.18), RcppEigen (>= 0.3.3.4.0), ruv, S4Vectors (>= 0.23.19), SingleCellExperiment (>= 1.7.3), SummarizedExperiment LinkingTo: Rcpp (>= 0.12.18), RcppEigen, testthat Suggests: BiocStyle, covr, HDF5Array, knitr, Matrix, rmarkdown, scales, scater, testthat, badger License: GPL-3 Archs: i386, x64 MD5sum: 71667dbd5c459a7db1c812ae8a7d13c1 NeedsCompilation: yes Title: scMerge: Merging multiple batches of scRNA-seq data Description: Like all gene expression data, single-cell RNA-seq (scRNA-Seq) data suffers from batch effects and other unwanted variations that makes accurate biological interpretations difficult. The scMerge method leverages factor analysis, stably expressed genes (SEGs) and (pseudo-) replicates to remove unwanted variations and merge multiple scRNA-Seq data. This package contains all the necessary functions in the scMerge pipeline, including the identification of SEGs, replication-identification methods, and merging of scRNA-Seq data. biocViews: BatchEffect, GeneExpression, Normalization, RNASeq, Sequencing, SingleCell, Software, Transcriptomics Author: Kevin Wang [aut, cre], Yingxin Lin [aut], Sydney Bioinformatics and Biometrics Group [fnd] Maintainer: Kevin Wang URL: https://github.com/SydneyBioX/scMerge VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scMerge/issues git_url: https://git.bioconductor.org/packages/scMerge git_branch: RELEASE_3_10 git_last_commit: b2effcb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/scMerge_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/scMerge_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scMerge_1.2.0.tgz vignettes: vignettes/scMerge/inst/doc/scMerge.html vignetteTitles: scMerge hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMerge/inst/doc/scMerge.R dependencyCount: 134 Package: scmeth Version: 1.6.0 Depends: R (>= 3.5.0) Imports: knitr, rmarkdown, bsseq, AnnotationHub, GenomicRanges, reshape2, stats, utils, BSgenome, DelayedArray (>= 0.5.15), annotatr, SummarizedExperiment (>= 1.5.6), GenomeInfoDb, Biostrings, DT, HDF5Array (>= 1.7.5) Suggests: BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, Biobase, BiocGenerics, ggplot2, ggthemes License: GPL-2 MD5sum: fa96b1ceaf9c03d23a16dc1d263a8dda NeedsCompilation: no Title: Functions to conduct quality control analysis in methylation data Description: Functions to analyze methylation data can be found here. Some functions are relevant for single cell methylation data but most other functions can be used for any methylation data. Highlight of this workflow is the comprehensive quality control report. biocViews: DNAMethylation, QualityControl, Preprocessing, SingleCell, ImmunoOncology Author: Divy Kangeyan Maintainer: Divy Kangeyan VignetteBuilder: knitr BugReports: https://github.com/aryeelab/scmeth/issues git_url: https://git.bioconductor.org/packages/scmeth git_branch: RELEASE_3_10 git_last_commit: c00688a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/scmeth_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/scmeth_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scmeth_1.6.0.tgz vignettes: vignettes/scmeth/inst/doc/my-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scmeth/inst/doc/my-vignette.R suggestsMe: biscuiteer dependencyCount: 155 Package: SCnorm Version: 1.8.2 Depends: R (>= 3.4.0), Imports: SingleCellExperiment, SummarizedExperiment, stats, methods, graphics, grDevices, parallel, quantreg, cluster, moments, data.table, BiocParallel, S4Vectors, ggplot2, forcats, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown, devtools License: GPL (>= 2) MD5sum: df71bb49708a0c1175474175f4b2f279 NeedsCompilation: no Title: Normalization of single cell RNA-seq data Description: This package implements SCnorm — a method to normalize single-cell RNA-seq data. biocViews: Normalization, RNASeq, SingleCell, ImmunoOncology Author: Rhonda Bacher Maintainer: Rhonda Bacher URL: https://github.com/rhondabacher/SCnorm VignetteBuilder: knitr BugReports: https://github.com/rhondabacher/SCnorm/issues git_url: https://git.bioconductor.org/packages/SCnorm git_branch: RELEASE_3_10 git_last_commit: fd88534 git_last_commit_date: 2019-11-21 Date/Publication: 2019-11-22 source.ver: src/contrib/SCnorm_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/SCnorm_1.8.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SCnorm_1.8.2.tgz vignettes: vignettes/SCnorm/inst/doc/SCnorm.pdf vignetteTitles: SCnorm Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCnorm/inst/doc/SCnorm.R dependencyCount: 85 Package: scone Version: 1.10.0 Depends: R (>= 3.4), methods, SummarizedExperiment Imports: graphics, stats, utils, aroma.light, BiocParallel, class, cluster, compositions, diptest, edgeR, fpc, gplots, grDevices, hexbin, limma, matrixStats, mixtools, RColorBrewer, boot, rhdf5, RUVSeq, rARPACK Suggests: BiocStyle, DT, ggplot2, knitr, miniUI, NMF, plotly, reshape2, rmarkdown, scran, scRNAseq, shiny, testthat, visNetwork, doParallel, BatchJobs License: Artistic-2.0 Archs: i386, x64 MD5sum: aef6aaf5cb6c0bd44186071262c1e6f4 NeedsCompilation: no Title: Single Cell Overview of Normalized Expression data Description: SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses. biocViews: ImmunoOncology, Normalization, Preprocessing, QualityControl, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell, Coverage Author: Michael Cole [aut, cph], Davide Risso [aut, cre, cph] Maintainer: Davide Risso VignetteBuilder: knitr BugReports: https://github.com/YosefLab/scone/issues git_url: https://git.bioconductor.org/packages/scone git_branch: RELEASE_3_10 git_last_commit: 41dbdaf git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/scone_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/scone_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scone_1.10.0.tgz vignettes: vignettes/scone/inst/doc/sconeTutorial.html vignetteTitles: scone Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scone/inst/doc/sconeTutorial.R dependencyCount: 137 Package: Sconify Version: 1.6.0 Depends: R (>= 3.5) Imports: tibble, dplyr, FNN, flowCore, Rtsne, ggplot2, magrittr, utils, stats, readr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: a45a53bc271fe9c4de1477631a3e315c NeedsCompilation: no Title: A toolkit for performing KNN-based statistics for flow and mass cytometry data Description: This package does k-nearest neighbor based statistics and visualizations with flow and mass cytometery data. This gives tSNE maps"fold change" functionality and provides a data quality metric by assessing manifold overlap between fcs files expected to be the same. Other applications using this package include imputation, marker redundancy, and testing the relative information loss of lower dimension embeddings compared to the original manifold. biocViews: ImmunoOncology, SingleCell, FlowCytometry, Software, MultipleComparison, Visualization Author: Tyler J Burns Maintainer: Tyler J Burns VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Sconify git_branch: RELEASE_3_10 git_last_commit: 4aa17be git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Sconify_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Sconify_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Sconify_1.6.0.tgz vignettes: vignettes/Sconify/inst/doc/DataQuality.html, vignettes/Sconify/inst/doc/FindingIdealK.html, vignettes/Sconify/inst/doc/Step1.PreProcessing.html, vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.html, vignettes/Sconify/inst/doc/Step3.PostProcessing.html vignetteTitles: Data Quality, Finding Ideal K, How to process FCS files for downstream use in R, General Scone Analysis, Final Post-Processing Steps for Scone hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Sconify/inst/doc/DataQuality.R, vignettes/Sconify/inst/doc/FindingIdealK.R, vignettes/Sconify/inst/doc/Step1.PreProcessing.R, vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.R, vignettes/Sconify/inst/doc/Step3.PostProcessing.R dependencyCount: 71 Package: scoreInvHap Version: 1.8.0 Depends: R (>= 3.4.0) Imports: Biostrings, methods, snpStats, VariantAnnotation, GenomicRanges, BiocParallel, graphics, SummarizedExperiment Suggests: testthat, knitr, BiocStyle, rmarkdown License: file LICENSE MD5sum: b3c2da04eaa4af3c8865a3fa5cb9224f NeedsCompilation: no Title: Get inversion status in predefined regions Description: scoreInvHap can get the samples' inversion status of known inversions. scoreInvHap uses SNP data as input and requires the following information about the inversion: genotype frequencies in the different haplotypes, R2 between the region SNPs and inversion status and heterozygote genotypes in the reference. The package include this data for two well known inversions (8p23 and 17q21.31) and for two additional regions. biocViews: SNP, Genetics, GenomicVariation Author: Carlos Ruiz [aut, cre], Juan R. Gonzalez [aut] Maintainer: Carlos Ruiz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scoreInvHap git_branch: RELEASE_3_10 git_last_commit: 67141e5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/scoreInvHap_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/scoreInvHap_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scoreInvHap_1.8.0.tgz vignettes: vignettes/scoreInvHap/inst/doc/scoreInvHap.html vignetteTitles: Call haplotype inversions with scoreInvHap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scoreInvHap/inst/doc/scoreInvHap.R dependencyCount: 88 Package: scPCA Version: 1.0.0 Depends: R (>= 3.6) Imports: stats, methods, assertthat, tibble, dplyr, purrr, stringr, Rdpack, BiocParallel, elasticnet, cluster, kernlab, origami Suggests: testthat (>= 2.1.0), covr, knitr, rmarkdown, BiocStyle, Matrix, ggplot2, ggpubr, splatter, SingleCellExperiment License: MIT + file LICENSE MD5sum: da5ad612b6b0ee32ca77b1fd3200f1dd NeedsCompilation: no Title: Sparse Contrastive Principal Component Analysis Description: A toolbox for sparse contrastive principal component analysis (scPCA) of high-dimensional biological data. scPCA combines the stability and interpretability of sparse PCA with contrastive PCA's ability to disentangle biological signal from techical noise through the use of control data. Also implements and extends cPCA. biocViews: PrincipalComponent, GeneExpression, DifferentialExpression, Sequencing, Microarray, RNASeq Author: Philippe Boileau [aut, cre, cph] (), Nima Hejazi [aut] (), Sandrine Dudoit [ctb, ths] () Maintainer: Philippe Boileau URL: https://github.com/PhilBoileau/scPCA VignetteBuilder: knitr BugReports: https://github.com/PhilBoileau/scPCA/issues git_url: https://git.bioconductor.org/packages/scPCA git_branch: RELEASE_3_10 git_last_commit: 83250fc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/scPCA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/scPCA_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scPCA_1.0.0.tgz vignettes: vignettes/scPCA/inst/doc/scpca_intro.html vignetteTitles: Sparse contrastive principal component analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scPCA/inst/doc/scpca_intro.R dependencyCount: 52 Package: scPipe Version: 1.8.0 Depends: R (>= 3.4), ggplot2, methods, SingleCellExperiment Imports: Rhtslib, biomaRt, GGally, MASS, mclust, Rcpp (>= 0.11.3), reshape, BiocGenerics, robustbase, scales, utils, stats, S4Vectors, SummarizedExperiment, AnnotationDbi, org.Hs.eg.db, org.Mm.eg.db, stringr, rtracklayer, hashmap, dplyr, GenomicRanges, magrittr, glue LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc, testthat Suggests: Rsubread, knitr, rmarkdown, testthat License: GPL (>= 2) MD5sum: 218f9b23a6d5ea6310bd98990a9c6efb NeedsCompilation: yes Title: pipeline for single cell RNA-seq data analysis Description: A preprocessing pipeline for single cell RNA-seq data that starts from the fastq files and produces a gene count matrix with associated quality control information. It can process fastq data generated by CEL-seq, MARS-seq, Drop-seq, Chromium 10x and SMART-seq protocols. biocViews: ImmunoOncology, Software, Sequencing, RNASeq, GeneExpression, SingleCell, Visualization, SequenceMatching, Preprocessing, QualityControl, GenomeAnnotation Author: Luyi Tian Maintainer: Luyi Tian URL: https://github.com/LuyiTian/scPipe SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: https://github.com/LuyiTian/scPipe git_url: https://git.bioconductor.org/packages/scPipe git_branch: RELEASE_3_10 git_last_commit: 8b19bf1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/scPipe_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/scPipe_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scPipe_1.8.0.tgz vignettes: vignettes/scPipe/inst/doc/scPipe_tutorial.html vignetteTitles: scPipe: flexible data preprocessing pipeline for 3' end scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scPipe/inst/doc/scPipe_tutorial.R dependencyCount: 119 Package: scran Version: 1.14.6 Depends: SingleCellExperiment Imports: SummarizedExperiment, S4Vectors, BiocGenerics, BiocParallel, Rcpp, stats, methods, utils, Matrix, scater, edgeR, limma, BiocNeighbors, igraph, statmod, DelayedArray, DelayedMatrixStats, BiocSingular, dqrng LinkingTo: Rcpp, beachmat, BH, dqrng Suggests: testthat, BiocStyle, knitr, beachmat, HDF5Array, scRNAseq, dynamicTreeCut, DESeq2, monocle, Biobase, aroma.light, pheatmap License: GPL-3 MD5sum: 04c9ccec2e065cc94cf183b6ac836032 NeedsCompilation: yes Title: Methods for Single-Cell RNA-Seq Data Analysis Description: Implements functions for low-level analyses of single-cell RNA-seq data. Methods are provided for normalization of cell-specific biases, assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, Visualization, BatchEffect, Clustering Author: Aaron Lun [aut, cre], Karsten Bach [aut], Jong Kyoung Kim [ctb], Antonio Scialdone [ctb] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scran git_branch: RELEASE_3_10 git_last_commit: 240fad2 git_last_commit_date: 2020-01-30 Date/Publication: 2020-02-03 source.ver: src/contrib/scran_1.14.6.tar.gz win.binary.ver: bin/windows/contrib/3.6/scran_1.14.6.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scran_1.14.6.tgz vignettes: vignettes/scran/inst/doc/scran.html vignetteTitles: Using scran to analyze scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scran/inst/doc/scran.R importsMe: BASiCS, scDblFinder, scDD suggestsMe: batchelor, CellTrails, clusterExperiment, fcoex, PCAtools, schex, scone, SingleR, splatter dependencyCount: 102 Package: scRecover Version: 1.2.0 Depends: R (>= 3.4.0) Imports: stats, utils, methods, graphics, doParallel, foreach, parallel, penalized, kernlab, rsvd, Matrix (>= 1.2-14), MASS (>= 7.3-45), pscl (>= 1.4.9), bbmle (>= 1.0.18), gamlss (>= 4.4-0), preseqR (>= 4.0.0), SAVER (>= 1.1.1), Rmagic (>= 1.3.0), BiocParallel (>= 1.12.0) Suggests: knitr, rmarkdown, SingleCellExperiment, testthat License: GPL MD5sum: 605f7faf7dfef3fbfbe392499310cd5c NeedsCompilation: no Title: scRecover for imputation of single-cell RNA-seq data Description: scRecover is an R package for imputation of single-cell RNA-seq (scRNA-seq) data. It will detect and impute dropout values in a scRNA-seq raw read counts matrix while keeping the real zeros unchanged, since there are both dropout zeros and real zeros in scRNA-seq data. By combination with scImpute, SAVER and MAGIC, scRecover not only detects dropout and real zeros at higher accuracy, but also improve the downstream clustering and visualization results. biocViews: GeneExpression, SingleCell, RNASeq, Transcriptomics, Sequencing, Preprocessing, Software Author: Zhun Miao, Xuegong Zhang Maintainer: Zhun Miao URL: https://miaozhun.github.io/scRecover VignetteBuilder: knitr BugReports: https://github.com/miaozhun/scRecover/issues git_url: https://git.bioconductor.org/packages/scRecover git_branch: RELEASE_3_10 git_last_commit: 1498e40 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/scRecover_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/scRecover_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scRecover_1.2.0.tgz vignettes: vignettes/scRecover/inst/doc/scRecover.html vignetteTitles: scRecover hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scRecover/inst/doc/scRecover.R dependencyCount: 90 Package: scruff Version: 1.4.2 Depends: R (>= 3.5.0) Imports: data.table, GenomicAlignments, GenomicFeatures, GenomicRanges, Rsamtools, ShortRead, parallel, plyr, BiocGenerics, BiocParallel, S4Vectors, AnnotationDbi, Biostrings, methods, ggplot2, ggthemes, scales, GenomeInfoDb, stringdist, ggbio, rtracklayer, SingleCellExperiment, SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, Rsubread, testthat License: MIT + file LICENSE MD5sum: 0501d543e1f1c2b3280d0c66235cb1aa NeedsCompilation: no Title: Single Cell RNA-Seq UMI Filtering Facilitator (scruff) Description: A pipeline which processes single cell RNA-seq (scRNA-seq) reads from CEL-seq and CEL-seq2 protocols. Demultiplex scRNA-seq FASTQ files, align reads to reference genome using Rsubread, and generate UMI filtered count matrix. Also provide visualizations of read alignments and pre- and post-alignment QC metrics. biocViews: Software, Technology, Sequencing, Alignment, RNASeq, SingleCell, WorkflowStep, Preprocessing, QualityControl, Visualization, ImmunoOncology Author: Zhe Wang [aut, cre], Junming Hu [aut], Joshua Campbell [aut] Maintainer: Zhe Wang VignetteBuilder: knitr BugReports: https://github.com/campbio/scruff/issues git_url: https://git.bioconductor.org/packages/scruff git_branch: RELEASE_3_10 git_last_commit: 5ba4097 git_last_commit_date: 2019-12-31 Date/Publication: 2019-12-31 source.ver: src/contrib/scruff_1.4.2.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scruff_1.4.2.tgz vignettes: vignettes/scruff/inst/doc/scruff.html vignetteTitles: Process Single Cell RNA-Seq reads using scruff hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scruff/inst/doc/scruff.R dependencyCount: 156 Package: scsR Version: 1.22.0 Depends: R (>= 2.14.0), STRINGdb, methods, BiocGenerics, Biostrings, IRanges, plyr, tcltk Imports: sqldf, hash, ggplot2, graphics,grDevices, RColorBrewer Suggests: RUnit License: GPL-2 MD5sum: 9a4ddb0ac265666e64bc51950d437333 NeedsCompilation: no Title: SiRNA correction for seed mediated off-target effect Description: Corrects genome-wide siRNA screens for seed mediated off-target effect. Suitable functions to identify the effective seeds/miRNAs and to visualize their effect are also provided in the package. biocViews: Preprocessing Author: Andrea Franceschini Maintainer: Andrea Franceschini , Roger Meier , Christian von Mering git_url: https://git.bioconductor.org/packages/scsR git_branch: RELEASE_3_10 git_last_commit: 65182ce git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/scsR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/scsR_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scsR_1.22.0.tgz vignettes: vignettes/scsR/inst/doc/scsR.pdf vignetteTitles: scsR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scsR/inst/doc/scsR.R dependencyCount: 88 Package: scTensor Version: 1.2.1 Depends: R (>= 3.5.0) Imports: methods, RSQLite, igraph, S4Vectors, plotly, reactome.db, AnnotationDbi, SummarizedExperiment, SingleCellExperiment, nnTensor, rTensor, abind, plotrix, heatmaply, tagcloud, rmarkdown, BiocStyle, knitr, AnnotationHub, MeSHDbi, grDevices, graphics, stats, utils, outliers, Category, meshr, GOstats, ReactomePA, DOSE, crayon, checkmate, BiocManager, visNetwork Suggests: testthat, LRBase.Hsa.eg.db, MeSH.Hsa.eg.db, LRBase.Mmu.eg.db, MeSH.Mmu.eg.db, LRBaseDbi, Seurat, Homo.sapiens License: Artistic-2.0 Archs: i386, x64 MD5sum: f02d0af02b63e3368dbfa7425197cc7e NeedsCompilation: no Title: Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition Description: The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor. biocViews: DimensionReduction, SingleCell, Software, GeneExpression Author: Koki Tsuyuzaki [aut, cre], Kozo Nishida [aut] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTensor git_branch: RELEASE_3_10 git_last_commit: c4b8069 git_last_commit_date: 2019-11-08 Date/Publication: 2019-11-09 source.ver: src/contrib/scTensor_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/scTensor_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scTensor_1.2.1.tgz vignettes: vignettes/scTensor/inst/doc/scTensor_1_Data_format_ID_Conversion.html, vignettes/scTensor/inst/doc/scTensor_2_Report_Interpretation.html, vignettes/scTensor/inst/doc/scTensor_3_CCI_Simulation.html, vignettes/scTensor/inst/doc/scTensor_4_Reanalysis.html, vignettes/scTensor/inst/doc/scTensor.html vignetteTitles: scTensor: 1. Data format and ID conversion, scTensor: 2. Interpretation of HTML report, scTensor: 3. Simulation of CCI, scTensor: 4. Reanalysis of the results of scTensor, scTensor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTensor/inst/doc/scTensor_1_Data_format_ID_Conversion.R, vignettes/scTensor/inst/doc/scTensor_2_Report_Interpretation.R, vignettes/scTensor/inst/doc/scTensor_3_CCI_Simulation.R, vignettes/scTensor/inst/doc/scTensor_4_Reanalysis.R, vignettes/scTensor/inst/doc/scTensor.R dependencyCount: 248 Package: scTGIF Version: 1.0.0 Depends: R (>= 3.6.0) Imports: GSEABase, Biobase, SingleCellExperiment, BiocStyle, plotly, tagcloud, rmarkdown, Rcpp, grDevices, graphics, utils, knitr, S4Vectors, SummarizedExperiment, RColorBrewer, nnTensor, methods, scales, msigdbr Suggests: testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 704f14f0be35d49505d366c939ed4c2e NeedsCompilation: no Title: Cell type annotation for unannotated single-cell RNA-Seq data Description: scTGIF connects the cells and the related gene functions without cell type label. biocViews: DimensionReduction, QualityControl, SingleCell, Software, GeneExpression Author: Koki Tsuyuzaki [aut, cre] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTGIF git_branch: RELEASE_3_10 git_last_commit: 5e82bd9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/scTGIF_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/scTGIF_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/scTGIF_1.0.0.tgz vignettes: vignettes/scTGIF/inst/doc/scTGIF.html vignetteTitles: scTGIF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTGIF/inst/doc/scTGIF.R dependencyCount: 134 Package: SDAMS Version: 1.6.0 Depends: R(>= 3.5), SummarizedExperiment Imports: trust, qvalue, methods, stats, utils Suggests: testthat License: GPL MD5sum: e7b2bfc31bc7214a74610e325bbf7546 NeedsCompilation: no Title: Differential Abundant Analysis for Metabolomics and Proteomics Data Description: This Package utilizes a Semi-parametric Differential Abundance analysis (SDA) method for metabolomics and proteomics data from mass spectrometry. SDA is able to robustly handle non-normally distributed data and provides a clear quantification of the effect size. biocViews: ImmunoOncology, DifferentialExpression, Metabolomics, Proteomics, MassSpectrometry Author: Yuntong Li , Chi Wang , Li Chen Maintainer: Yuntong Li git_url: https://git.bioconductor.org/packages/SDAMS git_branch: RELEASE_3_10 git_last_commit: eaa3c3c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SDAMS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SDAMS_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SDAMS_1.6.0.tgz vignettes: vignettes/SDAMS/inst/doc/SDAMS.pdf vignetteTitles: SDAMS Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SDAMS/inst/doc/SDAMS.R dependencyCount: 83 Package: segmentSeq Version: 2.20.0 Depends: R (>= 3.0.0), methods, baySeq (>= 2.9.0), S4Vectors, parallel, GenomicRanges, ShortRead, stats Imports: Rsamtools, IRanges, GenomeInfoDb, graphics, grDevices, utils, abind Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 0224dcb2fb78bae6c6329f49de097409 NeedsCompilation: no Title: Methods for identifying small RNA loci from high-throughput sequencing data Description: High-throughput sequencing technologies allow the production of large volumes of short sequences, which can be aligned to the genome to create a set of matches to the genome. By looking for regions of the genome which to which there are high densities of matches, we can infer a segmentation of the genome into regions of biological significance. The methods in this package allow the simultaneous segmentation of data from multiple samples, taking into account replicate data, in order to create a consensus segmentation. This has obvious applications in a number of classes of sequencing experiments, particularly in the discovery of small RNA loci and novel mRNA transcriptome discovery. biocViews: MultipleComparison, Sequencing, Alignment, DifferentialExpression, QualityControl, DataImport Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/segmentSeq git_branch: RELEASE_3_10 git_last_commit: 1012f7a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/segmentSeq_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/segmentSeq_2.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/segmentSeq_2.20.0.tgz vignettes: vignettes/segmentSeq/inst/doc/methylationAnalysis.pdf, vignettes/segmentSeq/inst/doc/segmentSeq.pdf vignetteTitles: segmentsSeq: Methylation locus identification, segmentSeq: small RNA locus detection hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/segmentSeq/inst/doc/methylationAnalysis.R, vignettes/segmentSeq/inst/doc/segmentSeq.R dependencyCount: 48 Package: SELEX Version: 1.18.0 Depends: R (>= 2.7.0), rJava (>= 0.5-0), Biostrings (>= 2.26.0) License: GPL (>=2) MD5sum: ffe0fd9a6d854508b3147396438dedd7 NeedsCompilation: no Title: Functions for analyzing SELEX-seq data Description: Tools for quantifying DNA binding specificities based on SELEX-seq data biocViews: Software, MotifDiscovery, MotifAnnotation, GeneRegulation, Transcription Author: Chaitanya Rastogi, Dahong Liu, and Harmen Bussemaker Maintainer: Harmen Bussemaker URL: http://bussemakerlab.org/software/SELEX/ SystemRequirements: Java (>= 1.5) git_url: https://git.bioconductor.org/packages/SELEX git_branch: RELEASE_3_10 git_last_commit: 878a017 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SELEX_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SELEX_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SELEX_1.18.0.tgz vignettes: vignettes/SELEX/inst/doc/SELEX.pdf vignetteTitles: Motif Discovery with SELEX-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SELEX/inst/doc/SELEX.R dependencyCount: 13 Package: SemDist Version: 1.20.0 Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate Suggests: GOSemSim License: GPL (>= 2) MD5sum: 6ccbdbba997ffdf59e3d091ffc085eba NeedsCompilation: no Title: Information Accretion-based Function Predictor Evaluation Description: This package implements methods to calculate information accretion for a given version of the gene ontology and uses this data to calculate remaining uncertainty, misinformation, and semantic similarity for given sets of predicted annotations and true annotations from a protein function predictor. biocViews: Classification, Annotation, GO, Software Author: Ian Gonzalez and Wyatt Clark Maintainer: Ian Gonzalez URL: http://github.com/iangonzalez/SemDist git_url: https://git.bioconductor.org/packages/SemDist git_branch: RELEASE_3_10 git_last_commit: 68b2b83 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SemDist_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SemDist_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SemDist_1.20.0.tgz vignettes: vignettes/SemDist/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SemDist/inst/doc/introduction.R dependencyCount: 32 Package: semisup Version: 1.10.0 Depends: R (>= 3.5.0) Imports: VGAM Suggests: knitr, testthat, SummarizedExperiment License: GPL-3 MD5sum: 39b963548fa0c661fd230e6b150928a4 NeedsCompilation: no Title: Semi-Supervised Mixture Model Description: Useful for detecting SNPs with interactive effects on a quantitative trait. This R packages moves away from testing interaction terms, and moves towards testing whether an individual SNP is involved in any interaction. This reduces the multiple testing burden to one test per SNP, and allows for interactions with unobserved factors. Analysing one SNP at a time, it splits the individuals into two groups, based on the number of minor alleles. If the quantitative trait differs in mean between the two groups, the SNP has a main effect. If the quantitative trait differs in distribution between some individuals in one group and all other individuals, it possibly has an interactive effect. Implicitly, the membership probabilities may suggest potential interacting variables. biocViews: SNP, GenomicVariation, SomaticMutation, Genetics, Classification, Clustering, DNASeq, Microarray, MultipleComparison Author: Armin Rauschenberger [aut, cre] Maintainer: Armin Rauschenberger URL: https://github.com/rauschenberger/semisup VignetteBuilder: knitr BugReports: https://github.com/rauschenberger/semisup/issues git_url: https://git.bioconductor.org/packages/semisup git_branch: RELEASE_3_10 git_last_commit: eb90888 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/semisup_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/semisup_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/semisup_1.10.0.tgz vignettes: vignettes/semisup/inst/doc/semisup.pdf, vignettes/semisup/inst/doc/article.html, vignettes/semisup/inst/doc/vignette.html vignetteTitles: vignette source, article frame, vignette frame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/semisup/inst/doc/semisup.R dependencyCount: 5 Package: SEPIRA Version: 1.6.0 Depends: R (>= 3.5.0) Imports: limma (>= 3.32.5), corpcor (>= 1.6.9), parallel (>= 3.3.1), stats Suggests: knitr, rmarkdown, testthat, igraph License: GPL-3 MD5sum: 6ec7f5617147b0d80c82caaf690451b3 NeedsCompilation: no Title: Systems EPigenomics Inference of Regulatory Activity Description: SEPIRA (Systems EPigenomics Inference of Regulatory Activity) is an algorithm that infers sample-specific transcription factor activity from the genome-wide expression or DNA methylation profile of the sample. biocViews: GeneExpression, Transcription, GeneRegulation, GeneTarget, NetworkInference, Network, Software Author: Yuting Chen [aut, cre], Andrew Teschendorff [aut] Maintainer: Yuting Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SEPIRA git_branch: RELEASE_3_10 git_last_commit: f7bf8e8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SEPIRA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SEPIRA_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SEPIRA_1.6.0.tgz vignettes: vignettes/SEPIRA/inst/doc/SEPIRA.html vignetteTitles: Introduction to `SEPIRA` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SEPIRA/inst/doc/SEPIRA.R dependencyCount: 8 Package: seq2pathway Version: 1.18.0 Depends: R (>= 2.10.0) Imports: nnet, WGCNA, GSA, biomaRt, GenomicRanges, seq2pathway.data License: GPL-2 MD5sum: 6963117e7c00b3237d17f6ebfcf28ea9 NeedsCompilation: no Title: a novel tool for functional gene-set (or termed as pathway) analysis of next-generation sequencing data Description: Seq2pathway is a novel tool for functional gene-set (or termed as pathway) analysis of next-generation sequencing data, consisting of "seq2gene" and "gene2path" components. The seq2gene links sequence-level measurements of genomic regions (including SNPs or point mutation coordinates) to gene-level scores, and the gene2pathway summarizes gene scores to pathway-scores for each sample. The seq2gene has the feasibility to assign both coding and non-exon regions to a broader range of neighboring genes than only the nearest one, thus facilitating the study of functional non-coding regions. The gene2pathway takes into account the quantity of significance for gene members within a pathway compared those outside a pathway. The output of seq2pathway is a general structure of quantitative pathway-level scores, thus allowing one to functional interpret such datasets as RNA-seq, ChIP-seq, GWAS, and derived from other next generational sequencing experiments. biocViews: Software Author: Xinan Yang ; Bin Wang Maintainer: Xinan Yang with contribution from Zhezhen Wang git_url: https://git.bioconductor.org/packages/seq2pathway git_branch: RELEASE_3_10 git_last_commit: 292ac36 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/seq2pathway_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/seq2pathway_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/seq2pathway_1.18.0.tgz vignettes: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.pdf vignetteTitles: An R package for sequence hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.R dependencyCount: 132 Package: SeqArray Version: 1.26.2 Depends: R (>= 3.5.0), gdsfmt (>= 1.18.0) Imports: methods, parallel, IRanges, GenomicRanges, GenomeInfoDb, Biostrings, S4Vectors LinkingTo: gdsfmt Suggests: Biobase, BiocGenerics, BiocParallel, RUnit, Rcpp, SNPRelate, digest, crayon, knitr, Rsamtools, VariantAnnotation License: GPL-3 MD5sum: a2dfba8da2942bd0559b7d29f29c7d9e NeedsCompilation: yes Title: Data Management of Large-scale Whole-genome Sequence Variant Calls Description: Data management of large-scale whole-genome sequencing variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language. biocViews: Infrastructure, DataRepresentation, Sequencing, Genetics Author: Xiuwen Zheng [aut, cre] (), Stephanie Gogarten [aut], David Levine [ctb], Cathy Laurie [ctb] Maintainer: Xiuwen Zheng URL: http://github.com/zhengxwen/SeqArray VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/SeqArray/issues git_url: https://git.bioconductor.org/packages/SeqArray git_branch: RELEASE_3_10 git_last_commit: e9a1b0d git_last_commit_date: 2019-12-26 Date/Publication: 2019-12-26 source.ver: src/contrib/SeqArray_1.26.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/SeqArray_1.26.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SeqArray_1.26.2.tgz vignettes: vignettes/SeqArray/inst/doc/OverviewSlides.html, vignettes/SeqArray/inst/doc/SeqArray.html, vignettes/SeqArray/inst/doc/SeqArrayTutorial.html vignetteTitles: SeqArray Overview, R Integration, SeqArray Data Format and Access hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqArray/inst/doc/SeqArray.R, vignettes/SeqArray/inst/doc/SeqArrayTutorial.R dependsOnMe: SAIGEgds, SeqVarTools importsMe: GDSArray, GENESIS, VariantExperiment suggestsMe: DelayedDataFrame, VCFArray dependencyCount: 18 Package: seqbias Version: 1.34.0 Depends: R (>= 3.0.2), GenomicRanges (>= 0.1.0), Biostrings (>= 2.15.0), methods LinkingTo: Rhtslib (>= 1.15.3) Suggests: Rsamtools, ggplot2 License: LGPL-3 MD5sum: 143a9b594c7e162ef08c5ca6b152ebaa NeedsCompilation: yes Title: Estimation of per-position bias in high-throughput sequencing data Description: This package implements a model of per-position sequencing bias in high-throughput sequencing data using a simple Bayesian network, the structure and parameters of which are trained on a set of aligned reads and a reference genome sequence. biocViews: Sequencing Author: Daniel Jones Maintainer: Daniel Jones SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/seqbias git_branch: RELEASE_3_10 git_last_commit: 9d5b1f7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/seqbias_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/seqbias_1.33.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/seqbias_1.34.0.tgz vignettes: vignettes/seqbias/inst/doc/overview.pdf vignetteTitles: Assessing and Adjusting for Technical Bias in High Throughput Sequencing Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqbias/inst/doc/overview.R dependsOnMe: ReQON dependencyCount: 18 Package: seqCAT Version: 1.8.1 Depends: R (>= 3.6), GenomicRanges (>= 1.26.4), VariantAnnotation(>= 1.20.3) Imports: dplyr (>= 0.5.0), GenomeInfoDb (>= 1.13.4), ggplot2 (>= 2.2.1), grid (>= 3.5.0), IRanges (>= 2.8.2), methods, rtracklayer, rlang, scales (>= 0.4.1), S4Vectors (>= 0.12.2), stats, SummarizedExperiment (>= 1.4.0), tidyr (>= 0.6.1), utils Suggests: knitr, BiocStyle, rmarkdown, testthat, BiocManager License: MIT + file LICENCE MD5sum: 50586b82f734a2239e1371e0a757c2be NeedsCompilation: no Title: High Throughput Sequencing Cell Authentication Toolkit Description: The seqCAT package uses variant calling data (in the form of VCF files) from high throughput sequencing technologies to authenticate and validate the source, function and characteristics of biological samples used in scientific endeavours. biocViews: Coverage, GenomicVariation, Sequencing, VariantAnnotation Author: Erik Fasterius [aut, cre] Maintainer: Erik Fasterius VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/seqCAT git_branch: RELEASE_3_10 git_last_commit: 4617d4e git_last_commit_date: 2020-03-27 Date/Publication: 2020-03-27 source.ver: src/contrib/seqCAT_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/seqCAT_1.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/seqCAT_1.8.1.tgz vignettes: vignettes/seqCAT/inst/doc/seqCAT.html vignetteTitles: seqCAT: The High Throughput Sequencing Cell Authentication Toolkit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCAT/inst/doc/seqCAT.R dependencyCount: 113 Package: seqCNA Version: 1.32.0 Depends: R (>= 3.0), GLAD (>= 2.14), doSNOW (>= 1.0.5), adehabitatLT (>= 0.3.4), seqCNA.annot (>= 0.99), methods License: GPL-3 MD5sum: e12dc93565001d38fad1f5399135ba2e NeedsCompilation: yes Title: Copy number analysis of high-throughput sequencing cancer data Description: Copy number analysis of high-throughput sequencing cancer data with fast summarization, extensive filtering and improved normalization biocViews: CopyNumberVariation, Genetics, Sequencing Author: David Mosen-Ansorena Maintainer: David Mosen-Ansorena SystemRequirements: samtools git_url: https://git.bioconductor.org/packages/seqCNA git_branch: RELEASE_3_10 git_last_commit: e178c8f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/seqCNA_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/seqCNA_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/seqCNA_1.32.0.tgz vignettes: vignettes/seqCNA/inst/doc/seqCNA.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCNA/inst/doc/seqCNA.R dependencyCount: 26 Package: seqcombo Version: 1.8.0 Depends: R (>= 3.4.0) Imports: Biostrings, cowplot, dplyr, ggplot2, grid, igraph, magrittr, methods, rvcheck, utils Suggests: emojifont, knitr, prettydoc, tibble License: Artistic-2.0 Archs: i386, x64 MD5sum: 31282e6d977d13d2abf2674f0bfa3841 NeedsCompilation: no Title: Visualization Tool for Sequence Recombination and Reassortment Description: Provides useful functions for visualizing sequence recombination and virus reassortment events. biocViews: Alignment, Software, Visualization Author: Guangchuang Yu [aut, cre] Maintainer: Guangchuang Yu VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/seqcombo/issues git_url: https://git.bioconductor.org/packages/seqcombo git_branch: RELEASE_3_10 git_last_commit: 416915f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/seqcombo_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/seqcombo_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/seqcombo_1.8.0.tgz vignettes: vignettes/seqcombo/inst/doc/reassortment.html, vignettes/seqcombo/inst/doc/seqcombo.html vignetteTitles: Reassortment, seqcombo introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqcombo/inst/doc/reassortment.R, vignettes/seqcombo/inst/doc/seqcombo.R dependencyCount: 71 Package: SeqGSEA Version: 1.26.0 Depends: Biobase, doParallel, DESeq Imports: methods, biomaRt Suggests: easyRNASeq, GenomicRanges License: GPL (>= 3) Archs: i386, x64 MD5sum: 91a4442561d3495bd69c6e673685a6bb NeedsCompilation: no Title: Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing Description: The package generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene's differential expression and splicing, respectively. biocViews: Sequencing, RNASeq, GeneSetEnrichment, GeneExpression, DifferentialExpression, DifferentialSplicing, ImmunoOncology Author: Xi Wang Maintainer: Xi Wang git_url: https://git.bioconductor.org/packages/SeqGSEA git_branch: RELEASE_3_10 git_last_commit: 80d09f7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SeqGSEA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SeqGSEA_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SeqGSEA_1.26.0.tgz vignettes: vignettes/SeqGSEA/inst/doc/SeqGSEA.pdf vignetteTitles: Gene set enrichment analysis of RNA-Seq data with the SeqGSEA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqGSEA/inst/doc/SeqGSEA.R dependencyCount: 77 Package: seqLogo Version: 1.52.0 Depends: methods, grid Imports: stats4 License: LGPL (>= 2) MD5sum: 4df973b10b87c62034a92a3d721baa4b NeedsCompilation: no Title: Sequence logos for DNA sequence alignments Description: seqLogo takes the position weight matrix of a DNA sequence motif and plots the corresponding sequence logo as introduced by Schneider and Stephens (1990). biocViews: SequenceMatching Author: Oliver Bembom Maintainer: Oliver Bembom git_url: https://git.bioconductor.org/packages/seqLogo git_branch: RELEASE_3_10 git_last_commit: c68b076 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/seqLogo_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/seqLogo_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/seqLogo_1.52.0.tgz vignettes: vignettes/seqLogo/inst/doc/seqLogo.pdf vignetteTitles: Sequence logos for DNA sequence alignments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqLogo/inst/doc/seqLogo.R dependsOnMe: motifRG, rGADEM importsMe: igvR, IntEREst, PWMEnrich, rGADEM, riboSeqR, SPLINTER, TFBSTools suggestsMe: BCRANK, DiffLogo, Logolas, motifcounter, universalmotif dependencyCount: 3 Package: seqPattern Version: 1.18.0 Depends: methods, R (>= 2.15.0) Imports: Biostrings, GenomicRanges, IRanges, KernSmooth, plotrix Suggests: BSgenome.Drerio.UCSC.danRer7, CAGEr, RUnit, BiocGenerics, BiocStyle Enhances: parallel License: GPL-3 Archs: i386, x64 MD5sum: 2f1ba0555af7ef9264f89f5097058c12 NeedsCompilation: no Title: Visualising oligonucleotide patterns and motif occurrences across a set of sorted sequences Description: Visualising oligonucleotide patterns and sequence motifs occurrences across a large set of sequences centred at a common reference point and sorted by a user defined feature. biocViews: Visualization, SequenceMatching Author: Vanja Haberle Maintainer: Vanja Haberle git_url: https://git.bioconductor.org/packages/seqPattern git_branch: RELEASE_3_10 git_last_commit: 2f504b4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/seqPattern_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/seqPattern_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/seqPattern_1.18.0.tgz vignettes: vignettes/seqPattern/inst/doc/seqPattern.pdf vignetteTitles: seqPattern hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqPattern/inst/doc/seqPattern.R importsMe: genomation dependencyCount: 20 Package: seqplots Version: 1.24.0 Depends: R (>= 3.2.0) Imports: methods, IRanges, BSgenome, digest, rtracklayer, GenomicRanges, Biostrings, shiny (>= 0.13.0), DBI, RSQLite, plotrix, fields, grid, kohonen, parallel, GenomeInfoDb, class, S4Vectors, ggplot2, reshape2, gridExtra, jsonlite, DT (>= 0.1.0), RColorBrewer, Rsamtools, GenomicAlignments, BiocManager Suggests: testthat, BiocStyle, knitr, rmarkdown, covr License: GPL-3 MD5sum: a66ed641c8a0d65bfd98628b6cbda004 NeedsCompilation: no Title: An interactive tool for visualizing NGS signals and sequence motif densities along genomic features using average plots and heatmaps Description: SeqPlots is a tool for plotting next generation sequencing (NGS) based experiments' signal tracks, e.g. reads coverage from ChIP-seq, RNA-seq and DNA accessibility assays like DNase-seq and MNase-seq, over user specified genomic features, e.g. promoters, gene bodies, etc. It can also calculate sequence motif density profiles from reference genome. The data are visualized as average signal profile plot, with error estimates (standard error and 95% confidence interval) shown as fields, or as series of heatmaps that can be sorted and clustered using hierarchical clustering, k-means algorithm and self organising maps. Plots can be prepared using R programming language or web browser based graphical user interface (GUI) implemented using Shiny framework. The dual-purpose implementation allows running the software locally on desktop or deploying it on server. SeqPlots is useful for both for exploratory data analyses and preparing replicable, publication quality plots. Other features of the software include collaboration and data sharing capabilities, as well as ability to store pre-calculated result matrixes, that combine many sequencing experiments and in-silico generated tracks with multiple different features. These binaries can be further used to generate new combination plots on fly, run automated batch operations or share with colleagues, who can adjust their plotting parameters without loading actual tracks and recalculating numeric values. SeqPlots relays on Bioconductor packages, mainly on rtracklayer for data input and BSgenome packages for reference genome sequence and annotations. biocViews: ImmunoOncology, ChIPSeq, RNASeq, Sequencing, Software, Visualization Author: Przemyslaw Stempor [aut, cph, cre] Maintainer: Przemyslaw Stempor URL: http://github.com/przemol/seqplots VignetteBuilder: knitr BugReports: http://github.com/przemol/seqplots/issues git_url: https://git.bioconductor.org/packages/seqplots git_branch: RELEASE_3_10 git_last_commit: 95b0a96 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/seqplots_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/seqplots_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/seqplots_1.24.0.tgz vignettes: vignettes/seqplots/inst/doc/QuickStart.html, vignettes/seqplots/inst/doc/SeqPlotsGUI.html vignetteTitles: SeqPlots Quick Start, SeqPlots GUI hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqplots/inst/doc/QuickStart.R, vignettes/seqplots/inst/doc/SeqPlotsGUI.R importsMe: ChIPSeqSpike dependencyCount: 119 Package: seqsetvis Version: 1.6.0 Depends: R (>= 3.5), ggplot2 Imports: data.table, eulerr, GenomeInfoDb, GenomicAlignments, GenomicRanges, grDevices, grid, IRanges, limma, methods, parallel, pbapply, png, RColorBrewer, Rsamtools, rtracklayer, S4Vectors, stats Suggests: BiocFileCache, BiocManager, BiocStyle, ChIPpeakAnno, covr, cowplot, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 10da95c3807e4b08e81a20dc404e894a NeedsCompilation: no Title: Set Based Visualizations for Next-Gen Sequencing Data Description: seqsetvis enables the visualization and analysis of multiple genomic samples. Although seqsetvis was designed for the comparison of mulitple ChIP-seq samples, this package is domain-agnostic and allows the processing of multiple genomic coordinate files (bed-like files) and signal files (bigwig files or bam pileups). biocViews: Software, ChIPSeq, MultipleComparison, Sequencing, Visualization Author: Joseph R Boyd [aut, cre] Maintainer: Joseph R Boyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/seqsetvis git_branch: RELEASE_3_10 git_last_commit: 17b5f4f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/seqsetvis_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/seqsetvis_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/seqsetvis_1.6.0.tgz vignettes: vignettes/seqsetvis/inst/doc/seqsetvis_overview.html vignetteTitles: Overview and Use Cases hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/seqsetvis/inst/doc/seqsetvis_overview.R dependencyCount: 92 Package: SeqSQC Version: 1.8.1 Depends: R (>= 3.4), ExperimentHub (>= 1.3.7), SNPRelate (>= 1.10.2) Imports: e1071, GenomicRanges, gdsfmt, ggplot2, GGally, IRanges, methods, rbokeh, RColorBrewer, reshape2, rmarkdown, S4Vectors, stats, utils Suggests: BiocStyle, knitr, testthat License: GPL-3 MD5sum: 47e9ef506d9e7c67c4a6d6531e0704b2 NeedsCompilation: no Title: A bioconductor package for sample quality check with next generation sequencing data Description: The SeqSQC is designed to identify problematic samples in NGS data, including samples with gender mismatch, contamination, cryptic relatedness, and population outlier. biocViews: Experiment Data, Homo_sapiens_Data, Sequencing Data, Project1000genomes, Genome Author: Qian Liu [aut, cre] Maintainer: Qian Liu URL: https://github.com/Liubuntu/SeqSQC VignetteBuilder: knitr BugReports: https://github.com/Liubuntu/SeqSQC/issues git_url: https://git.bioconductor.org/packages/SeqSQC git_branch: RELEASE_3_10 git_last_commit: fbc7a0d git_last_commit_date: 2020-02-14 Date/Publication: 2020-02-14 source.ver: src/contrib/SeqSQC_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/SeqSQC_1.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SeqSQC_1.8.1.tgz vignettes: vignettes/SeqSQC/inst/doc/vignette.html vignetteTitles: Sample Quality Check for Next-Generation Sequencing Data with SeqSQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqSQC/inst/doc/vignette.R dependencyCount: 130 Package: seqTools Version: 1.20.0 Depends: methods,utils,zlibbioc LinkingTo: zlibbioc Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: a95db5dd95072e4d568b89ef2fa103f2 NeedsCompilation: yes Title: Analysis of nucleotide, sequence and quality content on fastq files Description: Analyze read length, phred scores and alphabet frequency and DNA k-mers on uncompressed and compressed fastq files. biocViews: QualityControl,Sequencing Author: Wolfgang Kaisers Maintainer: Wolfgang Kaisers git_url: https://git.bioconductor.org/packages/seqTools git_branch: RELEASE_3_10 git_last_commit: 9a80776 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/seqTools_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/seqTools_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/seqTools_1.20.0.tgz vignettes: vignettes/seqTools/inst/doc/seqTools_qual_report.pdf, vignettes/seqTools/inst/doc/seqTools.pdf vignetteTitles: seqTools_qual_report, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqTools/inst/doc/seqTools_qual_report.R, vignettes/seqTools/inst/doc/seqTools.R importsMe: qckitfastq dependencyCount: 3 Package: SeqVarTools Version: 1.24.1 Depends: SeqArray Imports: grDevices, graphics, stats, methods, Biobase, gdsfmt, GenomicRanges, IRanges, S4Vectors, GWASExactHW, logistf, Matrix, dplyr, rlang, tidyr Suggests: BiocGenerics, BiocStyle, RUnit, stringr License: GPL-3 MD5sum: 848bdbd13d0dc40c515ed5a75f8250a2 NeedsCompilation: no Title: Tools for variant data Description: An interface to the fast-access storage format for VCF data provided in SeqArray, with tools for common operations and analysis. biocViews: SNP, GeneticVariability, Sequencing, Genetics Author: Stephanie M. Gogarten, Xiuwen Zheng, Adrienne Stilp Maintainer: Stephanie M. Gogarten URL: https://github.com/smgogarten/SeqVarTools git_url: https://git.bioconductor.org/packages/SeqVarTools git_branch: RELEASE_3_10 git_last_commit: f0ee218 git_last_commit_date: 2020-03-16 Date/Publication: 2020-03-17 source.ver: src/contrib/SeqVarTools_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/SeqVarTools_1.24.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SeqVarTools_1.24.1.tgz vignettes: vignettes/SeqVarTools/inst/doc/Iterators.pdf, vignettes/SeqVarTools/inst/doc/SeqVarTools.pdf vignetteTitles: Iterators in SeqVarTools, Introduction to SeqVarTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqVarTools/inst/doc/Iterators.R, vignettes/SeqVarTools/inst/doc/SeqVarTools.R importsMe: GENESIS, VariantExperiment dependencyCount: 61 Package: sesame Version: 1.4.0 Depends: R (>= 3.6), sesameData, methods Imports: BiocParallel, R6, grDevices, utils, illuminaio, MASS, GenomicRanges, IRanges, grid, preprocessCore, stats, S4Vectors, randomForest, wheatmap, ggplot2, parallel, matrixStats, DNAcopy Suggests: scales, knitr, rmarkdown, testthat, minfi, SummarizedExperiment, FlowSorted.CordBloodNorway.450k, FlowSorted.Blood.450k, dplyr, tidyr, BiocStyle License: MIT + file LICENSE MD5sum: 35d0b2495a491568137ec0ee7aea4117 NeedsCompilation: no Title: Tools For Analyzing Illumina Infinium DNA Methylation Arrays Description: Tools For analyzing Illumina Infinium DNA methylation arrays. biocViews: DNAMethylation, MethylationArray, Preprocessing, QualityControl Author: Wanding Zhou [aut, cre], Hui Shen [aut], Timothy Triche [ctb], Bret Barnes [ctb] Maintainer: Wanding Zhou URL: https://github.com/zwdzwd/sesame VignetteBuilder: knitr BugReports: https://github.com/zwdzwd/sesame/issues git_url: https://git.bioconductor.org/packages/sesame git_branch: RELEASE_3_10 git_last_commit: 55af6fe git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sesame_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sesame_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sesame_1.4.0.tgz vignettes: vignettes/sesame/inst/doc/largeData.html, vignettes/sesame/inst/doc/minfi.html, vignettes/sesame/inst/doc/QC.html, vignettes/sesame/inst/doc/sesame.html vignetteTitles: 2. Large Data, 3. Minfi Interaction, 1. Quality Controls, "0. SeSAMe User Guide" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sesame/inst/doc/largeData.R, vignettes/sesame/inst/doc/minfi.R, vignettes/sesame/inst/doc/QC.R, vignettes/sesame/inst/doc/sesame.R importsMe: TCGAbiolinksGUI suggestsMe: TCGAbiolinks dependencyCount: 117 Package: SEtools Version: 1.0.0 Depends: R (>= 3.6) Imports: SummarizedExperiment, data.table, pheatmap, seriation, ComplexHeatmap, circlize, methods Suggests: BiocStyle, knitr, rmarkdown, ggplot2 License: GPL MD5sum: 824c673a36aa31cfd9bc24ae8dca2177 NeedsCompilation: no Title: SEtools: tools for working with SummarizedExperiment Description: This includes a set of tools for working with the SummarizedExperiment class, including handy merging and plotting functions. biocViews: DataRepresentation, Visualization Author: Pierre-Luc Germain [cre, aut] () Maintainer: Pierre-Luc Germain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SEtools git_branch: RELEASE_3_10 git_last_commit: ef2a1d2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SEtools_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SEtools_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SEtools_1.0.0.tgz vignettes: vignettes/SEtools/inst/doc/SEtools.html vignetteTitles: SEtools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SEtools/inst/doc/SEtools.R dependencyCount: 105 Package: sevenbridges Version: 1.16.1 Depends: methods, utils, stats Imports: httr, jsonlite, yaml, objectProperties, stringr, S4Vectors, docopt, curl, uuid, dplyr Suggests: knitr, rmarkdown, testthat, readr License: Apache License 2.0 | file LICENSE Archs: i386, x64 MD5sum: 53c9f8090fc354f0fc2ee459dda97093 NeedsCompilation: no Title: Seven Bridges Platform API Client and Common Workflow Language Tool Builder in R Description: R client and utilities for Seven Bridges platform API, from Cancer Genomics Cloud to other Seven Bridges supported platforms. biocViews: Software, DataImport, ThirdPartyClient Author: Nan Xiao [aut, cre], Tengfei Yin [aut], Emile Young [ctb], Dusan Randjelovic [ctb], Seven Bridges Genomics [cph, fnd] Maintainer: Nan Xiao URL: https://www.sevenbridges.com, https://sbg.github.io/sevenbridges-r/, https://github.com/sbg/sevenbridges-r VignetteBuilder: knitr BugReports: https://github.com/sbg/sevenbridges-r/issues git_url: https://git.bioconductor.org/packages/sevenbridges git_branch: RELEASE_3_10 git_last_commit: fe93e09 git_last_commit_date: 2020-03-26 Date/Publication: 2020-03-26 source.ver: src/contrib/sevenbridges_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/sevenbridges_1.16.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sevenbridges_1.16.1.tgz vignettes: vignettes/sevenbridges/inst/doc/api.html, vignettes/sevenbridges/inst/doc/apps.html, vignettes/sevenbridges/inst/doc/bioc-workflow.html, vignettes/sevenbridges/inst/doc/cgc-datasets.html, vignettes/sevenbridges/inst/doc/docker.html, vignettes/sevenbridges/inst/doc/rstudio.html vignetteTitles: Complete Guide for Seven Bridges API R Client, Describe and Execute CWL Tools/Workflows in R, Master Tutorial: Use R for Cancer Genomics Cloud, Find Data on CGC via Data Browser and Datasets API, Creating Your Docker Container and Command Line Interface (with docopt), IDE Container: RStudio Server,, Shiny Server,, and More hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sevenbridges/inst/doc/api.R, vignettes/sevenbridges/inst/doc/apps.R, vignettes/sevenbridges/inst/doc/bioc-workflow.R, vignettes/sevenbridges/inst/doc/cgc-datasets.R, vignettes/sevenbridges/inst/doc/docker.R, vignettes/sevenbridges/inst/doc/rstudio.R dependencyCount: 46 Package: sevenC Version: 1.6.0 Depends: R (>= 3.5), InteractionSet (>= 1.2.0) Imports: rtracklayer (>= 1.34.1), BiocGenerics (>= 0.22.0), GenomeInfoDb (>= 1.12.2), GenomicRanges (>= 1.28.5), IRanges (>= 2.10.3), S4Vectors (>= 0.14.4), readr (>= 1.1.0), purrr (>= 0.2.2), data.table (>= 1.10.4), boot (>= 1.3-20), methods (>= 3.4.1) Suggests: testthat, BiocStyle, knitr, rmarkdown, GenomicInteractions, covr License: GPL-3 MD5sum: 9ac3bdbce366b09eab13ccd68ed3587b NeedsCompilation: no Title: Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs Description: Chromatin looping is an essential feature of eukaryotic genomes and can bring regulatory sequences, such as enhancers or transcription factor binding sites, in the close physical proximity of regulated target genes. Here, we provide sevenC, an R package that uses protein binding signals from ChIP-seq and sequence motif information to predict chromatin looping events. Cross-linking of proteins that bind close to loop anchors result in ChIP-seq signals at both anchor loci. These signals are used at CTCF motif pairs together with their distance and orientation to each other to predict whether they interact or not. The resulting chromatin loops might be used to associate enhancers or transcription factor binding sites (e.g., ChIP-seq peaks) to regulated target genes. biocViews: DNA3DStructure, ChIPchip, Coverage, DataImport, Epigenetics, FunctionalGenomics, Classification, Regression, ChIPSeq, HiC, Annotation Author: Jonas Ibn-Salem [aut, cre] Maintainer: Jonas Ibn-Salem URL: https://github.com/ibn-salem/sevenC VignetteBuilder: knitr BugReports: https://github.com/ibn-salem/sevenC/issues git_url: https://git.bioconductor.org/packages/sevenC git_branch: RELEASE_3_10 git_last_commit: 206f5fd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sevenC_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sevenC_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sevenC_1.6.0.tgz vignettes: vignettes/sevenC/inst/doc/sevenC.html vignetteTitles: Introduction to sevenC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sevenC/inst/doc/sevenC.R dependencyCount: 62 Package: SGSeq Version: 1.20.0 Depends: R (>= 3.5.0), IRanges (>= 2.13.15), GenomicRanges (>= 1.31.10), Rsamtools (>= 1.31.2), SummarizedExperiment, methods Imports: AnnotationDbi, BiocGenerics (>= 0.31.5), Biostrings (>= 2.47.6), GenomicAlignments (>= 1.15.7), GenomicFeatures (>= 1.31.5), GenomeInfoDb, RUnit, S4Vectors (>= 0.23.19), grDevices, graphics, igraph, parallel, rtracklayer (>= 1.39.7), stats Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown License: Artistic-2.0 MD5sum: e9857e69975fe2cb4ffeb35e4a1b38db NeedsCompilation: no Title: Splice event prediction and quantification from RNA-seq data Description: SGSeq is a software package for analyzing splice events from RNA-seq data. Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation. biocViews: AlternativeSplicing, ImmunoOncology, RNASeq, Transcription Author: Leonard Goldstein Maintainer: Leonard Goldstein VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SGSeq git_branch: RELEASE_3_10 git_last_commit: 8200c5f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SGSeq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SGSeq_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SGSeq_1.20.0.tgz vignettes: vignettes/SGSeq/inst/doc/SGSeq.html vignetteTitles: SGSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SGSeq/inst/doc/SGSeq.R dependsOnMe: EventPointer importsMe: Rhisat2 dependencyCount: 85 Package: SharedObject Version: 1.0.0 Depends: R (>= 3.6.0) Imports: Rcpp, methods, stats, xptr, BiocGenerics LinkingTo: BH, Rcpp Suggests: testthat, parallel, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: f0605035882f181d2f2c490d35506443 NeedsCompilation: yes Title: Sharing R objects across multiple R processes without memory duplication Description: This package is developed for facilitating parallel computing in R. It is capable to create an R object in the shared memory space and share the data across multiple R processes. It avoids the overhead of memory dulplication and data transfer, which make sharing big data object across many clusters possible. biocViews: Infrastructure Author: Jiefei Wang Maintainer: Jiefei Wang VignetteBuilder: knitr BugReports: https://github.com/Jiefei-Wang/SharedObject/issues git_url: https://git.bioconductor.org/packages/SharedObject git_branch: RELEASE_3_10 git_last_commit: 5a594e2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-30 source.ver: src/contrib/SharedObject_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SharedObject_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SharedObject_1.0.0.tgz vignettes: vignettes/SharedObject/inst/doc/quick_start_guide.html vignetteTitles: quickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SharedObject/inst/doc/quick_start_guide.R dependencyCount: 9 Package: shinyMethyl Version: 1.22.0 Depends: methods, BiocGenerics (>= 0.3.2), shiny (>= 0.13.2), minfi (>= 1.18.2), IlluminaHumanMethylation450kmanifest, matrixStats, R (>= 3.0.0) Imports: RColorBrewer Suggests: shinyMethylData, minfiData, BiocStyle, RUnit, digest, knitr License: Artistic-2.0 MD5sum: 85f55fb07d7522ec2b2a6cf4ec967ed6 NeedsCompilation: no Title: Interactive visualization for Illumina methylation arrays Description: Interactive tool for visualizing Illumina methylation array data. Both the 450k and EPIC array are supported. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl Author: Jean-Philippe Fortin [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Jean-Philippe Fortin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/shinyMethyl git_branch: RELEASE_3_10 git_last_commit: 2477228 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/shinyMethyl_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/shinyMethyl_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/shinyMethyl_1.22.0.tgz vignettes: vignettes/shinyMethyl/inst/doc/shinyMethyl.pdf vignetteTitles: shinyMethyl: interactive visualization of Illumina 450K methylation arrays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/shinyMethyl/inst/doc/shinyMethyl.R dependencyCount: 130 Package: shinyTANDEM Version: 1.24.0 Depends: rTANDEM (>= 1.3.5), shiny, mixtools, methods, xtable License: GPL-3 MD5sum: 0fe8360601d647cd545016b4664b3367 NeedsCompilation: no Title: Provides a GUI for rTANDEM Description: This package provides a GUI interface for rTANDEM. The GUI is primarily designed to visualize rTANDEM result object or result xml files. But it will also provides an interface for creating parameter objects, launching searches or performing conversions between R objects and xml files. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Frederic Fournier , Arnaud Droit Maintainer: Frederic Fournier git_url: https://git.bioconductor.org/packages/shinyTANDEM git_branch: RELEASE_3_10 git_last_commit: 78060f0 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/shinyTANDEM_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/shinyTANDEM_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/shinyTANDEM_1.24.0.tgz vignettes: vignettes/shinyTANDEM/inst/doc/shinyTANDEM.pdf vignetteTitles: shinyTANDEM user guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 35 Package: ShortRead Version: 1.44.3 Depends: BiocGenerics (>= 0.23.3), BiocParallel, Biostrings (>= 2.47.6), Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6) Imports: Biobase, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomeInfoDb (>= 1.15.2), GenomicRanges (>= 1.31.8), hwriter, methods, zlibbioc, lattice, latticeExtra, LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: BiocStyle, RUnit, biomaRt, GenomicFeatures, yeastNagalakshmi License: Artistic-2.0 MD5sum: 71cda3a16e9e7e225af0d993510e03e3 NeedsCompilation: yes Title: FASTQ input and manipulation Description: This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats. biocViews: DataImport, Sequencing, QualityControl Author: Martin Morgan, Michael Lawrence, Simon Anders Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/ShortRead git_branch: RELEASE_3_10 git_last_commit: d97933c git_last_commit_date: 2020-02-03 Date/Publication: 2020-02-03 source.ver: src/contrib/ShortRead_1.44.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/ShortRead_1.44.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ShortRead_1.44.3.tgz vignettes: vignettes/ShortRead/inst/doc/Overview.pdf vignetteTitles: An introduction to ShortRead hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ShortRead/inst/doc/Overview.R dependsOnMe: chipseq, EDASeq, esATAC, girafe, HTSeqGenie, OTUbase, Rqc, segmentSeq, systemPipeR importsMe: amplican, ArrayExpressHTS, basecallQC, BEAT, chipseq, ChIPseqR, ChIPsim, dada2, easyRNASeq, FastqCleaner, GOTHiC, icetea, IONiseR, MACPET, ngsReports, nucleR, QuasR, R453Plus1Toolbox, RSVSim, scruff suggestsMe: BiocParallel, CSAR, DBChIP, GenomicAlignments, Genominator, PICS, PING, Repitools, Rsamtools, S4Vectors dependencyCount: 41 Package: SIAMCAT Version: 1.6.0 Depends: R (>= 3.5.0), mlr, phyloseq Imports: beanplot, glmnet, graphics, grDevices, grid, gridBase, gridExtra, LiblineaR, matrixStats, methods, ParamHelpers, pROC, PRROC, RColorBrewer, scales, stats, stringr, utils, infotheo, progress, corrplot Suggests: BiocStyle, optparse, testthat, knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: eab9d832385eb7a268d8fe0da44ea722 NeedsCompilation: no Title: Statistical Inference of Associations between Microbial Communities And host phenoTypes Description: Pipeline for Statistical Inference of Associations between Microbial Communities And host phenoTypes (SIAMCAT). A primary goal of analyzing microbiome data is to determine changes in community composition that are associated with environmental factors. In particular, linking human microbiome composition to host phenotypes such as diseases has become an area of intense research. For this, robust statistical modeling and biomarker extraction toolkits are crucially needed. SIAMCAT provides a full pipeline supporting data preprocessing, statistical association testing, statistical modeling (LASSO logistic regression) including tools for evaluation and interpretation of these models (such as cross validation, parameter selection, ROC analysis and diagnostic model plots). biocViews: ImmunoOncology, Metagenomics, Classification, Microbiome, Sequencing, Preprocessing, Clustering, FeatureExtraction, GeneticVariability, MultipleComparison,Regression Author: Konrad Zych [aut, cre] (), Jakob Wirbel [aut] (), Georg Zeller [aut] (), Morgan Essex [ctb], Nicolai Karcher [ctb], Kersten Breuer [ctb] Maintainer: Konrad Zych VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SIAMCAT git_branch: RELEASE_3_10 git_last_commit: 68044d5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SIAMCAT_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SIAMCAT_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SIAMCAT_1.6.0.tgz vignettes: vignettes/SIAMCAT/inst/doc/SIAMCAT_holdout.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_read-in.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_vignette.html vignetteTitles: SIAMCAT holdout testing vignette, SIAMCAT.input, SIAMCAT basic vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIAMCAT/inst/doc/SIAMCAT_holdout.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_read-in.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_vignette.R dependencyCount: 106 Package: SICtools Version: 1.16.0 Depends: R (>= 3.0.0), methods, Rsamtools (>= 1.18.1), doParallel (>= 1.0.8), Biostrings (>= 2.32.1), stringr (>= 0.6.2), matrixStats (>= 0.10.0), plyr (>= 1.8.3), GenomicRanges (>= 1.22.4), IRanges (>= 2.4.8) Suggests: knitr, RUnit, BiocGenerics License: GPL (>=2) MD5sum: a64b607f4b6d8833bbf67c4b53d61d9f NeedsCompilation: yes Title: Find SNV/Indel differences between two bam files with near relationship Description: This package is to find SNV/Indel differences between two bam files with near relationship in a way of pairwise comparison thourgh each base position across the genome region of interest. The difference is inferred by fisher test and euclidean distance, the input of which is the base count (A,T,G,C) in a given position and read counts for indels that span no less than 2bp on both sides of indel region. biocViews: Alignment, Sequencing, Coverage, SequenceMatching, QualityControl, DataImport, Software, SNP, VariantDetection Author: Xiaobin Xing, Wu Wei Maintainer: Xiaobin Xing VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SICtools git_branch: RELEASE_3_10 git_last_commit: 3e51fb9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SICtools_1.16.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SICtools_1.16.0.tgz vignettes: vignettes/SICtools/inst/doc/SICtools.pdf vignetteTitles: Using SICtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SICtools/inst/doc/SICtools.R dependencyCount: 38 Package: sigaR Version: 1.34.0 Depends: Biobase, CGHbase, methods, mvtnorm, Imports: corpcor (>= 1.6.2), graphics, igraph, limma, marray, MASS, penalized, quadprog, snowfall, stats Suggests: CGHcall License: GPL (>= 2) MD5sum: d46036ac2e2e93e041fbff71d925a699 NeedsCompilation: no Title: Statistics for Integrative Genomics Analyses in R Description: Facilitates the joint analysis of high-throughput data from multiple molecular levels. Contains functions for manipulation of objects, various analysis types, and some visualization. biocViews: Microarray, DifferentialExpression, aCGH, GeneExpression, Pathways Author: Wessel N. van Wieringen Maintainer: Wessel N. van Wieringen URL: http://www.few.vu.nl/~wvanwie git_url: https://git.bioconductor.org/packages/sigaR git_branch: RELEASE_3_10 git_last_commit: 8c164ca git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sigaR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sigaR_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sigaR_1.34.0.tgz vignettes: vignettes/sigaR/inst/doc/sigaR.pdf vignetteTitles: sigaR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigaR/inst/doc/sigaR.R dependencyCount: 28 Package: SigCheck Version: 2.18.0 Depends: R (>= 3.2.0), MLInterfaces, Biobase, e1071, BiocParallel, survival Imports: graphics, stats, utils, methods Suggests: BiocStyle, breastCancerNKI, qusage License: Artistic-2.0 MD5sum: 53564e4873c772ce6adc920f98647424 NeedsCompilation: no Title: Check a gene signature's prognostic performance against random signatures, known signatures, and permuted data/metadata Description: While gene signatures are frequently used to predict phenotypes (e.g. predict prognosis of cancer patients), it it not always clear how optimal or meaningful they are (cf David Venet, Jacques E. Dumont, and Vincent Detours' paper "Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome"). Based on suggestions in that paper, SigCheck accepts a data set (as an ExpressionSet) and a gene signature, and compares its performance on survival and/or classification tasks against a) random gene signatures of the same length; b) known, related and unrelated gene signatures; and c) permuted data and/or metadata. biocViews: GeneExpression, Classification, GeneSetEnrichment Author: Rory Stark and Justin Norden Maintainer: Rory Stark git_url: https://git.bioconductor.org/packages/SigCheck git_branch: RELEASE_3_10 git_last_commit: 09c2cce git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SigCheck_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SigCheck_2.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SigCheck_2.18.0.tgz vignettes: vignettes/SigCheck/inst/doc/SigCheck.pdf vignetteTitles: Checking gene expression signatures against random and known signatures with SigCheck hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigCheck/inst/doc/SigCheck.R dependencyCount: 101 Package: sigFeature Version: 1.4.0 Depends: R (>= 3.5.0) Imports: biocViews, nlme, e1071, openxlsx, pheatmap, RColorBrewer, Matrix, SparseM, graphics, stats, utils, SummarizedExperiment, BiocParallel, methods Suggests: RUnit, BiocGenerics, knitr License: GPL Archs: i386, x64 MD5sum: 89dd1bab013c57234acfdd94a8416a89 NeedsCompilation: no Title: sigFeature: Significant feature selection using SVM-RFE & t-statistic Description: This package provides a novel feature selection algorithm for binary classification using support vector machine recursive feature elimination SVM-RFE and t-statistic. In this feature selection process, the selected features are differentially significant between the two classes and also they are good classifier with higher degree of classification accuracy. biocViews: FeatureExtraction, GeneExpression, Microarray, Transcription, mRNAMicroarray, GenePrediction, Normalization, Classification, SupportVectorMachine Author: Pijush Das Developer [aut, cre], Dr. Susanta Roychudhury User [ctb], Dr. Sucheta Tripathy User [ctb] Maintainer: Pijush Das Developer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sigFeature git_branch: RELEASE_3_10 git_last_commit: 74224f6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sigFeature_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sigFeature_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sigFeature_1.4.0.tgz vignettes: vignettes/sigFeature/inst/doc/vignettes.pdf vignetteTitles: sigFeature hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sigFeature/inst/doc/vignettes.R dependencyCount: 60 Package: SigFuge Version: 1.24.0 Depends: R (>= 3.1.1), GenomicRanges Imports: ggplot2, matlab, reshape, sigclust Suggests: org.Hs.eg.db, prebsdata, Rsamtools (>= 1.17.0), TxDb.Hsapiens.UCSC.hg19.knownGene, BiocStyle License: GPL-3 MD5sum: 9e64e549bd25b3048671984fdee3ab33 NeedsCompilation: no Title: SigFuge Description: Algorithm for testing significance of clustering in RNA-seq data. biocViews: Clustering, Visualization, RNASeq, ImmunoOncology Author: Patrick Kimes, Christopher Cabanski Maintainer: Patrick Kimes git_url: https://git.bioconductor.org/packages/SigFuge git_branch: RELEASE_3_10 git_last_commit: bc27402 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SigFuge_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SigFuge_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SigFuge_1.24.0.tgz vignettes: vignettes/SigFuge/inst/doc/SigFuge.pdf vignetteTitles: SigFuge Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigFuge/inst/doc/SigFuge.R dependencyCount: 70 Package: siggenes Version: 1.60.0 Depends: Biobase, multtest, splines, methods Imports: stats4, grDevices, graphics, stats, scrime (>= 1.2.5) Suggests: affy, annotate, genefilter, KernSmooth License: LGPL (>= 2) MD5sum: bdd9c7b381cfa504b7c13d639b2a27a2 NeedsCompilation: no Title: Multiple Testing using SAM and Efron's Empirical Bayes Approaches Description: Identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM). biocViews: MultipleComparison, Microarray, GeneExpression, SNP, ExonArray, DifferentialExpression Author: Holger Schwender Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/siggenes git_branch: RELEASE_3_10 git_last_commit: 3cb3d04 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/siggenes_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/siggenes_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/siggenes_1.60.0.tgz vignettes: vignettes/siggenes/inst/doc/siggenes.pdf, vignettes/siggenes/inst/doc/siggenesRnews.pdf, vignettes/siggenes/inst/doc/identify.sam.html, vignettes/siggenes/inst/doc/plot.ebam.html, vignettes/siggenes/inst/doc/plot.finda0.html, vignettes/siggenes/inst/doc/plot.sam.html, vignettes/siggenes/inst/doc/print.ebam.html, vignettes/siggenes/inst/doc/print.finda0.html, vignettes/siggenes/inst/doc/print.sam.html, vignettes/siggenes/inst/doc/summary.ebam.html, vignettes/siggenes/inst/doc/summary.sam.html vignetteTitles: siggenes Manual, siggenesRnews.pdf, identify.sam.html, plot.ebam.html, plot.finda0.html, plot.sam.html, print.ebam.html, print.finda0.html, print.sam.html, summary.ebam.html, summary.sam.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/siggenes/inst/doc/siggenes.R dependsOnMe: KCsmart importsMe: coexnet, DAPAR, minfi, trio, XDE suggestsMe: logicFS dependencyCount: 17 Package: sights Version: 1.12.0 Depends: R(>= 3.3) Imports: MASS(>= 7.3), qvalue(>= 2.2), ggplot2(>= 2.0), reshape2(>= 1.4), lattice(>= 0.2), stats(>= 3.3) Suggests: testthat, knitr, rmarkdown, ggthemes, gridExtra, xlsx License: GPL-3 | file LICENSE MD5sum: 6a78fb6d754ff94cd0de786b21b786d6 NeedsCompilation: no Title: Statistics and dIagnostic Graphs for HTS Description: SIGHTS is a suite of normalization methods, statistical tests, and diagnostic graphical tools for high throughput screening (HTS) assays. HTS assays use microtitre plates to screen large libraries of compounds for their biological, chemical, or biochemical activity. biocViews: ImmunoOncology, CellBasedAssays, MicrotitrePlateAssay, Normalization, MultipleComparison, Preprocessing, QualityControl, BatchEffect, Visualization Author: Elika Garg [aut, cre], Carl Murie [aut], Heydar Ensha [ctb], Robert Nadon [aut] Maintainer: Elika Garg URL: https://eg-r.github.io/sights/ VignetteBuilder: knitr BugReports: https://github.com/eg-r/sights/issues git_url: https://git.bioconductor.org/packages/sights git_branch: RELEASE_3_10 git_last_commit: 39a40e1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sights_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sights_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sights_1.12.0.tgz vignettes: vignettes/sights/inst/doc/sights.html vignetteTitles: Using **SIGHTS** R-package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sights/inst/doc/sights.R dependencyCount: 59 Package: signatureSearch Version: 1.0.4 Depends: R(>= 3.6.0), Rcpp, SummarizedExperiment Imports: AnnotationDbi, ggplot2, data.table, ExperimentHub, HDF5Array, magrittr, RSQLite, dplyr, fgsea, scales, methods, qvalue, stats, utils, reshape2, visNetwork, BiocParallel, fastmatch, Matrix, clusterProfiler, readr, DOSE, rhdf5, GSEABase, DelayedArray LinkingTo: Rcpp Suggests: knitr, testthat, rmarkdown, BiocStyle, org.Hs.eg.db License: Artistic-2.0 MD5sum: 80f6147b7df8edc9921c015599ea8fb4 NeedsCompilation: yes Title: Environment for Gene Expression Searching Combined with Functional Enrichment Analysis Description: This package implements algorithms and data structures for performing gene expression signature (GES) searches, and subsequently interpreting the results functionally with specialized enrichment methods. biocViews: Software, GeneExpression, GO, KEGG, NetworkEnrichment, Sequencing, Coverage, DifferentialExpression Author: Yuzhu Duan [cre, aut], Thomas Girke [aut] Maintainer: Yuzhu Duan URL: https://github.com/yduan004/signatureSearch/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/yduan004/signatureSearch/issues git_url: https://git.bioconductor.org/packages/signatureSearch git_branch: RELEASE_3_10 git_last_commit: 712e3fb git_last_commit_date: 2020-04-06 Date/Publication: 2020-04-06 source.ver: src/contrib/signatureSearch_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/signatureSearch_1.0.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/signatureSearch_1.0.4.tgz vignettes: vignettes/signatureSearch/inst/doc/signatureSearch.html vignetteTitles: signatureSearch hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signatureSearch/inst/doc/signatureSearch.R dependencyCount: 161 Package: signeR Version: 1.12.0 Depends: VariantAnnotation, NMF Imports: BiocGenerics, Biostrings, class, graphics, grDevices, GenomeInfoDb, GenomicRanges, IRanges, nloptr, methods, stats, utils, PMCMR LinkingTo: Rcpp, RcppArmadillo (>= 0.7.100) Suggests: knitr, rtracklayer, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 MD5sum: c2d6fe26f8351a51ee5df2ca8d73a120 NeedsCompilation: yes Title: Empirical Bayesian approach to mutational signature discovery Description: The signeR package provides an empirical Bayesian approach to mutational signature discovery. It is designed to analyze single nucleotide variaton (SNV) counts in cancer genomes, but can also be applied to other features as well. Functionalities to characterize signatures or genome samples according to exposure patterns are also provided. biocViews: GenomicVariation, SomaticMutation, StatisticalMethod, Visualization Author: Rafael Rosales, Rodrigo Drummond, Renan Valieris, Israel Tojal da Silva Maintainer: Renan Valieris URL: https://github.com/rvalieris/signeR SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/signeR git_branch: RELEASE_3_10 git_last_commit: 0579a0a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/signeR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/signeR_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/signeR_1.12.0.tgz vignettes: vignettes/signeR/inst/doc/signeR-vignette.html vignetteTitles: signeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signeR/inst/doc/signeR-vignette.R dependencyCount: 132 Package: signet Version: 1.6.0 Depends: R (>= 3.4.0) Imports: graph, igraph, RBGL, graphics, utils, stats, methods Suggests: graphite, BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 9b0c49383971ef6545060185474c0060 NeedsCompilation: no Title: signet: Selection Inference in Gene NETworks Description: An R package to detect selection in biological pathways. Using gene selection scores and biological pathways data, one can search for high-scoring subnetworks of genes within pathways and test their significance. biocViews: Software, Pathways, DifferentialExpression, GeneExpression, NetworkEnrichment, GraphAndNetwork, KEGG Author: Alexandre Gouy [aut, cre] Maintainer: Alexandre Gouy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/signet git_branch: RELEASE_3_10 git_last_commit: fd60d46 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/signet_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/signet_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/signet_1.6.0.tgz vignettes: vignettes/signet/inst/doc/signet.html vignetteTitles: signet tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signet/inst/doc/signet.R dependencyCount: 17 Package: sigPathway Version: 1.54.0 Depends: R (>= 2.10) Suggests: hgu133a.db (>= 1.10.0), XML (>= 1.6-3), AnnotationDbi (>= 1.3.12) License: GPL-2 MD5sum: d1af8cbd405246a2010d0c3a88ac9b3f NeedsCompilation: yes Title: Pathway Analysis Description: Conducts pathway analysis by calculating the NT_k and NE_k statistics as described in Tian et al. (2005) biocViews: DifferentialExpression, MultipleComparison Author: Weil Lai (optimized R and C code), Lu Tian and Peter Park (algorithm development and initial R code) Maintainer: Weil Lai URL: http://www.pnas.org/cgi/doi/10.1073/pnas.0506577102, http://www.chip.org/~ppark/Supplements/PNAS05.html git_url: https://git.bioconductor.org/packages/sigPathway git_branch: RELEASE_3_10 git_last_commit: 2217a34 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sigPathway_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sigPathway_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sigPathway_1.54.0.tgz vignettes: vignettes/sigPathway/inst/doc/sigPathway-vignette.pdf vignetteTitles: sigPathway hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigPathway/inst/doc/sigPathway-vignette.R dependsOnMe: tRanslatome dependencyCount: 0 Package: SigsPack Version: 1.0.0 Depends: R (>= 3.6) Imports: quadprog (>= 1.5-5), methods, Biobase, BSgenome (>= 1.46.0), VariantAnnotation (>= 1.24.5), Biostrings, GenomeInfoDb, GenomicRanges, rtracklayer, SummarizedExperiment, graphics, stats, utils Suggests: IRanges, BSgenome.Hsapiens.UCSC.hg19, BiocStyle, knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 3a0745fcfd4da303a518fadcbc794797 NeedsCompilation: no Title: Mutational Signature Estimation for Single Samples Description: Single sample estimation of exposure to mutational signatures. Exposures to known mutational signatures are estimated for single samples, based on quadratic programming algorithms. Bootstrapping the input mutational catalogues provides estimations on the stability of these exposures. The effect of the sequence composition of mutational context can be taken into account by normalising the catalogues. biocViews: SomaticMutation, SNP, VariantAnnotation, BiomedicalInformatics, DNASeq Author: Franziska Schumann Maintainer: Franziska Schumann URL: https://github.com/bihealth/SigsPack VignetteBuilder: knitr BugReports: https://github.com/bihealth/SigsPack/issues git_url: https://git.bioconductor.org/packages/SigsPack git_branch: RELEASE_3_10 git_last_commit: 07a6d89 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SigsPack_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SigsPack_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SigsPack_1.0.0.tgz vignettes: vignettes/SigsPack/inst/doc/SigsPack.html vignetteTitles: Introduction to SigsPack hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigsPack/inst/doc/SigsPack.R dependencyCount: 86 Package: sigsquared Version: 1.18.0 Depends: R (>= 3.2.0), methods Imports: Biobase, survival Suggests: RUnit, BiocGenerics License: GPL version 3 Archs: i386, x64 MD5sum: 5a4641401e312c4b3544eacf636e4122 NeedsCompilation: no Title: Gene signature generation for functionally validated signaling pathways Description: By leveraging statistical properties (log-rank test for survival) of patient cohorts defined by binary thresholds, poor-prognosis patients are identified by the sigsquared package via optimization over a cost function reducing type I and II error. Author: UnJin Lee Maintainer: UnJin Lee git_url: https://git.bioconductor.org/packages/sigsquared git_branch: RELEASE_3_10 git_last_commit: 08c23d3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sigsquared_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sigsquared_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sigsquared_1.18.0.tgz vignettes: vignettes/sigsquared/inst/doc/sigsquared.pdf vignetteTitles: SigSquared hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigsquared/inst/doc/sigsquared.R dependencyCount: 13 Package: SIM Version: 1.56.0 Depends: R (>= 2.4), quantreg Imports: graphics, stats, globaltest, quantsmooth Suggests: biomaRt, RColorBrewer License: GPL (>= 2) MD5sum: d44ad018a76f687777855a22f318c552 NeedsCompilation: yes Title: Integrated Analysis on two human genomic datasets Description: Finds associations between two human genomic datasets. biocViews: Microarray, Visualization Author: Renee X. de Menezes and Judith M. Boer Maintainer: Renee X. de Menezes git_url: https://git.bioconductor.org/packages/SIM git_branch: RELEASE_3_10 git_last_commit: c4f8db6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SIM_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SIM_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SIM_1.56.0.tgz vignettes: vignettes/SIM/inst/doc/SIM.pdf vignetteTitles: SIM vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIM/inst/doc/SIM.R dependencyCount: 42 Package: SIMAT Version: 1.18.0 Depends: R (>= 3.5.0), Rcpp (>= 0.11.3) Imports: mzR, ggplot2, grid, reshape2, grDevices, stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 Archs: i386, x64 MD5sum: c848028bd8c9ac42d12ade895ef1f469 NeedsCompilation: no Title: GC-SIM-MS data processing and alaysis tool Description: This package provides a pipeline for analysis of GC-MS data acquired in selected ion monitoring (SIM) mode. The tool also provides a guidance in choosing appropriate fragments for the targets of interest by using an optimization algorithm. This is done by considering overlapping peaks from a provided library by the user. biocViews: ImmunoOncology, Software, Metabolomics, MassSpectrometry Author: M. R. Nezami Ranjbar Maintainer: M. R. Nezami Ranjbar URL: http://omics.georgetown.edu/SIMAT.html git_url: https://git.bioconductor.org/packages/SIMAT git_branch: RELEASE_3_10 git_last_commit: d28e88b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SIMAT_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SIMAT_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SIMAT_1.18.0.tgz vignettes: vignettes/SIMAT/inst/doc/SIMAT-vignette.pdf vignetteTitles: SIMAT Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIMAT/inst/doc/SIMAT-vignette.R dependencyCount: 66 Package: SimBindProfiles Version: 1.24.0 Depends: R (>= 2.10), methods, Ringo Imports: limma, mclust, Biobase License: GPL-3 MD5sum: fe112ecccee8cbd58eeef97995d24480 NeedsCompilation: no Title: Similar Binding Profiles Description: SimBindProfiles identifies common and unique binding regions in genome tiling array data. This package does not rely on peak calling, but directly compares binding profiles processed on the same array platform. It implements a simple threshold approach, thus allowing retrieval of commonly and differentially bound regions between datasets as well as events of compensation and increased binding. biocViews: Microarray, Software Author: Bettina Fischer, Enrico Ferrero, Robert Stojnic, Steve Russell Maintainer: Bettina Fischer git_url: https://git.bioconductor.org/packages/SimBindProfiles git_branch: RELEASE_3_10 git_last_commit: 29a037d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SimBindProfiles_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SimBindProfiles_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SimBindProfiles_1.24.0.tgz vignettes: vignettes/SimBindProfiles/inst/doc/SimBindProfiles.pdf vignetteTitles: SimBindProfiles: Similar Binding Profiles,, identifies common and unique regions in array genome tiling array data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SimBindProfiles/inst/doc/SimBindProfiles.R dependencyCount: 85 Package: SIMD Version: 1.4.0 Depends: R (>= 3.5.0) Imports: edgeR, statmod, methylMnM, stats, utils Suggests: BiocStyle, knitr,rmarkdown License: GPL-3 MD5sum: ac0428e92fe2f83a2cfdaa6e4168f21c NeedsCompilation: yes Title: Statistical Inferences with MeDIP-seq Data (SIMD) to infer the methylation level for each CpG site Description: This package provides a inferential analysis method for detecting differentially expressed CpG sites in MeDIP-seq data. It uses statistical framework and EM algorithm, to identify differentially expressed CpG sites. The methods on this package are described in the article 'Methylation-level Inferences and Detection of Differential Methylation with Medip-seq Data' by Yan Zhou, Jiadi Zhu, Mingtao Zhao, Baoxue Zhang, Chunfu Jiang and Xiyan Yang (2018, pending publication). biocViews: ImmunoOncology, DifferentialMethylation,SingleCell, DifferentialExpression Author: Yan Zhou Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SIMD git_branch: RELEASE_3_10 git_last_commit: a79f651 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SIMD_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SIMD_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SIMD_1.4.0.tgz vignettes: vignettes/SIMD/inst/doc/SIMD.html vignetteTitles: SIMD Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIMD/inst/doc/SIMD.R dependencyCount: 13 Package: similaRpeak Version: 1.18.0 Depends: R6 (>= 2.0) Imports: stats Suggests: RUnit, BiocGenerics, knitr, Rsamtools, GenomicAlignments, rtracklayer, rmarkdown, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 026c11fcefc58cb763ecd5ccae6ef25d NeedsCompilation: no Title: Metrics to estimate a level of similarity between two ChIP-Seq profiles Description: This package calculates metrics which assign a level of similarity between ChIP-Seq profiles. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, DifferentialExpression Author: Astrid Deschenes [cre, aut], Elsa Bernatchez [aut], Charles Joly Beauparlant [aut], Fabien Claude Lamaze [aut], Rawane Samb [aut], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschenes URL: https://github.com/adeschen/similaRpeak VignetteBuilder: knitr BugReports: https://github.com/adeschen/similaRpeak/issues git_url: https://git.bioconductor.org/packages/similaRpeak git_branch: RELEASE_3_10 git_last_commit: 6ad8fdb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/similaRpeak_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/similaRpeak_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/similaRpeak_1.18.0.tgz vignettes: vignettes/similaRpeak/inst/doc/similaRpeak.html vignetteTitles: Similarity between two ChIP-Seq profiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/similaRpeak/inst/doc/similaRpeak.R suggestsMe: metagene dependencyCount: 2 Package: SIMLR Version: 1.12.0 Depends: R (>= 3.6), Imports: parallel, Matrix, stats, methods, Rcpp, pracma, RcppAnnoy, RSpectra LinkingTo: Rcpp Suggests: BiocGenerics, BiocStyle, testthat, knitr, igraph License: file LICENSE MD5sum: 3a41869c7cc9d55ba0bc5d5ecc3c21d0 NeedsCompilation: yes Title: Single-cell Interpretation via Multi-kernel LeaRning (SIMLR) Description: Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical for the identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. We develop a novel similarity-learning framework, SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization. biocViews: ImmunoOncology, Clustering, GeneExpression, Sequencing, SingleCell Author: Daniele Ramazzotti [aut, cre], Bo Wang [aut], Luca De Sano [aut], Serafim Batzoglou [ctb] Maintainer: Luca De Sano URL: https://github.com/BatzoglouLabSU/SIMLR VignetteBuilder: knitr BugReports: https://github.com/BatzoglouLabSU/SIMLR git_url: https://git.bioconductor.org/packages/SIMLR git_branch: RELEASE_3_10 git_last_commit: 2fa414e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SIMLR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SIMLR_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SIMLR_1.12.0.tgz vignettes: vignettes/SIMLR/inst/doc/vignette.pdf vignetteTitles: Single-cell Interpretation via Multi-kernel LeaRning (\Biocpkg{SIMLR}) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SIMLR/inst/doc/vignette.R dependencyCount: 14 Package: simpleaffy Version: 2.62.0 Depends: R (>= 2.0.0), methods, utils, grDevices, graphics, stats, BiocGenerics (>= 0.1.12), Biobase, affy (>= 1.33.6), genefilter, gcrma Imports: methods, utils, grDevices, graphics, stats, BiocGenerics, Biobase, affy, genefilter, gcrma License: GPL (>= 2) MD5sum: e095a6c376370187a5f9decc7bbc1874 NeedsCompilation: yes Title: Very simple high level analysis of Affymetrix data Description: Provides high level functions for reading Affy .CEL files, phenotypic data, and then computing simple things with it, such as t-tests, fold changes and the like. Makes heavy use of the affy library. Also has some basic scatter plot functions and mechanisms for generating high resolution journal figures... biocViews: Microarray, OneChannel, QualityControl, Preprocessing, Transcription, DataImport, DifferentialExpression, Annotation, ReportWriting, Visualization Author: Crispin J Miller Maintainer: Crispin Miller URL: http://www.bioconductor.org, http://bioinformatics.picr.man.ac.uk/simpleaffy/ git_url: https://git.bioconductor.org/packages/simpleaffy git_branch: RELEASE_3_10 git_last_commit: 6418a82 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/simpleaffy_2.62.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/simpleaffy_2.62.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/simpleaffy_2.62.0.tgz vignettes: vignettes/simpleaffy/inst/doc/simpleAffy.pdf vignetteTitles: simpleaffy primer hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simpleaffy/inst/doc/simpleAffy.R dependsOnMe: yaqcaffy importsMe: affyQCReport, arrayMvout suggestsMe: AffyExpress, ArrayTools, ELBOW dependencyCount: 46 Package: simulatorZ Version: 1.20.0 Depends: R (>= 3.5), Biobase, SummarizedExperiment, survival, CoxBoost, BiocGenerics Imports: graphics, stats, gbm, Hmisc, GenomicRanges, methods Suggests: RUnit, BiocStyle, curatedOvarianData, parathyroidSE, superpc License: Artistic-2.0 Archs: i386, x64 MD5sum: a269400a0e96953611de70bf10af9679 NeedsCompilation: yes Title: Simulator for Collections of Independent Genomic Data Sets Description: simulatorZ is a package intended primarily to simulate collections of independent genomic data sets, as well as performing training and validation with predicting algorithms. It supports ExpressionSet and RangedSummarizedExperiment objects. biocViews: Survival Author: Yuqing Zhang, Christoph Bernau, Levi Waldron Maintainer: Yuqing Zhang URL: https://github.com/zhangyuqing/simulatorZ BugReports: https://github.com/zhangyuqing/simulatorZ git_url: https://git.bioconductor.org/packages/simulatorZ git_branch: RELEASE_3_10 git_last_commit: 43f5b72 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/simulatorZ_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/simulatorZ_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/simulatorZ_1.20.0.tgz vignettes: vignettes/simulatorZ/inst/doc/simulatorZ-vignette.pdf vignetteTitles: SimulatorZ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simulatorZ/inst/doc/simulatorZ-vignette.R suggestsMe: doppelgangR dependencyCount: 112 Package: sincell Version: 1.18.0 Depends: R (>= 3.0.2), igraph Imports: Rcpp (>= 0.11.2), entropy, scatterplot3d, MASS, TSP, ggplot2, reshape2, fields, proxy, parallel, Rtsne, fastICA, cluster, statmod LinkingTo: Rcpp Suggests: BiocStyle, knitr, biomaRt, stringr, monocle License: GPL (>= 2) MD5sum: 54192e25a013d3c4dd606929eea9829a NeedsCompilation: yes Title: R package for the statistical assessment of cell state hierarchies from single-cell RNA-seq data Description: Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell-states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies. biocViews: ImmunoOncology, Sequencing, RNASeq, Clustering, GraphAndNetwork, Visualization, GeneExpression, GeneSetEnrichment, BiomedicalInformatics, CellBiology, FunctionalGenomics, SystemsBiology Author: Miguel Julia , Amalio Telenti , Antonio Rausell Maintainer: Miguel Julia , Antonio Rausell URL: http://bioconductor.org/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sincell git_branch: RELEASE_3_10 git_last_commit: d599c69 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sincell_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sincell_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sincell_1.18.0.tgz vignettes: vignettes/sincell/inst/doc/sincell-vignette.pdf vignetteTitles: Sincell: Analysis of cell state hierarchies from single-cell RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sincell/inst/doc/sincell-vignette.R dependencyCount: 75 Package: SingleCellExperiment Version: 1.8.0 Depends: SummarizedExperiment Imports: S4Vectors (>= 0.23.19), methods, BiocGenerics, utils, stats Suggests: testthat, BiocStyle, knitr, rmarkdown, scRNAseq, Rtsne, Matrix License: GPL-3 Archs: i386, x64 MD5sum: e60a4c8a92e1ab7ab75aa9cd3dd7b473 NeedsCompilation: no Title: S4 Classes for Single Cell Data Description: Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries. biocViews: ImmunoOncology, DataRepresentation, DataImport, Infrastructure, SingleCell Author: Aaron Lun [aut, cph], Davide Risso [aut, cre, cph], Keegan Korthauer [ctb], Kevin Rue-Albrecht [ctb] Maintainer: Davide Risso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SingleCellExperiment git_branch: RELEASE_3_10 git_last_commit: 11bd3ff git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SingleCellExperiment_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SingleCellExperiment_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SingleCellExperiment_1.8.0.tgz vignettes: vignettes/SingleCellExperiment/inst/doc/devel.html, vignettes/SingleCellExperiment/inst/doc/intro.html vignetteTitles: 2. Developing around the SingleCellExperiment class, 1. An introduction to the SingleCellExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleCellExperiment/inst/doc/devel.R, vignettes/SingleCellExperiment/inst/doc/intro.R dependsOnMe: BASiCS, batchelor, CellBench, CellTrails, CHETAH, clusterExperiment, cydar, DropletUtils, iSEE, LoomExperiment, MAST, scAlign, scater, scGPS, schex, scPipe, scran, singleCellTK, Spaniel, splatter, switchde, TreeSummarizedExperiment, zinbwave importsMe: bayNorm, BEARscc, CATALYST, ccfindR, CellMixS, CoGAPS, destiny, fcoex, infercnv, LineagePulse, mbkmeans, muscat, netSmooth, phemd, SC3, scBFA, scDblFinder, scDD, scds, scfind, scmap, scMerge, SCnorm, scruff, scTensor, scTGIF, slalom, slingshot, tradeSeq, waddR suggestsMe: DEsingle, FCBF, M3Drop, phenopath, scFeatureFilter, scPCA, scRecover, SingleR dependencyCount: 32 Package: singleCellTK Version: 1.6.0 Depends: R (>= 3.5), SummarizedExperiment, SingleCellExperiment, DelayedArray, Biobase Imports: ape, colourpicker, cluster, ComplexHeatmap, data.table, DESeq2, DT, ggplot2, ggtree, gridExtra, GSVA (>= 1.26.0), GSVAdata, limma, MAST, matrixStats, methods, multtest, plotly, RColorBrewer, Rtsne, S4Vectors, shiny, shinyjs, shinyBS, sva, reshape2, AnnotationDbi, shinyalert, circlize, enrichR, celda, shinycssloaders, shinythemes, umap Suggests: testthat, Rsubread, BiocStyle, knitr, lintr, bladderbatch, rmarkdown, org.Mm.eg.db, org.Hs.eg.db, scRNAseq, xtable, spelling, GSEABase License: MIT + file LICENSE Archs: i386, x64 MD5sum: 2c9431a0a5b413562050cb4f6de5b1c7 NeedsCompilation: no Title: Interactive Analysis of Single Cell RNA-Seq Data Description: Run common single cell analysis directly through your browser including differential expression, downsampling analysis, and clustering. biocViews: SingleCell, GeneExpression, DifferentialExpression, Alignment, Clustering, ImmunoOncology Author: David Jenkins Maintainer: David Jenkins URL: https://compbiomed.github.io/sctk_docs/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/singleCellTK/issues git_url: https://git.bioconductor.org/packages/singleCellTK git_branch: RELEASE_3_10 git_last_commit: 89de302 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/singleCellTK_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/singleCellTK_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/singleCellTK_1.6.0.tgz vignettes: vignettes/singleCellTK/inst/doc/v01-Introduction_to_singleCellTK.html, vignettes/singleCellTK/inst/doc/v02-Processing_and_Visualizing_Data_in_the_SingleCellTK.html vignetteTitles: 1. Introduction to singleCellTK, 2. Processing and Visualizing Data in the Single Cell Toolkit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/singleCellTK/inst/doc/v01-Introduction_to_singleCellTK.R, vignettes/singleCellTK/inst/doc/v02-Processing_and_Visualizing_Data_in_the_SingleCellTK.R dependencyCount: 203 Package: SingleR Version: 1.0.6 Depends: SummarizedExperiment Imports: methods, Matrix, S4Vectors, DelayedArray, DelayedMatrixStats, BiocNeighbors, BiocParallel, stats, utils, Rcpp, ExperimentHub LinkingTo: Rcpp, beachmat Suggests: testthat, knitr, rmarkdown, BiocStyle, beachmat, SingleCellExperiment, scater, scRNAseq, scran, BiocGenerics, ggplot2, pheatmap, grDevices, viridis, AnnotationHub, AnnotationDbi License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: 00d98a80f1cba6bbb3e00dbc2a7ccf5a NeedsCompilation: yes Title: Reference-Based Single-Cell RNA-Seq Annotation Description: Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently. biocViews: Software, SingleCell, GeneExpression, Transcriptomics, Classification, Clustering, Annotation Author: Dvir Aran [aut, cph], Aaron Lun [ctb, cre], Daniel Bunis [ctb], Jared Andrews [ctb], Friederike Dündar [ctb] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SingleR git_branch: RELEASE_3_10 git_last_commit: 5803a1f git_last_commit_date: 2020-04-08 Date/Publication: 2020-04-08 source.ver: src/contrib/SingleR_1.0.6.tar.gz win.binary.ver: bin/windows/contrib/3.6/SingleR_1.0.6.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SingleR_1.0.6.tgz vignettes: vignettes/SingleR/inst/doc/SingleR.html vignetteTitles: Annotating scRNA-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SingleR/inst/doc/SingleR.R dependencyCount: 92 Package: singscore Version: 1.6.0 Depends: R (>= 3.5),GSEABase Imports: methods, stats, graphics, ggplot2, grDevices, ggrepel, plotly, tidyr, plyr, magrittr, reshape, edgeR, RColorBrewer, Biobase, BiocParallel, SummarizedExperiment, matrixStats, reshape2, S4Vectors Suggests: knitr, rmarkdown, testthat License: GPL-3 Archs: i386, x64 MD5sum: 453c28159ff84b553c8ab71c295c2200 NeedsCompilation: no Title: Rank-based single-sample gene set scoring method Description: A simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample's gene expression profile. It scores the expression activities of gene sets at a single-sample level. biocViews: Software, GeneExpression, GeneSetEnrichment Author: Ruqian Lyu [aut, ctb], Momeneh Foroutan [aut, ctb] (), Dharmesh D. Bhuva [aut, cre] () Maintainer: Dharmesh D. Bhuva URL: https://davislaboratory.github.io/singscore VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/singscore/issues git_url: https://git.bioconductor.org/packages/singscore git_branch: RELEASE_3_10 git_last_commit: e80a3f9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/singscore_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/singscore_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/singscore_1.6.0.tgz vignettes: vignettes/singscore/inst/doc/singscore.html vignetteTitles: 1. Differential co-expression analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/singscore/inst/doc/singscore.R dependencyCount: 121 Package: SISPA Version: 1.16.0 Depends: R (>= 3.2),genefilter,GSVA,changepoint Imports: data.table, plyr, ggplot2 Suggests: knitr License: GPL-2 MD5sum: 0ffe6aab3318390f1acff282b275e1dc NeedsCompilation: no Title: SISPA: Method for Sample Integrated Set Profile Analysis Description: Sample Integrated Set Profile Analysis (SISPA) is a method designed to define sample groups with similar gene set enrichment profiles. biocViews: GeneSetEnrichment,GenomeWideAssociation Author: Bhakti Dwivedi and Jeanne Kowalski Maintainer: Bhakti Dwivedi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SISPA git_branch: RELEASE_3_10 git_last_commit: dd721df git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SISPA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SISPA_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SISPA_1.16.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 94 Package: sitePath Version: 1.2.3 Depends: R (>= 3.6.0) Imports: ape, seqinr, Rcpp, methods, graphics, utils, stats LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 49cb68cdb5f3dcd3b22b38b63b36bd6a NeedsCompilation: yes Title: Detection of sites with fixation of amino acid substitutions in protein evolution Description: The package does hierarchical search for fixation events given multiple sequence alignment and phylogenetic tree. These fixation events can be specific to a phylogenetic lineages or shared by multiple lineages. biocViews: Alignment, MultipleSequenceAlignment, Software Author: Chengyang Ji, Aiping Wu Maintainer: Chengyang Ji URL: https://wuaipinglab.github.io/sitePath/ VignetteBuilder: knitr BugReports: https://github.com/wuaipinglab/sitePath/issues git_url: https://git.bioconductor.org/packages/sitePath git_branch: RELEASE_3_10 git_last_commit: 86e44d4 git_last_commit_date: 2020-04-01 Date/Publication: 2020-04-02 source.ver: src/contrib/sitePath_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/sitePath_1.2.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sitePath_1.2.3.tgz vignettes: vignettes/sitePath/inst/doc/sitePathTutorial.html vignetteTitles: A basic workflow for sitePath hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sitePath/inst/doc/sitePathTutorial.R dependencyCount: 18 Package: sizepower Version: 1.56.0 Depends: stats License: LGPL Archs: i386, x64 MD5sum: 5dc82654fc4fbc2a5f6fc16683332148 NeedsCompilation: no Title: Sample Size and Power Calculation in Micorarray Studies Description: This package has been prepared to assist users in computing either a sample size or power value for a microarray experimental study. The user is referred to the cited references for technical background on the methodology underpinning these calculations. This package provides support for five types of sample size and power calculations. These five types can be adapted in various ways to encompass many of the standard designs encountered in practice. biocViews: Microarray Author: Weiliang Qiu and Mei-Ling Ting Lee and George Alex Whitmore Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/sizepower git_branch: RELEASE_3_10 git_last_commit: fd7ee84 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sizepower_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sizepower_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sizepower_1.56.0.tgz vignettes: vignettes/sizepower/inst/doc/sizepower.pdf vignetteTitles: Sample Size and Power Calculation in Microarray Studies Using the \Rpackage{sizepower} package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sizepower/inst/doc/sizepower.R dependencyCount: 1 Package: skewr Version: 1.18.0 Depends: R (>= 3.1.1), methylumi, wateRmelon, mixsmsn, IlluminaHumanMethylation450kmanifest Imports: minfi, S4Vectors (>= 0.19.1), RColorBrewer Suggests: GEOquery, knitr, minfiData License: GPL-2 MD5sum: e4de866acd3a76f5deba544c6d199bf3 NeedsCompilation: no Title: Visualize Intensities Produced by Illumina's Human Methylation 450k BeadChip Description: The skewr package is a tool for visualizing the output of the Illumina Human Methylation 450k BeadChip to aid in quality control. It creates a panel of nine plots. Six of the plots represent the density of either the methylated intensity or the unmethylated intensity given by one of three subsets of the 485,577 total probes. These subsets include Type I-red, Type I-green, and Type II.The remaining three distributions give the density of the Beta-values for these same three subsets. Each of the nine plots optionally displays the distributions of the "rs" SNP probes and the probes associated with imprinted genes as series of 'tick' marks located above the x-axis. biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl Author: Ryan Putney [cre, aut], Steven Eschrich [aut], Anders Berglund [aut] Maintainer: Ryan Putney VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/skewr git_branch: RELEASE_3_10 git_last_commit: 138b781 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/skewr_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/skewr_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/skewr_1.18.0.tgz vignettes: vignettes/skewr/inst/doc/skewr.pdf vignetteTitles: An Introduction to the skewr Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/skewr/inst/doc/skewr.R dependencyCount: 167 Package: slalom Version: 1.8.0 Depends: R (>= 3.4) Imports: Rcpp (>= 0.12.8), RcppArmadillo, BH, ggplot2, grid, GSEABase, methods, rsvd, SingleCellExperiment, SummarizedExperiment, stats LinkingTo: Rcpp, RcppArmadillo, BH Suggests: knitr, rhdf5, scater, testthat License: GPL-2 MD5sum: 6dbc79144e424b937c737b5e9f7a3f8b NeedsCompilation: yes Title: Factorial Latent Variable Modeling of Single-Cell RNA-Seq Data Description: slalom is a scalable modelling framework for single-cell RNA-seq data that uses gene set annotations to dissect single-cell transcriptome heterogeneity, thereby allowing to identify biological drivers of cell-to-cell variability and model confounding factors. biocViews: ImmunoOncology, SingleCell, RNASeq, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, Reactome Author: Florian Buettner [aut], Naruemon Pratanwanich [aut], Davis McCarthy [aut, cre], John Marioni [aut], Oliver Stegle [aut] Maintainer: Davis McCarthy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/slalom git_branch: RELEASE_3_10 git_last_commit: c7a016f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/slalom_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/slalom_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/slalom_1.8.0.tgz vignettes: vignettes/slalom/inst/doc/vignette.html vignetteTitles: Introduction to slalom hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/slalom/inst/doc/vignette.R dependencyCount: 93 Package: SLGI Version: 1.46.0 Depends: R (>= 2.10), ScISI, lattice Imports: AnnotationDbi, Biobase, GO.db, ScISI, graphics, lattice, methods, stats, BiocGenerics Suggests: GO.db, org.Sc.sgd.db License: Artistic-2.0 Archs: i386, x64 MD5sum: 52f7ee5ae03d7d9995fa3a687ec4dd8e NeedsCompilation: no Title: Synthetic Lethal Genetic Interaction Description: A variety of data files and functions for the analysis of genetic interactions biocViews: GraphAndNetwork, Proteomics, Genetics, Network Author: Nolwenn LeMeur, Zhen Jiang, Ting-Yuan Liu, Jess Mar and Robert Gentleman Maintainer: Nolwenn Le Meur git_url: https://git.bioconductor.org/packages/SLGI git_branch: RELEASE_3_10 git_last_commit: 2252de8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SLGI_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SLGI_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SLGI_1.46.0.tgz vignettes: vignettes/SLGI/inst/doc/SLGI.pdf vignetteTitles: SLGI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SLGI/inst/doc/SLGI.R dependsOnMe: PCpheno dependencyCount: 43 Package: slingshot Version: 1.4.0 Depends: R (>= 3.5), princurve (>= 2.0.4), stats Imports: ape, graphics, grDevices, igraph, matrixStats, methods, SingleCellExperiment, SummarizedExperiment Suggests: BiocGenerics, BiocStyle, clusterExperiment, destiny, gam, knitr, mclust, mgcv, RColorBrewer, rgl, rmarkdown, testthat, covr License: Artistic-2.0 MD5sum: a70274449a6b4b45f4e60850f0d862d0 NeedsCompilation: no Title: Tools for ordering single-cell sequencing Description: Provides functions for inferring continuous, branching lineage structures in low-dimensional data. Slingshot was designed to model developmental trajectories in single-cell RNA sequencing data and serve as a component in an analysis pipeline after dimensionality reduction and clustering. It is flexible enough to handle arbitrarily many branching events and allows for the incorporation of prior knowledge through supervised graph construction. biocViews: Clustering, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, Sequencing, SingleCell, Transcriptomics, Visualization Author: Kelly Street [aut, cre, cph], Davide Risso [aut], Diya Das [aut], Sandrine Dudoit [ths], Koen Van den Berge [ctb], Robrecht Cannoodt [ctb] Maintainer: Kelly Street VignetteBuilder: knitr BugReports: https://github.com/kstreet13/slingshot/issues git_url: https://git.bioconductor.org/packages/slingshot git_branch: RELEASE_3_10 git_last_commit: 35e457a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/slingshot_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/slingshot_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/slingshot_1.4.0.tgz vignettes: vignettes/slingshot/inst/doc/conditionsVignette.html, vignettes/slingshot/inst/doc/vignette.html vignetteTitles: Differential Topology, Slingshot hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/slingshot/inst/doc/conditionsVignette.R, vignettes/slingshot/inst/doc/vignette.R importsMe: tradeSeq dependencyCount: 40 Package: slinky Version: 1.4.0 Depends: R (>= 3.5.0) Imports: SummarizedExperiment, curl, dplyr, foreach, httr, stats, utils, methods, readr, rhdf5, jsonlite, tidyr Suggests: GeoDE, doParallel, testthat, knitr, rmarkdown, ggplot2, Rtsne, Biobase, BiocStyle License: MIT + file LICENSE MD5sum: 7036231e87ab7be22866297914851bea NeedsCompilation: no Title: Putting the fun in LINCS L1000 data analysis Description: Wrappers to query the L1000 metadata available via the clue.io REST API as well as helpers for dealing with LINCS gctx files, extracting data sets of interest, converting to SummarizedExperiment objects, and some facilities for performing streamlined differential expression analysis of these data sets. biocViews: DataImport, ThirdPartyClient, GeneExpression, DifferentialExpression, GeneSetEnrichment, PatternLogic Author: Eric J. Kort Maintainer: Eric J. Kort VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/slinky git_branch: RELEASE_3_10 git_last_commit: f98864e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/slinky_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/slinky_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/slinky_1.4.0.tgz vignettes: vignettes/slinky/inst/doc/LINCS-analysis.html vignetteTitles: "LINCS analysis with slinky" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/slinky/inst/doc/LINCS-analysis.R dependencyCount: 70 Package: SLqPCR Version: 1.52.0 Depends: R(>= 2.4.0) Imports: stats Suggests: RColorBrewer License: GPL (>= 2) Archs: i386, x64 MD5sum: 265accb7e94b68ac774c2cee461d349c NeedsCompilation: no Title: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH Description: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH biocViews: MicrotitrePlateAssay, qPCR Author: Matthias Kohl Maintainer: Matthias Kohl git_url: https://git.bioconductor.org/packages/SLqPCR git_branch: RELEASE_3_10 git_last_commit: 5bc3b32 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SLqPCR_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SLqPCR_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SLqPCR_1.52.0.tgz vignettes: vignettes/SLqPCR/inst/doc/SLqPCR.pdf vignetteTitles: SLqPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SLqPCR/inst/doc/SLqPCR.R suggestsMe: EasyqpcR dependencyCount: 1 Package: SMAD Version: 1.2.0 Depends: R (>= 3.6.0) Imports: magrittr (>= 1.5), matrixStats, dplyr, stats, tidyr, utils, Rcpp (>= 1.0.0) LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle License: MIT + file LICENSE MD5sum: 6cf543bebd188cdc5956770f6c4d4bb4 NeedsCompilation: yes Title: Statistical Modelling of AP-MS Data (SMAD) Description: Assigning probability scores to prey proteins captured in affinity purification mass spectrometry (AP-MS) expriments to infer protein- protein interactions. The output would facilitate non-specific background removal as contaminants are commonly found in AP-MS data. biocViews: MassSpectrometry, Proteomics, Software Author: Qingzhou Zhang [aut, cre] Maintainer: Qingzhou Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SMAD git_branch: RELEASE_3_10 git_last_commit: 9ae385b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SMAD_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SMAD_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SMAD_1.2.0.tgz vignettes: vignettes/SMAD/inst/doc/quickstart.html vignetteTitles: SMAD Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SMAD/inst/doc/quickstart.R dependencyCount: 30 Package: SMAP Version: 1.50.0 Depends: R (>= 2.10), methods License: GPL-2 MD5sum: 12493761a3c95528322a1cdaead6af97 NeedsCompilation: yes Title: A Segmental Maximum A Posteriori Approach to Array-CGH Copy Number Profiling Description: Functions and classes for DNA copy number profiling of array-CGH data biocViews: Microarray, TwoChannel, CopyNumberVariation Author: Robin Andersson Maintainer: Robin Andersson git_url: https://git.bioconductor.org/packages/SMAP git_branch: RELEASE_3_10 git_last_commit: adc04ac git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SMAP_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SMAP_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SMAP_1.50.0.tgz vignettes: vignettes/SMAP/inst/doc/SMAP.pdf vignetteTitles: SMAP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SMAP/inst/doc/SMAP.R dependencyCount: 1 Package: SMITE Version: 1.14.0 Depends: R (>= 3.3), GenomicRanges Imports: scales, plyr, Hmisc, AnnotationDbi, org.Hs.eg.db, ggplot2, reactome.db, KEGG.db, BioNet, goseq, methods, IRanges, igraph, Biobase,tools, S4Vectors, geneLenDataBase, grDevices, graphics, stats, utils Suggests: knitr License: GPL (>=2) MD5sum: 0237f548050507b5ff7375ec68f48aa2 NeedsCompilation: no Title: Significance-based Modules Integrating the Transcriptome and Epigenome Description: This package builds on the Epimods framework which facilitates finding weighted subnetworks ("modules") on Illumina Infinium 27k arrays using the SpinGlass algorithm, as implemented in the iGraph package. We have created a class of gene centric annotations associated with p-values and effect sizes and scores from any researchers prior statistical results to find functional modules. biocViews: ImmunoOncology, DifferentialMethylation, DifferentialExpression, SystemsBiology, NetworkEnrichment,GenomeAnnotation,Network, Sequencing, RNASeq, Coverage Author: Neil Ari Wijetunga, Andrew Damon Johnston, John Murray Greally Maintainer: Neil Ari Wijetunga , Andrew Damon Johnston URL: https://github.com/GreallyLab/SMITE VignetteBuilder: knitr BugReports: https://github.com/GreallyLab/SMITE/issues git_url: https://git.bioconductor.org/packages/SMITE git_branch: RELEASE_3_10 git_last_commit: c3867a6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SMITE_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SMITE_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SMITE_1.14.0.tgz vignettes: vignettes/SMITE/inst/doc/SMITE.pdf vignetteTitles: SMITE Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SMITE/inst/doc/SMITE.R dependencyCount: 146 Package: SNAGEE Version: 1.26.0 Depends: R (>= 2.6.0), SNAGEEdata Suggests: ALL, hgu95av2.db Enhances: parallel License: Artistic-2.0 MD5sum: 2bc485137555bd1ce8c06b3db5bf5ee3 NeedsCompilation: no Title: Signal-to-Noise applied to Gene Expression Experiments Description: Signal-to-Noise applied to Gene Expression Experiments. Signal-to-noise ratios can be used as a proxy for quality of gene expression studies and samples. The SNRs can be calculated on any gene expression data set as long as gene IDs are available, no access to the raw data files is necessary. This allows to flag problematic studies and samples in any public data set. biocViews: Microarray, OneChannel, TwoChannel, QualityControl Author: David Venet Maintainer: David Venet URL: http://bioconductor.org/ git_url: https://git.bioconductor.org/packages/SNAGEE git_branch: RELEASE_3_10 git_last_commit: 3243393 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SNAGEE_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SNAGEE_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SNAGEE_1.26.0.tgz vignettes: vignettes/SNAGEE/inst/doc/SNAGEE.pdf vignetteTitles: SNAGEE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNAGEE/inst/doc/SNAGEE.R dependencyCount: 1 Package: snapCGH Version: 1.56.0 Depends: R (>= 3.5.0) Imports: aCGH, cluster, DNAcopy, GLAD, graphics, grDevices, limma, methods, stats, tilingArray, utils License: GPL Archs: i386, x64 MD5sum: cd3bbe2f94af5792f2b5d1b5cb3e076c NeedsCompilation: yes Title: Segmentation, normalisation and processing of aCGH data Description: Methods for segmenting, normalising and processing aCGH data; including plotting functions for visualising raw and segmented data for individual and multiple arrays. biocViews: Microarray, CopyNumberVariation, TwoChannel, Preprocessing Author: Mike L. Smith, John C. Marioni, Steven McKinney, Thomas Hardcastle, Natalie P. Thorne Maintainer: John Marioni git_url: https://git.bioconductor.org/packages/snapCGH git_branch: RELEASE_3_10 git_last_commit: 1a870eb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/snapCGH_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/snapCGH_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/snapCGH_1.56.0.tgz vignettes: vignettes/snapCGH/inst/doc/snapCGHguide.pdf vignetteTitles: Segmentation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snapCGH/inst/doc/snapCGHguide.R importsMe: ADaCGH2 suggestsMe: beadarraySNP dependencyCount: 96 Package: snm Version: 1.34.0 Depends: R (>= 2.12.0) Imports: corpcor, lme4 (>= 1.0), splines License: LGPL Archs: i386, x64 MD5sum: 3188e262b97c8a5af7b4396cde8b911c NeedsCompilation: no Title: Supervised Normalization of Microarrays Description: SNM is a modeling strategy especially designed for normalizing high-throughput genomic data. The underlying premise of our approach is that your data is a function of what we refer to as study-specific variables. These variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest. biocViews: Microarray, OneChannel, TwoChannel, MultiChannel, DifferentialExpression, ExonArray, GeneExpression, Transcription, MultipleComparison, Preprocessing, QualityControl Author: Brig Mecham and John D. Storey Maintainer: John D. Storey git_url: https://git.bioconductor.org/packages/snm git_branch: RELEASE_3_10 git_last_commit: 7247237 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/snm_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/snm_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/snm_1.34.0.tgz vignettes: vignettes/snm/inst/doc/snm.pdf vignetteTitles: snm Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snm/inst/doc/snm.R importsMe: edge dependencyCount: 20 Package: SNPchip Version: 2.32.0 Depends: R (>= 2.14.0) Imports: methods, graphics, lattice, grid, foreach, utils, Biobase, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, oligoClasses (>= 1.31.1) Suggests: crlmm (>= 1.17.14), RUnit Enhances: doSNOW, VanillaICE (>= 1.21.24), RColorBrewer License: LGPL (>= 2) MD5sum: 76e932350a2c196663a591c76d35f706 NeedsCompilation: no Title: Visualizations for copy number alterations Description: Functions for plotting SNP array data; maintained for historical reasons biocViews: CopyNumberVariation, SNP, GeneticVariability, Visualization Author: Robert Scharpf and Ingo Ruczinski Maintainer: Robert Scharpf URL: http://www.biostat.jhsph.edu/~iruczins/software/snpchip.html PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/SNPchip git_branch: RELEASE_3_10 git_last_commit: 9c3147f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SNPchip_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SNPchip_2.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SNPchip_2.32.0.tgz vignettes: vignettes/SNPchip/inst/doc/PlottingIdiograms.pdf vignetteTitles: Plotting Idiograms hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPchip/inst/doc/PlottingIdiograms.R dependsOnMe: mBPCR importsMe: crlmm, phenoTest suggestsMe: MinimumDistance, oligoClasses, VanillaICE dependencyCount: 54 Package: SNPediaR Version: 1.12.0 Depends: R (>= 3.0.0) Imports: RCurl, jsonlite Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 638b2ada1454ecccf2447dd85eb7005d NeedsCompilation: no Title: Query data from SNPedia Description: SNPediaR provides some tools for downloading and parsing data from the SNPedia web site . The implemented functions allow users to import the wiki text available in SNPedia pages and to extract the most relevant information out of them. If some information in the downloaded pages is not automatically processed by the library functions, users can easily implement their own parsers to access it in an efficient way. biocViews: SNP, VariantAnnotation Author: David Montaner [aut, cre] Maintainer: David Montaner URL: https://github.com/genometra/SNPediaR VignetteBuilder: knitr BugReports: https://github.com/genometra/SNPediaR/issues git_url: https://git.bioconductor.org/packages/SNPediaR git_branch: RELEASE_3_10 git_last_commit: f652276 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SNPediaR_1.12.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SNPediaR_1.12.0.tgz vignettes: vignettes/SNPediaR/inst/doc/SNPediaR.html vignetteTitles: SNPediaR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPediaR/inst/doc/SNPediaR.R dependencyCount: 4 Package: SNPhood Version: 1.16.0 Depends: R (>= 3.1), GenomicRanges, Rsamtools, data.table, checkmate Imports: DESeq2, cluster, ggplot2, lattice, GenomeInfoDb, BiocParallel, VariantAnnotation, BiocGenerics, IRanges, methods, SummarizedExperiment, RColorBrewer, Biostrings, grDevices, gridExtra, stats, grid, utils, reshape2, scales, S4Vectors Suggests: BiocStyle, knitr, pryr, rmarkdown, SNPhoodData, corrplot License: LGPL (>= 3) MD5sum: e78afd56058624060d94b5036fe4466e NeedsCompilation: no Title: SNPhood: Investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data Description: To date, thousands of single nucleotide polymorphisms (SNPs) have been found to be associated with complex traits and diseases. However, the vast majority of these disease-associated SNPs lie in the non-coding part of the genome, and are likely to affect regulatory elements, such as enhancers and promoters, rather than function of a protein. Thus, to understand the molecular mechanisms underlying genetic traits and diseases, it becomes increasingly important to study the effect of a SNP on nearby molecular traits such as chromatin environment or transcription factor (TF) binding. Towards this aim, we developed SNPhood, a user-friendly *Bioconductor* R package to investigate and visualize the local neighborhood of a set of SNPs of interest for NGS data such as chromatin marks or transcription factor binding sites from ChIP-Seq or RNA- Seq experiments. SNPhood comprises a set of easy-to-use functions to extract, normalize and summarize reads for a genomic region, perform various data quality checks, normalize read counts using additional input files, and to cluster and visualize the regions according to the binding pattern. The regions around each SNP can be binned in a user-defined fashion to allow for analysis of very broad patterns as well as a detailed investigation of specific binding shapes. Furthermore, SNPhood supports the integration with genotype information to investigate and visualize genotype-specific binding patterns. Finally, SNPhood can be employed for determining, investigating, and visualizing allele-specific binding patterns around the SNPs of interest. biocViews: Software Author: Christian Arnold [aut, cre], Pooja Bhat [aut], Judith Zaugg [aut] Maintainer: Christian Arnold URL: https://bioconductor.org/packages/SNPhood VignetteBuilder: knitr BugReports: christian.arnold@embl.de git_url: https://git.bioconductor.org/packages/SNPhood git_branch: RELEASE_3_10 git_last_commit: 545f6a7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SNPhood_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SNPhood_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SNPhood_1.16.0.tgz vignettes: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.html, vignettes/SNPhood/inst/doc/workflow.html vignetteTitles: Introduction and Methodological Details, Workflow example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.R, vignettes/SNPhood/inst/doc/workflow.R dependencyCount: 145 Package: SNPRelate Version: 1.20.1 Depends: R (>= 2.15), gdsfmt (>= 1.8.3) Imports: methods LinkingTo: gdsfmt Suggests: parallel, Matrix, RUnit, knitr, MASS, BiocGenerics Enhances: SeqArray (>= 1.12.0) License: GPL-3 Archs: i386, x64 MD5sum: 9fd26cbc81ba903d4d36098bda3dbe8a NeedsCompilation: yes Title: Parallel Computing Toolset for Relatedness and Principal Component Analysis of SNP Data Description: Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed an R package SNPRelate to provide a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variations (SNVs), insertion/deletion polymorphism (indel) and structural variation calls in whole-genome and whole-exome variant data. biocViews: Infrastructure, Genetics, StatisticalMethod, PrincipalComponent Author: Xiuwen Zheng [aut, cre, cph] (), Stephanie Gogarten [ctb], Cathy Laurie [ctb], Bruce Weir [ctb, ths] () Maintainer: Xiuwen Zheng URL: http://github.com/zhengxwen/SNPRelate, http://corearray.sourceforge.net/tutorials/SNPRelate/ VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/SNPRelate/issues git_url: https://git.bioconductor.org/packages/SNPRelate git_branch: RELEASE_3_10 git_last_commit: 210b76d git_last_commit_date: 2019-11-22 Date/Publication: 2019-11-22 source.ver: src/contrib/SNPRelate_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/SNPRelate_1.20.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SNPRelate_1.20.1.tgz vignettes: vignettes/SNPRelate/inst/doc/SNPRelate.html vignetteTitles: SNPRelate Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPRelate/inst/doc/SNPRelate.R dependsOnMe: SeqSQC importsMe: CNVRanger, GDSArray, GENESIS, gwasurvivr, VariantExperiment suggestsMe: GWASTools, HIBAG, SAIGEgds, SeqArray dependencyCount: 2 Package: snpStats Version: 1.36.0 Depends: R(>= 2.10.0), survival, Matrix, methods Imports: graphics, grDevices, stats, utils, BiocGenerics, zlibbioc Suggests: hexbin License: GPL-3 MD5sum: e01e082a740f9ddfe2a413642d066c0b NeedsCompilation: yes Title: SnpMatrix and XSnpMatrix classes and methods Description: Classes and statistical methods for large SNP association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes. biocViews: Microarray, SNP, GeneticVariability Author: David Clayton Maintainer: David Clayton git_url: https://git.bioconductor.org/packages/snpStats git_branch: RELEASE_3_10 git_last_commit: 6838a7c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/snpStats_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/snpStats_1.36.0.zip vignettes: vignettes/snpStats/inst/doc/data-input-vignette.pdf, vignettes/snpStats/inst/doc/differences.pdf, vignettes/snpStats/inst/doc/Fst-vignette.pdf, vignettes/snpStats/inst/doc/imputation-vignette.pdf, vignettes/snpStats/inst/doc/ld-vignette.pdf, vignettes/snpStats/inst/doc/pca-vignette.pdf, vignettes/snpStats/inst/doc/snpStats-vignette.pdf, vignettes/snpStats/inst/doc/tdt-vignette.pdf vignetteTitles: Data input, snpMatrix-differences, Fst, Imputation and meta-analysis, LD statistics, Principal components analysis, snpStats introduction, TDT tests hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snpStats/inst/doc/data-input-vignette.R, vignettes/snpStats/inst/doc/Fst-vignette.R, vignettes/snpStats/inst/doc/imputation-vignette.R, vignettes/snpStats/inst/doc/ld-vignette.R, vignettes/snpStats/inst/doc/pca-vignette.R, vignettes/snpStats/inst/doc/snpStats-vignette.R, vignettes/snpStats/inst/doc/tdt-vignette.R dependsOnMe: GGBase importsMe: FunciSNP, GeneGeneInteR, GGtools, gQTLstats, ldblock, martini, RVS, scoreInvHap suggestsMe: crlmm, gwascat, GWASTools, omicRexposome, omicsPrint, VariantAnnotation dependencyCount: 13 Package: soGGi Version: 1.18.0 Depends: R (>= 3.2.0), BiocGenerics, SummarizedExperiment Imports: methods, reshape2, ggplot2, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, GenomicAlignments, rtracklayer, preprocessCore, chipseq, BiocParallel Suggests: testthat, BiocStyle, knitr License: GPL (>= 3) MD5sum: 5bce0a493c9a3fb36e8f07b4f800de13 NeedsCompilation: no Title: Visualise ChIP-seq, MNase-seq and motif occurrence as aggregate plots Summarised Over Grouped Genomic Intervals Description: The soGGi package provides a toolset to create genomic interval aggregate/summary plots of signal or motif occurence from BAM and bigWig files as well as PWM, rlelist, GRanges and GAlignments Bioconductor objects. soGGi allows for normalisation, transformation and arithmetic operation on and between summary plot objects as well as grouping and subsetting of plots by GRanges objects and user supplied metadata. Plots are created using the GGplot2 libary to allow user defined manipulation of the returned plot object. Coupled together, soGGi features a broad set of methods to visualise genomics data in the context of groups of genomic intervals such as genes, superenhancers and transcription factor binding events. biocViews: Sequencing, ChIPSeq, Coverage Author: Gopuraja Dharmalingam, Tom Carroll Maintainer: Tom Carroll VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/soGGi git_branch: RELEASE_3_10 git_last_commit: ff379f2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/soGGi_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/soGGi_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/soGGi_1.18.0.tgz vignettes: vignettes/soGGi/inst/doc/soggi.pdf vignetteTitles: soggi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/soGGi/inst/doc/soggi.R importsMe: DChIPRep, profileplyr dependencyCount: 94 Package: sojourner Version: 1.0.2 Imports: ggplot2,dplyr,reshape2,gridExtra,EBImage,MASS,R.matlab,Rcpp,boot,fitdistrplus,mclust,minpack.lm,mixtools,mltools,nls2,plyr,rtiff,sampSurf,scales,shiny,shinyjs,sp,truncnorm,utils,stats,pixmap,rlang,graphics,grDevices,grid,compiler,lattice Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 5878575d770a0ae262ad5cfcf06060f5 NeedsCompilation: no Title: sojourner: An R package for statistical analysis of single molecule trajectories Description: Single molecule tracking has evolved as a novel new approach complementing genomic sequencing, it reports live biophysical properties of molecules being investigated besides properties relating their coding sequence; here we provided "sojourner" package, to address statistical and bioinformatic needs related to the analysis and comprehension of high throughput single molecule tracking data. biocViews: Technology, WorkflowStep Author: Sheng Liu [aut], Sun Jay Yoo [aut], Xiao Na Tang [aut], Young Soo Sung [aut], Carl Wu [aut], Anand Ranjan [ctb], Vu Nguyen [ctb], Sojourner Developer [cre] Maintainer: Sojourner Developer URL: https://github.com/sheng-liu/sojourner VignetteBuilder: knitr BugReports: https://github.com/sheng-liu/sojourner/issues git_url: https://git.bioconductor.org/packages/sojourner git_branch: RELEASE_3_10 git_last_commit: 2c59633 git_last_commit_date: 2020-04-05 Date/Publication: 2020-04-06 source.ver: src/contrib/sojourner_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/sojourner_1.0.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sojourner_1.0.2.tgz vignettes: vignettes/sojourner/inst/doc/sojourner-vignette.html vignetteTitles: Sojourner: an R package for statistical analysis of single molecule trajectories hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sojourner/inst/doc/sojourner-vignette.R dependencyCount: 117 Package: SomaticSignatures Version: 2.22.0 Depends: R (>= 3.1.0), VariantAnnotation, GenomicRanges, NMF Imports: S4Vectors, IRanges, GenomeInfoDb, Biostrings, ggplot2, ggbio, reshape2, NMF, pcaMethods, Biobase, methods, proxy Suggests: testthat, knitr, parallel, BSgenome.Hsapiens.1000genomes.hs37d5, SomaticCancerAlterations, ggdendro, fastICA, sva License: MIT + file LICENSE Archs: i386, x64 MD5sum: 004e119655e42063a21e0ebd39267d80 NeedsCompilation: no Title: Somatic Signatures Description: The SomaticSignatures package identifies mutational signatures of single nucleotide variants (SNVs). It provides a infrastructure related to the methodology described in Nik-Zainal (2012, Cell), with flexibility in the matrix decomposition algorithms. biocViews: Sequencing, SomaticMutation, Visualization, Clustering, GenomicVariation, StatisticalMethod Author: Julian Gehring Maintainer: Julian Gehring URL: https://github.com/juliangehring/SomaticSignatures VignetteBuilder: knitr BugReports: https://support.bioconductor.org git_url: https://git.bioconductor.org/packages/SomaticSignatures git_branch: RELEASE_3_10 git_last_commit: 7876f52 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SomaticSignatures_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SomaticSignatures_2.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SomaticSignatures_2.22.0.tgz vignettes: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.html vignetteTitles: SomaticSignatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.R importsMe: Rariant, YAPSA dependencyCount: 164 Package: SpacePAC Version: 1.24.0 Depends: R(>= 2.15),iPAC Suggests: RUnit, BiocGenerics, rgl License: GPL-2 Archs: i386, x64 MD5sum: c32927c2fd004598588644c6a720bedb NeedsCompilation: no Title: Identification of Mutational Clusters in 3D Protein Space via Simulation. Description: Identifies clustering of somatic mutations in proteins via a simulation approach while considering the protein's tertiary structure. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/SpacePAC git_branch: RELEASE_3_10 git_last_commit: 9a92d75 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SpacePAC_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SpacePAC_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SpacePAC_1.24.0.tgz vignettes: vignettes/SpacePAC/inst/doc/SpacePAC.pdf vignetteTitles: SpacePAC: Identifying mutational clusters in 3D protein space using simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpacePAC/inst/doc/SpacePAC.R dependsOnMe: QuartPAC dependencyCount: 25 Package: Spaniel Version: 1.0.0 Depends: R (>= 3.6), Seurat, SingleCellExperiment, SummarizedExperiment, dplyr Imports: methods, ggplot2, scater (>= 1.13.27), shiny, jpeg, magrittr, utils, S4Vectors Suggests: knitr, rmarkdown, testthat, devtools License: MIT + file LICENSE MD5sum: b44ea8f281a73ebe4861586fb6f6afa9 NeedsCompilation: no Title: Spatial Transcriptomics Analysis Description: Spaniel includes a series of tools to aid the quality control and analysis of Spatial Transcriptomics data. The package contains functions to create either a Seurat object or SingleCellExperiment from a count matrix and spatial barcode file and provides a method of loading a histologial image into R. The spanielPlot function allows visualisation of metrics contained within the S4 object overlaid onto the image of the tissue. biocViews: SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, Transcriptomics, GeneExpression, Sequencing, Software, DataImport, DataRepresentation, Infrastructure, Coverage, Clustering Author: Rachel Queen Maintainer: Rachel Queen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Spaniel git_branch: RELEASE_3_10 git_last_commit: a0ddb42 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Spaniel_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Spaniel_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Spaniel_1.0.0.tgz vignettes: vignettes/Spaniel/inst/doc/spaniel-vignette.html vignetteTitles: Using Spaniel hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Spaniel/inst/doc/spaniel-vignette.R dependencyCount: 170 Package: sparseDOSSA Version: 1.10.0 Imports: stats, utils, optparse, MASS, tmvtnorm (>= 1.4.10), MCMCpack Suggests: knitr, BiocStyle, BiocGenerics, rmarkdown License: MIT + file LICENSE MD5sum: 8d9b26a8b9ae17633788abb3fa5cff13 NeedsCompilation: no Title: Sparse Data Observations for Simulating Synthetic Abundance Description: The package is to provide a model based Bayesian method to characterize and simulate microbiome data. sparseDOSSA's model captures the marginal distribution of each microbial feature as a truncated, zero-inflated log-normal distribution, with parameters distributed as a parent log-normal distribution. The model can be effectively fit to reference microbial datasets in order to parameterize their microbes and communities, or to simulate synthetic datasets of similar population structure. Most importantly, it allows users to include both known feature-feature and feature-metadata correlation structures and thus provides a gold standard to enable benchmarking of statistical methods for metagenomic data analysis. biocViews: ImmunoOncology, Bayesian, Microbiome, Metagenomics, Software Author: Boyu Ren, Emma Schwager, Timothy Tickle, Curtis Huttenhower Maintainer: Boyu Ren, Emma Schwager , George Weingart VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sparseDOSSA git_branch: RELEASE_3_10 git_last_commit: 17d196b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sparseDOSSA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sparseDOSSA_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sparseDOSSA_1.10.0.tgz vignettes: vignettes/sparseDOSSA/inst/doc/sparsedossa-vignette.html vignetteTitles: Sparse Data Observations for the Simulation of Synthetic Abundances (sparseDOSSA) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sparseDOSSA/inst/doc/sparsedossa-vignette.R dependencyCount: 23 Package: sparsenetgls Version: 1.4.0 Depends: R (>= 3.5.0), Matrix, MASS Imports: methods, glmnet, parcor, huge, stats, graphics, utils Suggests: testthat, lme4, BiocStyle, knitr, rmarkdown, roxygen2 (>= 5.0.0) License: GPL-3 MD5sum: 8062a387975d0bb3d1e5daa13ecd1a59 NeedsCompilation: no Title: Using Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regression Description: The package provides methods of combining the graph structure learning and generalized least squares regression to improve the regression estimation. The main function sparsenetgls() provides solutions for multivariate regression with Gaussian distributed dependant variables and explanatory variables utlizing multiple well-known graph structure learning approaches to estimating the precision matrix, and uses a penalized variance covariance matrix with a distance tuning parameter of the graph structure in deriving the sandwich estimators in generalized least squares (gls) regression. This package also provides functions for assessing a Gaussian graphical model which uses the penalized approach. It uses Receiver Operative Characteristics curve as a visualization tool in the assessment. biocViews: ImmunoOncology, GraphAndNetwork,Regression,Metabolomics,CopyNumberVariation,MassSpectrometry,Proteomics,Software,Visualization Author: Irene Zeng [aut, cre], Thomas Lumley [ctb] Maintainer: Irene Zeng SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sparsenetgls git_branch: RELEASE_3_10 git_last_commit: 12e5920 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sparsenetgls_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sparsenetgls_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sparsenetgls_1.4.0.tgz vignettes: vignettes/sparsenetgls/inst/doc/vignettes_sparsenetgls.html vignetteTitles: Introduction to sparsenetgls hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sparsenetgls/inst/doc/vignettes_sparsenetgls.R dependencyCount: 38 Package: SparseSignatures Version: 1.6.0 Depends: R (>= 3.6), NMF Imports: BiocGenerics (>= 0.31.6), nnlasso, nnls, parallel, data.table, Biostrings, GenomicRanges, IRanges, BSgenome, BSgenome.Hsapiens.1000genomes.hs37d5, GenomeInfoDb, ggplot2, gridExtra Suggests: BiocStyle, testthat, knitr, License: file LICENSE MD5sum: 1289fc098d42ef0a7e71e4b837d0c082 NeedsCompilation: no Title: SparseSignatures Description: Point mutations occurring in a genome can be divided into 96 categories based on the base being mutated, the base it is mutated into and its two flanking bases. Therefore, for any patient, it is possible to represent all the point mutations occurring in that patient’s tumor as a vector of length 96, where each element represents the count of mutations for a given category in the patient. A mutational signature represents the pattern of mutations produced by a mutagen or mutagenic process inside the cell. Each signature can also be represented by a vector of length 96, where each element represents the probability that this particular mutagenic process generates a mutation of the 96 above mentioned categories. In this R package, we provide a set of functions to extract and visualize the mutational signatures that best explain the mutation counts of a large number of patients. biocViews: BiomedicalInformatics, SomaticMutation Author: Daniele Ramazzotti [cre, aut], Avantika Lal [aut], Keli Liu [ctb], Luca De Sano [ctb], Robert Tibshirani [ctb], Arend Sidow [aut] Maintainer: Luca De Sano URL: https://github.com/danro9685/SparseSignatures VignetteBuilder: knitr BugReports: https://github.com/danro9685/SparseSignatures git_url: https://git.bioconductor.org/packages/SparseSignatures git_branch: RELEASE_3_10 git_last_commit: f703f63 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SparseSignatures_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SparseSignatures_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SparseSignatures_1.6.0.tgz vignettes: vignettes/SparseSignatures/inst/doc/vignette.pdf vignetteTitles: SparseSignatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SparseSignatures/inst/doc/vignette.R dependencyCount: 106 Package: SpatialCPie Version: 1.2.0 Depends: R (>= 3.6) Imports: colorspace (>= 1.3-2), data.table (>= 1.12.2), dplyr (>= 0.7.6), ggforce (>= 0.3.0), ggiraph (>= 0.5.0), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), grid (>= 3.5.1), igraph (>= 1.2.2), lpSolve (>= 5.6.13), methods (>= 3.5.0), purrr (>= 0.2.5), readr (>= 1.1.1), rlang (>= 0.2.2), shiny (>= 1.1.0), shinycssloaders (>= 0.2.0), shinyjs (>= 1.0), shinyWidgets (>= 0.4.8), stats (>= 3.6.0), SummarizedExperiment (>= 1.10.1), tibble (>= 1.4.2), tidyr (>= 0.8.1), tidyselect (>= 0.2.4), tools (>= 3.6.0), utils (>= 3.5.0), zeallot (>= 0.1.0) Suggests: BiocStyle (>= 2.8.2), jpeg (>= 0.1-8), knitr (>= 1.20), rmarkdown (>= 1.10), testthat (>= 2.0.0) License: MIT + file LICENSE MD5sum: 5e4cb4b872b69e6353f2ec6e57791617 NeedsCompilation: no Title: Cluster analysis of Spatial Transcriptomics data Description: SpatialCPie is an R package designed to facilitate cluster evaluation for spatial transcriptomics data by providing intuitive visualizations that display the relationships between clusters in order to guide the user during cluster identification and other downstream applications. The package is built around a shiny "gadget" to allow the exploration of the data with multiple plots in parallel and an interactive UI. The user can easily toggle between different cluster resolutions in order to choose the most appropriate visual cues. biocViews: Transcriptomics, Clustering, RNASeq, Software Author: Joseph Bergenstraahle [aut, cre] Maintainer: Joseph Bergenstraahle VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SpatialCPie git_branch: RELEASE_3_10 git_last_commit: f860fcd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SpatialCPie_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SpatialCPie_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SpatialCPie_1.2.0.tgz vignettes: vignettes/SpatialCPie/inst/doc/SpatialCPie.html vignetteTitles: SpatialCPie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpatialCPie/inst/doc/SpatialCPie.R dependencyCount: 115 Package: specL Version: 1.20.0 Depends: R (>= 3.6), DBI (>= 0.5), methods (>= 3.3), protViz (>= 0.4), RSQLite (>= 1.1), seqinr (>= 3.3) Suggests: BiocGenerics, BiocStyle (>= 2.2), knitr (>= 1.15), rmarkdown, RUnit (>= 0.4) License: GPL-3 MD5sum: 7ccd6b66bb7d34579125ef37bde9fed3 NeedsCompilation: no Title: specL - Prepare Peptide Spectrum Matches for Use in Targeted Proteomics Description: provides a function for generating spectra libraries that can be used for MRM SRM MS workflows in proteomics. The package provides a BiblioSpec reader, a function which can add the protein information using a FASTA formatted amino acid file, and an export method for using the created library in the Spectronaut software. biocViews: MassSpectrometry, Proteomics Author: Christian Panse [aut, cre] (), Jonas Grossmann [aut] (), Christian Trachsel [aut], Witold E. Wolski [ctb] Maintainer: Christian Panse URL: http://bioconductor.org/packages/specL/ VignetteBuilder: knitr BugReports: https://github.com/fgcz/specL/issues git_url: https://git.bioconductor.org/packages/specL git_branch: RELEASE_3_10 git_last_commit: 4c45ebb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/specL_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/specL_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/specL_1.20.0.tgz vignettes: vignettes/specL/inst/doc/specL.pdf, vignettes/specL/inst/doc/report.html vignetteTitles: Introduction to specL, Automatic Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/specL/inst/doc/report.R, vignettes/specL/inst/doc/specL.R dependencyCount: 29 Package: SpeCond Version: 1.40.0 Depends: R (>= 2.10.0), mclust (>= 3.3.1), Biobase (>= 1.15.13), fields, hwriter (>= 1.1), RColorBrewer, methods License: LGPL (>=2) MD5sum: a5fad2ec925a2c2cbee5fac0a8928c12 NeedsCompilation: no Title: Condition specific detection from expression data Description: This package performs a gene expression data analysis to detect condition-specific genes. Such genes are significantly up- or down-regulated in a small number of conditions. It does so by fitting a mixture of normal distributions to the expression values. Conditions can be environmental conditions, different tissues, organs or any other sources that you wish to compare in terms of gene expression. biocViews: Microarray, DifferentialExpression, MultipleComparison, Clustering, ReportWriting Author: Florence Cavalli Maintainer: Florence Cavalli git_url: https://git.bioconductor.org/packages/SpeCond git_branch: RELEASE_3_10 git_last_commit: 63c360c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SpeCond_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SpeCond_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SpeCond_1.40.0.tgz vignettes: vignettes/SpeCond/inst/doc/SpeCond.pdf vignetteTitles: SpeCond hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpeCond/inst/doc/SpeCond.R dependencyCount: 16 Package: SpectralTAD Version: 1.2.0 Depends: R (>= 3.6) Imports: dplyr, PRIMME, cluster, Matrix, parallel, BiocParallel, magrittr, HiCcompare, GenomicRanges Suggests: BiocCheck, BiocManager, BiocStyle, knitr, rmarkdown, microbenchmark, testthat, covr License: MIT + file LICENSE MD5sum: 5cb1b4aba2ae749a0630b9577dfc890a NeedsCompilation: no Title: SpectralTAD: Hierarchical TAD detection using spectral clustering Description: SpectralTAD is an R package designed to identify Topologically Associated Domains (TADs) from Hi-C contact matrices. It uses a modified version of spectral clustering that uses a sliding window to quickly detect TADs. The function works on a range of different formats of contact matrices and returns a bed file of TAD coordinates. The method does not require users to adjust any parameters to work and gives them control over the number of hierarchical levels to be returned. biocViews: Software, HiC, Sequencing, FeatureExtraction, Clustering Author: Kellen Cresswell , John Stansfield , Mikhail Dozmorov Maintainer: Kellen Cresswell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SpectralTAD git_branch: RELEASE_3_10 git_last_commit: 9bde8c8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SpectralTAD_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SpectralTAD_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SpectralTAD_1.2.0.tgz vignettes: vignettes/SpectralTAD/inst/doc/SpectralTAD.html vignetteTitles: SpectralTAD hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpectralTAD/inst/doc/SpectralTAD.R dependencyCount: 111 Package: SPEM Version: 1.26.0 Depends: R (>= 2.15.1), Rsolnp, Biobase, methods License: GPL-2 MD5sum: 25f7d60fe090cd0243a29feae2dc5ff7 NeedsCompilation: no Title: S-system parameter estimation method Description: This package can optimize the parameter in S-system models given time series data biocViews: Network, NetworkInference, Software Author: Xinyi YANG Developer, Jennifer E. DENT Developer and Christine NARDINI Supervisor Maintainer: Xinyi YANG git_url: https://git.bioconductor.org/packages/SPEM git_branch: RELEASE_3_10 git_last_commit: 5d3610b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SPEM_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SPEM_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SPEM_1.26.0.tgz vignettes: vignettes/SPEM/inst/doc/SPEM-package.pdf vignetteTitles: Vignette for SPEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPEM/inst/doc/SPEM-package.R importsMe: TMixClust dependencyCount: 9 Package: SPIA Version: 2.38.0 Depends: R (>= 2.14.0), graphics, KEGGgraph Imports: graphics Suggests: graph, Rgraphviz, hgu133plus2.db License: file LICENSE License_restricts_use: yes MD5sum: ab4aa8129d9dd98d268e736df2c86ff9 NeedsCompilation: no Title: Signaling Pathway Impact Analysis (SPIA) using combined evidence of pathway over-representation and unusual signaling perturbations Description: This package implements the Signaling Pathway Impact Analysis (SPIA) which uses the information form a list of differentially expressed genes and their log fold changes together with signaling pathways topology, in order to identify the pathways most relevant to the condition under the study. biocViews: Microarray, GraphAndNetwork Author: Adi Laurentiu Tarca , Purvesh Kathri and Sorin Draghici Maintainer: Adi Laurentiu Tarca URL: http://bioinformatics.oxfordjournals.org/cgi/reprint/btn577v1 git_url: https://git.bioconductor.org/packages/SPIA git_branch: RELEASE_3_10 git_last_commit: 70ebe78 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SPIA_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SPIA_2.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SPIA_2.38.0.tgz vignettes: vignettes/SPIA/inst/doc/SPIA.pdf vignetteTitles: SPIA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SPIA/inst/doc/SPIA.R importsMe: EnrichmentBrowser suggestsMe: graphite, KEGGgraph dependencyCount: 12 Package: SpidermiR Version: 1.16.2 Depends: R (>= 3.0.0) Imports: httr, igraph, utils, stats, miRNAtap, miRNAtap.db, AnnotationDbi, org.Hs.eg.db, ggplot2, gridExtra, gplots, grDevices, lattice, latticeExtra, visNetwork, TCGAbiolinks, gdata, MAGeCKFlute,networkD3 Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2 License: GPL (>= 3) MD5sum: 230f6a7c88299515517779ef99929b65 NeedsCompilation: no Title: SpidermiR: An R/Bioconductor package for integrative network analysis with miRNA data Description: The aims of SpidermiR are : i) facilitate the network open-access data retrieval from GeneMania data, ii) prepare the data using the appropriate gene nomenclature, iii) integration of miRNA data in a specific network, iv) provide different standard analyses and v) allow the user to visualize the results. In more detail, the package provides multiple methods for query, prepare and download network data (GeneMania), and the integration with validated and predicted miRNA data (mirWalk, miRTarBase, miRandola, Miranda, PicTar and TargetScan). Furthermore, we also present a statistical test to identify pharmaco-mir relationships using the gene-drug interactions derived by DGIdb and MATADOR database. biocViews: GeneRegulation, miRNA, Network Author: Claudia Cava, Antonio Colaprico, Alex Graudenzi, Gloria Bertoli, Tiago C. Silva, Catharina Olsen, Houtan Noushmehr, Gianluca Bontempi, Giancarlo Mauri, Isabella Castiglioni Maintainer: Claudia Cava URL: https://github.com/claudiacava/SpidermiR VignetteBuilder: knitr BugReports: https://github.com/claudiacava/SpidermiR/issues git_url: https://git.bioconductor.org/packages/SpidermiR git_branch: RELEASE_3_10 git_last_commit: c969a6c git_last_commit_date: 2020-01-13 Date/Publication: 2020-01-13 source.ver: src/contrib/SpidermiR_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/SpidermiR_1.16.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SpidermiR_1.16.2.tgz vignettes: vignettes/SpidermiR/inst/doc/SpidermiR.html vignetteTitles: Working with SpidermiR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpidermiR/inst/doc/SpidermiR.R importsMe: StarBioTrek dependencyCount: 226 Package: spikeLI Version: 2.46.0 Imports: graphics, grDevices, stats, utils License: GPL-2 MD5sum: c411bfaa418cd6b1be33be2e2300ef67 NeedsCompilation: no Title: Affymetrix Spike-in Langmuir Isotherm Data Analysis Tool Description: SpikeLI is a package that performs the analysis of the Affymetrix spike-in data using the Langmuir Isotherm. The aim of this package is to show the advantages of a physical-chemistry based analysis of the Affymetrix microarray data compared to the traditional methods. The spike-in (or Latin square) data for the HGU95 and HGU133 chipsets have been downloaded from the Affymetrix web site. The model used in the spikeLI package is described in details in E. Carlon and T. Heim, Physica A 362, 433 (2006). biocViews: Microarray, QualityControl Author: Delphine Baillon, Paul Leclercq , Sarah Ternisien, Thomas Heim, Enrico Carlon Maintainer: Enrico Carlon git_url: https://git.bioconductor.org/packages/spikeLI git_branch: RELEASE_3_10 git_last_commit: 78d6a31 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/spikeLI_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/spikeLI_2.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/spikeLI_2.46.0.tgz vignettes: vignettes/spikeLI/inst/doc/spikeLI.pdf vignetteTitles: spikeLI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: spkTools Version: 1.42.0 Depends: R (>= 2.7.0), Biobase (>= 2.5.5) Imports: Biobase (>= 2.5.5), graphics, grDevices, gtools, methods, RColorBrewer, stats, utils Suggests: xtable License: GPL (>= 2) MD5sum: da854c696808de4191107fe342c5c1f3 NeedsCompilation: no Title: Methods for Spike-in Arrays Description: The package contains functions that can be used to compare expression measures on different array platforms. biocViews: Software, Technology, Microarray Author: Matthew N McCall , Rafael A Irizarry Maintainer: Matthew N McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/spkTools git_branch: RELEASE_3_10 git_last_commit: 1b1d063 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/spkTools_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/spkTools_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/spkTools_1.42.0.tgz vignettes: vignettes/spkTools/inst/doc/spkDoc.pdf vignetteTitles: spkTools: Spike-in Data Analysis and Visualization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spkTools/inst/doc/spkDoc.R dependencyCount: 10 Package: splatter Version: 1.10.1 Depends: R (>= 3.6), SingleCellExperiment Imports: akima, BiocGenerics, BiocParallel, checkmate (>= 2.0.0), edgeR, fitdistrplus, ggplot2, locfit, matrixStats, methods, scales, scater (>= 1.13.27), stats, SummarizedExperiment, utils, crayon, S4Vectors, rlang Suggests: BiocStyle, covr, cowplot, knitr, limSolve, lme4, progress, pscl, testthat, rmarkdown, scDD, scran, mfa, phenopath, BASiCS (>= 1.7.10), zinbwave, SparseDC, BiocManager, spelling, igraph, DropletUtils License: GPL-3 + file LICENSE MD5sum: e5a154a4ba5a788764c5906540fadf25 NeedsCompilation: no Title: Simple Simulation of Single-cell RNA Sequencing Data Description: Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets. biocViews: SingleCell, RNASeq, Transcriptomics, GeneExpression, Sequencing, Software, ImmunoOncology Author: Luke Zappia [aut, cre] (), Belinda Phipson [aut] (), Alicia Oshlack [aut] () Maintainer: Luke Zappia URL: https://github.com/Oshlack/splatter VignetteBuilder: knitr BugReports: https://github.com/Oshlack/splatter/issues git_url: https://git.bioconductor.org/packages/splatter git_branch: RELEASE_3_10 git_last_commit: 39a9ae7 git_last_commit_date: 2020-02-14 Date/Publication: 2020-02-14 source.ver: src/contrib/splatter_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/splatter_1.10.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/splatter_1.10.1.tgz vignettes: vignettes/splatter/inst/doc/splat_params.html, vignettes/splatter/inst/doc/splatter.html vignetteTitles: Splat simulation parameters, An introduction to the Splatter package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/splatter/inst/doc/splat_params.R, vignettes/splatter/inst/doc/splatter.R suggestsMe: scPCA, SummarizedBenchmark dependencyCount: 105 Package: splicegear Version: 1.58.0 Depends: R (>= 2.6.0), methods, Biobase(>= 2.5.5) Imports: annotate, Biobase, graphics, grDevices, grid, methods, utils, XML License: LGPL MD5sum: d20b3ff1b53a21366c64fc54b6e33930 NeedsCompilation: no Title: splicegear Description: A set of tools to work with alternative splicing biocViews: Infrastructure, Transcription Author: Laurent Gautier Maintainer: Laurent Gautier git_url: https://git.bioconductor.org/packages/splicegear git_branch: RELEASE_3_10 git_last_commit: 118b955 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/splicegear_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/splicegear_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/splicegear_1.58.0.tgz vignettes: vignettes/splicegear/inst/doc/splicegear.pdf vignetteTitles: splicegear Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splicegear/inst/doc/splicegear.R dependencyCount: 33 Package: SplicingGraphs Version: 1.26.1 Depends: GenomicFeatures (>= 1.17.13), GenomicAlignments (>= 1.1.22), Rgraphviz (>= 2.3.7) Imports: methods, utils, graphics, igraph, BiocGenerics, S4Vectors (>= 0.17.5), BiocParallel, IRanges (>= 2.20.1), GenomeInfoDb, GenomicRanges (>= 1.23.21), GenomicFeatures, Rsamtools, GenomicAlignments, graph, Rgraphviz Suggests: igraph, Gviz, TxDb.Hsapiens.UCSC.hg19.knownGene, RNAseqData.HNRNPC.bam.chr14, RUnit License: Artistic-2.0 MD5sum: 2a6b80ba2ec6e69b261838ebd694a520 NeedsCompilation: no Title: Create, manipulate, visualize splicing graphs, and assign RNA-seq reads to them Description: This package allows the user to create, manipulate, and visualize splicing graphs and their bubbles based on a gene model for a given organism. Additionally it allows the user to assign RNA-seq reads to the edges of a set of splicing graphs, and to summarize them in different ways. biocViews: Genetics, Annotation, DataRepresentation, Visualization, Sequencing, RNASeq, GeneExpression, AlternativeSplicing, Transcription, ImmunoOncology Author: D. Bindreither, M. Carlson, M. Morgan, H. Pagès Maintainer: H. Pagès git_url: https://git.bioconductor.org/packages/SplicingGraphs git_branch: RELEASE_3_10 git_last_commit: 42f49b4 git_last_commit_date: 2019-11-20 Date/Publication: 2019-11-20 source.ver: src/contrib/SplicingGraphs_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/SplicingGraphs_1.26.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SplicingGraphs_1.26.1.tgz vignettes: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.pdf vignetteTitles: Splicing graphs and RNA-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.R dependencyCount: 86 Package: splineTimeR Version: 1.14.0 Depends: R (>= 3.3), Biobase, igraph, limma, GSEABase, gtools, splines, GeneNet (>= 1.2.13), longitudinal (>= 1.1.12), FIs Suggests: knitr License: GPL-3 MD5sum: bc235c7ab78ac945827f2be7a6d85ced NeedsCompilation: no Title: Time-course differential gene expression data analysis using spline regression models followed by gene association network reconstruction Description: This package provides functions for differential gene expression analysis of gene expression time-course data. Natural cubic spline regression models are used. Identified genes may further be used for pathway enrichment analysis and/or the reconstruction of time dependent gene regulatory association networks. biocViews: GeneExpression, DifferentialExpression, TimeCourse, Regression, GeneSetEnrichment, NetworkEnrichment, NetworkInference, GraphAndNetwork Author: Agata Michna Maintainer: Herbert Braselmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/splineTimeR git_branch: RELEASE_3_10 git_last_commit: adf92f1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/splineTimeR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/splineTimeR_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/splineTimeR_1.14.0.tgz vignettes: vignettes/splineTimeR/inst/doc/splineTimeR.pdf vignetteTitles: splineTimeR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splineTimeR/inst/doc/splineTimeR.R dependencyCount: 47 Package: SPLINTER Version: 1.12.0 Depends: R (>= 3.6.0), grDevices, stats Imports: graphics, ggplot2, seqLogo, Biostrings, biomaRt, GenomicAlignments, GenomicRanges, GenomicFeatures, Gviz, IRanges, S4Vectors, GenomeInfoDb, utils, plyr,stringr, methods, BSgenome.Mmusculus.UCSC.mm9, googleVis Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 99f63e357b6b493431981e4c652b7967 NeedsCompilation: no Title: Splice Interpreter of Transcripts Description: Provides tools to analyze alternative splicing sites, interpret outcomes based on sequence information, select and design primers for site validiation and give visual representation of the event to guide downstream experiments. biocViews: ImmunoOncology, GeneExpression, RNASeq, Visualization, AlternativeSplicing Author: Diana Low [aut, cre] Maintainer: Diana Low URL: https://github.com/dianalow/SPLINTER/ VignetteBuilder: knitr BugReports: https://github.com/dianalow/SPLINTER/issues git_url: https://git.bioconductor.org/packages/SPLINTER git_branch: RELEASE_3_10 git_last_commit: f03565b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SPLINTER_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SPLINTER_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SPLINTER_1.12.0.tgz vignettes: vignettes/SPLINTER/inst/doc/vignette.pdf vignetteTitles: SPLINTER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPLINTER/inst/doc/vignette.R dependencyCount: 147 Package: splots Version: 1.52.0 Imports: grid, RColorBrewer License: LGPL MD5sum: df8c4ed16e0cab7dd8874444b6391501 NeedsCompilation: no Title: Visualization of high-throughput assays in microtitre plate or slide format Description: The splots package provides the plotScreen function for visualising data in microtitre plate or slide format. biocViews: Visualization, Sequencing, MicrotitrePlateAssay Author: Wolfgang Huber, Oleg Sklyar Maintainer: Wolfgang Huber git_url: https://git.bioconductor.org/packages/splots git_branch: RELEASE_3_10 git_last_commit: 788a780 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/splots_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/splots_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/splots_1.52.0.tgz vignettes: vignettes/splots/inst/doc/splotsHOWTO.pdf vignetteTitles: Visualization of data from assays in microtitre plate or slide format hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splots/inst/doc/splotsHOWTO.R dependsOnMe: cellHTS2 importsMe: RNAinteract, RNAither dependencyCount: 2 Package: SPONGE Version: 1.8.0 Depends: R (>= 3.4) Imports: Biobase, stats, ppcor, logging, foreach, doRNG, data.table, MASS, expm, gRbase, glmnet, igraph, iterators, Suggests: testthat, knitr, rmarkdown, visNetwork, ggplot2, ggrepel, gridExtra, digest, doParallel, bigmemory License: GPL (>=3) MD5sum: 72a077ff5bf22060ac19b6d7aa684970 NeedsCompilation: no Title: Sparse Partial Correlations On Gene Expression Description: This package provides methods to efficiently detect competitive endogeneous RNA interactions between two genes. Such interactions are mediated by one or several miRNAs such that both gene and miRNA expression data for a larger number of samples is needed as input. biocViews: GeneExpression, Transcription, GeneRegulation, NetworkInference, Transcriptomics, SystemsBiology, Regression Author: Markus List, Azim Dehghani Amirabad, Dennis Kostka, Marcel H. Schulz Maintainer: Markus List VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SPONGE git_branch: RELEASE_3_10 git_last_commit: 4c39efb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SPONGE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SPONGE_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SPONGE_1.8.0.tgz vignettes: vignettes/SPONGE/inst/doc/SPONGE.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPONGE/inst/doc/SPONGE.R dependencyCount: 36 Package: spotSegmentation Version: 1.60.0 Depends: R (>= 2.10), mclust License: GPL (>= 2) MD5sum: 15dd8f599a691ac983e08eeba4458bfa NeedsCompilation: no Title: Microarray Spot Segmentation and Gridding for Blocks of Microarray Spots Description: Spot segmentation via model-based clustering and gridding for blocks within microarray slides, as described in Li et al, Robust Model-Based Segmentation of Microarray Images, Technical Report no. 473, Department of Statistics, University of Washington. biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Qunhua Li, Chris Fraley, Adrian Raftery Department of Statistics, University of Washington Maintainer: Chris Fraley URL: http://www.stat.washington.edu/fraley git_url: https://git.bioconductor.org/packages/spotSegmentation git_branch: RELEASE_3_10 git_last_commit: 34c4ba9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/spotSegmentation_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/spotSegmentation_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/spotSegmentation_1.60.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 5 Package: SQLDataFrame Version: 1.0.0 Depends: R (>= 3.6), dplyr (>= 0.8.0.1), dbplyr (>= 1.4.0), S4Vectors Imports: DBI, lazyeval, methods, tools, stats, BiocGenerics, RSQLite, tibble Suggests: RMySQL, bigrquery, testthat, knitr, rmarkdown, DelayedArray License: GPL-3 MD5sum: 609112b7b76e4a12ed2440e642625f3f NeedsCompilation: no Title: Representation of SQL database in DataFrame metaphor Description: SQLDataFrame is developed to lazily represent and efficiently analyze SQL-based tables in _R_. SQLDataFrame supports common and familiar 'DataFrame' operations such as '[' subsetting, rbind, cbind, etc.. The internal implementation is based on the widely adopted dplyr grammar and SQL commands. In-memory datasets or plain text files (.txt, .csv, etc.) could also be easily converted into SQLDataFrames objects (which generates a new database on-disk). biocViews: Infrastructure, DataRepresentation Author: Qian Liu [aut, cre] (), Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/SQLDataFrame VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/SQLDataFrame/issues git_url: https://git.bioconductor.org/packages/SQLDataFrame git_branch: RELEASE_3_10 git_last_commit: 6839d85 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SQLDataFrame_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SQLDataFrame_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SQLDataFrame_1.0.0.tgz vignettes: vignettes/SQLDataFrame/inst/doc/SQLDataFrame-internal.html, vignettes/SQLDataFrame/inst/doc/SQLDataFrame.html vignetteTitles: SQLDataFrame Internal Implementation, SQLDataFrame: Lazy representation of SQL database in DataFrame metaphor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SQLDataFrame/inst/doc/SQLDataFrame-internal.R, vignettes/SQLDataFrame/inst/doc/SQLDataFrame.R dependencyCount: 40 Package: SQUADD Version: 1.36.0 Depends: R (>= 2.11.0) Imports: graphics, grDevices, methods, RColorBrewer, stats, utils License: GPL (>=2) MD5sum: abc5aee21b685163ba2badc1b7e5e9a2 NeedsCompilation: no Title: Add-on of the SQUAD Software Description: This package SQUADD is a SQUAD add-on. It permits to generate SQUAD simulation matrix, prediction Heat-Map and Correlation Circle from PCA analysis. biocViews: GraphAndNetwork, Network, Visualization Author: Martial Sankar, supervised by Christian Hardtke and Ioannis Xenarios Maintainer: Martial Sankar URL: http://www.unil.ch/dbmv/page21142_en.html git_url: https://git.bioconductor.org/packages/SQUADD git_branch: RELEASE_3_10 git_last_commit: 7d06b6d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SQUADD_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SQUADD_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SQUADD_1.36.0.tgz vignettes: vignettes/SQUADD/inst/doc/SQUADD_ERK.pdf, vignettes/SQUADD/inst/doc/SQUADD.pdf vignetteTitles: SQUADD ERK exemple, SQUADD HOW-TO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SQUADD/inst/doc/SQUADD_ERK.R, vignettes/SQUADD/inst/doc/SQUADD.R dependencyCount: 6 Package: sRACIPE Version: 1.2.0 Depends: R (>= 3.5.0),SummarizedExperiment, methods, Rcpp, shiny Imports: shinyBS, ggplot2, reshape2, MASS, RColorBrewer, gridExtra,visNetwork, gplots, umap, htmlwidgets, S4Vectors, BiocGenerics, grDevices, stats, utils, graphics, shinyjs LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown License: MIT + file LICENSE MD5sum: 7fdefaa0ff3b3d5a63ac91da40d6d414 NeedsCompilation: yes Title: Systems biology tool to simulate gene regulatory circuits Description: sRACIPE implements a randomization-based method for gene circuit modeling. It allows us to study the effect of both the gene expression noise and the parametric variation on any gene regulatory circuit (GRC) using only its topology, and simulates an ensemble of models with random kinetic parameters at multiple noise levels. Statistical analysis of the generated gene expressions reveals the basin of attraction and stability of various phenotypic states and their changes associated with intrinsic and extrinsic noises. sRACIPE provides a holistic picture to evaluate the effects of both the stochastic nature of cellular processes and the parametric variation. biocViews: ResearchField, SystemsBiology, MathematicalBiology, GeneExpression, GeneRegulation, GeneTarget Author: Vivek Kohar Maintainer: Vivek Kohar URL: https://github.com/vivekkohar/sRACIPE VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sRACIPE git_branch: RELEASE_3_10 git_last_commit: 9f721f8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sRACIPE_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sRACIPE_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sRACIPE_1.2.0.tgz vignettes: vignettes/sRACIPE/inst/doc/sRACIPE.html vignetteTitles: A systems biology tool for gene regulatory circuit simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sRACIPE/inst/doc/sRACIPE.R dependencyCount: 110 Package: SRAdb Version: 1.48.2 Depends: RSQLite, graph, RCurl Imports: GEOquery Suggests: Rgraphviz License: Artistic-2.0 MD5sum: f0d8f02d2ccb2d19e2d2f05bfcfca9a6 NeedsCompilation: no Title: A compilation of metadata from NCBI SRA and tools Description: The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, and others. However, finding data of interest can be challenging using current tools. SRAdb is an attempt to make access to the metadata associated with submission, study, sample, experiment and run much more feasible. This is accomplished by parsing all the NCBI SRA metadata into a SQLite database that can be stored and queried locally. Fulltext search in the package make querying metadata very flexible and powerful. fastq and sra files can be downloaded for doing alignment locally. Beside ftp protocol, the SRAdb has funcitons supporting fastp protocol (ascp from Aspera Connect) for faster downloading large data files over long distance. The SQLite database is updated regularly as new data is added to SRA and can be downloaded at will for the most up-to-date metadata. biocViews: Infrastructure, Sequencing, DataImport Author: Jack Zhu and Sean Davis Maintainer: Jack Zhu URL: http://gbnci.abcc.ncifcrf.gov/sra/ BugReports: https://github.com/seandavi/SRAdb/issues/new git_url: https://git.bioconductor.org/packages/SRAdb git_branch: RELEASE_3_10 git_last_commit: 874c719 git_last_commit_date: 2019-12-24 Date/Publication: 2019-12-24 source.ver: src/contrib/SRAdb_1.48.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/SRAdb_1.48.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SRAdb_1.48.2.tgz vignettes: vignettes/SRAdb/inst/doc/SRAdb.pdf vignetteTitles: Using SRAdb to Query the Sequence Read Archive hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SRAdb/inst/doc/SRAdb.R dependencyCount: 56 Package: sRAP Version: 1.26.0 Depends: WriteXLS Imports: gplots, pls, ROCR, qvalue License: GPL-3 MD5sum: 41f56582e6bdd908167d4ad57a2a9fa4 NeedsCompilation: no Title: Simplified RNA-Seq Analysis Pipeline Description: This package provides a pipeline for gene expression analysis (primarily for RNA-Seq data). The normalization function is specific for RNA-Seq analysis, but all other functions (Quality Control Figures, Differential Expression and Visualization, and Functional Enrichment via BD-Func) will work with any type of gene expression data. biocViews: GeneExpression, RNAseq, Microarray, Preprocessing, QualityControl, Statistics, DifferentialExpression, Visualization, GeneSetEnrichment, GO Author: Charles Warden Maintainer: Charles Warden git_url: https://git.bioconductor.org/packages/sRAP git_branch: RELEASE_3_10 git_last_commit: 00ca0b2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sRAP_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sRAP_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sRAP_1.26.0.tgz vignettes: vignettes/sRAP/inst/doc/sRAP.pdf vignetteTitles: sRAP Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sRAP/inst/doc/sRAP.R dependencyCount: 68 Package: SRGnet Version: 1.12.0 Depends: R (>= 3.3.1), EBcoexpress, MASS, igraph, pvclust (>= 2.0-0), gbm (>= 2.1.1), limma, DMwR (>= 0.4.1), matrixStats, Hmisc Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 8e26f7024968872c2c151b413e3e68b0 NeedsCompilation: no Title: SRGnet: An R package for studying synergistic response to gene mutations from transcriptomics data Description: We developed SRGnet to analyze synergistic regulatory mechanisms in transcriptome profiles that act to enhance the overall cell response to combination of mutations, drugs or environmental exposure. This package can be used to identify regulatory modules downstream of synergistic response genes, prioritize synergistic regulatory genes that may be potential intervention targets, and contextualize gene perturbation experiments. biocViews: Software, StatisticalMethod, Regression Author: Isar Nassiri [aut, cre], Matthew McCall [aut, cre] Maintainer: Isar Nassiri VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SRGnet git_branch: RELEASE_3_10 git_last_commit: 7fc03d2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SRGnet_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SRGnet_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SRGnet_1.12.0.tgz vignettes: vignettes/SRGnet/inst/doc/vignette.html vignetteTitles: SRGnet An R package for studying synergistic response to gene mutations from transcriptomics data \ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 109 Package: srnadiff Version: 1.6.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.12.8), methods, devtools, S4Vectors, GenomeInfoDb, rtracklayer, SummarizedExperiment, IRanges, GenomicRanges, DESeq2, Rsamtools, GenomicFeatures, GenomicAlignments, grDevices, Gviz, BiocParallel, BiocManager, BiocStyle LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 Archs: i386, x64 MD5sum: 4dd07b4c21af2b4bacea056346482c29 NeedsCompilation: yes Title: Finding differentially expressed unannotated genomic regions from RNA-seq data Description: srnadiff is a package that finds differently expressed regions from RNA-seq data at base-resolution level without relying on existing annotation. To do so, the package implements the identify-then-annotate methodology that builds on the idea of combining two pipelines approachs differential expressed regions detection and differential expression quantification. biocViews: ImmunoOncology, GeneExpression, Coverage, SmallRNA, Epigenetics, StatisticalMethod, Preprocessing, DifferentialExpression Author: Zytnicki Matthias [aut, cre], Gonzalez Ignacio [aut] Maintainer: Zytnicki Matthias SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/srnadiff git_branch: RELEASE_3_10 git_last_commit: 179be34 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/srnadiff_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/srnadiff_1.5.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/srnadiff_1.6.0.tgz vignettes: vignettes/srnadiff/inst/doc/srnadiff.html vignetteTitles: The srnadiff package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/srnadiff/inst/doc/srnadiff.R dependencyCount: 179 Package: sscore Version: 1.58.0 Depends: R (>= 1.8.0), affy, affyio Suggests: affydata License: GPL (>= 2) MD5sum: 0485c962d38cf9874533fb225bcaab96 NeedsCompilation: no Title: S-Score Algorithm for Affymetrix Oligonucleotide Microarrays Description: This package contains an implementation of the S-Score algorithm as described by Zhang et al (2002). biocViews: DifferentialExpression Author: Richard Kennedy , based on C++ code from Li Zhang and Borland Delphi code from Robnet Kerns . Maintainer: Richard Kennedy URL: http://home.att.net/~richard-kennedy/professional.html git_url: https://git.bioconductor.org/packages/sscore git_branch: RELEASE_3_10 git_last_commit: 6ec9d7b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sscore_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sscore_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sscore_1.58.0.tgz vignettes: vignettes/sscore/inst/doc/sscore.pdf vignetteTitles: SScore primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sscore/inst/doc/sscore.R dependencyCount: 13 Package: sscu Version: 2.16.0 Depends: R (>= 3.3) Imports: Biostrings (>= 2.36.4), seqinr (>= 3.1-3), BiocGenerics (>= 0.16.1) Suggests: knitr, rmarkdown License: GPL (>= 2) MD5sum: e8b7709423dc51ee3b10b35cf2c027c8 NeedsCompilation: no Title: Strength of Selected Codon Usage Description: The package calculates the indexes for selective stength in codon usage in bacteria species. (1) The package can calculate the strength of selected codon usage bias (sscu, also named as s_index) based on Paul Sharp's method. The method take into account of background mutation rate, and focus only on four pairs of codons with universal translational advantages in all bacterial species. Thus the sscu index is comparable among different species. (2) The package can detect the strength of translational accuracy selection by Akashi's test. The test tabulating all codons into four categories with the feature as conserved/variable amino acids and optimal/non-optimal codons. (3) Optimal codon lists (selected codons) can be calculated by either op_highly function (by using the highly expressed genes compared with all genes to identify optimal codons), or op_corre_CodonW/op_corre_NCprime function (by correlative method developed by Hershberg & Petrov). Users will have a list of optimal codons for further analysis, such as input to the Akashi's test. (4) The detailed codon usage information, such as RSCU value, number of optimal codons in the highly/all gene set, as well as the genomic gc3 value, can be calculate by the optimal_codon_statistics and genomic_gc3 function. (5) Furthermore, we added one test function low_frequency_op in the package. The function try to find the low frequency optimal codons, among all the optimal codons identified by the op_highly function. biocViews: Genetics, GeneExpression, WholeGenome Author: Yu Sun Maintainer: Yu Sun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sscu git_branch: RELEASE_3_10 git_last_commit: 570a7b2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sscu_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sscu_2.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sscu_2.16.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 21 Package: sSeq Version: 1.24.0 Depends: R (>= 3.0), caTools, RColorBrewer License: GPL (>= 3) MD5sum: 9a759ec2878efc76eb9bbf49b3554354 NeedsCompilation: no Title: Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size Description: The purpose of this package is to discover the genes that are differentially expressed between two conditions in RNA-seq experiments. Gene expression is measured in counts of transcripts and modeled with the Negative Binomial (NB) distribution using a shrinkage approach for dispersion estimation. The method of moment (MM) estimates for dispersion are shrunk towards an estimated target, which minimizes the average squared difference between the shrinkage estimates and the initial estimates. The exact per-gene probability under the NB model is calculated, and used to test the hypothesis that the expected expression of a gene in two conditions identically follow a NB distribution. biocViews: ImmunoOncology, RNASeq Author: Danni Yu , Wolfgang Huber and Olga Vitek Maintainer: Danni Yu git_url: https://git.bioconductor.org/packages/sSeq git_branch: RELEASE_3_10 git_last_commit: 8d8954f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sSeq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sSeq_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sSeq_1.24.0.tgz vignettes: vignettes/sSeq/inst/doc/sSeq.pdf vignetteTitles: sSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sSeq/inst/doc/sSeq.R importsMe: MLSeq dependencyCount: 3 Package: ssize Version: 1.60.0 Depends: gdata, xtable License: LGPL Archs: i386, x64 MD5sum: 7b27151fb70892a6819cfbff66ce8dee NeedsCompilation: no Title: Estimate Microarray Sample Size Description: Functions for computing and displaying sample size information for gene expression arrays. biocViews: Microarray, DifferentialExpression Author: Gregory R. Warnes, Peng Liu, and Fasheng Li Maintainer: Gregory R. Warnes git_url: https://git.bioconductor.org/packages/ssize git_branch: RELEASE_3_10 git_last_commit: 154816f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ssize_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ssize_1.60.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ssize_1.60.0.tgz vignettes: vignettes/ssize/inst/doc/ssize.pdf vignetteTitles: Sample Size Estimation for Microarray Experiments Using the \code{ssize} package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssize/inst/doc/ssize.R dependencyCount: 6 Package: SSPA Version: 2.26.0 Depends: R (>= 2.12), methods, qvalue, lattice, limma Imports: graphics, stats Suggests: BiocStyle, genefilter, edgeR, DESeq License: GPL (>= 2) MD5sum: 3151d8e660a5d69b4d5bdabf0b3ff6fe NeedsCompilation: yes Title: General Sample Size and Power Analysis for Microarray and Next-Generation Sequencing Data Description: General Sample size and power analysis for microarray and next-generation sequencing data. biocViews: ImmunoOncology, GeneExpression, RNASeq, Microarray, StatisticalMethod Author: Maarten van Iterson Maintainer: Maarten van Iterson URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html git_url: https://git.bioconductor.org/packages/SSPA git_branch: RELEASE_3_10 git_last_commit: 52a549f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SSPA_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SSPA_2.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SSPA_2.26.0.tgz vignettes: vignettes/SSPA/inst/doc/SSPA.pdf vignetteTitles: SSPA Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SSPA/inst/doc/SSPA.R dependencyCount: 60 Package: ssPATHS Version: 1.0.0 Depends: SummarizedExperiment Imports: ROCR, dml, MESS Suggests: ggplot2, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 8bf3d124ae029c40bc61db1ed46aedaa NeedsCompilation: no Title: ssPATHS: Single Sample PATHway Score Description: This package generates pathway scores from expression data for single samples after training on a reference cohort. The score is generated by taking the expression of a gene set (pathway) from a reference cohort and performing linear discriminant analysis to distinguish samples in the cohort that have the pathway augmented and not. The separating hyperplane is then used to score new samples. biocViews: Software, GeneExpression, BiomedicalInformatics, RNASeq, Pathways, Transcriptomics, DimensionReduction, Classification Author: Natalie R. Davidson Maintainer: Natalie R. Davidson git_url: https://git.bioconductor.org/packages/ssPATHS git_branch: RELEASE_3_10 git_last_commit: 3eaea9c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ssPATHS_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ssPATHS_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ssPATHS_1.0.0.tgz vignettes: vignettes/ssPATHS/inst/doc/ssPATHS.pdf vignetteTitles: Using ssPATHS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ssPATHS/inst/doc/ssPATHS.R dependencyCount: 118 Package: ssrch Version: 1.2.0 Depends: R (>= 3.6), methods Imports: shiny, DT, utils Suggests: knitr, testthat License: Artistic-2.0 MD5sum: 467061c5511c292e3060d496a8134545 NeedsCompilation: no Title: a simple search engine Description: Demonstrate tokenization and a search gadget for collections of CSV files. biocViews: Infrastructure Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ssrch git_branch: RELEASE_3_10 git_last_commit: 1045733 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ssrch_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ssrch_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ssrch_1.2.0.tgz vignettes: vignettes/ssrch/inst/doc/ssrch.html vignetteTitles: ssrch: small search engine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssrch/inst/doc/ssrch.R importsMe: HumanTranscriptomeCompendium dependencyCount: 27 Package: ssviz Version: 1.20.0 Depends: R (>= 2.15.1),methods,Rsamtools,Biostrings,reshape,ggplot2,RColorBrewer,stats Suggests: knitr License: GPL-2 MD5sum: b28027774447e3e14ea107913889fb12 NeedsCompilation: no Title: A small RNA-seq visualizer and analysis toolkit Description: Small RNA sequencing viewer biocViews: ImmunoOncology, Sequencing,RNASeq,Visualization,MultipleComparison,Genetics Author: Diana Low Maintainer: Diana Low VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ssviz git_branch: RELEASE_3_10 git_last_commit: 9f0b36b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ssviz_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ssviz_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ssviz_1.20.0.tgz vignettes: vignettes/ssviz/inst/doc/ssviz.pdf vignetteTitles: ssviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssviz/inst/doc/ssviz.R dependencyCount: 78 Package: stageR Version: 1.8.0 Depends: R (>= 3.4), SummarizedExperiment Imports: methods, stats Suggests: knitr, rmarkdown, BiocStyle, methods, Biobase, edgeR, limma, DEXSeq, testthat License: GNU General Public License version 3 Archs: i386, x64 MD5sum: 86093935342ac6e5abf93d5a2d6cbf97 NeedsCompilation: no Title: stageR: stage-wise analysis of high throughput gene expression data in R Description: The stageR package allows automated stage-wise analysis of high-throughput gene expression data. The method is published in Genome Biology at https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1277-0 biocViews: Software, StatisticalMethod Author: Koen Van den Berge and Lieven Clement Maintainer: Koen Van den Berge VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/stageR git_branch: RELEASE_3_10 git_last_commit: 80c262e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/stageR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/stageR_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/stageR_1.8.0.tgz vignettes: vignettes/stageR/inst/doc/stageRVignette.html vignetteTitles: stageR: stage-wise analysis of high-throughput gene expression data in R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/stageR/inst/doc/stageRVignette.R dependencyCount: 32 Package: STAN Version: 2.14.0 Depends: methods, poilog, parallel Imports: GenomicRanges, IRanges, S4Vectors, BiocGenerics, GenomeInfoDb, Gviz, Rsolnp Suggests: BiocStyle, gplots, knitr License: GPL (>= 2) MD5sum: 10a4bba67aba2c94cc95affae646e22d NeedsCompilation: yes Title: The Genomic STate ANnotation Package Description: Genome segmentation with hidden Markov models has become a useful tool to annotate genomic elements, such as promoters and enhancers. STAN (genomic STate ANnotation) implements (bidirectional) hidden Markov models (HMMs) using a variety of different probability distributions, which can model a wide range of current genomic data (e.g. continuous, discrete, binary). STAN de novo learns and annotates the genome into a given number of 'genomic states'. The 'genomic states' may for instance reflect distinct genome-associated protein complexes (e.g. 'transcription states') or describe recurring patterns of chromatin features (referred to as 'chromatin states'). Unlike other tools, STAN also allows for the integration of strand-specific (e.g. RNA) and non-strand-specific data (e.g. ChIP). biocViews: HiddenMarkovModel, GenomeAnnotation, Microarray, Sequencing, ChIPSeq, RNASeq, ChipOnChip, Transcription, ImmunoOncology Author: Benedikt Zacher, Julia Ertl, Rafael Campos-Martin, Julien Gagneur, Achim Tresch Maintainer: Rafael Campos-Martin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/STAN git_branch: RELEASE_3_10 git_last_commit: 2d374bb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/STAN_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/STAN_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/STAN_2.14.0.tgz vignettes: vignettes/STAN/inst/doc/STAN-knitr.pdf vignetteTitles: The genomic STate ANnotation package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STAN/inst/doc/STAN-knitr.R dependencyCount: 146 Package: staRank Version: 1.28.0 Depends: methods, cellHTS2, R (>= 2.10) License: GPL MD5sum: bbced3e50bc3d4b9bace0c9eba68b015 NeedsCompilation: no Title: Stability Ranking Description: Detecting all relevant variables from a data set is challenging, especially when only few samples are available and data is noisy. Stability ranking provides improved variable rankings of increased robustness using resampling or subsampling. biocViews: ImmunoOncology, MultipleComparison, CellBiology, CellBasedAssays, MicrotitrePlateAssay Author: Juliane Siebourg, Niko Beerenwinkel Maintainer: Juliane Siebourg git_url: https://git.bioconductor.org/packages/staRank git_branch: RELEASE_3_10 git_last_commit: 6b20662 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/staRank_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/staRank_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/staRank_1.28.0.tgz vignettes: vignettes/staRank/inst/doc/staRank.pdf vignetteTitles: Using staRank hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/staRank/inst/doc/staRank.R dependencyCount: 97 Package: StarBioTrek Version: 1.12.0 Depends: R (>= 3.3) Imports: SpidermiR, graphite, AnnotationDbi, e1071, ROCR, MLmetrics, grDevices, igraph, reshape2, ggplot2 Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2, qgraph, png, grid License: GPL (>= 3) MD5sum: 62fec11ff2a139d648c3d13373027292 NeedsCompilation: no Title: StarBioTrek Description: This tool StarBioTrek presents some methodologies to measure pathway activity and cross-talk among pathways integrating also the information of network data. biocViews: GeneRegulation, Network, Pathways, KEGG Author: Claudia Cava, Isabella Castiglioni Maintainer: Claudia Cava URL: https://github.com/claudiacava/StarBioTrek VignetteBuilder: knitr BugReports: https://github.com/claudiacava/StarBioTrek/issues git_url: https://git.bioconductor.org/packages/StarBioTrek git_branch: RELEASE_3_10 git_last_commit: ea1e82e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/StarBioTrek_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/StarBioTrek_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/StarBioTrek_1.12.0.tgz vignettes: vignettes/StarBioTrek/inst/doc/StarBioTrek.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/StarBioTrek/inst/doc/StarBioTrek.R dependencyCount: 233 Package: Starr Version: 1.42.0 Depends: Ringo, affy, affxparser Imports: pspline, MASS, zlibbioc License: Artistic-2.0 MD5sum: f3748bac5ca58ab7540112837e496369 NeedsCompilation: yes Title: Simple tiling array analysis of Affymetrix ChIP-chip data Description: Starr facilitates the analysis of ChIP-chip data, in particular that of Affymetrix tiling arrays. The package provides functions for data import, quality assessment, data visualization and exploration. Furthermore, it includes high-level analysis features like association of ChIP signals with annotated features, correlation analysis of ChIP signals and other genomic data (e.g. gene expression), peak-finding with the CMARRT algorithm and comparative display of multiple clusters of ChIP-profiles. It uses the basic Bioconductor classes ExpressionSet and probeAnno for maximum compatibility with other software on Bioconductor. All functions from Starr can be used to investigate preprocessed data from the Ringo package, and vice versa. An important novel tool is the the automated generation of correct, up-to-date microarray probe annotation (bpmap) files, which relies on an efficient mapping of short sequences (e.g. the probe sequences on a microarray) to an arbitrary genome. biocViews: Microarray,OneChannel,DataImport,QualityControl,Preprocessing,ChIPchip Author: Benedikt Zacher, Johannes Soeding, Pei Fen Kuan, Matthias Siebert, Achim Tresch Maintainer: Benedikt Zacher git_url: https://git.bioconductor.org/packages/Starr git_branch: RELEASE_3_10 git_last_commit: 16465ff git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Starr_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Starr_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Starr_1.42.0.tgz vignettes: vignettes/Starr/inst/doc/Starr.pdf vignetteTitles: Simple tiling array analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Starr/inst/doc/Starr.R suggestsMe: nucleR dependencyCount: 86 Package: STATegRa Version: 1.22.0 Depends: R (>= 2.10) Imports: Biobase, gridExtra, ggplot2, methods, stats, grid, MASS, calibrate, gplots, edgeR, limma, foreach, affy Suggests: RUnit, BiocGenerics, knitr (>= 1.6), rmarkdown, BiocStyle (>= 1.3), roxygen2, doSNOW License: GPL-2 Archs: i386, x64 MD5sum: 3f35e9d4f4b21f06ae3bb15b594ca0d7 NeedsCompilation: no Title: Classes and methods for multi-omics data integration Description: Classes and tools for multi-omics data integration. biocViews: Software, StatisticalMethod, Clustering, DimensionReduction, PrincipalComponent Author: STATegra Consortia Maintainer: David Gomez-Cabrero , Núria Planell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/STATegRa git_branch: RELEASE_3_10 git_last_commit: ffd403f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/STATegRa_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/STATegRa_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/STATegRa_1.22.0.tgz vignettes: vignettes/STATegRa/inst/doc/STATegRa.html vignetteTitles: STATegRa User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STATegRa/inst/doc/STATegRa.R dependencyCount: 76 Package: statTarget Version: 1.16.1 Depends: R (>= 3.6.0) Imports: randomForest,plyr,pdist,ROC,utils,grDevices,graphics,rrcov,stats, pls,impute Suggests: testthat, BiocStyle, knitr, rmarkdown, gWidgets2,gWidgets2RGtk2,RGtk2 License: LGPL (>= 3) MD5sum: 2f9606374cbaf28fee7ebc4a6b3f1725 NeedsCompilation: no Title: Statistical Analysis of Molecular Profiles Description: A streamlined tool provides a graphical user interface for quality control based signal drift correction (QC-RFSC), integration of data from multi-batch MS-based experiments, and the comprehensive statistical analysis in metabolomics and proteomics. biocViews: ImmunoOncology, Metabolomics, Proteomics, Machine Learning, Lipidomics, MassSpectrometry, QualityControl, Normalization, QC-RFSC, QC-RLSC, ComBat, DifferentialExpression, BatchEffect, Visualization, MultipleComparison,Preprocessing, GUI, Software Author: Hemi Luan Maintainer: Hemi Luan URL: https://stattarget.github.io VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/statTarget git_branch: RELEASE_3_10 git_last_commit: e70a4b3 git_last_commit_date: 2019-10-30 Date/Publication: 2019-10-30 source.ver: src/contrib/statTarget_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/statTarget_1.16.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/statTarget_1.16.1.tgz vignettes: vignettes/statTarget/inst/doc/Combat.html, vignettes/statTarget/inst/doc/pathway_analysis.html, vignettes/statTarget/inst/doc/statTarget.html vignetteTitles: QC_free approach with Combat method, statTarget2 for pathway analysis, statTarget2 On using the Graphical User Interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/statTarget/inst/doc/Combat.R, vignettes/statTarget/inst/doc/pathway_analysis.R, vignettes/statTarget/inst/doc/statTarget.R dependencyCount: 32 Package: stepNorm Version: 1.58.0 Depends: R (>= 1.8.0), marray, methods Imports: marray, MASS, methods, stats License: LGPL MD5sum: a0d03941c56f36b72dd4545282c23ce5 NeedsCompilation: no Title: Stepwise normalization functions for cDNA microarrays Description: Stepwise normalization functions for cDNA microarray data. biocViews: Microarray, TwoChannel, Preprocessing Author: Yuanyuan Xiao , Yee Hwa (Jean) Yang Maintainer: Yuanyuan Xiao URL: http://www.biostat.ucsf.edu/jean/ git_url: https://git.bioconductor.org/packages/stepNorm git_branch: RELEASE_3_10 git_last_commit: d6edf9e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/stepNorm_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/stepNorm_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/stepNorm_1.58.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: strandCheckR Version: 1.4.0 Imports: dplyr, magrittr, GenomeInfoDb, GenomicAlignments, GenomicRanges, IRanges, Rsamtools, S4Vectors, grid, BiocGenerics, ggplot2, reshape2, stats, gridExtra, TxDb.Hsapiens.UCSC.hg38.knownGene, methods, stringr Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: 335ee69c69e703cd44c5974c3e1c364b NeedsCompilation: no Title: Calculate strandness information of a bam file Description: This package aims to quantify and remove putative double strand DNA from a strand-specific RNA sample. There are also options and methods to plot the positive/negative proportions of all sliding windows, which allow users to have an idea of how much the sample was contaminated and the appropriate threshold to be used for filtering. biocViews: RNASeq, Alignment, QualityControl, Coverage, ImmunoOncology Author: Thu-Hien To [aut, cre], Steve Pederson [aut] Maintainer: Thu-Hien To VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/strandCheckR git_branch: RELEASE_3_10 git_last_commit: c5f3d37 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/strandCheckR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/strandCheckR_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/strandCheckR_1.4.0.tgz vignettes: vignettes/strandCheckR/inst/doc/strandCheckR.html vignetteTitles: An Introduction To strandCheckR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/strandCheckR/inst/doc/strandCheckR.R dependencyCount: 114 Package: Streamer Version: 1.32.1 Imports: methods, graph, RBGL, parallel, BiocGenerics Suggests: RUnit, Rsamtools (>= 1.5.53), GenomicAlignments, Rgraphviz License: Artistic-2.0 MD5sum: 3de186918ac0f434a08cce9be223612e NeedsCompilation: yes Title: Enabling stream processing of large files Description: Large data files can be difficult to work with in R, where data generally resides in memory. This package encourages a style of programming where data is 'streamed' from disk into R via a `producer' and through a series of `consumers' that, typically reduce the original data to a manageable size. The package provides useful Producer and Consumer stream components for operations such as data input, sampling, indexing, and transformation; see package?Streamer for details. biocViews: Infrastructure, DataImport Author: Martin Morgan, Nishant Gopalakrishnan Maintainer: Martin Morgan git_url: https://git.bioconductor.org/packages/Streamer git_branch: RELEASE_3_10 git_last_commit: 4862eed git_last_commit_date: 2019-12-19 Date/Publication: 2019-12-19 source.ver: src/contrib/Streamer_1.32.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/Streamer_1.32.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Streamer_1.32.1.tgz vignettes: vignettes/Streamer/inst/doc/Streamer.pdf vignetteTitles: Streamer: A simple example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Streamer/inst/doc/Streamer.R importsMe: plethy dependencyCount: 10 Package: STRINGdb Version: 1.26.0 Depends: R (>= 2.14.0) Imports: png, sqldf, plyr, igraph, RCurl, methods, RColorBrewer, gplots, hash, plotrix Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 85330b8d3e07daf93e93bbbb0a20430e NeedsCompilation: no Title: STRINGdb (Search Tool for the Retrieval of Interacting proteins database) Description: The STRINGdb package provides a R interface to the STRING protein-protein interactions database (http://www.string-db.org). biocViews: Network Author: Andrea Franceschini Maintainer: Damian Szklarczyk git_url: https://git.bioconductor.org/packages/STRINGdb git_branch: RELEASE_3_10 git_last_commit: 848db67 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/STRINGdb_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/STRINGdb_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/STRINGdb_1.26.0.tgz vignettes: vignettes/STRINGdb/inst/doc/STRINGdb.pdf vignetteTitles: STRINGdb Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STRINGdb/inst/doc/STRINGdb.R dependsOnMe: PPInfer, scsR importsMe: coexnet, glmSparseNet, IMMAN, pwOmics, RITAN, XINA suggestsMe: epiNEM, GeneNetworkBuilder, netSmooth, PCAN dependencyCount: 41 Package: STROMA4 Version: 1.10.0 Depends: R (>= 3.4), Biobase, BiocParallel, cluster, matrixStats, stats, graphics, utils Suggests: breastCancerMAINZ License: GPL-3 MD5sum: f14fc373406adb85ddfe4aeb8a58cad0 NeedsCompilation: no Title: Assign Properties to TNBC Patients Description: This package estimates four stromal properties identified in TNBC patients in each patient of a gene expression datasets. These stromal property assignments can be combined to subtype patients. These four stromal properties were identified in Triple negative breast cancer (TNBC) patients and represent the presence of different cells in the stroma: T-cells (T), B-cells (B), stromal infiltrating epithelial cells (E), and desmoplasia (D). Additionally this package can also be used to estimate generative properties for the Lehmann subtypes, an alternative TNBC subtyping scheme (PMID: 21633166). biocViews: ImmunoOncology, GeneExpression, BiomedicalInformatics, Classification, Microarray, RNASeq, Software Author: Sadiq Saleh [aut, cre], Michael Hallett [aut] Maintainer: Sadiq Saleh git_url: https://git.bioconductor.org/packages/STROMA4 git_branch: RELEASE_3_10 git_last_commit: a2618e7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/STROMA4_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/STROMA4_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/STROMA4_1.10.0.tgz vignettes: vignettes/STROMA4/inst/doc/STROMA4-vignette.pdf vignetteTitles: Using the STROMA4 package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STROMA4/inst/doc/STROMA4-vignette.R dependencyCount: 17 Package: Structstrings Version: 1.2.0 Depends: R (>= 3.6), S4Vectors (>= 0.23.18), IRanges, Biostrings Imports: methods, assertive, BiocGenerics, XVector, stringr, stringi LinkingTo: IRanges, S4Vectors Suggests: testthat, knitr, rmarkdown, tRNAscanImport, BiocStyle License: Artistic-2.0 MD5sum: e0aee5c917276bb97595d28abaf07ab4 NeedsCompilation: yes Title: Implementation of the dot bracket annotations with Biostrings Description: The Structstrings package implements the widely used dot bracket annotation for storing base pairing information in structured RNA. Structstrings uses the infrastructure provided by the Biostrings package and derives the DotBracketString and related classes from the BString class. From these, base pair tables can be produced for in depth analysis. In addition, the loop indices of the base pairs can be retrieved as well. For better efficiency, information conversion is implemented in C, inspired to a large extend by the ViennaRNA package. biocViews: DataImport, DataRepresentation, Infrastructure, Sequencing, Software, Alignment, SequenceMatching Author: Felix G.M. Ernst [aut, cre] () Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/Structstrings VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/Structstrings/issues git_url: https://git.bioconductor.org/packages/Structstrings git_branch: RELEASE_3_10 git_last_commit: eb9142f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Structstrings_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Structstrings_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Structstrings_1.2.0.tgz vignettes: vignettes/Structstrings/inst/doc/Structstrings.html vignetteTitles: Structstrings hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Structstrings/inst/doc/Structstrings.R dependsOnMe: tRNA, tRNAdbImport importsMe: tRNAscanImport dependencyCount: 41 Package: StructuralVariantAnnotation Version: 1.2.0 Depends: GenomicRanges, rtracklayer, VariantAnnotation, BiocGenerics, R (>= 3.6.0) Imports: assertthat, Biostrings, stringr, dplyr, methods, rlang Suggests: BSgenome.Hsapiens.UCSC.hg19, ggplot2, devtools, testthat, roxygen2, covr, knitr, plyranges, ggbio, biovizBase, circlize, tictoc, GenomeInfoDb, IRanges, S4Vectors, SummarizedExperiment License: GPL-3 Archs: i386, x64 MD5sum: da340adef69eab266b2994f86b84fc53 NeedsCompilation: no Title: Variant annotations for structural variants Description: StructuralVariantAnnotation contains useful helper functions for dealing with structural variants in VCF format. The packages contains functions for parsing VCFs from a number of popular callers as well as functions for dealing with breakpoints involving two separate genomic loci encoded as GRanges objects. biocViews: DataImport, Sequencing, Annotation, Genetics, VariantAnnotation Author: Daniel Cameron [aut, cre] (), Ruining Dong [aut] () Maintainer: Daniel Cameron VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/StructuralVariantAnnotation git_branch: RELEASE_3_10 git_last_commit: 2d91969 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/StructuralVariantAnnotation_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/StructuralVariantAnnotation_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/StructuralVariantAnnotation_1.2.0.tgz vignettes: vignettes/StructuralVariantAnnotation/inst/doc/vignettes.html vignetteTitles: Structural Variant Annotation Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/StructuralVariantAnnotation/inst/doc/vignettes.R dependencyCount: 85 Package: SubCellBarCode Version: 1.2.6 Depends: R (>= 3.6) Imports: Rtsne, scatterplot3d, caret, e1071, ggplot2, gridExtra, networkD3, ggrepel, graphics, stats, org.Hs.eg.db, AnnotationDbi Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 6a09a20449ab5d0c5402a8691f07b5d8 NeedsCompilation: no Title: SubCellBarCode: Integrated workflow for robust mapping and visualizing whole human spatial proteome Description: Mass-Spectrometry based spatial proteomics have enabled the proteome-wide mapping of protein subcellular localization (Orre et al. 2019, Molecular Cell). SubCellBarCode R package robustly classifies proteins into corresponding subcellular localization. biocViews: Proteomics, MassSpectrometry, Classification Author: Taner Arslan Maintainer: Taner Arslan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SubCellBarCode git_branch: RELEASE_3_10 git_last_commit: b71aaa3 git_last_commit_date: 2020-01-13 Date/Publication: 2020-01-13 source.ver: src/contrib/SubCellBarCode_1.2.6.tar.gz win.binary.ver: bin/windows/contrib/3.6/SubCellBarCode_1.2.6.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SubCellBarCode_1.2.6.tgz vignettes: vignettes/SubCellBarCode/inst/doc/SubCellBarCode.html vignetteTitles: SubCellBarCode R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SubCellBarCode/inst/doc/SubCellBarCode.R dependencyCount: 111 Package: subSeq Version: 1.16.0 Depends: R (>= 3.2) Imports: data.table, dplyr, tidyr, ggplot2, magrittr, qvalue (>= 1.99), digest, Biobase Suggests: limma, edgeR, DESeq2, DEXSeq (>= 1.9.7), testthat, knitr License: MIT + file LICENSE MD5sum: a12b662d9aa1715f6aa83c22fd62a688 NeedsCompilation: no Title: Subsampling of high-throughput sequencing count data Description: Subsampling of high throughput sequencing count data for use in experiment design and analysis. biocViews: ImmunoOncology, Sequencing, Transcription, RNASeq, GeneExpression, DifferentialExpression Author: David Robinson, John D. Storey, with contributions from Andrew J. Bass Maintainer: Andrew J. Bass , John D. Storey URL: http://github.com/StoreyLab/subSeq VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/subSeq git_branch: RELEASE_3_10 git_last_commit: f672225 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/subSeq_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/subSeq_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/subSeq_1.16.0.tgz vignettes: vignettes/subSeq/inst/doc/subSeq.pdf vignetteTitles: subSeq Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/subSeq/inst/doc/subSeq.R dependencyCount: 69 Package: SummarizedBenchmark Version: 2.4.0 Depends: R (>= 3.6), tidyr, SummarizedExperiment, S4Vectors, BiocGenerics, methods, UpSetR, rlang, stringr, utils, BiocParallel, ggplot2, mclust, dplyr, digest, sessioninfo, crayon, tibble Suggests: iCOBRA, BiocStyle, knitr, magrittr, IHW, qvalue, testthat, DESeq2, edgeR, limma, tximport, readr, scRNAseq, splatter, scater, rnaseqcomp, biomaRt License: GPL (>= 3) Archs: i386, x64 MD5sum: 170eab2467c9ea7883e9a916365c2ef6 NeedsCompilation: no Title: Classes and methods for performing benchmark comparisons Description: This package defines the BenchDesign and SummarizedBenchmark classes for building, executing, and evaluating benchmark experiments of computational methods. The SummarizedBenchmark class extends the RangedSummarizedExperiment object, and is designed to provide infrastructure to store and compare the results of applying different methods to a shared data set. This class provides an integrated interface to store metadata such as method parameters and software versions as well as ground truths (when these are available) and evaluation metrics. biocViews: Software, Infrastructure Author: Alejandro Reyes [aut] (), Patrick Kimes [aut, cre] () Maintainer: Patrick Kimes URL: https://github.com/areyesq89/SummarizedBenchmark, http://bioconductor.org/packages/SummarizedBenchmark/ VignetteBuilder: knitr BugReports: https://github.com/areyesq89/SummarizedBenchmark/issues git_url: https://git.bioconductor.org/packages/SummarizedBenchmark git_branch: RELEASE_3_10 git_last_commit: 5fc84cb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SummarizedBenchmark_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SummarizedBenchmark_2.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SummarizedBenchmark_2.4.0.tgz vignettes: vignettes/SummarizedBenchmark/inst/doc/CaseStudy-RNAseqQuantification.html, vignettes/SummarizedBenchmark/inst/doc/CaseStudy-SingleCellSimulation.html, vignettes/SummarizedBenchmark/inst/doc/Feature-ErrorHandling.html, vignettes/SummarizedBenchmark/inst/doc/Feature-Iterative.html, vignettes/SummarizedBenchmark/inst/doc/Feature-Parallel.html, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-ClassDetails.html, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-FullCaseStudy.html, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-Introduction.html vignetteTitles: Case Study: Benchmarking non-R Methods, Case Study: Single-Cell RNA-Seq Simulation, Feature: Error Handling, Feature: Iterative Benchmarking, Feature: Parallelization, SummarizedBenchmark: Class Details, SummarizedBenchmark: Full Case Study, SummarizedBenchmark: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SummarizedBenchmark/inst/doc/CaseStudy-RNAseqQuantification.R, vignettes/SummarizedBenchmark/inst/doc/CaseStudy-SingleCellSimulation.R, vignettes/SummarizedBenchmark/inst/doc/Feature-ErrorHandling.R, vignettes/SummarizedBenchmark/inst/doc/Feature-Iterative.R, vignettes/SummarizedBenchmark/inst/doc/Feature-Parallel.R, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-ClassDetails.R, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-FullCaseStudy.R, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-Introduction.R dependencyCount: 89 Package: SummarizedExperiment Version: 1.16.1 Depends: R (>= 3.2), methods, GenomicRanges (>= 1.33.6), Biobase, DelayedArray (>= 0.3.20) Imports: utils, stats, tools, Matrix, BiocGenerics (>= 0.15.3), S4Vectors (>= 0.23.20), IRanges (>= 2.13.16), GenomeInfoDb (>= 1.13.1) Suggests: annotate, AnnotationDbi, hgu95av2.db, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, BiocStyle, knitr, rmarkdown, digest, jsonlite, rhdf5, HDF5Array (>= 1.7.5), airway, RUnit, testthat License: Artistic-2.0 MD5sum: 33fbb7b4c9bad189ecfa7b0e3830d0d9 NeedsCompilation: no Title: SummarizedExperiment container Description: The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples. biocViews: Genetics, Infrastructure, Sequencing, Annotation, Coverage, GenomeAnnotation Author: Martin Morgan, Valerie Obenchain, Jim Hester, Hervé Pagès Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SummarizedExperiment git_branch: RELEASE_3_10 git_last_commit: 1004fc2 git_last_commit_date: 2019-12-19 Date/Publication: 2019-12-19 source.ver: src/contrib/SummarizedExperiment_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/SummarizedExperiment_1.16.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SummarizedExperiment_1.16.1.tgz vignettes: vignettes/SummarizedExperiment/inst/doc/Extensions.html, vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html vignetteTitles: 2. Extending the SummarizedExperiment class, 1. SummarizedExperiment for Coordinating Experimental Assays,, Samples,, and Regions of Interest hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SummarizedExperiment/inst/doc/Extensions.R, vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.R dependsOnMe: AffiXcan, AllelicImbalance, ASpediaFI, BDMMAcorrect, BiocSklearn, BiSeq, bnbc, bsseq, CAGEfightR, celaref, clusterExperiment, coseq, csaw, DaMiRseq, deco, deepSNV, DeMixT, DESeq2, DEXSeq, DiffBind, diffcoexp, diffHic, divergence, DMCFB, DMCHMM, DMRcate, EnrichmentBrowser, epigenomix, evaluomeR, EventPointer, ExpressionAtlas, GenoGAM, GenomicAlignments, GenomicFiles, genoset, GRmetrics, GSEABenchmarkeR, HelloRanges, hipathia, IgGeneUsage, InteractionSet, IntEREst, iSEE, isomiRs, ivygapSE, JunctionSeq, lipidr, LoomExperiment, MBASED, methrix, methylPipe, minfi, miRmine, mpra, MultiAssayExperiment, NADfinder, NBAMSeq, OUTRIDER, PowerExplorer, profileplyr, recount, REMP, RIPSeeker, rqt, runibic, Scale4C, scGPS, scone, SDAMS, SGSeq, signatureSearch, simulatorZ, SingleCellExperiment, singleCellTK, SingleR, soGGi, Spaniel, ssPATHS, stageR, SummarizedBenchmark, survtype, TimeSeriesExperiment, TissueEnrich, TNBC.CMS, VanillaICE, VariantAnnotation, VariantExperiment, yamss, zinbwave importsMe: ADAM, adaptest, ALDEx2, alpine, anamiR, animalcules, anota2seq, APAlyzer, apeglm, appreci8R, ASICS, AUCell, BASiCS, batchelor, bayNorm, BBCAnalyzer, BiocOncoTK, biotmle, biovizBase, biscuiteer, BiSeq, blacksheepr, BUMHMM, BUScorrect, CAGEr, CAMTHC, CATALYST, ccfindR, celda, CellMixS, CellTrails, CHARGE, CHETAH, ChIPpeakAnno, chromVAR, CNVfilteR, CNVRanger, coexnet, CoGAPS, compartmap, consensusDE, CopyNumberPlots, countsimQC, cydar, DChIPRep, debCAM, debrowser, DEComplexDisease, decompTumor2Sig, DEFormats, DEGreport, deltaCaptureC, DEP, DEScan2, destiny, DEWSeq, diffcyt, DiscoRhythm, DominoEffect, doppelgangR, doseR, easyRNASeq, ELMER, ensemblVEP, epivizrData, erma, FCBF, fcScan, fishpond, FourCSeq, GARS, GenomicDataCommons, GGBase, ggbio, glmSparseNet, gQTLBase, gQTLstats, GreyListChIP, gscreend, gwasurvivr, HTSeqGenie, HumanTranscriptomeCompendium, iasva, icetea, ideal, ImpulseDE2, infercnv, INSPEcT, InterMineR, iteremoval, LineagePulse, lionessR, M3D, MADSEQ, MAST, mbkmeans, MBQN, mCSEA, MEAL, MEB, MetaNeighbor, methyAnalysis, MethylAid, methylumi, methyvim, MinimumDistance, miRSM, MLSeq, MoonlightR, motifbreakR, motifmatchr, MPRAnalyze, msgbsR, MTseeker, MultiDataSet, multiOmicsViz, muscat, MutationalPatterns, MWASTools, netSmooth, NormalyzerDE, oligoClasses, omicRexposome, OmicsLonDA, omicsPrint, oncomix, ORFik, OVESEG, PAIRADISE, pcaExplorer, phemd, phenopath, proDA, psichomics, pulsedSilac, PureCN, qsmooth, R453Plus1Toolbox, RaggedExperiment, RareVariantVis, RcisTarget, readat, regionReport, regsplice, rgsepd, Rmmquant, RNAsense, roar, rScudo, RTCGAToolbox, RTN, SBGNview, SC3, scater, scBFA, scDblFinder, scDD, scds, scfind, scmap, scMerge, scmeth, SCnorm, scoreInvHap, scPipe, scran, scruff, scTensor, scTGIF, seqCAT, SEtools, sigFeature, SigsPack, singscore, slalom, slingshot, slinky, SNPchip, SNPhood, SpatialCPie, splatter, srnadiff, SVAPLSseq, switchde, systemPipeR, TCGAbiolinks, TCGAbiolinksGUI, TCGAutils, TCseq, tenXplore, TOAST, ToPASeq, tradeSeq, TreeSummarizedExperiment, Trendy, TSRchitect, TTMap, TVTB, tximeta, TxRegInfra, VariantFiltering, vidger, zFPKM suggestsMe: AnnotationHub, biobroom, dcanr, DelayedArray, epivizr, epivizrChart, esetVis, GENIE3, GenomicRanges, Glimma, globalSeq, gsean, HDF5Array, interactiveDisplay, MSnbase, pathprint, pathwayPCA, podkat, RiboProfiling, S4Vectors, scFeatureFilter, semisup, sesame, StructuralVariantAnnotation, TFutils dependencyCount: 31 Package: supraHex Version: 1.24.0 Depends: R (>= 3.3), hexbin Imports: ape, MASS, grDevices, graphics, stats, utils License: GPL-2 MD5sum: 0cafd4be1e01dddb6493b9b01ab578da NeedsCompilation: no Title: supraHex: a supra-hexagonal map for analysing tabular omics data Description: A supra-hexagonal map is a giant hexagon on a 2-dimensional grid seamlessly consisting of smaller hexagons. It is supposed to train, analyse and visualise a high-dimensional omics input data. The supraHex is able to carry out gene clustering/meta-clustering and sample correlation, plus intuitive visualisations to facilitate exploratory analysis. More importantly, it allows for overlaying additional data onto the trained map to explore relations between input and additional data. So with supraHex, it is also possible to carry out multilayer omics data comparisons. Newly added utilities are advanced heatmap visualisation and tree-based analysis of sample relationships. Uniquely to this package, users can ultrafastly understand any tabular omics data, both scientifically and artistically, especially in a sample-specific fashion but without loss of information on large genes. biocViews: Software, Clustering, Visualization, GeneExpression Author: Hai Fang and Julian Gough Maintainer: Hai Fang URL: http://suprahex.r-forge.r-project.org git_url: https://git.bioconductor.org/packages/supraHex git_branch: RELEASE_3_10 git_last_commit: 0cd30fc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/supraHex_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/supraHex_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/supraHex_1.24.0.tgz vignettes: vignettes/supraHex/inst/doc/supraHex_vignettes.pdf vignetteTitles: supraHex User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/supraHex/inst/doc/supraHex_vignettes.R importsMe: Pi suggestsMe: TCGAbiolinks dependencyCount: 14 Package: survcomp Version: 1.36.1 Depends: survival, prodlim, R (>= 3.4) Imports: ipred, SuppDists, KernSmooth, survivalROC, bootstrap, grid, rmeta, stats, graphics Suggests: Hmisc, CPE, clinfun, xtable, Biobase, BiocManager License: Artistic-2.0 MD5sum: 6622297fe8cd4acc75120848846c39eb NeedsCompilation: yes Title: Performance Assessment and Comparison for Survival Analysis Description: Assessment and Comparison for Performance of Risk Prediction (Survival) Models. biocViews: GeneExpression, DifferentialExpression, Visualization Author: Benjamin Haibe-Kains, Markus Schroeder, Catharina Olsen, Christos Sotiriou, Gianluca Bontempi, John Quackenbush, Samuel Branders, Zhaleh Safikhani Maintainer: Benjamin Haibe-Kains , Markus Schroeder , Catharina Olsen URL: http://www.pmgenomics.ca/bhklab/ git_url: https://git.bioconductor.org/packages/survcomp git_branch: RELEASE_3_10 git_last_commit: f69c9f2 git_last_commit_date: 2020-01-31 Date/Publication: 2020-01-31 source.ver: src/contrib/survcomp_1.36.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/survcomp_1.36.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/survcomp_1.36.1.tgz vignettes: vignettes/survcomp/inst/doc/survcomp.pdf vignetteTitles: SurvComp: a package for performance assessment and comparison for survival analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/survcomp/inst/doc/survcomp.R dependsOnMe: genefu importsMe: GenRank suggestsMe: glmSparseNet, metaseqR dependencyCount: 25 Package: survtype Version: 1.2.0 Depends: SummarizedExperiment, pheatmap, survival, survminer, clustvarsel, stats, utils Suggests: maftools, scales, knitr License: Artistic-2.0 MD5sum: 09a25e3ed047972bf58ff2d0e86892a6 NeedsCompilation: no Title: Subtype Identification with Survival Data Description: Subtypes are defined as groups of samples that have distinct molecular and clinical features. Genomic data can be analyzed for discovering patient subtypes, associated with clinical data, especially for survival information. This package is aimed to identify subtypes that are both clinically relevant and biologically meaningful. biocViews: Software, StatisticalMethod, GeneExpression, Survival, Clustering, Sequencing, Coverage Author: Dongmin Jung Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/survtype git_branch: RELEASE_3_10 git_last_commit: a615bc1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/survtype_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/survtype_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/survtype_1.2.0.tgz vignettes: vignettes/survtype/inst/doc/survtype.html vignetteTitles: survtype hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/survtype/inst/doc/survtype.R dependencyCount: 125 Package: Sushi Version: 1.24.0 Depends: R (>= 2.10), zoo,biomaRt Imports: graphics, grDevices License: GPL (>= 2) MD5sum: 79d12bd6416a487621c4664f2eec420f NeedsCompilation: no Title: Tools for visualizing genomics data Description: Flexible, quantitative, and integrative genomic visualizations for publication-quality multi-panel figures biocViews: DataRepresentation, Visualization, Genetics, Sequencing, Infrastructure, HiC Author: Douglas H Phanstiel Maintainer: Douglas H Phanstiel git_url: https://git.bioconductor.org/packages/Sushi git_branch: RELEASE_3_10 git_last_commit: 5d49303 git_last_commit_date: 2019-10-29 Date/Publication: 2019-12-04 source.ver: src/contrib/Sushi_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Sushi_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Sushi_1.24.0.tgz vignettes: vignettes/Sushi/inst/doc/Sushi.pdf vignetteTitles: Sushi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Sushi/inst/doc/Sushi.R importsMe: diffloop, Ularcirc dependencyCount: 61 Package: sva Version: 3.34.0 Depends: R (>= 3.2), mgcv, genefilter, BiocParallel Imports: matrixStats, stats, graphics, utils, limma, Suggests: pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat License: Artistic-2.0 MD5sum: f863c00e300dd557f5f642c82d7edb85 NeedsCompilation: yes Title: Surrogate Variable Analysis Description: The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing batch effects with known control probes (Leek 2014 biorXiv). Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics). biocViews: ImmunoOncology, Microarray, StatisticalMethod, Preprocessing, MultipleComparison, Sequencing, RNASeq, BatchEffect, Normalization Author: Jeffrey T. Leek , W. Evan Johnson , Hilary S. Parker , Elana J. Fertig , Andrew E. Jaffe , John D. Storey , Yuqing Zhang , Leonardo Collado Torres Maintainer: Jeffrey T. Leek , John D. Storey , W. Evan Johnson git_url: https://git.bioconductor.org/packages/sva git_branch: RELEASE_3_10 git_last_commit: 814ff26 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/sva_3.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/sva_3.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/sva_3.34.0.tgz vignettes: vignettes/sva/inst/doc/sva.pdf vignetteTitles: sva tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sva/inst/doc/sva.R dependsOnMe: SCAN.UPC importsMe: ASSIGN, ballgown, BatchQC, bnbc, crossmeta, DaMiRseq, debrowser, doppelgangR, edge, flowSpy, KnowSeq, LINC, MAGeCKFlute, omicRexposome, PAA, proBatch, PROPS, qsmooth, singleCellTK, TCGAbiolinks, trigger suggestsMe: Harman, iasva, RnBeads, SomaticSignatures dependencyCount: 48 Package: SVAPLSseq Version: 1.12.0 Depends: R (>= 3.4) Imports: methods, stats, SummarizedExperiment, edgeR, ggplot2, limma, lmtest, parallel, pls Suggests: BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: b23dfc8c0964d97cbf24273de164ab1e NeedsCompilation: no Title: SVAPLSseq-An R package to estimate the hidden factors of unwanted variability and adjust for them to enable a more powerful and accurate differential expression analysis based on RNAseq data Description: The package contains functions that are intended for extracting the signatures of latent variation in RNAseq data and using them to perform an improved differential expression analysis for a set of features (genes, transcripts) between two specified biological groups. biocViews: ImmunoOncology, GeneExpression, RNASeq, Normalization, BatchEffect Author: Sutirtha Chakraborty Maintainer: Sutirtha Chakraborty git_url: https://git.bioconductor.org/packages/SVAPLSseq git_branch: RELEASE_3_10 git_last_commit: 46d16c4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SVAPLSseq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SVAPLSseq_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SVAPLSseq_1.12.0.tgz vignettes: vignettes/SVAPLSseq/inst/doc/SVAPLSseq.pdf vignetteTitles: SVAPLSseq tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SVAPLSseq/inst/doc/SVAPLSseq.R dependencyCount: 83 Package: SWATH2stats Version: 1.16.0 Depends: R(>= 2.10.0) Imports: data.table, reshape2, grid, ggplot2, stats, grDevices, graphics, utils, biomaRt Suggests: testthat, knitr Enhances: imsbInfer, MSstats, PECA, aLFQ License: GPL-3 MD5sum: f105d060b3a088cb5ea67bae47e4b66d NeedsCompilation: no Title: Transform and Filter SWATH Data for Statistical Packages Description: This package is intended to transform SWATH data from the OpenSWATH software into a format readable by other statistics packages while performing filtering, annotation and FDR estimation. biocViews: Proteomics, Annotation, ExperimentalDesign, Preprocessing, MassSpectrometry, ImmunoOncology Author: Peter Blattmann, Moritz Heusel and Ruedi Aebersold Maintainer: Peter Blattmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SWATH2stats git_branch: RELEASE_3_10 git_last_commit: a6faa31 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SWATH2stats_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SWATH2stats_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SWATH2stats_1.16.0.tgz vignettes: vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.pdf, vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.pdf vignetteTitles: SWATH2stats example script, SWATH2stats package Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.R, vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.R dependencyCount: 91 Package: SwathXtend Version: 2.8.0 Depends: e1071, openxlsx, VennDiagram, lattice License: GPL-2 MD5sum: 773242dbf45829e8c6f96ddea7fcba77 NeedsCompilation: no Title: SWATH extended library generation and statistical data analysis Description: Contains utility functions for integrating spectral libraries for SWATH and statistical data analysis for SWATH generated data. biocViews: Software Author: J WU and D Pascovici Maintainer: Jemma Wu git_url: https://git.bioconductor.org/packages/SwathXtend git_branch: RELEASE_3_10 git_last_commit: 42d292b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SwathXtend_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SwathXtend_2.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SwathXtend_2.8.0.tgz vignettes: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.pdf vignetteTitles: SwathXtend hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.R dependencyCount: 20 Package: swfdr Version: 1.12.0 Depends: R (>= 3.4) Imports: splines, stats4, stats Suggests: dplyr, ggplot2, BiocStyle, knitr, qvalue, reshape2, rmarkdown, testthat License: GPL (>= 3) Archs: i386, x64 MD5sum: d809535f296fc9a6a1b856925e54c2a4 NeedsCompilation: no Title: Science-wise false discovery rate and proportion of true null hypotheses estimation Description: This package allows users to estimate the science-wise false discovery rate from Jager and Leek, "Empirical estimates suggest most published medical research is true," 2013, Biostatistics, using an EM approach due to the presence of rounding and censoring. It also allows users to estimate the proportion of true null hypotheses in the presence of covariates, using a regression framework, as per Boca and Leek, "A direct approach to estimating false discovery rates conditional on covariates," 2018, PeerJ. biocViews: MultipleComparison, StatisticalMethod, Software Author: Jeffrey T. Leek, Leah Jager, Simina M. Boca, Tomasz Konopka Maintainer: Simina M. Boca , Jeffrey T. Leek VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/swfdr git_branch: RELEASE_3_10 git_last_commit: 3fc9bef git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/swfdr_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/swfdr_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/swfdr_1.12.0.tgz vignettes: vignettes/swfdr/inst/doc/swfdrQ.pdf, vignettes/swfdr/inst/doc/swfdrTutorial.pdf vignetteTitles: Computing covariate-adjusted q-values, Tutorial for swfdr package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/swfdr/inst/doc/swfdrQ.R, vignettes/swfdr/inst/doc/swfdrTutorial.R dependencyCount: 3 Package: SwimR Version: 1.24.0 Depends: R (>= 3.0.0), methods, gplots (>= 2.10.1), heatmap.plus (>= 1.3), signal (>= 0.7), R2HTML (>= 2.2.1) Imports: methods License: LGPL-2 MD5sum: 5110a178253a0e4a01ebdde7d01b3d89 NeedsCompilation: no Title: SwimR: A Suite of Analytical Tools for Quantification of C. elegans Swimming Behavior Description: SwimR is an R-based suite that calculates, analyses, and plots the frequency of C. elegans swimming behavior over time. It places a particular emphasis on identifying paralysis and quantifying the kinetic elements of paralysis during swimming. Data is input to SwipR from a custom built program that fits a 5 point morphometric spine to videos of single worms swimming in a buffer called Worm Tracker. biocViews: Visualization Author: Jing Wang , Andrew Hardaway and Bing Zhang Maintainer: Randy Blakely git_url: https://git.bioconductor.org/packages/SwimR git_branch: RELEASE_3_10 git_last_commit: b365599 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SwimR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SwimR_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SwimR_1.24.0.tgz vignettes: vignettes/SwimR/inst/doc/SwimR.pdf vignetteTitles: SwimR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SwimR/inst/doc/SwimR.R dependencyCount: 15 Package: switchBox Version: 1.22.0 Depends: R (>= 2.13.1), pROC, gplots License: GPL-2 MD5sum: f40656f75b37f8db4c3b269fbaecefa7 NeedsCompilation: yes Title: Utilities to train and validate classifiers based on pair switching using the K-Top-Scoring-Pair (KTSP) algorithm Description: The package offer different classifiers based on comparisons of pair of features (TSP), using various decision rules (e.g., majority wins principle). biocViews: Software, StatisticalMethod, Classification Author: Bahman Afsari , Luigi Marchionni , Wikum Dinalankara Maintainer: Bahman Afsari , Luigi Marchionni , Wikum Dinalankara git_url: https://git.bioconductor.org/packages/switchBox git_branch: RELEASE_3_10 git_last_commit: 7ae60d4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/switchBox_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/switchBox_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/switchBox_1.22.0.tgz vignettes: vignettes/switchBox/inst/doc/switchBox.pdf vignetteTitles: Working with the switchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/switchBox/inst/doc/switchBox.R dependencyCount: 12 Package: switchde Version: 1.12.0 Depends: R (>= 3.4), SingleCellExperiment Imports: SummarizedExperiment, dplyr, ggplot2, methods, stats Suggests: knitr, rmarkdown, BiocStyle, testthat, numDeriv, tidyr License: GPL (>= 2) MD5sum: a8ab91d87149ca3257bffafb9527b59e NeedsCompilation: no Title: Switch-like differential expression across single-cell trajectories Description: Inference and detection of switch-like differential expression across single-cell RNA-seq trajectories. biocViews: ImmunoOncology, Software, Transcriptomics, GeneExpression, RNASeq, Regression, DifferentialExpression, SingleCell Author: Kieran Campbell [aut, cre] Maintainer: Kieran Campbell URL: https://github.com/kieranrcampbell/switchde VignetteBuilder: knitr BugReports: https://github.com/kieranrcampbell/switchde git_url: https://git.bioconductor.org/packages/switchde git_branch: RELEASE_3_10 git_last_commit: dca856a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/switchde_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/switchde_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/switchde_1.12.0.tgz vignettes: vignettes/switchde/inst/doc/switchde_vignette.html vignetteTitles: An overview of the switchde package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/switchde/inst/doc/switchde_vignette.R dependencyCount: 82 Package: synapter Version: 2.10.0 Depends: R (>= 3.1.0), methods, MSnbase (>= 2.1.2) Imports: RColorBrewer, lattice, qvalue, multtest, utils, tools, Biobase, knitr, Biostrings, cleaver (>= 1.3.3), readr (>= 0.2), rmarkdown (>= 1.0) Suggests: synapterdata (>= 1.13.2), xtable, testthat (>= 0.8), BRAIN, BiocStyle License: GPL-2 MD5sum: 17037dcec26bdf62e4334de8c64f0f49 NeedsCompilation: no Title: Label-free data analysis pipeline for optimal identification and quantitation Description: The synapter package provides functionality to reanalyse label-free proteomics data acquired on a Synapt G2 mass spectrometer. One or several runs, possibly processed with additional ion mobility separation to increase identification accuracy can be combined to other quantitation files to maximise identification and quantitation accuracy. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, QualityControl Author: Laurent Gatto, Nick J. Bond, Pavel V. Shliaha and Sebastian Gibb. Maintainer: Laurent Gatto and Sebastian Gibb URL: https://lgatto.github.io/synapter/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synapter git_branch: RELEASE_3_10 git_last_commit: 7316aec git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/synapter_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/synapter_2.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/synapter_2.10.0.tgz vignettes: vignettes/synapter/inst/doc/fragmentmatching.html, vignettes/synapter/inst/doc/synapter.html, vignettes/synapter/inst/doc/synapter2.html vignetteTitles: Fragment matching using 'synapter', Combining HDMSe/MSe data using 'synapter' to optimise identification and quantitation, Synapter2 and synergise2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synapter/inst/doc/fragmentmatching.R, vignettes/synapter/inst/doc/synapter.R, vignettes/synapter/inst/doc/synapter2.R suggestsMe: pRoloc dependencyCount: 112 Package: synergyfinder Version: 2.0.12 Depends: R (>= 3.6.0), methods Imports: drc (>= 2.5-12), reshape2 (>= 1.4.1), ggplot2 (>= 2.1.0), grid (>= 3.2.4), lattice (>= 0.20-33), nleqslv(>= 3.0), stats (>= 3.3.0), graphics (>= 3.3.0), grDevices (>= 3.3.0) Suggests: knitr, rmarkdown License: Mozilla Public License 2.0 MD5sum: 1802404199706ff1b955085d4bb69273 NeedsCompilation: yes Title: Calculate and Visualize Synergy Scores for Drug Combinations Description: Efficient implementations for all the popular synergy scoring models for drug combinations, including HSA, Loewe, Bliss and ZIP and visualization of the synergy scores as either a two-dimensional or a three-dimensional interaction surface over the dose matrix. biocViews: Software, StatisticalMethod Author: Liye He , Jing Tang , Shuyu Zheng Maintainer: Shuyu Zheng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synergyfinder git_branch: RELEASE_3_10 git_last_commit: a09d792 git_last_commit_date: 2020-03-17 Date/Publication: 2020-03-17 source.ver: src/contrib/synergyfinder_2.0.12.tar.gz win.binary.ver: bin/windows/contrib/3.6/synergyfinder_2.0.12.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/synergyfinder_2.0.12.tgz vignettes: vignettes/synergyfinder/inst/doc/synergyfinder.pdf vignetteTitles: synergyfinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synergyfinder/inst/doc/synergyfinder.R dependencyCount: 104 Package: synlet Version: 1.16.0 Depends: R (>= 3.2.0), ggplot2 Imports: doBy, dplyr, grid, magrittr, RColorBrewer, RankProd, reshape2 Suggests: knitr, testthat License: GPL-3 Archs: i386, x64 MD5sum: 1c14138600751d9faa39ff81b426e446 NeedsCompilation: no Title: Hits Selection for Synthetic Lethal RNAi Screen Data Description: Select hits from synthetic lethal RNAi screen data. For example, there are two identical celllines except one gene is knocked-down in one cellline. The interest is to find genes that lead to stronger lethal effect when they are knocked-down further by siRNA. Quality control and various visualisation tools are implemented. Four different algorithms could be used to pick up the interesting hits. This package is designed based on 384 wells plates, but may apply to other platforms with proper configuration. biocViews: ImmunoOncology, CellBasedAssays, QualityControl, Preprocessing, Visualization, FeatureExtraction Author: Chunxuan Shao Maintainer: Chunxuan Shao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synlet git_branch: RELEASE_3_10 git_last_commit: 1e35acb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/synlet_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/synlet_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/synlet_1.16.0.tgz vignettes: vignettes/synlet/inst/doc/synlet-vignette.html vignetteTitles: A working Demo for synlet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synlet/inst/doc/synlet-vignette.R dependencyCount: 79 Package: SynMut Version: 1.2.0 Imports: seqinr, methods, Biostrings, stringr, BiocGenerics Suggests: BiocManager, knitr, rmarkdown, testthat, devtools, prettydoc, glue License: GPL-2 MD5sum: f0733262fb5d1ea6fa066cc1a805f45c NeedsCompilation: no Title: SynMut: Designing Synonymously Mutated Sequences with Different Genomic Signatures Description: There are increasing demands on designing virus mutants with specific dinucleotide or codon composition. This tool can take both dinucleotide preference and/or codon usage bias into account while designing mutants. It is a powerful tool for in silico designs of DNA sequence mutants. biocViews: SequenceMatching, ExperimentalDesign, Preprocessing Author: Haogao Gu [aut, cre], Leo L.M. Poon [led] Maintainer: Haogao Gu URL: https://github.com/Koohoko/SynMut VignetteBuilder: knitr BugReports: https://github.com/Koohoko/SynMut/issues git_url: https://git.bioconductor.org/packages/SynMut git_branch: RELEASE_3_10 git_last_commit: bff7fc6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/SynMut_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/SynMut_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/SynMut_1.2.0.tgz vignettes: vignettes/SynMut/inst/doc/SynMut.html vignetteTitles: SynMut: Designing Synonymous Mutants for DNA Sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SynMut/inst/doc/SynMut.R dependencyCount: 26 Package: systemPipeR Version: 1.20.0 Depends: Rsamtools (>= 1.31.2), Biostrings, ShortRead (>= 1.37.1), methods Imports: BiocGenerics, GenomicRanges, GenomicFeatures (>= 1.31.3), SummarizedExperiment, VariantAnnotation (>= 1.25.11), rjson, ggplot2, grid, limma, edgeR, DESeq2, GOstats, GO.db, annotate, pheatmap, batchtools, yaml Suggests: ape, RUnit, BiocStyle, knitr, rmarkdown, biomaRt, BiocParallel, BiocManager License: Artistic-2.0 MD5sum: 00ba373eb92200b5efb0493d3d15cd08 NeedsCompilation: no Title: systemPipeR: NGS workflow and report generation environment Description: R package for building and running automated end-to-end analysis workflows for a wide range of next generation sequence (NGS) applications such as RNA-Seq, ChIP-Seq, VAR-Seq and Ribo-Seq. Important features include a uniform workflow interface across different NGS applications, automated report generation, and support for running both R and command-line software, such as NGS aligners or peak/variant callers, on local computers or compute clusters. Efficient handling of complex sample sets and experimental designs is facilitated by a consistently implemented sample annotation infrastructure. Instructions for using systemPipeR are given in the Overview Vignette (HTML). The remaining Vignettes, linked below, are workflow templates for common NGS use cases. biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, RiboSeq, ChIPSeq, MethylSeq, SNP, GeneExpression, Coverage, GeneSetEnrichment, Alignment, QualityControl, ImmunoOncology Author: Thomas Girke Maintainer: Thomas Girke URL: http://girke.bioinformatics.ucr.edu/systemPipeR/ SystemRequirements: systemPipeR can be used to run external command-line software (e.g. short read aligners), but the corresponding tool needs to be installed on a system. VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/systemPipeR git_branch: RELEASE_3_10 git_last_commit: 70b884d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/systemPipeR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/systemPipeR_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/systemPipeR_1.20.0.tgz vignettes: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.html, vignettes/systemPipeR/inst/doc/systemPipeR.html vignetteTitles: systemPipeR: Workflows collection, systemPipeR: NGS workflow and report generation environment hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.R, vignettes/systemPipeR/inst/doc/systemPipeR.R importsMe: DiffBind, RNASeqR dependencyCount: 161 Package: target Version: 1.0.0 Depends: R (>= 3.6) Imports: BiocGenerics, GenomicRanges, IRanges, matrixStats, methods, stats, graphics, shiny Suggests: testthat (>= 2.1.0), knitr, rmarkdown, shinytest, shinyBS, covr License: GPL-3 Archs: i386, x64 MD5sum: 76f5b4ffac1c3cf975c9ed4ecce96d49 NeedsCompilation: no Title: Predict Combined Function of Transcription Factors Description: Implement the BETA algorithm for infering direct target genes from DNA-binding and perturbation expression data Wang et al. (2013) . Extend the algorithm to predict the combined function of two DNA-binding elements from comprable binding and expression data. biocViews: Software, StatisticalMethod, Transcription Author: Mahmoud Ahmed [aut, cre] Maintainer: Mahmoud Ahmed URL: https://github.com/MahShaaban/target VignetteBuilder: knitr BugReports: https://github.com/MahShaaban/target/issues git_url: https://git.bioconductor.org/packages/target git_branch: RELEASE_3_10 git_last_commit: 3cae12e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/target_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/target_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/target_1.0.0.tgz vignettes: vignettes/target/inst/doc/extend-target.html, vignettes/target/inst/doc/target.html vignetteTitles: Using target to predict combined binding, Using the target package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/target/inst/doc/extend-target.R, vignettes/target/inst/doc/target.R dependencyCount: 36 Package: TargetScore Version: 1.24.0 Depends: pracma, Matrix Suggests: TargetScoreData, gplots, Biobase, GEOquery License: GPL-2 MD5sum: 22fc203f7e09bb65a2a8d277b770924f NeedsCompilation: no Title: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information Description: Infer the posterior distributions of microRNA targets by probabilistically modelling the likelihood microRNA-overexpression fold-changes and sequence-based scores. Variaitonal Bayesian Gaussian mixture model (VB-GMM) is applied to log fold-changes and sequence scores to obtain the posteriors of latent variable being the miRNA targets. The final targetScore is computed as the sigmoid-transformed fold-change weighted by the averaged posteriors of target components over all of the features. biocViews: miRNA Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/software.html git_url: https://git.bioconductor.org/packages/TargetScore git_branch: RELEASE_3_10 git_last_commit: b2267e9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TargetScore_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TargetScore_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TargetScore_1.24.0.tgz vignettes: vignettes/TargetScore/inst/doc/TargetScore.pdf vignetteTitles: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetScore/inst/doc/TargetScore.R dependencyCount: 9 Package: TargetSearch Version: 1.42.1 Depends: ncdf4 Imports: graphics, grDevices, methods, stats, utils, stringr, assertthat Suggests: TargetSearchData, BiocStyle, knitr License: GPL (>= 2) MD5sum: 8732eb5f79e58bcb34cb73ffbbbf7d1f NeedsCompilation: yes Title: A package for the analysis of GC-MS metabolite profiling data Description: This packages provides a targeted pre-processing method for GC-MS data. biocViews: MassSpectrometry, Preprocessing, DecisionTree, ImmunoOncology Author: Alvaro Cuadros-Inostroza , Jan Lisec, Henning Redestig, Matt Hannah Maintainer: Alvaro Cuadros-Inostroza URL: https://github.com/acinostroza/TargetSearch VignetteBuilder: knitr BugReports: https://github.com/acinostroza/TargetSearch/issues git_url: https://git.bioconductor.org/packages/TargetSearch git_branch: RELEASE_3_10 git_last_commit: 25537d9 git_last_commit_date: 2019-12-05 Date/Publication: 2019-12-05 source.ver: src/contrib/TargetSearch_1.42.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/TargetSearch_1.42.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TargetSearch_1.42.1.tgz vignettes: vignettes/TargetSearch/inst/doc/RICorrection.pdf, vignettes/TargetSearch/inst/doc/TargetSearch.pdf vignetteTitles: RI correction extra, The TargetSearch Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetSearch/inst/doc/RetentionIndexCorrection.R, vignettes/TargetSearch/inst/doc/RICorrection.R, vignettes/TargetSearch/inst/doc/TargetSearch.R dependencyCount: 12 Package: TarSeqQC Version: 1.16.0 Depends: R (>= 3.5.1), methods, GenomicRanges, Rsamtools (>= 1.9.2), ggplot2, plyr, openxlsx Imports: grDevices, stats, utils, S4Vectors, IRanges, BiocGenerics, reshape2, GenomeInfoDb, BiocParallel, Biostrings, cowplot, graphics, GenomicAlignments, Hmisc Suggests: BiocManager, RUnit License: GPL (>=2) MD5sum: df5ba2cec9500dc878a1b1a24bfbcbe0 NeedsCompilation: no Title: TARgeted SEQuencing Experiment Quality Control Description: The package allows the representation of targeted experiment in R. This is based on current packages and incorporates functions to do a quality control over this kind of experiments and a fast exploration of the sequenced regions. An xlsx file is generated as output. biocViews: Software, Sequencing, TargetedResequencing, QualityControl, Visualization, Coverage, Alignment, DataImport Author: Gabriela A. Merino, Cristobal Fresno, Yanina Murua, Andrea S. Llera and Elmer A. Fernandez Maintainer: Gabriela Merino URL: http://www.bdmg.com.ar git_url: https://git.bioconductor.org/packages/TarSeqQC git_branch: RELEASE_3_10 git_last_commit: e258522 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TarSeqQC_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TarSeqQC_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TarSeqQC_1.16.0.tgz vignettes: vignettes/TarSeqQC/inst/doc/TarSeqQC-vignette.pdf vignetteTitles: TarSeqQC: Targeted Sequencing Experiment Quality Control hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TarSeqQC/inst/doc/TarSeqQC-vignette.R dependencyCount: 114 Package: TCC Version: 1.26.0 Depends: R (>= 3.0), methods, DESeq, DESeq2, edgeR, baySeq, ROC Suggests: RUnit, BiocGenerics Enhances: snow License: GPL-2 MD5sum: 102d5ca1e357ce4166dba5844d8156a6 NeedsCompilation: no Title: TCC: Differential expression analysis for tag count data with robust normalization strategies Description: This package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in depended packages. biocViews: ImmunoOncology, Sequencing, DifferentialExpression, RNASeq Author: Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji Kadota Maintainer: Jianqiang Sun , Tomoaki Nishiyama git_url: https://git.bioconductor.org/packages/TCC git_branch: RELEASE_3_10 git_last_commit: 1a87441 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TCC_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TCC_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TCC_1.26.0.tgz vignettes: vignettes/TCC/inst/doc/TCC.pdf vignetteTitles: TCC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCC/inst/doc/TCC.R suggestsMe: compcodeR dependencyCount: 127 Package: TCGAbiolinks Version: 2.14.1 Depends: R (>= 3.5) Imports: downloader (>= 0.4), survminer, grDevices, dplyr, gridExtra, graphics, tibble, grid, GenomicRanges, XML (>= 3.98.0), data.table, EDASeq (>= 2.0.0), edgeR (>= 3.0.0), jsonlite (>= 1.0.0), plyr, knitr, methods, biomaRt, ggplot2, ggthemes, survival, stringr (>= 1.0.0), IRanges, scales, rvest (>= 0.3.0), stats, utils, selectr, S4Vectors, R.utils, SummarizedExperiment (>= 1.4.0), genefilter, readr, RColorBrewer, doParallel, GenomeInfoDb, GenomicFeatures, parallel, tools, tidyr, sva, limma, purrr, xml2, httr (>= 1.2.1), purrrogress, ggrepel (>= 0.6.3) Suggests: jpeg, png, BiocStyle, rmarkdown, devtools, maftools, parmigene, c3net, minet, dnet, Biobase, affy, testthat, sesame, pathview, clusterProfiler, ComplexHeatmap, circlize, ConsensusClusterPlus, igraph, TCGAbiolinksGUI.data, supraHex License: GPL (>= 3) MD5sum: a8b5726c87f63cd53137b8f967865059 NeedsCompilation: no Title: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data Description: The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) to easily reproduce earlier research results. In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Sequencing, Survival, Software Author: Antonio Colaprico, Tiago Chedraoui Silva, Catharina Olsen, Luciano Garofano, Davide Garolini, Claudia Cava, Thais Sabedot, Tathiane Malta, Stefano M. Pagnotta, Isabella Castiglioni, Michele Ceccarelli, Gianluca Bontempi, Houtan Noushmehr Maintainer: Antonio Colaprico , Tiago Chedraoui Silva URL: https://github.com/BioinformaticsFMRP/TCGAbiolinks VignetteBuilder: knitr BugReports: https://github.com/BioinformaticsFMRP/TCGAbiolinks/issues git_url: https://git.bioconductor.org/packages/TCGAbiolinks git_branch: RELEASE_3_10 git_last_commit: b369afea git_last_commit_date: 2020-02-27 Date/Publication: 2020-02-27 source.ver: src/contrib/TCGAbiolinks_2.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/TCGAbiolinks_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TCGAbiolinks_2.14.1.tgz vignettes: vignettes/TCGAbiolinks/inst/doc/analysis.html, vignettes/TCGAbiolinks/inst/doc/casestudy.html, vignettes/TCGAbiolinks/inst/doc/classifiers.html, vignettes/TCGAbiolinks/inst/doc/clinical.html, vignettes/TCGAbiolinks/inst/doc/download_prepare.html, vignettes/TCGAbiolinks/inst/doc/extension.html, vignettes/TCGAbiolinks/inst/doc/gui.html, vignettes/TCGAbiolinks/inst/doc/index.html, vignettes/TCGAbiolinks/inst/doc/mutation.html, vignettes/TCGAbiolinks/inst/doc/query.html, vignettes/TCGAbiolinks/inst/doc/subtypes.html vignetteTitles: 7. Analyzing and visualizing TCGA data, 8. Case Studies, 10. Classifiers, "4. Clinical data", "3. Downloading and preparing files for analysis", "10. TCGAbiolinks_Extension", "9. Graphical User Interface (GUI)", "1. Introduction", "5. Mutation data", "2. Searching GDC database", 6. Compilation of TCGA molecular subtypes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAbiolinks/inst/doc/analysis.R, vignettes/TCGAbiolinks/inst/doc/casestudy.R, vignettes/TCGAbiolinks/inst/doc/classifiers.R, vignettes/TCGAbiolinks/inst/doc/clinical.R, vignettes/TCGAbiolinks/inst/doc/download_prepare.R, vignettes/TCGAbiolinks/inst/doc/extension.R, vignettes/TCGAbiolinks/inst/doc/gui.R, vignettes/TCGAbiolinks/inst/doc/index.R, vignettes/TCGAbiolinks/inst/doc/mutation.R, vignettes/TCGAbiolinks/inst/doc/query.R, vignettes/TCGAbiolinks/inst/doc/subtypes.R importsMe: ELMER, MoonlightR, SpidermiR, TCGAbiolinksGUI dependencyCount: 173 Package: TCGAbiolinksGUI Version: 1.12.0 Depends: R (>= 3.3.1), shinydashboard (>= 0.5.3), TCGAbiolinksGUI.data Imports: shiny (>= 0.14.1), downloader (>= 0.4), grid, DT, plotly, readr, maftools, stringr (>= 1.1.0), SummarizedExperiment, ggrepel, data.table, caret, shinyFiles (>= 0.6.2), ggplot2 (>= 2.1.0), pathview, ELMER (>= 2.0.0), clusterProfiler, parallel, TCGAbiolinks (>= 2.5.5), shinyjs (>= 0.7), colourpicker, sesame, shinyBS (>= 0.61) Suggests: testthat, dplyr, knitr, roxygen2, devtools, rvest, xml2, BiocStyle, animation, pander License: GPL (>= 3) Archs: i386, x64 MD5sum: e1391204086360db1606a9686d2b2929 NeedsCompilation: no Title: "TCGAbiolinksGUI: A Graphical User Interface to analyze cancer molecular and clinical data" Description: "TCGAbiolinksGUI: A Graphical User Interface to analyze cancer molecular and clinical data. A demo version of GUI is found in https://tcgabiolinksgui.shinyapps.io/tcgabiolinks/" biocViews: Genetics, GUI, DNAMethylation, StatisticalMethod, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Sequencing, Pathways, Network, DNASeq Author: Tiago Chedraoui Silva , Antonio Colaprico , Catharina Olsen , Michele Ceccarelli, Gianluca Bontempi , Benjamin P. Berman , Houtan Noushmehr Maintainer: Tiago C. Silva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TCGAbiolinksGUI git_branch: RELEASE_3_10 git_last_commit: 049cf46 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TCGAbiolinksGUI_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TCGAbiolinksGUI_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TCGAbiolinksGUI_1.12.0.tgz vignettes: vignettes/TCGAbiolinksGUI/inst/doc/analysis.html, vignettes/TCGAbiolinksGUI/inst/doc/Cases.html, vignettes/TCGAbiolinksGUI/inst/doc/data.html, vignettes/TCGAbiolinksGUI/inst/doc/index.html, vignettes/TCGAbiolinksGUI/inst/doc/integrative.html vignetteTitles: "3. Analysis menu", "5. Cases study", "2. Data menu", "1. Introduction", "4. Integrative analysis menu" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAbiolinksGUI/inst/doc/data.R, vignettes/TCGAbiolinksGUI/inst/doc/index.R dependencyCount: 286 Package: TCGAutils Version: 1.6.2 Depends: R (>= 3.6.0) Imports: AnnotationDbi, BiocGenerics, GenomeInfoDb, GenomicFeatures, GenomicRanges, GenomicDataCommons, IRanges, methods, MultiAssayExperiment, RaggedExperiment (>= 1.5.7), rvest, S4Vectors, stats, stringr, SummarizedExperiment, utils, xml2 Suggests: BiocFileCache, BiocStyle, curatedTCGAData, ComplexHeatmap, devtools, dplyr, IlluminaHumanMethylation450kanno.ilmn12.hg19, impute, knitr, magrittr, mirbase.db, org.Hs.eg.db, RColorBrewer, readr, RTCGAToolbox (>= 2.7.5), rtracklayer, R.utils, testthat, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 1717cd6ce8d1ebf72e9821476cf49090 NeedsCompilation: no Title: TCGA utility functions for data management Description: A suite of helper functions for checking and manipulating TCGA data including data obtained from the curatedTCGAData experiment package. These functions aim to simplify and make working with TCGA data more manageable. biocViews: Software, WorkflowStep, Preprocessing Author: Marcel Ramos [aut, cre], Lucas Schiffer [aut], Sean Davis [ctb], Levi Waldron [aut] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/waldronlab/TCGAutils/issues git_url: https://git.bioconductor.org/packages/TCGAutils git_branch: RELEASE_3_10 git_last_commit: 7f5faef git_last_commit_date: 2020-01-18 Date/Publication: 2020-01-18 source.ver: src/contrib/TCGAutils_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/TCGAutils_1.6.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TCGAutils_1.6.2.tgz vignettes: vignettes/TCGAutils/inst/doc/TCGAutils.html vignetteTitles: TCGAutils Essentials hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAutils/inst/doc/TCGAutils.R importsMe: RTCGAToolbox suggestsMe: CNVRanger, glmSparseNet dependencyCount: 92 Package: TCseq Version: 1.10.0 Depends: R (>= 3.4) Imports: edgeR, BiocGenerics, reshape2, GenomicRanges, IRanges, SummarizedExperiment, GenomicAlignments, Rsamtools, e1071, cluster, ggplot2, grid, grDevices, stats, utils, methods, locfit Suggests: testthat License: GPL (>= 2) MD5sum: 6d80d29e166bf8cc9388e668c1bdc620 NeedsCompilation: no Title: Time course sequencing data analysis Description: Quantitative and differential analysis of epigenomic and transcriptomic time course sequencing data, clustering analysis and visualization of temporal patterns of time course data. biocViews: Epigenetics, TimeCourse, Sequencing, ChIPSeq, RNASeq, DifferentialExpression, Clustering, Visualization Author: Mengjun Wu , Lei Gu Maintainer: Mengjun Wu git_url: https://git.bioconductor.org/packages/TCseq git_branch: RELEASE_3_10 git_last_commit: 3c0e3c9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TCseq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TCseq_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TCseq_1.10.0.tgz vignettes: vignettes/TCseq/inst/doc/TCseq.pdf vignetteTitles: TCseq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCseq/inst/doc/TCseq.R dependencyCount: 91 Package: TDARACNE Version: 1.36.0 Depends: GenKern, Rgraphviz, Biobase License: GPL-2 MD5sum: cb028b69bbf50f47d55033960f56545a NeedsCompilation: no Title: Network reverse engineering from time course data. Description: To infer gene networks from time-series measurements is a current challenge into bioinformatics research area. In order to detect dependencies between genes at different time delays, we propose an approach to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory. The proposed algorithm is expected to be useful in reconstruction of small biological directed networks from time course data. biocViews: Microarray, TimeCourse Author: Zoppoli P.,Morganella S., Ceccarelli M. Maintainer: Zoppoli Pietro git_url: https://git.bioconductor.org/packages/TDARACNE git_branch: RELEASE_3_10 git_last_commit: e276b46 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TDARACNE_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TDARACNE_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TDARACNE_1.36.0.tgz vignettes: vignettes/TDARACNE/inst/doc/TDARACNE.pdf vignetteTitles: TDARACNE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TDARACNE/inst/doc/TDARACNE.R dependencyCount: 14 Package: tenXplore Version: 1.8.0 Depends: R (>= 3.4), shiny, restfulSE (>= 0.99.12) Imports: methods, ontoProc (>= 0.99.7), SummarizedExperiment, AnnotationDbi, matrixStats, org.Mm.eg.db, stats, utils Suggests: org.Hs.eg.db, testthat, knitr License: Artistic-2.0 MD5sum: bc07987e789496c42073d8bb032267df NeedsCompilation: no Title: ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics Description: Perform ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics. biocViews: ImmunoOncology, DimensionReduction, PrincipalComponent, Transcriptomics, SingleCell Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tenXplore git_branch: RELEASE_3_10 git_last_commit: 8748ad4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/tenXplore_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/tenXplore_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/tenXplore_1.8.0.tgz vignettes: vignettes/tenXplore/inst/doc/tenXplore.html vignetteTitles: tenXplore: ontology for scRNA-seq,, applied to 10x 1.3 million neurons hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tenXplore/inst/doc/tenXplore.R dependencyCount: 107 Package: TEQC Version: 4.8.0 Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5), Rsamtools, hwriter Imports: Biobase (>= 2.15.1) License: GPL (>= 2) MD5sum: db5d306de0cc2c6ca6f5730eb2f23558 NeedsCompilation: no Title: Quality control for target capture experiments Description: Target capture experiments combine hybridization-based (in solution or on microarrays) capture and enrichment of genomic regions of interest (e.g. the exome) with high throughput sequencing of the captured DNA fragments. This package provides functionalities for assessing and visualizing the quality of the target enrichment process, like specificity and sensitivity of the capture, per-target read coverage and so on. biocViews: QualityControl, Microarray, Sequencing, Genetics Author: M. Hummel, S. Bonnin, E. Lowy, G. Roma Maintainer: Manuela Hummel git_url: https://git.bioconductor.org/packages/TEQC git_branch: RELEASE_3_10 git_last_commit: 77692f1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TEQC_4.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TEQC_4.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TEQC_4.8.0.tgz vignettes: vignettes/TEQC/inst/doc/TEQC.pdf vignetteTitles: TEQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TEQC/inst/doc/TEQC.R dependencyCount: 28 Package: ternarynet Version: 1.30.0 Depends: R (>= 2.10.0), methods Imports: utils, igraph License: GPL (>= 2) MD5sum: d80ef14c392c232a5970595a185e6152 NeedsCompilation: yes Title: Ternary Network Estimation Description: A computational Bayesian approach to ternary gene regulatory network estimation from gene perturbation experiments. biocViews: Software, CellBiology, GraphAndNetwork Author: Matthew N. McCall , Anthony Almudevar , David Burton , Harry Stern Maintainer: Matthew N. McCall git_url: https://git.bioconductor.org/packages/ternarynet git_branch: RELEASE_3_10 git_last_commit: 3617d67 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ternarynet_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ternarynet_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ternarynet_1.30.0.tgz vignettes: vignettes/ternarynet/inst/doc/ternarynet.pdf vignetteTitles: ternarynet: A Computational Bayesian Approach to Ternary Network Estimation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ternarynet/inst/doc/ternarynet.R dependencyCount: 11 Package: TFARM Version: 1.8.0 Depends: R (>= 3.4) Imports: arules, fields, GenomicRanges, graphics, stringr, methods, stats, gplots Suggests: BiocStyle, knitr, plyr License: Artistic-2.0 MD5sum: 9a5e34cd8837b759df5b5776c965f323 NeedsCompilation: no Title: Transcription Factors Association Rules Miner Description: It searches for relevant associations of transcription factors with a transcription factor target, in specific genomic regions. It also allows to evaluate the Importance Index distribution of transcription factors (and combinations of transcription factors) in association rules. biocViews: BiologicalQuestion, Infrastructure, StatisticalMethod, Transcription Author: Liuba Nausicaa Martino, Alice Parodi, Gaia Ceddia, Piercesare Secchi, Stefano Campaner, Marco Masseroli Maintainer: Liuba Nausicaa Martino VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFARM git_branch: RELEASE_3_10 git_last_commit: 23db14e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TFARM_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TFARM_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TFARM_1.8.0.tgz vignettes: vignettes/TFARM/inst/doc/TFARM.pdf vignetteTitles: Transcription Factor Association Rule Miner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFARM/inst/doc/TFARM.R dependencyCount: 35 Package: TFBSTools Version: 1.24.0 Depends: R (>= 3.2.2) Imports: Biobase(>= 2.28), Biostrings(>= 2.36.4), BiocGenerics(>= 0.14.0), BiocParallel(>= 1.2.21), BSgenome(>= 1.36.3), caTools(>= 1.17.1), CNEr(>= 1.4.0), DirichletMultinomial(>= 1.10.0), GenomeInfoDb(>= 1.6.1), GenomicRanges(>= 1.20.6), gtools(>= 3.5.0), grid, IRanges(>= 2.2.7), methods, DBI (>= 0.6), RSQLite(>= 1.0.0), rtracklayer(>= 1.28.10), seqLogo(>= 1.34.0), S4Vectors(>= 0.9.25), TFMPvalue(>= 0.0.5), XML(>= 3.98-1.3), XVector(>= 0.8.0), parallel Suggests: BiocStyle(>= 1.7.7), JASPAR2014(>= 1.4.0), knitr(>= 1.11), testthat, JASPAR2016(>= 1.0.0), JASPAR2018(>= 1.0.0) License: GPL-2 MD5sum: af2c594d6ade542bae4b1c8d37f86dce NeedsCompilation: yes Title: Software Package for Transcription Factor Binding Site (TFBS) Analysis Description: TFBSTools is a package for the analysis and manipulation of transcription factor binding sites. It includes matrices conversion between Position Frequency Matirx (PFM), Position Weight Matirx (PWM) and Information Content Matrix (ICM). It can also scan putative TFBS from sequence/alignment, query JASPAR database and provides a wrapper of de novo motif discovery software. biocViews: MotifAnnotation, GeneRegulation, MotifDiscovery, Transcription, Alignment Author: Ge Tan [aut, cre] Maintainer: Ge Tan URL: https://github.com/ge11232002/TFBSTools VignetteBuilder: knitr BugReports: https://github.com/ge11232002/TFBSTools/issues git_url: https://git.bioconductor.org/packages/TFBSTools git_branch: RELEASE_3_10 git_last_commit: 952afaa git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TFBSTools_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TFBSTools_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TFBSTools_1.24.0.tgz vignettes: vignettes/TFBSTools/inst/doc/TFBSTools.html vignetteTitles: Transcription factor binding site (TFBS) analysis with the "TFBSTools" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFBSTools/inst/doc/TFBSTools.R importsMe: chromVAR, enrichTF, esATAC, MatrixRider, motifmatchr, primirTSS suggestsMe: universalmotif dependencyCount: 122 Package: TFEA.ChIP Version: 1.6.0 Depends: R (>= 3.3) Imports: GenomicRanges, IRanges, biomaRt, GenomicFeatures, grDevices, dplyr, stats, utils, R.utils, methods, org.Hs.eg.db Suggests: knitr, rmarkdown, S4Vectors, plotly, scales, tidyr, ggplot2, GSEABase, DESeq2, BiocGenerics, ggrepel, rcompanion, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: f7599281402369eeaa8d62c52d4118de NeedsCompilation: no Title: Analyze Transcription Factor Enrichment Description: Package to analize transcription factor enrichment in a gene set using data from ChIP-Seq experiments. biocViews: Transcription, GeneRegulation, GeneSetEnrichment, Transcriptomics, Sequencing, ChIPSeq, RNASeq, ImmunoOncology Author: Laura Puente Santamaría, Luis del Peso Maintainer: Laura Puente Santamaría VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFEA.ChIP git_branch: RELEASE_3_10 git_last_commit: 7319820 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TFEA.ChIP_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TFEA.ChIP_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TFEA.ChIP_1.6.0.tgz vignettes: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.html vignetteTitles: TFEA.ChIP: a tool kit for transcription factor enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.R dependencyCount: 87 Package: TFHAZ Version: 1.8.0 Depends: R(>= 3.4) Imports: GenomicRanges, S4Vectors, grDevices, graphics, stats, utils, IRanges, methods Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: cf168bd093aed49285fdc4a8174efcba NeedsCompilation: no Title: Transcription Factor High Accumulation Zones Description: It finds trascription factor (TF) high accumulation DNA zones, i.e., regions along the genome where there is a high presence of different transcription factors. Starting from a dataset containing the genomic positions of TF binding regions, for each base of the selected chromosome the accumulation of TFs is computed. Three different types of accumulation (TF, region and base accumulation) are available, together with the possibility of considering, in the single base accumulation computing, the TFs present not only in that single base, but also in its neighborhood, within a window of a given width. Two different methods for the search of TF high accumulation DNA zones, called "binding regions" and "overlaps", are available. In addition, some functions are provided in order to analyze, visualize and compare results obtained with different input parameters. biocViews: Software, BiologicalQuestion, Transcription, ChIPSeq, Coverage Author: Alberto Marchesi, Marco Masseroli Maintainer: Alberto Marchesi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFHAZ git_branch: RELEASE_3_10 git_last_commit: ec9ac9d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TFHAZ_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TFHAZ_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TFHAZ_1.8.0.tgz vignettes: vignettes/TFHAZ/inst/doc/TFHAZ.html vignetteTitles: TFHAZ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFHAZ/inst/doc/TFHAZ.R dependencyCount: 17 Package: TFutils Version: 1.6.0 Depends: R (>= 3.5.0) Imports: methods, dplyr, magrittr, miniUI, shiny, Rsamtools, GSEABase Suggests: knitr, data.table, testthat, AnnotationDbi, AnnotationFilter, Biobase, GenomicFeatures, GenomicRanges, Gviz, IRanges, Rsamtools, S4Vectors, org.Hs.eg.db, EnsDb.Hsapiens.v75, BiocParallel, BiocStyle, GO.db, GenomicFiles, GenomeInfoDb, SummarizedExperiment, UpSetR, ggplot2, png, gwascat, MotifDb, motifStack, RColorBrewer License: Artistic-2.0 MD5sum: 3ceb4666eb074006100683afd8d3da73 NeedsCompilation: no Title: TFutils Description: Package to work with TF metadata from various sources. biocViews: Transcriptomics Author: Vincent Carey [aut], Shweta Gopaulakrishnan [cre, aut] Maintainer: Shweta Gopaulakrishnan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFutils git_branch: RELEASE_3_10 git_last_commit: 7ae9a5f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TFutils_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TFutils_1.5.7.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TFutils_1.6.0.tgz vignettes: vignettes/TFutils/inst/doc/fimo16.html, vignettes/TFutils/inst/doc/TFutils.html vignetteTitles: A note on fimo16, TFutils -- representing TFBS and TF target sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFutils/inst/doc/fimo16.R, vignettes/TFutils/inst/doc/TFutils.R suggestsMe: TxRegInfra dependencyCount: 72 Package: tigre Version: 1.40.0 Depends: R (>= 2.11.0), BiocGenerics, Biobase Imports: methods, AnnotationDbi, gplots, graphics, grDevices, stats, utils, annotate, DBI, RSQLite Suggests: drosgenome1.db, puma, lumi, BiocStyle, BiocManager License: AGPL-3 Archs: i386, x64 MD5sum: c789945260f55ef0b06ea2f6e8418af8 NeedsCompilation: yes Title: Transcription factor Inference through Gaussian process Reconstruction of Expression Description: The tigre package implements our methodology of Gaussian process differential equation models for analysis of gene expression time series from single input motif networks. The package can be used for inferring unobserved transcription factor (TF) protein concentrations from expression measurements of known target genes, or for ranking candidate targets of a TF. biocViews: Microarray, TimeCourse, GeneExpression, Transcription, GeneRegulation, NetworkInference, Bayesian Author: Antti Honkela, Pei Gao, Jonatan Ropponen, Miika-Petteri Matikainen, Magnus Rattray, Neil D. Lawrence Maintainer: Antti Honkela URL: https://github.com/ahonkela/tigre BugReports: https://github.com/ahonkela/tigre/issues git_url: https://git.bioconductor.org/packages/tigre git_branch: RELEASE_3_10 git_last_commit: 8735a46 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/tigre_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/tigre_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/tigre_1.40.0.tgz vignettes: vignettes/tigre/inst/doc/tigre_quick.pdf, vignettes/tigre/inst/doc/tigre.pdf vignetteTitles: tigre Quick Guide, tigre User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tigre/inst/doc/tigre_quick.R, vignettes/tigre/inst/doc/tigre.R dependencyCount: 37 Package: tilingArray Version: 1.64.0 Depends: R (>= 2.11.0), Biobase, methods, pixmap Imports: strucchange, affy, vsn, genefilter, RColorBrewer, grid, stats4 License: Artistic-2.0 MD5sum: fb26abb0a6ee35bc9ee9fd27cd9c4de3 NeedsCompilation: yes Title: Transcript mapping with high-density oligonucleotide tiling arrays Description: The package provides functionality that can be useful for the analysis of high-density tiling microarray data (such as from Affymetrix genechips) for measuring transcript abundance and architecture. The main functionalities of the package are: 1. the class 'segmentation' for representing partitionings of a linear series of data; 2. the function 'segment' for fitting piecewise constant models using a dynamic programming algorithm that is both fast and exact; 3. the function 'confint' for calculating confidence intervals using the strucchange package; 4. the function 'plotAlongChrom' for generating pretty plots; 5. the function 'normalizeByReference' for probe-sequence dependent response adjustment from a (set of) reference hybridizations. biocViews: Microarray, OneChannel, Preprocessing, Visualization Author: Wolfgang Huber, Zhenyu Xu, Joern Toedling with contributions from Matt Ritchie Maintainer: Zhenyu Xu git_url: https://git.bioconductor.org/packages/tilingArray git_branch: RELEASE_3_10 git_last_commit: c1975d2 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/tilingArray_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/tilingArray_1.64.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/tilingArray_1.64.0.tgz vignettes: vignettes/tilingArray/inst/doc/assessNorm.pdf, vignettes/tilingArray/inst/doc/costMatrix.pdf, vignettes/tilingArray/inst/doc/findsegments.pdf, vignettes/tilingArray/inst/doc/plotAlongChrom.pdf, vignettes/tilingArray/inst/doc/segmentation.pdf vignetteTitles: Normalisation with the normalizeByReference function in the tilingArray package, Supplement. Calculation of the cost matrix, Introduction to using the segment function to fit a piecewise constant curve, Introduction to the plotAlongChrom function, Segmentation demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tilingArray/inst/doc/findsegments.R, vignettes/tilingArray/inst/doc/plotAlongChrom.R importsMe: ADaCGH2, snapCGH dependencyCount: 87 Package: timecourse Version: 1.58.0 Depends: R (>= 2.1.1), MASS, methods Imports: Biobase, graphics, limma (>= 1.8.6), MASS, marray, methods, stats License: LGPL MD5sum: 6da9c111e0bc56f06e688a9d84fa73de NeedsCompilation: no Title: Statistical Analysis for Developmental Microarray Time Course Data Description: Functions for data analysis and graphical displays for developmental microarray time course data. biocViews: Microarray, TimeCourse, DifferentialExpression Author: Yu Chuan Tai Maintainer: Yu Chuan Tai URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/timecourse git_branch: RELEASE_3_10 git_last_commit: c6296f9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/timecourse_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/timecourse_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/timecourse_1.58.0.tgz vignettes: vignettes/timecourse/inst/doc/timecourse.pdf vignetteTitles: timecourse manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timecourse/inst/doc/timecourse.R dependencyCount: 11 Package: timescape Version: 1.10.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), stringr (>= 1.0.0), dplyr (>= 0.4.3), gtools (>= 3.5.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: f0a030619dd73ca09a5fc371e75638d2 NeedsCompilation: no Title: Patient Clonal Timescapes Description: TimeScape is an automated tool for navigating temporal clonal evolution data. The key attributes of this implementation involve the enumeration of clones, their evolutionary relationships and their shifting dynamics over time. TimeScape requires two inputs: (i) the clonal phylogeny and (ii) the clonal prevalences. Optionally, TimeScape accepts a data table of targeted mutations observed in each clone and their allele prevalences over time. The output is the TimeScape plot showing clonal prevalence vertically, time horizontally, and the plot height optionally encoding tumour volume during tumour-shrinking events. At each sampling time point (denoted by a faint white line), the height of each clone accurately reflects its proportionate prevalence. These prevalences form the anchors for bezier curves that visually represent the dynamic transitions between time points. biocViews: Visualization, BiomedicalInformatics Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/timescape git_branch: RELEASE_3_10 git_last_commit: 0bd0f18 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/timescape_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/timescape_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/timescape_1.10.0.tgz vignettes: vignettes/timescape/inst/doc/timescape_vignette.html vignetteTitles: TimeScape vignette hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timescape/inst/doc/timescape_vignette.R dependencyCount: 34 Package: TimeSeriesExperiment Version: 1.4.0 Depends: R (>= 3.5.0), S4Vectors (>= 0.19.23), SummarizedExperiment (>= 1.11.6) Imports: dynamicTreeCut, dplyr, edgeR, DESeq2, ggplot2 (>= 3.0.0), graphics, Hmisc, limma, methods, magrittr, proxy, stats, tibble, tidyr, vegan, viridis, utils Suggests: Biobase, BiocFileCache (>= 1.5.8), circlize, ComplexHeatmap, GO.db, grDevices, grid, knitr, org.Mm.eg.db, org.Hs.eg.db, MASS, RColorBrewer, rmarkdown, UpSetR, License: LGPL (>= 3) Archs: i386, x64 MD5sum: ebd9d5357fbd2a67a7c543573c958ee0 NeedsCompilation: no Title: Analysis for short time-series data Description: Visualization and analysis toolbox for short time course data which includes dimensionality reduction, clustering, two-sample differential expression testing and gene ranking techniques. The package also provides methods for retrieving enriched pathways. biocViews: TimeCourse, Sequencing, RNASeq, Microbiome, GeneExpression, ImmunoOncology, Transcription, Normalization, DifferentialExpression, PrincipalComponent, Clustering, Visualization, Pathways Author: Lan Huong Nguyen Maintainer: Lan Huong Nguyen URL: https://github.com/nlhuong/TimeSeriesExperiment VignetteBuilder: knitr BugReports: https://github.com/nlhuong/TimeSeriesExperiment/issues git_url: https://git.bioconductor.org/packages/TimeSeriesExperiment git_branch: RELEASE_3_10 git_last_commit: a10390b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TimeSeriesExperiment_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TimeSeriesExperiment_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TimeSeriesExperiment_1.4.0.tgz vignettes: vignettes/TimeSeriesExperiment/inst/doc/cop1_knockout_timecourse.html vignetteTitles: Gene expression time course data analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TimeSeriesExperiment/inst/doc/cop1_knockout_timecourse.R dependencyCount: 131 Package: TIN Version: 1.18.0 Depends: R (>= 2.12.0), data.table, impute, aroma.affymetrix Imports: WGCNA, squash, stringr Suggests: knitr, aroma.light, affxparser, RUnit, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: 55167eb639b889f0521a8124e7803f10 NeedsCompilation: no Title: Transcriptome instability analysis Description: The TIN package implements a set of tools for transcriptome instability analysis based on exon expression profiles. Deviating exon usage is studied in the context of splicing factors to analyse to what degree transcriptome instability is correlated to splicing factor expression. In the transcriptome instability correlation analysis, the data is compared to both random permutations of alternative splicing scores and expression of random gene sets. biocViews: ExonArray, Microarray, GeneExpression, AlternativeSplicing, Genetics, DifferentialSplicing Author: Bjarne Johannessen, Anita Sveen and Rolf I. Skotheim Maintainer: Bjarne Johannessen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TIN git_branch: RELEASE_3_10 git_last_commit: 89d0cf6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TIN_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TIN_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TIN_1.18.0.tgz vignettes: vignettes/TIN/inst/doc/TIN.pdf vignetteTitles: Introduction to the TIN package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TIN/inst/doc/TIN.R dependencyCount: 127 Package: TissueEnrich Version: 1.6.0 Depends: R (>= 3.5), ensurer (>= 1.1.0), ggplot2 (>= 2.2.1), tidyr (>= 0.8.0), SummarizedExperiment (>= 1.6.5), GSEABase (>= 1.38.2) Imports: dplyr (>= 0.7.3), stats Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 113159e6e0525fdbe63f6f06f7ad0f5f NeedsCompilation: no Title: Tissue-specific gene enrichment analysis Description: The TissueEnrich package is used to calculate enrichment of tissue-specific genes in a set of input genes. For example, the user can input the most highly expressed genes from RNA-Seq data, or gene co-expression modules to determine which tissue-specific genes are enriched in those datasets. Tissue-specific genes were defined by processing RNA-Seq data from the Human Protein Atlas (HPA) (Uhlén et al. 2015), GTEx (Ardlie et al. 2015), and mouse ENCODE (Shen et al. 2012) using the algorithm from the HPA (Uhlén et al. 2015).The hypergeometric test is being used to determine if the tissue-specific genes are enriched among the input genes. Along with tissue-specific gene enrichment, the TissueEnrich package can also be used to define tissue-specific genes from expression datasets provided by the user, which can then be used to calculate tissue-specific gene enrichments. biocViews: GeneSetEnrichment, GeneExpression, Sequencing Author: Ashish Jain [aut, cre], Geetu Tuteja [aut] Maintainer: Ashish Jain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TissueEnrich git_branch: RELEASE_3_10 git_last_commit: 00cc666 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TissueEnrich_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TissueEnrich_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TissueEnrich_1.6.0.tgz vignettes: vignettes/TissueEnrich/inst/doc/TissueEnrich.html vignetteTitles: TissueEnrich hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TissueEnrich/inst/doc/TissueEnrich.R dependencyCount: 96 Package: TitanCNA Version: 1.24.0 Depends: R (>= 3.5.1) Imports: BiocGenerics (>= 0.31.6), IRanges (>= 2.6.1), GenomicRanges (>= 1.24.3), VariantAnnotation (>= 1.18.7), foreach (>= 1.4.3), GenomeInfoDb (>= 1.8.7), data.table (>= 1.10.4), dplyr (>= 0.5.0), License: GPL-3 MD5sum: 9d2d87ce5f4d4d4d16f476a398606c66 NeedsCompilation: yes Title: Subclonal copy number and LOH prediction from whole genome sequencing of tumours Description: Hidden Markov model to segment and predict regions of subclonal copy number alterations (CNA) and loss of heterozygosity (LOH), and estimate cellular prevalence of clonal clusters in tumour whole genome sequencing data. biocViews: Sequencing, WholeGenome, DNASeq, ExomeSeq, StatisticalMethod, CopyNumberVariation, HiddenMarkovModel, Genetics, GenomicVariation, ImmunoOncology Author: Gavin Ha Maintainer: Gavin Ha URL: https://github.com/gavinha/TitanCNA git_url: https://git.bioconductor.org/packages/TitanCNA git_branch: RELEASE_3_10 git_last_commit: 7fad13c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TitanCNA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TitanCNA_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TitanCNA_1.24.0.tgz vignettes: vignettes/TitanCNA/inst/doc/TitanCNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TitanCNA/inst/doc/TitanCNA.R dependencyCount: 89 Package: tkWidgets Version: 1.64.0 Depends: R (>= 2.0.0), methods, widgetTools (>= 1.1.7), DynDoc (>= 1.3.0), tools Suggests: Biobase, hgu95av2 License: Artistic-2.0 MD5sum: 23961a0eabf254457cf450cf2713a7d7 NeedsCompilation: no Title: R based tk widgets Description: Widgets to provide user interfaces. tcltk should have been installed for the widgets to run. biocViews: Infrastructure Author: J. Zhang Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/tkWidgets git_branch: RELEASE_3_10 git_last_commit: b052f74 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/tkWidgets_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/tkWidgets_1.64.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/tkWidgets_1.64.0.tgz vignettes: vignettes/tkWidgets/inst/doc/importWizard.pdf, vignettes/tkWidgets/inst/doc/tkWidgets.pdf vignetteTitles: tkWidgets importWizard, tkWidgets contents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tkWidgets/inst/doc/importWizard.R, vignettes/tkWidgets/inst/doc/tkWidgets.R importsMe: Mfuzz, OLINgui suggestsMe: affy, affyQCReport, annotate, Biobase, genefilter, marray dependencyCount: 6 Package: TMixClust Version: 1.8.0 Depends: R (>= 3.4) Imports: gss, mvtnorm, stats, zoo, cluster, utils, BiocParallel, flexclust, grDevices, graphics, Biobase, SPEM Suggests: rmarkdown, knitr, BiocStyle, testthat License: GPL (>=2) MD5sum: b85b732fbc12b96d0149cc5cb72f64f2 NeedsCompilation: no Title: Time Series Clustering of Gene Expression with Gaussian Mixed-Effects Models and Smoothing Splines Description: Implementation of a clustering method for time series gene expression data based on mixed-effects models with Gaussian variables and non-parametric cubic splines estimation. The method can robustly account for the high levels of noise present in typical gene expression time series datasets. biocViews: Software, StatisticalMethod, Clustering, TimeCourse, GeneExpression Author: Monica Golumbeanu Maintainer: Monica Golumbeanu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TMixClust git_branch: RELEASE_3_10 git_last_commit: 28defa4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TMixClust_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TMixClust_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TMixClust_1.8.0.tgz vignettes: vignettes/TMixClust/inst/doc/TMixClust.pdf vignetteTitles: Clustering time series gene expression data with TMixClust hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TMixClust/inst/doc/TMixClust.R dependencyCount: 29 Package: TNBC.CMS Version: 1.2.0 Depends: R (>= 3.6.0), e1071, quadprog, SummarizedExperiment Imports: GSVA (>= 1.26.0), pheatmap, grDevices, RColorBrewer, pracma, GGally, R.utils, forestplot, ggplot2, ggpubr, survival, grid, stats, methods Suggests: knitr License: GPL-3 MD5sum: 9b420268e761c00fb2f84670487d2b87 NeedsCompilation: no Title: TNBC.CMS: Prediction of TNBC Consensus Molecular Subtypes Description: This package implements a machine learning-based classifier for the assignment of consensus molecular subtypes to TNBC samples. It also provides functions to summarize genomic and clinical characteristics. biocViews: Classification, Clustering, GeneExpression, GenePrediction, SupportVectorMachine Author: Doyeong Yu, Jihyun Kim, In Hae Park, Charny Park Maintainer: Doyeong Yu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TNBC.CMS git_branch: RELEASE_3_10 git_last_commit: 71f3cfc git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TNBC.CMS_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TNBC.CMS_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TNBC.CMS_1.2.0.tgz vignettes: vignettes/TNBC.CMS/inst/doc/TNBC.CMS.pdf vignetteTitles: TNBC.CMS.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TNBC.CMS/inst/doc/TNBC.CMS.R dependencyCount: 130 Package: TnT Version: 1.8.1 Depends: R (>= 3.4), GenomicRanges Imports: methods, stats, utils, grDevices, htmlwidgets, jsonlite, data.table, Biobase, GenomeInfoDb, IRanges, S4Vectors, knitr Suggests: GenomicFeatures, shiny, BiocManager, rmarkdown, testthat License: AGPL-3 Archs: i386, x64 MD5sum: a5189ad0071cd22a121494b3821bcbaa NeedsCompilation: no Title: Interactive Visualization for Genomic Features Description: A R interface to the TnT javascript library (https://github.com/ tntvis) to provide interactive and flexible visualization of track-based genomic data. biocViews: Infrastructure, Visualization Author: Jialin Ma [cre, aut], Miguel Pignatelli [aut], Toby Hocking [aut] Maintainer: Jialin Ma URL: https://github.com/Marlin-Na/TnT VignetteBuilder: knitr BugReports: https://github.com/Marlin-Na/TnT/issues git_url: https://git.bioconductor.org/packages/TnT git_branch: RELEASE_3_10 git_last_commit: cd8a55d git_last_commit_date: 2020-01-30 Date/Publication: 2020-01-30 source.ver: src/contrib/TnT_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/TnT_1.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TnT_1.8.1.tgz vignettes: vignettes/TnT/inst/doc/introduction.html vignetteTitles: Introduction to TnT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TnT/inst/doc/introduction.R dependencyCount: 37 Package: TOAST Version: 1.0.0 Depends: R (>= 3.6), RefFreeEWAS, EpiDISH Imports: stats, methods, SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, csSAM, gplots, matrixStats, Matrix License: GPL-2 MD5sum: 4f92cdb5215db766154c976c8fa5134e NeedsCompilation: no Title: Tools for the analysis of heterogeneous tissues Description: This package is devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include 1. detect cell-type specific or cross-cell type differential signals 2. improve variable selection in reference-free deconvolution. biocViews: DNAMethylation, GeneExpression, DifferentialExpression, DifferentialMethylation, Microarray, GeneTarget, Epigenetics, MethylationArray Author: Ziyi Li and Hao Wu Maintainer: Ziyi Li VignetteBuilder: knitr BugReports: https://github.com/ziyili20/TOAST/issues git_url: https://git.bioconductor.org/packages/TOAST git_branch: RELEASE_3_10 git_last_commit: 5525311 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TOAST_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TOAST_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TOAST_1.0.0.tgz vignettes: vignettes/TOAST/inst/doc/TOAST.html vignetteTitles: The TOAST User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TOAST/inst/doc/TOAST.R dependencyCount: 44 Package: tofsims Version: 1.14.0 Depends: R (>= 3.3.0), methods, utils, ProtGenerics Imports: Rcpp (>= 0.11.2), ALS, ChemometricsWithR, signal, KernSmooth, graphics, grDevices, stats LinkingTo: Rcpp, RcppArmadillo Suggests: EBImage, knitr, rmarkdown, testthat, tofsimsData, BiocParallel, RColorBrewer Enhances: parallel License: GPL-3 MD5sum: ed21a9406bc2e34f7fe14c183790e630 NeedsCompilation: yes Title: Import, process and analysis of Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) imaging data Description: This packages offers a pipeline for import, processing and analysis of ToF-SIMS 2D image data. Import of Iontof and Ulvac-Phi raw or preprocessed data is supported. For rawdata, mass calibration, peak picking and peak integration exist. General funcionality includes data binning, scaling, image subsetting and visualization. A range of multivariate tools common in the ToF-SIMS community are implemented (PCA, MCR, MAF, MNF). An interface to the bioconductor image processing package EBImage offers image segmentation functionality. biocViews: ImmunoOncology, Infrastructure, DataImport, MassSpectrometry, ImagingMassSpectrometry, Proteomics, Metabolomics Author: Lorenz Gerber, Viet Mai Hoang Maintainer: Lorenz Gerber URL: https://github.com/lorenzgerber/tofsims VignetteBuilder: knitr BugReports: https://github.com/lorenzgerber/tofsims/issues git_url: https://git.bioconductor.org/packages/tofsims git_branch: RELEASE_3_10 git_last_commit: 7f44d40 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/tofsims_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/tofsims_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/tofsims_1.14.0.tgz vignettes: vignettes/tofsims/inst/doc/workflow.html vignetteTitles: Workflow with the `tofsims` package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tofsims/inst/doc/workflow.R dependencyCount: 92 Package: ToPASeq Version: 1.20.0 Depends: R(>= 3.5.0), graphite Imports: Rcpp, graph, methods, Biobase, RBGL, SummarizedExperiment, gRbase, limma, corpcor LinkingTo: Rcpp Suggests: BiocStyle, airway, knitr, rmarkdown, DESeq2, DESeq, edgeR, plotrix, breastCancerVDX, EnrichmentBrowser License: AGPL-3 MD5sum: 33572b0d742cd094ed43d8daf0884c5a NeedsCompilation: yes Title: Topology-based pathway analysis of RNA-seq data Description: Implementation of methods for topology-based pathway analysis of RNA-seq data. This includes Topological Analysis of Pathway Phenotype Association (TAPPA; Gao and Wang, 2007), PathWay Enrichment Analysis (PWEA; Hung et al., 2010), and the Pathway Regulation Score (PRS; Ibrahim et al., 2012). biocViews: ImmunoOncology, GeneExpression, RNASeq, DifferentialExpression, GraphAndNetwork, Pathways, NetworkEnrichment, Visualization Author: Ivana Ihnatova, Eva Budinska, Ludwig Geistlinger Maintainer: Ivana Ihnatova VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ToPASeq git_branch: RELEASE_3_10 git_last_commit: a760d80 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ToPASeq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ToPASeq_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ToPASeq_1.20.0.tgz vignettes: vignettes/ToPASeq/inst/doc/ToPASeq_allMethods.html, vignettes/ToPASeq/inst/doc/ToPASeq.html vignetteTitles: Eight methods for topology-based pathway analysis of RNA-seq data, Topology-based pathway analysis of RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ToPASeq/inst/doc/ToPASeq_allMethods.R, vignettes/ToPASeq/inst/doc/ToPASeq.R dependencyCount: 69 Package: topconfects Version: 1.2.0 Depends: R (>= 3.6.0) Imports: methods, utils, stats, assertthat, ggplot2 Suggests: limma, edgeR, DESeq2, NBPSeq, dplyr, testthat, knitr, rmarkdown, reshape2, tidyr, readr, org.At.tair.db, AnnotationDbi License: LGPL-2.1 | file LICENSE MD5sum: 8d7442f7fbbeb3d187fa8b7d1e7bb975 NeedsCompilation: no Title: Top Confident Effect Sizes Description: Rank results by confident effect sizes, while maintaining False Discovery Rate and False Coverage-statement Rate control. Topconfects is alternative presentation of TREAT results with improved usability, eliminating p-values and instead providing confidence bounds. The main application is differential gene expression analysis, providing genes ranked in order of confident log2 fold change, but it can be applied to any collection of effect sizes with associated standard errors. biocViews: GeneExpression, DifferentialExpression, Transcriptomics, RNASeq, mRNAMicroarray, Regression, MultipleComparison Author: Paul Harrison [aut, cre] () Maintainer: Paul Harrison URL: https://github.com/pfh/topconfects VignetteBuilder: knitr BugReports: https://github.com/pfh/topconfects/issues git_url: https://git.bioconductor.org/packages/topconfects git_branch: RELEASE_3_10 git_last_commit: c3fcb60 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/topconfects_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/topconfects_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/topconfects_1.2.0.tgz vignettes: vignettes/topconfects/inst/doc/an_overview.html, vignettes/topconfects/inst/doc/fold_change.html vignetteTitles: An overview of topconfects, Confident fold change hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/topconfects/inst/doc/an_overview.R, vignettes/topconfects/inst/doc/fold_change.R importsMe: MetaVolcanoR dependencyCount: 54 Package: topdownr Version: 1.8.0 Depends: R (>= 3.5), methods, BiocGenerics (>= 0.20.0), ProtGenerics (>= 1.10.0), Biostrings (>= 2.42.1), S4Vectors (>= 0.12.2) Imports: grDevices, stats, tools, utils, Biobase, Matrix (>= 1.2.10), MSnbase (>= 2.3.10), ggplot2 (>= 2.2.1), mzR (>= 2.11.4) Suggests: topdownrdata (>= 0.2), knitr, ranger, testthat, BiocStyle, xml2 License: GPL (>= 3) MD5sum: 26ca0fa4eecbc230cffc6ba550221006 NeedsCompilation: no Title: Investigation of Fragmentation Conditions in Top-Down Proteomics Description: The topdownr package allows automatic and systemic investigation of fragment conditions. It creates Thermo Orbitrap Fusion Lumos method files to test hundreds of fragmentation conditions. Additionally it provides functions to analyse and process the generated MS data and determine the best conditions to maximise overall fragment coverage. biocViews: ImmunoOncology, Infrastructure, Proteomics, MassSpectrometry, Coverage Author: Sebastian Gibb [aut, cre] (), Pavel Shliaha [aut] (), Ole Nørregaard Jensen [aut] () Maintainer: Sebastian Gibb URL: https://github.com/sgibb/topdownr/ VignetteBuilder: knitr BugReports: https://github.com/sgibb/topdownr/issues/ git_url: https://git.bioconductor.org/packages/topdownr git_branch: RELEASE_3_10 git_last_commit: c8ad7c1 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/topdownr_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/topdownr_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/topdownr_1.8.0.tgz vignettes: vignettes/topdownr/inst/doc/analysis.html, vignettes/topdownr/inst/doc/data-generation.html vignetteTitles: Fragmentation Analysis with topdownr, Data Generation for topdownr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/topdownr/inst/doc/analysis.R, vignettes/topdownr/inst/doc/data-generation.R dependencyCount: 91 Package: topGO Version: 2.38.1 Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.6), graph (>= 1.14.0), Biobase (>= 2.0.0), GO.db (>= 2.3.0), AnnotationDbi (>= 1.7.19), SparseM (>= 0.73) Imports: lattice, matrixStats, DBI Suggests: ALL, hgu95av2.db, hgu133a.db, genefilter, xtable, multtest, Rgraphviz, globaltest License: LGPL Archs: i386, x64 MD5sum: e839c596d3e51f003ca0ad07cd41d2b2 NeedsCompilation: no Title: Enrichment Analysis for Gene Ontology Description: topGO package provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied. biocViews: Microarray, Visualization Author: Adrian Alexa, Jorg Rahnenfuhrer Maintainer: Adrian Alexa git_url: https://git.bioconductor.org/packages/topGO git_branch: RELEASE_3_10 git_last_commit: cbe8856 git_last_commit_date: 2019-12-09 Date/Publication: 2019-12-16 source.ver: src/contrib/topGO_2.38.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/topGO_2.38.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/topGO_2.38.1.tgz vignettes: vignettes/topGO/inst/doc/topGO.pdf vignetteTitles: topGO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/topGO/inst/doc/topGO.R dependsOnMe: BgeeDB, cellTree, compEpiTools, EGSEA, ideal, KnowSeq, RCAS, RNAither, tRanslatome importsMe: cellity, EnrichmentBrowser, FoldGO, GOSim, OmaDB, pcaExplorer, psygenet2r, transcriptogramer, ViSEAGO suggestsMe: FGNet, IntramiRExploreR, miRNAtap, Ringo dependencyCount: 33 Package: TPP Version: 3.14.1 Depends: R (>= 3.4), Biobase, dplyr, magrittr, tidyr Imports: biobroom, broom, data.table, doParallel, foreach, futile.logger, ggplot2, grDevices, gridExtra, grid, knitr, limma, MASS, mefa, nls2, openxlsx (>= 2.4.0), parallel, plyr, purrr, RColorBrewer, RCurl, reshape2, rmarkdown, splines, stats, stringr, utils, VennDiagram, VGAM Suggests: BiocStyle, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: ea78366991bde87063f0f805fa23b471 NeedsCompilation: no Title: Analyze thermal proteome profiling (TPP) experiments Description: Analyze thermal proteome profiling (TPP) experiments with varying temperatures (TR) or compound concentrations (CCR). biocViews: ImmunoOncology, Proteomics, MassSpectrometry Author: Dorothee Childs, Nils Kurzawa, Holger Franken, Carola Doce, Mikhail Savitski and Wolfgang Huber Maintainer: Dorothee Childs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TPP git_branch: RELEASE_3_10 git_last_commit: 3470cf1 git_last_commit_date: 2020-01-31 Date/Publication: 2020-01-31 source.ver: src/contrib/TPP_3.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/TPP_3.14.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TPP_3.14.1.tgz vignettes: vignettes/TPP/inst/doc/NPARC_analysis_of_TPP_TR_data.pdf, vignettes/TPP/inst/doc/TPP_introduction_1D.pdf, vignettes/TPP/inst/doc/TPP_introduction_2D.pdf vignetteTitles: TPP_introduction_NPARC, TPP_introduction_1D, TPP_introduction_2D hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TPP/inst/doc/NPARC_analysis_of_TPP_TR_data.R, vignettes/TPP/inst/doc/TPP_introduction_1D.R, vignettes/TPP/inst/doc/TPP_introduction_2D.R dependencyCount: 102 Package: TPP2D Version: 1.2.3 Depends: R (>= 3.6.0), stats, utils, dplyr, methods Imports: ggplot2, tidyr, foreach, doParallel, openxlsx, stringr, RCurl, parallel, MASS Suggests: knitr, testthat License: GPL-3 MD5sum: 54a4deae002c1745cf0819a28d43b704 NeedsCompilation: no Title: FDR-controlled analysis of 2D-TPP experiments Description: FDR-controlled analysis of 2D-TPP experiments by functional analysis of dose-response curves across temperatures. biocViews: Software, Proteomics, DataImport Author: Nils Kurzawa [aut, cre], Holger Franken [aut], Simon Anders [aut], Wolfgang Huber [aut], Mikhail M. Savitski [aut] Maintainer: Nils Kurzawa URL: http://bioconductor.org/packages/TPP2D VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/TPP2D git_branch: RELEASE_3_10 git_last_commit: 4913466 git_last_commit_date: 2020-01-20 Date/Publication: 2020-01-20 source.ver: src/contrib/TPP2D_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/TPP2D_1.2.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TPP2D_1.2.3.tgz vignettes: vignettes/TPP2D/inst/doc/TPP2D.html vignetteTitles: Introduction to TPP2D for 2D-TPP analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TPP2D/inst/doc/TPP2D.R dependencyCount: 71 Package: tracktables Version: 1.20.0 Depends: R (>= 3.0.0) Imports: IRanges, GenomicRanges, XVector, Rsamtools, XML, tractor.base, stringr, RColorBrewer, methods Suggests: knitr, BiocStyle License: GPL (>= 3) MD5sum: f1f79b5ea55556a34a889b3463345ed9 NeedsCompilation: no Title: Build IGV tracks and HTML reports Description: Methods to create complex IGV genome browser sessions and dynamic IGV reports in HTML pages. biocViews: Sequencing, ReportWriting Author: Tom Carroll, Sanjay Khadayate, Anne Pajon, Ziwei Liang Maintainer: Tom Carroll VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tracktables git_branch: RELEASE_3_10 git_last_commit: 6532d08 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/tracktables_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/tracktables_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/tracktables_1.20.0.tgz vignettes: vignettes/tracktables/inst/doc/tracktables.pdf vignetteTitles: Creating IGV HTML reports with tracktables hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tracktables/inst/doc/tracktables.R dependencyCount: 39 Package: trackViewer Version: 1.22.1 Depends: R (>= 3.1.0), grDevices, methods, GenomicRanges, grid Imports: GenomeInfoDb, GenomicAlignments, GenomicFeatures, Gviz, Rsamtools, S4Vectors, rtracklayer, BiocGenerics, scales, tools, IRanges, AnnotationDbi, grImport, htmlwidgets, plotrix, Rgraphviz, InteractionSet, graph, utils Suggests: biomaRt, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, org.Hs.eg.db, BiocStyle, knitr, VariantAnnotation, httr, htmltools License: GPL (>= 2) MD5sum: 26345298ff212d43eb35b23974468da6 NeedsCompilation: no Title: A R/Bioconductor package with web interface for drawing elegant interactive tracks or lollipop plot to facilitate integrated analysis of multi-omics data Description: Visualize mapped reads along with annotation as track layers for NGS dataset such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq, SNPs and methylation data. biocViews: Visualization Author: Jianhong Ou and Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/trackViewer git_branch: RELEASE_3_10 git_last_commit: 0726fc8 git_last_commit_date: 2020-02-20 Date/Publication: 2020-02-20 source.ver: src/contrib/trackViewer_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/trackViewer_1.22.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/trackViewer_1.22.1.tgz vignettes: vignettes/trackViewer/inst/doc/trackViewer.html vignetteTitles: trackViewer Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trackViewer/inst/doc/trackViewer.R importsMe: NADfinder suggestsMe: ATACseqQC, ChIPpeakAnno dependencyCount: 148 Package: tradeSeq Version: 1.0.1 Depends: R (>= 3.6) Imports: mgcv, edgeR, SingleCellExperiment, SummarizedExperiment, slingshot, magrittr, RColorBrewer, clusterExperiment, BiocParallel, pbapply, ggplot2, princurve, methods, S4Vectors Suggests: knitr, rmarkdown, cowplot, dplyr, tidyr, testthat License: MIT + file LICENSE MD5sum: 51159828d84340417f9abdc4b873792d NeedsCompilation: no Title: trajectory-based differential expression analysis for sequencing data Description: tradeSeq provides a flexible method for finding genes that are differentially expressed along one or multiple trajectories, using a variety of tests suited to answer questions of interest, e.g. the discovery of genes that whose expression is associated with pseudotime, or who are differentially expressed (in a specific region) along the trajectory. It fits a generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM. biocViews: Clustering, Regression, TimeCourse, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, SingleCell, Transcriptomics, MultipleComparison, Visualization Author: Koen Van den Berge [aut], Hector Roux de Bezieux [aut, cre] (), Kelly Street [ctb], Lieven Clement [ctb], Sandrine Dudoit [ctb] Maintainer: Hector Roux de Bezieux URL: https://statomics.github.io/tradeSeq/index.html VignetteBuilder: knitr BugReports: https://github.com/statOmics/tradeSeq/issues git_url: https://git.bioconductor.org/packages/tradeSeq git_branch: RELEASE_3_10 git_last_commit: eb046aa git_last_commit_date: 2020-04-03 Date/Publication: 2020-04-03 source.ver: src/contrib/tradeSeq_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/tradeSeq_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/tradeSeq_1.0.1.tgz vignettes: vignettes/tradeSeq/inst/doc/tradeSeq.html vignetteTitles: 'Vignette for **tradeSeq** hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tradeSeq/inst/doc/tradeSeq.R dependencyCount: 149 Package: transcriptogramer Version: 1.8.0 Depends: R (>= 3.4), methods Imports: biomaRt, data.table, doSNOW, foreach, ggplot2, graphics, grDevices, igraph, limma, parallel, progress, RedeR, snow, stats, tidyr, topGO Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 1c50b33f0dda3cb242c4ce2f783b5db3 NeedsCompilation: no Title: Transcriptional analysis based on transcriptograms Description: R package for transcriptional analysis based on transcriptograms, a method to analyze transcriptomes that projects expression values on a set of ordered proteins, arranged such that the probability that gene products participate in the same metabolic pathway exponentially decreases with the increase of the distance between two proteins of the ordering. Transcriptograms are, hence, genome wide gene expression profiles that provide a global view for the cellular metabolism, while indicating gene sets whose expression are altered. biocViews: Software, Network, Visualization, SystemsBiology, GeneExpression, GeneSetEnrichment, GraphAndNetwork, Clustering, DifferentialExpression, Microarray, RNASeq, Transcription, ImmunoOncology Author: Diego Morais [aut, cre], Rodrigo Dalmolin [aut] Maintainer: Diego Morais URL: https://github.com/arthurvinx/transcriptogramer SystemRequirements: Java Runtime Environment (>= 6) VignetteBuilder: knitr BugReports: https://github.com/arthurvinx/transcriptogramer/issues git_url: https://git.bioconductor.org/packages/transcriptogramer git_branch: RELEASE_3_10 git_last_commit: 2c362a3 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/transcriptogramer_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/transcriptogramer_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/transcriptogramer_1.8.0.tgz vignettes: vignettes/transcriptogramer/inst/doc/transcriptogramer.html vignetteTitles: The transcriptogramer user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transcriptogramer/inst/doc/transcriptogramer.R dependencyCount: 103 Package: transcriptR Version: 1.14.0 Depends: methods, R (>= 3.3) Imports: BiocGenerics, caret, chipseq, e1071, GenomicAlignments, GenomicRanges, GenomicFeatures, GenomeInfoDb, ggplot2, graphics, grDevices, IRanges (>= 2.11.15), pROC, reshape2, Rsamtools, rtracklayer, S4Vectors, stats, utils Suggests: BiocStyle, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg19.knownGene, testthat License: GPL-3 MD5sum: 0497d98b5c1ffab51c01bb0df8e01e27 NeedsCompilation: no Title: An Integrative Tool for ChIP- And RNA-Seq Based Primary Transcripts Detection and Quantification Description: The differences in the RNA types being sequenced have an impact on the resulting sequencing profiles. mRNA-seq data is enriched with reads derived from exons, while GRO-, nucRNA- and chrRNA-seq demonstrate a substantial broader coverage of both exonic and intronic regions. The presence of intronic reads in GRO-seq type of data makes it possible to use it to computationally identify and quantify all de novo continuous regions of transcription distributed across the genome. This type of data, however, is more challenging to interpret and less common practice compared to mRNA-seq. One of the challenges for primary transcript detection concerns the simultaneous transcription of closely spaced genes, which needs to be properly divided into individually transcribed units. The R package transcriptR combines RNA-seq data with ChIP-seq data of histone modifications that mark active Transcription Start Sites (TSSs), such as, H3K4me3 or H3K9/14Ac to overcome this challenge. The advantage of this approach over the use of, for example, gene annotations is that this approach is data driven and therefore able to deal also with novel and case specific events. Furthermore, the integration of ChIP- and RNA-seq data allows the identification all known and novel active transcription start sites within a given sample. biocViews: ImmunoOncology, Transcription, Software, Sequencing, RNASeq, Coverage Author: Armen R. Karapetyan Maintainer: Armen R. Karapetyan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transcriptR git_branch: RELEASE_3_10 git_last_commit: 894be86 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/transcriptR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/transcriptR_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/transcriptR_1.14.0.tgz vignettes: vignettes/transcriptR/inst/doc/transcriptR.pdf vignetteTitles: transcriptR: an integrative tool for ChIP- and RNA-seq based primary transcripts detection and quantification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transcriptR/inst/doc/transcriptR.R dependencyCount: 142 Package: transite Version: 1.4.0 Depends: R (>= 3.5) Imports: BiocGenerics (>= 0.26.0), Biostrings (>= 2.48.0), dplyr (>= 0.7.6), GenomicRanges (>= 1.32.6), ggplot2 (>= 3.0.0), ggseqlogo (>= 0.1), gridExtra (>= 2.3), methods, parallel, Rcpp (>= 0.12.18), scales (>= 1.0.0), stats, TFMPvalue (>= 0.0.8), utils LinkingTo: Rcpp (>= 0.12.18) Suggests: knitr (>= 1.20), rmarkdown (>= 1.10), roxygen2 (>= 6.1.0) License: MIT + file LICENSE MD5sum: bf87f965a3129f4be34a844732555463 NeedsCompilation: yes Title: RNA-binding protein motif analysis Description: transite is a computational method that allows comprehensive analysis of the regulatory role of RNA-binding proteins in various cellular processes by leveraging preexisting gene expression data and current knowledge of binding preferences of RNA-binding proteins. biocViews: GeneExpression, Transcription, DifferentialExpression, Microarray, mRNAMicroarray, Genetics, GeneSetEnrichment Author: Konstantin Krismer [aut, cre, cph] (), Anna Gattinger [aut] (), Michael Yaffe [ths, cph] (), Ian Cannell [ths] () Maintainer: Konstantin Krismer URL: https://transite.mit.edu SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transite git_branch: RELEASE_3_10 git_last_commit: 6363139 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/transite_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/transite_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/transite_1.4.0.tgz vignettes: vignettes/transite/inst/doc/spma.html vignetteTitles: Spectrum Motif Analysis (SPMA) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/transite/inst/doc/spma.R dependencyCount: 75 Package: tRanslatome Version: 1.24.0 Depends: R (>= 2.15.0), methods, limma, sigPathway, anota, DESeq, edgeR, RankProd, topGO, org.Hs.eg.db, GOSemSim, Heatplus, gplots, plotrix, Biobase License: GPL-3 MD5sum: 4bbce3b7aa3b2d890862ea0d27c0e4bc NeedsCompilation: no Title: Comparison between multiple levels of gene expression Description: Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods: Rank Product, Translational Efficiency, t-test, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched post-transcriptional regulatory factors (RBPs, miRNAs, etc) and Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the enriched terms with heatmaps, radar plots and barplots. biocViews: CellBiology, GeneRegulation, Regulation, GeneExpression, DifferentialExpression, Microarray, HighThroughputSequencing, QualityControl, GO, MultipleComparisons, Bioinformatics Author: Toma Tebaldi, Erik Dassi, Galena Kostoska Maintainer: Toma Tebaldi , Erik Dassi git_url: https://git.bioconductor.org/packages/tRanslatome git_branch: RELEASE_3_10 git_last_commit: 3e4c8b5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/tRanslatome_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/tRanslatome_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/tRanslatome_1.24.0.tgz vignettes: vignettes/tRanslatome/inst/doc/tRanslatome_package.pdf vignetteTitles: tRanslatome hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tRanslatome/inst/doc/tRanslatome_package.R dependencyCount: 106 Package: TransView Version: 1.30.0 Depends: methods, GenomicRanges Imports: BiocGenerics, S4Vectors (>= 0.9.25), IRanges, zlibbioc, gplots LinkingTo: Rhtslib (>= 1.15.3) Suggests: RUnit, pasillaBamSubset, BiocManager License: GPL-3 MD5sum: 5d58fa816383ef25dbb815636fd437dd NeedsCompilation: yes Title: Read density map construction and accession. Visualization of ChIPSeq and RNASeq data sets Description: This package provides efficient tools to generate, access and display read densities of sequencing based data sets such as from RNA-Seq and ChIP-Seq. biocViews: ImmunoOncology, DNAMethylation, GeneExpression, Transcription, Microarray, Sequencing, Sequencing, ChIPSeq, RNASeq, MethylSeq, DataImport, Visualization, Clustering, MultipleComparison Author: Julius Muller Maintainer: Julius Muller URL: http://bioconductor.org/packages/release/bioc/html/TransView.html SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/TransView git_branch: RELEASE_3_10 git_last_commit: 8f39927 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TransView_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TransView_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TransView_1.30.0.tgz vignettes: vignettes/TransView/inst/doc/TransView.pdf vignetteTitles: An introduction to TransView hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TransView/inst/doc/TransView.R dependencyCount: 22 Package: traseR Version: 1.16.0 Depends: R (>= 3.2.0),GenomicRanges,IRanges,BSgenome.Hsapiens.UCSC.hg19 Suggests: BiocStyle,RUnit, BiocGenerics License: GPL MD5sum: a3efc2af21a8775af5f67fcda13fce71 NeedsCompilation: no Title: GWAS trait-associated SNP enrichment analyses in genomic intervals Description: traseR performs GWAS trait-associated SNP enrichment analyses in genomic intervals using different hypothesis testing approaches, also provides various functionalities to explore and visualize the results. biocViews: Genetics,Sequencing, Coverage, Alignment, QualityControl, DataImport Author: Li Chen, Zhaohui S.Qin Maintainer: li chen git_url: https://git.bioconductor.org/packages/traseR git_branch: RELEASE_3_10 git_last_commit: 5046524 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/traseR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/traseR_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/traseR_1.16.0.tgz vignettes: vignettes/traseR/inst/doc/traseR.pdf vignetteTitles: Perform GWAS trait-associated SNP enrichment analyses in genomic intervals hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/traseR/inst/doc/traseR.R dependencyCount: 40 Package: treeio Version: 1.10.0 Depends: R (>= 3.4.0) Imports: ape, dplyr, jsonlite, magrittr, methods, rlang, tibble, tidytree (>= 0.2.6), utils Suggests: Biostrings, ggplot2, ggtree, igraph, knitr, phangorn, prettydoc, testthat, tidyr, vroom License: Artistic-2.0 Archs: i386, x64 MD5sum: e21bfac4fae3027e4949e789ee0e31b1 NeedsCompilation: no Title: Base Classes and Functions for Phylogenetic Tree Input and Output Description: 'treeio' is an R package to make it easier to import and store phylogenetic tree with associated data; and to link external data from different sources to phylogeny. It also supports exporting phylogenetic tree with heterogeneous associated data to a single tree file and can be served as a platform for merging tree with associated data and converting file formats. biocViews: Software, Annotation, Clustering, DataImport, DataRepresentation, Alignment, MultipleSequenceAlignment Author: Guangchuang Yu [aut, cre] (), Tommy Tsan-Yuk Lam [ctb, ths], Casey Dunn [ctb], Bradley Jones [ctb], Tyler Bradley [ctb], Shuangbin Xu [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.github.io/treedata-book/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/treeio/issues git_url: https://git.bioconductor.org/packages/treeio git_branch: RELEASE_3_10 git_last_commit: 7321912 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/treeio_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/treeio_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/treeio_1.10.0.tgz vignettes: vignettes/treeio/inst/doc/treeio.html vignetteTitles: treeio hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/treeio/inst/doc/treeio.R importsMe: ggtree dependencyCount: 36 Package: TreeSummarizedExperiment Version: 1.2.0 Depends: R(>= 3.6.0), SingleCellExperiment, S4Vectors (>= 0.23.18) Imports: methods, utils, ape, dplyr, SummarizedExperiment Suggests: ggtree, BiocStyle, knitr, rmarkdown, testthat License: GPL (>=2) MD5sum: 4ae8d4e87f203dab184ad10c2655b571 NeedsCompilation: no Title: TreeSummarizedExperiment: a S4 Class for Data with Tree Structures Description: TreeSummarizedExperiment has extended SingleCellExperiment to include hierarchical information on the rows or columns of the rectangular data. biocViews: DataRepresentation, Infrastructure Author: Ruizhu Huang [aut, cre] () Maintainer: Ruizhu Huang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TreeSummarizedExperiment git_branch: RELEASE_3_10 git_last_commit: 96ad84f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TreeSummarizedExperiment_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TreeSummarizedExperiment_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TreeSummarizedExperiment_1.2.0.tgz vignettes: vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSummarizedExperiment.html vignetteTitles: Tree Aggregation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSummarizedExperiment.R dependencyCount: 56 Package: trena Version: 1.8.0 Depends: R (>= 3.5.0), utils, glmnet (>= 2.0.3), MotifDb (>= 1.19.17) Imports: RSQLite, RMySQL, lassopv, randomForest, flare, vbsr, xgboost, BiocParallel, RPostgreSQL, methods, DBI, BSgenome, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, SNPlocs.Hsapiens.dbSNP150.GRCh38, org.Hs.eg.db, Biostrings, GenomicRanges, biomaRt, AnnotationDbi Suggests: RUnit, plyr, knitr, BiocGenerics, rmarkdown, BSgenome.Scerevisiae.UCSC.sacCer3 License: GPL-3 MD5sum: 8e0d008ad855580117408ac55b50d992 NeedsCompilation: no Title: Fit transcriptional regulatory networks using gene expression, priors, machine learning Description: Methods for reconstructing transcriptional regulatory networks, especially in species for which genome-wide TF binding site information is available. biocViews: Transcription, GeneRegulation, NetworkInference, FeatureExtraction, Regression, SystemsBiology, GeneExpression Author: Seth Ament , Paul Shannon , Matthew Richards Maintainer: Paul Shannon URL: https://pricelab.github.io/trena/ VignetteBuilder: knitr BugReports: https://github.com/PriceLab/trena/issues git_url: https://git.bioconductor.org/packages/trena git_branch: RELEASE_3_10 git_last_commit: 18143a8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/trena_1.8.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/trena_1.8.0.tgz vignettes: vignettes/trena/inst/doc/TReNA_Vignette.html vignetteTitles: A Brief Introduction to TReNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trena/inst/doc/TReNA_Vignette.R dependencyCount: 106 Package: Trendy Version: 1.8.2 Depends: R (>= 3.4) Imports: stats, utils, graphics, grDevices, segmented, gplots, parallel, magrittr, BiocParallel, DT, S4Vectors, SummarizedExperiment, methods, shiny, shinyFiles Suggests: BiocStyle, knitr, rmarkdown, devtools License: GPL-3 Archs: i386, x64 MD5sum: 3ebcbb295169e8547999d597ed55eadb NeedsCompilation: no Title: Breakpoint analysis of time-course expression data Description: Trendy implements segmented (or breakpoint) regression models to estimate breakpoints which represent changes in expression for each feature/gene in high throughput data with ordered conditions. biocViews: TimeCourse, RNASeq, Regression, ImmunoOncology Author: Rhonda Bacher and Ning Leng Maintainer: Rhonda Bacher URL: https://github.com/rhondabacher/Trendy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Trendy git_branch: RELEASE_3_10 git_last_commit: 81cdb08 git_last_commit_date: 2019-11-13 Date/Publication: 2019-11-14 source.ver: src/contrib/Trendy_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/Trendy_1.8.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Trendy_1.8.2.tgz vignettes: vignettes/Trendy/inst/doc/Trendy_vignette.pdf vignetteTitles: Trendy Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Trendy/inst/doc/Trendy_vignette.R dependencyCount: 72 Package: triform Version: 1.28.0 Depends: R (>= 2.11.0), IRanges, yaml Imports: BiocGenerics, IRanges (>= 2.5.27), yaml Suggests: RUnit License: GPL-2 MD5sum: 8744d13213a0edf1b370b6c7c7254ace NeedsCompilation: no Title: Triform finds enriched regions (peaks) in transcription factor ChIP-sequencing data Description: The Triform algorithm uses model-free statistics to identify peak-like distributions of TF ChIP sequencing reads, taking advantage of an improved peak definition in combination with known profile characteristics. biocViews: Sequencing, ChIPSeq Author: Karl Kornacker Developer [aut], Tony Handstad Developer [aut, cre] Maintainer: Thomas Carroll git_url: https://git.bioconductor.org/packages/triform git_branch: RELEASE_3_10 git_last_commit: af94f12 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/triform_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/triform_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/triform_1.28.0.tgz vignettes: vignettes/triform/inst/doc/triform.pdf vignetteTitles: Triform users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/triform/inst/doc/triform.R dependencyCount: 10 Package: trigger Version: 1.32.0 Depends: R (>= 2.14.0), corpcor, qtl Imports: qvalue, methods, graphics, sva License: GPL-3 MD5sum: 14d7492e2fdc134559dd17f1e16dfeaf NeedsCompilation: yes Title: Transcriptional Regulatory Inference from Genetics of Gene ExpRession Description: This R package provides tools for the statistical analysis of integrative genomic data that involve some combination of: genotypes, high-dimensional intermediate traits (e.g., gene expression, protein abundance), and higher-order traits (phenotypes). The package includes functions to: (1) construct global linkage maps between genetic markers and gene expression; (2) analyze multiple-locus linkage (epistasis) for gene expression; (3) quantify the proportion of genome-wide variation explained by each locus and identify eQTL hotspots; (4) estimate pair-wise causal gene regulatory probabilities and construct gene regulatory networks; and (5) identify causal genes for a quantitative trait of interest. biocViews: GeneExpression, SNP, GeneticVariability, Microarray, Genetics Author: Lin S. Chen , Dipen P. Sangurdekar and John D. Storey Maintainer: John D. Storey git_url: https://git.bioconductor.org/packages/trigger git_branch: RELEASE_3_10 git_last_commit: e9f32f9 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/trigger_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/trigger_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/trigger_1.32.0.tgz vignettes: vignettes/trigger/inst/doc/trigger.pdf vignetteTitles: Trigger Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trigger/inst/doc/trigger.R dependencyCount: 92 Package: trio Version: 3.24.3 Depends: R (>= 3.0.1) Imports: grDevices, graphics, methods, stats, survival, utils, siggenes, LogicReg (>= 1.6.1) Suggests: haplo.stats, mcbiopi, splines, logicFS (>= 1.28.1), KernSmooth, VariantAnnotation License: LGPL-2 MD5sum: 5dd922e5476791e7a33f8330495e3a47 NeedsCompilation: no Title: Testing of SNPs and SNP Interactions in Case-Parent Trio Studies Description: Testing SNPs and SNP interactions with a genotypic TDT. This package furthermore contains functions for computing pairwise values of LD measures and for identifying LD blocks, as well as functions for setting up matched case pseudo-control genotype data for case-parent trios in order to run trio logic regression, for imputing missing genotypes in trios, for simulating case-parent trios with disease risk dependent on SNP interaction, and for power and sample size calculation in trio data. biocViews: SNP, GeneticVariability, Microarray, Genetics Author: Holger Schwender, Qing Li, Philipp Berger, Christoph Neumann, Margaret Taub, Ingo Ruczinski Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/trio git_branch: RELEASE_3_10 git_last_commit: 8f51c41 git_last_commit_date: 2020-04-11 Date/Publication: 2020-04-11 source.ver: src/contrib/trio_3.24.3.tar.gz win.binary.ver: bin/windows/contrib/3.6/trio_3.24.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/trio_3.24.3.tgz vignettes: vignettes/trio/inst/doc/trio.pdf vignetteTitles: Trio Logic Regression and genotypic TDT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trio/inst/doc/trio.R dependencyCount: 19 Package: triplex Version: 1.26.0 Depends: R (>= 2.15.0), S4Vectors (>= 0.5.14), IRanges (>= 2.5.27), XVector (>= 0.11.6), Biostrings (>= 2.39.10) Imports: methods, grid, GenomicRanges LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: rgl (>= 0.93.932), BSgenome.Celegans.UCSC.ce10, rtracklayer License: BSD_2_clause + file LICENSE MD5sum: 4b52cce32e796471e96bc9b3cb7b14bb NeedsCompilation: yes Title: Search and visualize intramolecular triplex-forming sequences in DNA Description: This package provides functions for identification and visualization of potential intramolecular triplex patterns in DNA sequence. The main functionality is to detect the positions of subsequences capable of folding into an intramolecular triplex (H-DNA) in a much larger sequence. The potential H-DNA (triplexes) should be made of as many cannonical nucleotide triplets as possible. The package includes visualization showing the exact base-pairing in 1D, 2D or 3D. biocViews: SequenceMatching, GeneRegulation Author: Jiri Hon, Matej Lexa, Tomas Martinek and Kamil Rajdl with contributions from Daniel Kopecek Maintainer: Jiri Hon URL: http://www.fi.muni.cz/~lexa/triplex/ git_url: https://git.bioconductor.org/packages/triplex git_branch: RELEASE_3_10 git_last_commit: 4c5b28a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/triplex_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/triplex_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/triplex_1.26.0.tgz vignettes: vignettes/triplex/inst/doc/triplex.pdf vignetteTitles: Triplex User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/triplex/inst/doc/triplex.R dependencyCount: 18 Package: tRNA Version: 1.4.0 Depends: R (>= 3.5), GenomicRanges, Structstrings Imports: stringr, S4Vectors, methods, assertive, BiocGenerics, IRanges, XVector, Biostrings, Modstrings, ggplot2, scales Suggests: knitr, rmarkdown, testthat, BiocStyle, tRNAscanImport License: GPL-3 + file LICENSE MD5sum: d8efb40c4ff7c7200194906132c15cfc NeedsCompilation: no Title: Analyzing tRNA sequences and structures Description: The tRNA package allows tRNA sequences and structures to be accessed and used for subsetting. In addition, it provides visualization tools to compare feature parameters of multiple tRNA sets and correlate them to additional data. The tRNA package uses GRanges objects as inputs requiring only few additional column data sets. biocViews: Software, Visualization Author: Felix GM Ernst [aut, cre] () Maintainer: Felix GM Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNA/issues git_url: https://git.bioconductor.org/packages/tRNA git_branch: RELEASE_3_10 git_last_commit: 92e84fa git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/tRNA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/tRNA_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/tRNA_1.4.0.tgz vignettes: vignettes/tRNA/inst/doc/tRNA.html vignetteTitles: tRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNA/inst/doc/tRNA.R dependsOnMe: tRNAdbImport, tRNAscanImport dependencyCount: 94 Package: tRNAdbImport Version: 1.4.0 Depends: R (>= 3.5), GenomicRanges, Modstrings, Structstrings, tRNA Imports: Biostrings, BiocGenerics, stringr, xml2, S4Vectors, assertive, methods, httr, IRanges, utils Suggests: knitr, rmarkdown, testthat, httptest, BiocStyle, rtracklayer License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: 3b2c2abe6f5e235c881c19055f85940d NeedsCompilation: no Title: Importing from tRNAdb and mitotRNAdb as GRanges objects Description: tRNAdbImport imports the entries of the tRNAdb and mtRNAdb (http://trna.bioinf.uni-leipzig.de) as GRanges object. biocViews: Software, Visualization, DataImport Author: Felix G.M. Ernst [aut, cre] () Maintainer: Felix G.M. Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNAdbImport/issues git_url: https://git.bioconductor.org/packages/tRNAdbImport git_branch: RELEASE_3_10 git_last_commit: 1e20206 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/tRNAdbImport_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/tRNAdbImport_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/tRNAdbImport_1.4.0.tgz vignettes: vignettes/tRNAdbImport/inst/doc/tRNAdbImport.html vignetteTitles: tRNAdbImport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNAdbImport/inst/doc/tRNAdbImport.R dependencyCount: 102 Package: tRNAscanImport Version: 1.6.0 Depends: R (>= 3.5), GenomicRanges, tRNA Imports: methods, assertive, stringr, BiocGenerics, Biostrings, Structstrings, S4Vectors, IRanges, XVector, GenomeInfoDb, rtracklayer, BSgenome, Rsamtools Suggests: BiocStyle, knitr, rmarkdown, testthat, ggplot2, BSgenome.Scerevisiae.UCSC.sacCer3 License: GPL-3 + file LICENSE MD5sum: b6fc4d8838979320c29967093e956cf5 NeedsCompilation: no Title: Importing a tRNAscan-SE result file as GRanges object Description: The package imports the result of tRNAscan-SE as a GRanges object. biocViews: Software, DataImport, WorkflowStep, Preprocessing, Visualization Author: Felix G.M. Ernst [aut, cre] () Maintainer: Felix G.M. Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNAscanImport/issues git_url: https://git.bioconductor.org/packages/tRNAscanImport git_branch: RELEASE_3_10 git_last_commit: fcbc9c6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/tRNAscanImport_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/tRNAscanImport_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/tRNAscanImport_1.6.0.tgz vignettes: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.html vignetteTitles: tRNAscanImport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.R suggestsMe: Structstrings, tRNA dependencyCount: 112 Package: TRONCO Version: 2.18.0 Depends: R (>= 3.6), Imports: bnlearn, Rgraphviz, gtools, parallel, foreach, doParallel, iterators, RColorBrewer, circlize, cgdsr, igraph, grid, gridExtra, xtable, gtable, scales, R.matlab, grDevices, graphics, stats, utils, methods Suggests: BiocGenerics, BiocStyle, testthat, knitr, rWikiPathways License: GPL-3 Archs: i386, x64 MD5sum: ea2cadbd0ffbd20b59720e21d81effcc NeedsCompilation: no Title: TRONCO, an R package for TRanslational ONCOlogy Description: The TRONCO (TRanslational ONCOlogy) R package collects algorithms to infer progression models via the approach of Suppes-Bayes Causal Network, both from an ensemble of tumors (cross-sectional samples) and within an individual patient (multi-region or single-cell samples). The package provides parallel implementation of algorithms that process binary matrices where each row represents a tumor sample and each column a single-nucleotide or a structural variant driving the progression; a 0/1 value models the absence/presence of that alteration in the sample. The tool can import data from plain, MAF or GISTIC format files, and can fetch it from the cBioPortal for cancer genomics. Functions for data manipulation and visualization are provided, as well as functions to import/export such data to other bioinformatics tools for, e.g, clustering or detection of mutually exclusive alterations. Inferred models can be visualized and tested for their confidence via bootstrap and cross-validation. TRONCO is used for the implementation of the Pipeline for Cancer Inference (PICNIC). biocViews: BiomedicalInformatics, Bayesian, GraphAndNetwork, SomaticMutation, NetworkInference, Network, Clustering, DataImport, SingleCell, ImmunoOncology Author: Marco Antoniotti [ctb], Giulio Caravagna [aut, cre], Luca De Sano [aut], Alex Graudenzi [aut], Giancarlo Mauri [ctb], Bud Mishra [ctb], Daniele Ramazzotti [aut] Maintainer: Luca De Sano URL: https://sites.google.com/site/troncopackage/ VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/TRONCO git_url: https://git.bioconductor.org/packages/TRONCO git_branch: RELEASE_3_10 git_last_commit: fe36bcd git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TRONCO_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TRONCO_2.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TRONCO_2.18.0.tgz vignettes: vignettes/TRONCO/inst/doc/vignette.pdf vignetteTitles: An R Package for TRanslational ONCOlogy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TRONCO/inst/doc/vignette.R dependencyCount: 52 Package: TSCAN Version: 1.24.0 Depends: R(>= 2.10.0) Imports: ggplot2, shiny, plyr, grid, fastICA, igraph, combinat, mgcv, mclust, gplots Suggests: knitr License: GPL(>=2) MD5sum: 01878ea77175cf0f6c6a1e622bf0a22f NeedsCompilation: no Title: TSCAN: Tools for Single-Cell ANalysis Description: TSCAN enables users to easily construct and tune pseudotemporal cell ordering as well as analyzing differentially expressed genes. TSCAN comes with a user-friendly GUI written in shiny. More features will come in the future. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji, Hongkai Ji Maintainer: Zhicheng Ji VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TSCAN git_branch: RELEASE_3_10 git_last_commit: 5847e11 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TSCAN_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TSCAN_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TSCAN_1.24.0.tgz vignettes: vignettes/TSCAN/inst/doc/TSCAN.pdf vignetteTitles: TSCAN: Tools for Single-Cell ANalysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSCAN/inst/doc/TSCAN.R dependencyCount: 76 Package: tspair Version: 1.44.0 Depends: R (>= 2.10), Biobase (>= 2.4.0) License: GPL-2 MD5sum: 730d22647d0fcdfb2c002b07296c6cfb NeedsCompilation: yes Title: Top Scoring Pairs for Microarray Classification Description: These functions calculate the pair of genes that show the maximum difference in ranking between two user specified groups. This "top scoring pair" maximizes the average of sensitivity and specificity over all rank based classifiers using a pair of genes in the data set. The advantage of classifying samples based on only the relative rank of a pair of genes is (a) the classifiers are much simpler and often more interpretable than more complicated classification schemes and (b) if arrays can be classified using only a pair of genes, PCR based tests could be used for classification of samples. See the references for the tspcalc() function for references regarding TSP classifiers. biocViews: Microarray Author: Jeffrey T. Leek Maintainer: Jeffrey T. Leek git_url: https://git.bioconductor.org/packages/tspair git_branch: RELEASE_3_10 git_last_commit: 2940fd5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/tspair_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/tspair_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/tspair_1.44.0.tgz vignettes: vignettes/tspair/inst/doc/tsp.pdf vignetteTitles: tspTutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tspair/inst/doc/tsp.R dependencyCount: 7 Package: TSRchitect Version: 1.12.4 Depends: R (>= 3.5) Imports: AnnotationHub, BiocGenerics, BiocParallel, dplyr, GenomicAlignments, GenomeInfoDb, GenomicRanges, gtools, IRanges, methods, readxl, Rsamtools (>= 1.14.3), rtracklayer, S4Vectors, SummarizedExperiment, tools, utils Suggests: ENCODExplorer, ggplot2, knitr, rmarkdown License: GPL-3 MD5sum: 22e900ea3861b9d190fef95729d5fdba NeedsCompilation: no Title: Promoter identification from large-scale TSS profiling data Description: In recent years, large-scale transcriptional sequence data has yielded considerable insights into the nature of gene expression and regulation in eukaryotes. Techniques that identify the 5' end of mRNAs, most notably CAGE, have mapped the promoter landscape across a number of model organisms. Due to the variability of TSS distributions and the transcriptional noise present in datasets, precisely identifying the active promoter(s) for genes from these datasets is not straightforward. TSRchitect allows the user to efficiently identify the putative promoter (the transcription start region, or TSR) from a variety of TSS profiling data types, including both single-end (e.g. CAGE) as well as paired-end (RAMPAGE, PEAT, STRIPE-seq). In addition, (new with version 1.3.0) TSRchitect provides the ability to import aligned EST and cDNA data. Along with the coordiantes of identified TSRs, TSRchitect also calculates the width, abundance and two forms of the Shape Index, and handles biological replicates for expression profiling. Finally, TSRchitect imports annotation files, allowing the user to associate identified promoters with genes and other genomic features. Three detailed examples of TSRchitect's utility are provided in the User's Guide, included with this package. biocViews: Clustering, FunctionalGenomics, GeneExpression, GeneRegulation, GenomeAnnotation, Sequencing, Transcription Author: R. Taylor Raborn [aut, cre, cph] Volker P. Brendel [aut, cph] Krishnakumar Sridharan [ctb] Maintainer: R. Taylor Raborn URL: https://github.com/brendelgroup/tsrchitect VignetteBuilder: knitr BugReports: https://github.com/brendelgroup/tsrchitect/issues git_url: https://git.bioconductor.org/packages/TSRchitect git_branch: RELEASE_3_10 git_last_commit: 309d642 git_last_commit_date: 2019-12-16 Date/Publication: 2019-12-16 source.ver: src/contrib/TSRchitect_1.12.4.tar.gz win.binary.ver: bin/windows/contrib/3.6/TSRchitect_1.12.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TSRchitect_1.12.4.tgz vignettes: vignettes/TSRchitect/inst/doc/TSRchitectUsersGuide.pdf, vignettes/TSRchitect/inst/doc/TSRchitect.html, vignettes/TSRchitect/inst/doc/TSRchitectUsersGuide.html vignetteTitles: TSRchitect User's Guide, TSRchitect vignette, TSRchitect User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TSRchitect/inst/doc/TSRchitect.R dependencyCount: 96 Package: TTMap Version: 1.8.0 Depends: rgl, colorRamps Imports: grDevices,graphics,stats,utils, methods, SummarizedExperiment, Biobase Suggests: BiocStyle, airway License: GPL-2 MD5sum: 15cd25d99d58b3d58de89dd16679c77f NeedsCompilation: no Title: Two-Tier Mapper: a clustering tool based on topological data analysis Description: TTMap is a clustering method that groups together samples with the same deviation in comparison to a control group. It is specially useful when the data is small. It is parameter free. biocViews: Software, Microarray, DifferentialExpression, MultipleComparison, Clustering, Classification Author: Rachel Jeitziner Maintainer: Rachel Jeitziner git_url: https://git.bioconductor.org/packages/TTMap git_branch: RELEASE_3_10 git_last_commit: 5656a3b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TTMap_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TTMap_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TTMap_1.8.0.tgz vignettes: vignettes/TTMap/inst/doc/TTMap.pdf vignetteTitles: Manual for the TTMap library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TTMap/inst/doc/TTMap.R dependencyCount: 70 Package: TurboNorm Version: 1.34.0 Depends: R (>= 2.12.0), convert, limma (>= 1.7.0), marray Imports: stats, grDevices, affy, lattice Suggests: BiocStyle, affydata License: LGPL MD5sum: ed517a2d681846003066387aabf182e3 NeedsCompilation: yes Title: A fast scatterplot smoother suitable for microarray normalization Description: A fast scatterplot smoother based on B-splines with second-order difference penalty. Functions for microarray normalization of single-colour data i.e. Affymetrix/Illumina and two-colour data supplied as marray MarrayRaw-objects or limma RGList-objects are available. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, DNAMethylation, CpGIsland, MethylationArray, Normalization Author: Maarten van Iterson and Chantal van Leeuwen Maintainer: Maarten van Iterson URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html git_url: https://git.bioconductor.org/packages/TurboNorm git_branch: RELEASE_3_10 git_last_commit: 35f0fec git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TurboNorm_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TurboNorm_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TurboNorm_1.34.0.tgz vignettes: vignettes/TurboNorm/inst/doc/turbonorm.pdf vignetteTitles: TurboNorm Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TurboNorm/inst/doc/turbonorm.R dependencyCount: 18 Package: TVTB Version: 1.12.0 Depends: R (>= 3.4), methods, utils, stats Imports: AnnotationFilter, BiocGenerics (>= 0.25.1), BiocParallel, Biostrings, ensembldb, ensemblVEP, GenomeInfoDb, GenomicRanges, GGally, ggplot2, Gviz, limma, IRanges (>= 2.7.1), reshape2, Rsamtools, S4Vectors (>= 0.11.11), SummarizedExperiment, VariantAnnotation (>= 1.19.9) Suggests: EnsDb.Hsapiens.v75 (>= 0.99.7), shiny (>= 0.13.2.9005), DT (>= 0.1.67), rtracklayer, BiocStyle (>= 2.5.19), knitr (>= 1.12), rmarkdown, testthat, covr, pander License: Artistic-2.0 MD5sum: 6d9f8069ece479dd95d3801e9de9c7d5 NeedsCompilation: no Title: TVTB: The VCF Tool Box Description: The package provides S4 classes and methods to filter, summarise and visualise genetic variation data stored in VCF files. In particular, the package extends the FilterRules class (S4Vectors package) to define news classes of filter rules applicable to the various slots of VCF objects. Functionalities are integrated and demonstrated in a Shiny web-application, the Shiny Variant Explorer (tSVE). biocViews: Software, Genetics, GeneticVariability, GenomicVariation, DataRepresentation, GUI, Genetics, DNASeq, WholeGenome, Visualization, MultipleComparison, DataImport, VariantAnnotation, Sequencing, Coverage, Alignment, SequenceMatching Author: Kevin Rue-Albrecht [aut, cre] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/TVTB VignetteBuilder: knitr BugReports: https://github.com/kevinrue/TVTB/issues git_url: https://git.bioconductor.org/packages/TVTB git_branch: RELEASE_3_10 git_last_commit: 09eb141 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TVTB_1.12.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TVTB_1.12.0.tgz vignettes: vignettes/TVTB/inst/doc/tSVE.pdf, vignettes/TVTB/inst/doc/Introduction.html, vignettes/TVTB/inst/doc/VcfFilterRules.html vignetteTitles: The Shiny Variant Explorer, Introduction to TVTB, VCF filter rules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TVTB/inst/doc/Introduction.R, vignettes/TVTB/inst/doc/tSVE.R, vignettes/TVTB/inst/doc/VcfFilterRules.R dependencyCount: 149 Package: tweeDEseq Version: 1.32.0 Depends: R (>= 2.12.0) Imports: MASS, limma, edgeR, parallel, cqn Suggests: tweeDEseqCountData, xtable License: GPL (>= 2) Archs: i386, x64 MD5sum: 7c9d94cadd14633a6ab6633ea58a0e79 NeedsCompilation: yes Title: RNA-seq data analysis using the Poisson-Tweedie family of distributions Description: Differential expression analysis of RNA-seq using the Poisson-Tweedie family of distributions. biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, Sequencing, RNASeq Author: Juan R Gonzalez and Mikel Esnaola (with contributions from Robert Castelo ) Maintainer: Juan R Gonzalez URL: http://www.creal.cat/jrgonzalez/software.htm git_url: https://git.bioconductor.org/packages/tweeDEseq git_branch: RELEASE_3_10 git_last_commit: f7b17df git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/tweeDEseq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/tweeDEseq_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/tweeDEseq_1.32.0.tgz vignettes: vignettes/tweeDEseq/inst/doc/tweeDEseq.pdf vignetteTitles: tweeDEseq: analysis of RNA-seq data using the Poisson-Tweedie family of distributions hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tweeDEseq/inst/doc/tweeDEseq.R dependencyCount: 22 Package: twilight Version: 1.62.0 Depends: R (>= 2.10), splines (>= 2.2.0), stats (>= 2.2.0), Biobase(>= 1.12.0) Imports: Biobase, graphics, grDevices, stats Suggests: golubEsets (>= 1.4.2), vsn (>= 1.7.2) License: GPL (>= 2) MD5sum: 0da1c0b8e813ece6325abf82a6ebd8b1 NeedsCompilation: yes Title: Estimation of local false discovery rate Description: In a typical microarray setting with gene expression data observed under two conditions, the local false discovery rate describes the probability that a gene is not differentially expressed between the two conditions given its corrresponding observed score or p-value level. The resulting curve of p-values versus local false discovery rate offers an insight into the twilight zone between clear differential and clear non-differential gene expression. Package 'twilight' contains two main functions: Function twilight.pval performs a two-condition test on differences in means for a given input matrix or expression set and computes permutation based p-values. Function twilight performs a stochastic downhill search to estimate local false discovery rates and effect size distributions. The package further provides means to filter for permutations that describe the null distribution correctly. Using filtered permutations, the influence of hidden confounders could be diminished. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Stefanie Scheid Maintainer: Stefanie Scheid URL: http://compdiag.molgen.mpg.de/software/twilight.shtml git_url: https://git.bioconductor.org/packages/twilight git_branch: RELEASE_3_10 git_last_commit: 11a8df6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/twilight_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/twilight_1.62.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/twilight_1.62.0.tgz vignettes: vignettes/twilight/inst/doc/tr_2004_01.pdf vignetteTitles: Estimation of Local False Discovery Rates hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/twilight/inst/doc/tr_2004_01.R dependsOnMe: OrderedList importsMe: OrderedList dependencyCount: 9 Package: twoddpcr Version: 1.10.0 Depends: R (>= 3.4) Imports: class, ggplot2, hexbin, methods, scales, shiny, stats, utils, RColorBrewer, S4Vectors Suggests: devtools, knitr, reshape2, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 4a1e40c21dfb072115a526d77893f135 NeedsCompilation: no Title: Classify 2-d Droplet Digital PCR (ddPCR) data and quantify the number of starting molecules Description: The twoddpcr package takes Droplet Digital PCR (ddPCR) droplet amplitude data from Bio-Rad's QuantaSoft and can classify the droplets. A summary of the positive/negative droplet counts can be generated, which can then be used to estimate the number of molecules using the Poisson distribution. This is the first open source package that facilitates the automatic classification of general two channel ddPCR data. Previous work includes 'definetherain' (Jones et al., 2014) and 'ddpcRquant' (Trypsteen et al., 2015) which both handle one channel ddPCR experiments only. The 'ddpcr' package available on CRAN (Attali et al., 2016) supports automatic gating of a specific class of two channel ddPCR experiments only. biocViews: ddPCR, Software, Classification Author: Anthony Chiu [aut, cre] Maintainer: Anthony Chiu URL: http://github.com/CRUKMI-ComputationalBiology/twoddpcr/ VignetteBuilder: knitr BugReports: http://github.com/CRUKMI-ComputationalBiology/twoddpcr/issues/ git_url: https://git.bioconductor.org/packages/twoddpcr git_branch: RELEASE_3_10 git_last_commit: bb6a5aa git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/twoddpcr_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/twoddpcr_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/twoddpcr_1.10.0.tgz vignettes: vignettes/twoddpcr/inst/doc/twoddpcr.html vignetteTitles: twoddpcr: A package for Droplet Digital PCR analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/twoddpcr/inst/doc/twoddpcr.R dependencyCount: 71 Package: tximeta Version: 1.4.5 Imports: SummarizedExperiment, tximport, jsonlite, S4Vectors, GenomicRanges, AnnotationDbi, GenomicFeatures, ensembldb, Biostrings, BiocFileCache, tibble, GenomeInfoDb, rappdirs, utils, methods Suggests: knitr, rmarkdown, testthat, tximportData, org.Dm.eg.db, DESeq2, edgeR, limma, devtools License: GPL-2 Archs: i386, x64 MD5sum: f41118965c460e25a5700b77c18cc8d0 NeedsCompilation: no Title: Transcript Quantification Import with Automatic Metadata Description: Transcript quantification import from Salmon with automatic population of metadata and transcript ranges. Filtered, combined, or de novo transcriptomes can be linked to the appropriate sources with linkedTxomes and shared for reproducible analyses. biocViews: Annotation, DataImport, Preprocessing, RNASeq, Transcriptomics, Transcription, GeneExpression, ImmunoOncology Author: Michael Love [aut, cre], Rob Patro [aut, ctb], Peter Hickey [aut, ctb], Charlotte Soneson [aut, ctb] Maintainer: Michael Love URL: https://github.com/mikelove/tximeta VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tximeta git_branch: RELEASE_3_10 git_last_commit: 6d7e03f git_last_commit_date: 2020-03-10 Date/Publication: 2020-03-11 source.ver: src/contrib/tximeta_1.4.5.tar.gz win.binary.ver: bin/windows/contrib/3.6/tximeta_1.4.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/tximeta_1.4.5.tgz vignettes: vignettes/tximeta/inst/doc/tximeta.html vignetteTitles: Transcript quantification import with automatic metadata hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tximeta/inst/doc/tximeta.R suggestsMe: DESeq2, fishpond dependencyCount: 88 Package: tximport Version: 1.14.2 Imports: utils, stats, methods Suggests: knitr, rmarkdown, testthat, tximportData, TxDb.Hsapiens.UCSC.hg19.knownGene, readr (>= 0.2.2), limma, edgeR, csaw, DESeq2 (>= 1.11.6), rhdf5, jsonlite, matrixStats, Matrix, fishpond License: GPL (>=2) Archs: i386, x64 MD5sum: 89af8d85d8f6daf942d27bd6ac4be965 NeedsCompilation: no Title: Import and summarize transcript-level estimates for transcript- and gene-level analysis Description: Imports transcript-level abundance, estimated counts and transcript lengths, and summarizes into matrices for use with downstream gene-level analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts. biocViews: DataImport, Preprocessing, RNASeq, Transcriptomics, Transcription, GeneExpression, ImmunoOncology Author: Michael Love [cre,aut], Charlotte Soneson [aut], Mark Robinson [aut], Rob Patro [ctb], Andrew Parker Morgan [ctb], Ryan C. Thompson [ctb], Matt Shirley [ctb], Avi Srivastava [ctb] Maintainer: Michael Love URL: https://github.com/mikelove/tximport VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tximport git_branch: RELEASE_3_10 git_last_commit: ac0c64b git_last_commit_date: 2020-03-22 Date/Publication: 2020-03-22 source.ver: src/contrib/tximport_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/tximport_1.14.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/tximport_1.14.2.tgz vignettes: vignettes/tximport/inst/doc/tximport.html vignetteTitles: Importing transcript abundance datasets with tximport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tximport/inst/doc/tximport.R importsMe: alevinQC, IsoformSwitchAnalyzeR, KnowSeq, RcwlPipelines, tximeta suggestsMe: BANDITS, DESeq2, HumanTranscriptomeCompendium, SummarizedBenchmark, variancePartition dependencyCount: 3 Package: TxRegInfra Version: 1.6.0 Depends: R (>= 3.5), RaggedExperiment (>= 1.3.11), mongolite Imports: methods, rjson, GenomicRanges, IRanges, BiocParallel, GenomeInfoDb, S4Vectors, SummarizedExperiment, utils Suggests: knitr, GenomicFiles, EnsDb.Hsapiens.v75, testthat, shiny, biovizBase (>= 1.27.2), Gviz, AnnotationFilter, ensembldb, ontoProc, rjson, graph, TFutils (>= 1.5.4) License: Artistic-2.0 Archs: i386, x64 MD5sum: 7976386c9dbc1c855e85b4635aa7f1f5 NeedsCompilation: no Title: Metadata management for multiomic specification of transcriptional regulatory networks Description: This package provides interfaces to genomic metadata employed in regulatory network creation, with a focus on noSQL solutions. Currently quantitative representations of eQTLs, DnaseI hypersensitivity sites and digital genomic footprints are assembled using an out-of-memory extension of the RaggedExperiment API. biocViews: Network Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TxRegInfra git_branch: RELEASE_3_10 git_last_commit: 0d523ea git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TxRegInfra_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TxRegInfra_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TxRegInfra_1.6.0.tgz vignettes: vignettes/TxRegInfra/inst/doc/shims.html, vignettes/TxRegInfra/inst/doc/TxRegInfra.html vignetteTitles: shims in TxRegInfra, TxRegInfra -- classes and methods for TxRegQuery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TxRegInfra/inst/doc/shims.R, vignettes/TxRegInfra/inst/doc/TxRegInfra.R dependencyCount: 40 Package: TypeInfo Version: 1.52.0 Depends: methods Suggests: Biobase License: BSD MD5sum: 78ea3b7a7a020311b943b6e85e26f6fd NeedsCompilation: no Title: Optional Type Specification Prototype Description: A prototype for a mechanism for specifying the types of parameters and the return value for an R function. This is meta-information that can be used to generate stubs for servers and various interfaces to these functions. Additionally, the arguments in a call to a typed function can be validated using the type specifications. We allow types to be specified as either i) by class name using either inheritance - is(x, className), or strict instance of - class(x) %in% className, or ii) a dynamic test given as an R expression which is evaluated at run-time. More precise information and interesting tests can be done via ii), but it is harder to use this information as meta-data as it requires more effort to interpret it and it is of course run-time information. It is typically more meaningful. biocViews: Infrastructure Author: Duncan Temple Lang Robert Gentleman () Maintainer: Duncan Temple Lang git_url: https://git.bioconductor.org/packages/TypeInfo git_branch: RELEASE_3_10 git_last_commit: d021a53 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/TypeInfo_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/TypeInfo_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/TypeInfo_1.52.0.tgz vignettes: vignettes/TypeInfo/inst/doc/TypeInfoNews.pdf vignetteTitles: TypeInfo R News hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TypeInfo/inst/doc/TypeInfoNews.R dependencyCount: 1 Package: Ularcirc Version: 1.4.0 Depends: R (>= 3.4.0) Imports: AnnotationHub, AnnotationDbi, BiocGenerics, Biostrings, BSgenome, data.table (>= 1.9.4), DT, GenomicFeatures, GenomeInfoDb, GenomeInfoDbData, GenomicAlignments, GenomicRanges, ggplot2, ggrepel, gsubfn, mirbase.db, moments, Organism.dplyr, S4Vectors, shiny, shinydashboard, shinyFiles, shinyjs, Sushi, yaml Suggests: BSgenome.Hsapiens.UCSC.hg38, BiocStyle, httpuv, knitr, org.Hs.eg.db, rmarkdown, TxDb.Hsapiens.UCSC.hg38.knownGene License: file LICENSE MD5sum: cb25fefc9225afa7448b89b09eca0512 NeedsCompilation: no Title: Shiny app for canonical and back splicing analysis (i.e. circular and mRNA analysis) Description: Ularcirc reads in STAR aligned splice junction files and provides visualisation and analysis tools for splicing analysis. Users can assess backsplice junctions and forward canonical junctions. biocViews: DataRepresentation,Visualization, Genetics, Sequencing, Annotation, Coverage, AlternativeSplicing, DifferentialSplicing Author: David Humphreys [aut, cre] Maintainer: David Humphreys VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Ularcirc git_branch: RELEASE_3_10 git_last_commit: c59323d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Ularcirc_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Ularcirc_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Ularcirc_1.4.0.tgz vignettes: vignettes/Ularcirc/inst/doc/Ularcirc.html vignetteTitles: Ularcirc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Ularcirc/inst/doc/Ularcirc.R dependencyCount: 142 Package: UNDO Version: 1.28.0 Depends: R (>= 2.15.2), methods, BiocGenerics, Biobase Imports: MASS, boot, nnls, stats, utils License: GPL-2 MD5sum: e235ce98eb89d1a22ef4740c39e315ec NeedsCompilation: no Title: Unsupervised Deconvolution of Tumor-Stromal Mixed Expressions Description: UNDO is an R package for unsupervised deconvolution of tumor and stromal mixed expression data. It detects marker genes and deconvolutes the mixing expression data without any prior knowledge. biocViews: Software Author: Niya Wang Maintainer: Niya Wang git_url: https://git.bioconductor.org/packages/UNDO git_branch: RELEASE_3_10 git_last_commit: 72f17e4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/UNDO_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/UNDO_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/UNDO_1.28.0.tgz vignettes: vignettes/UNDO/inst/doc/UNDO-vignette.pdf vignetteTitles: UNDO Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UNDO/inst/doc/UNDO-vignette.R dependencyCount: 11 Package: unifiedWMWqPCR Version: 1.22.0 Depends: methods Imports: BiocGenerics, stats, graphics, HTqPCR License: GPL (>=2) MD5sum: 218d3e4cbaa64742ad3e6c8273e82dcd NeedsCompilation: no Title: Unified Wilcoxon-Mann Whitney Test for testing differential expression in qPCR data Description: This packages implements the unified Wilcoxon-Mann-Whitney Test for qPCR data. This modified test allows for testing differential expression in qPCR data. biocViews: DifferentialExpression, GeneExpression, MicrotitrePlateAssay, MultipleComparison, QualityControl, Software, Visualization, qPCR Author: Jan R. De Neve & Joris Meys Maintainer: Joris Meys git_url: https://git.bioconductor.org/packages/unifiedWMWqPCR git_branch: RELEASE_3_10 git_last_commit: 9a07d3a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/unifiedWMWqPCR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/unifiedWMWqPCR_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/unifiedWMWqPCR_1.22.0.tgz vignettes: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.pdf vignetteTitles: Using unifiedWMWqPCR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.R dependencyCount: 23 Package: UniProt.ws Version: 2.26.0 Depends: methods, utils, RSQLite, RCurl, BiocGenerics (>= 0.13.8) Imports: AnnotationDbi, BiocFileCache, rappdirs Suggests: RUnit, BiocStyle, knitr License: Artistic License 2.0 MD5sum: 8d5524c3b55c80d1885b4b30a869dd54 NeedsCompilation: no Title: R Interface to UniProt Web Services Description: A collection of functions for retrieving, processing and repackaging the UniProt web services. biocViews: Annotation, Infrastructure, GO, KEGG, BioCarta Author: Marc Carlson [aut], Csaba Ortutay [ctb], Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/UniProt.ws git_branch: RELEASE_3_10 git_last_commit: 2ac203d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/UniProt.ws_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/UniProt.ws_2.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/UniProt.ws_2.26.0.tgz vignettes: vignettes/UniProt.ws/inst/doc/UniProt.ws.pdf vignetteTitles: UniProt.ws: A package for retrieving data from the UniProt web service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UniProt.ws/inst/doc/UniProt.ws.R importsMe: dagLogo suggestsMe: cleaver, qPLEXanalyzer dependencyCount: 53 Package: Uniquorn Version: 2.6.0 Depends: R (>= 3.5) Imports: stringr, R.utils, WriteXLS, stats, doParallel, foreach, GenomicRanges, IRanges, VariantAnnotation Suggests: testthat, knitr, rmarkdown, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: 5d4a3835e402f9205f71bf6dd506b253 NeedsCompilation: no Title: Identification of cancer cell lines based on their weighted mutational/ variational fingerprint Description: This packages enables users to identify cancer cell lines. Cancer cell line misidentification and cross-contamination reprents a significant challenge for cancer researchers. The identification is vital and in the frame of this package based on the locations/ loci of somatic and germline mutations/ variations. The input format is vcf/ vcf.gz and the files have to contain a single cancer cell line sample (i.e. a single member/genotype/gt column in the vcf file). The implemented method is optimized for the Next-generation whole exome and whole genome DNA-sequencing technology. RNA-seq data is very likely to work as well but hasn't been rigiously tested yet. Panel-seq will require manual adjustment of thresholds biocViews: ImmunoOncology, StatisticalMethod, WholeGenome, ExomeSeq Author: Raik Otto Maintainer: 'Raik Otto' VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Uniquorn git_branch: RELEASE_3_10 git_last_commit: c3d9c1d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Uniquorn_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Uniquorn_2.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Uniquorn_2.6.0.tgz vignettes: vignettes/Uniquorn/inst/doc/Uniquorn.html vignetteTitles: Uniquorn vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Uniquorn/inst/doc/Uniquorn.R dependencyCount: 93 Package: universalmotif Version: 1.4.10 Depends: R (>= 3.5.0) Imports: methods, stats, utils, MASS, ggplot2, ape, ggtree, ggseqlogo, yaml, Rcpp, Rdpack (>= 0.7), Biostrings, BiocGenerics, processx, S4Vectors, rlang LinkingTo: Rcpp, RcppThread Suggests: spelling, knitr, bookdown, TFBSTools, rmarkdown, MotifDb, testthat, Logolas, BiocParallel, seqLogo, motifStack, dplyr Enhances: MotIV, PWMEnrich, rGADEM, motifRG License: GPL-3 MD5sum: 758f7f897114f7fe95a88f8320fb5cc2 NeedsCompilation: yes Title: Import, Modify, and Export Motifs with R Description: Allows for importing most common motif types into R for use by functions provided by other Bioconductor motif-related packages. Motifs can be exported into most major motif formats from various classes as defined by other Bioconductor packages. A suite of motif and sequence manipulation and analysis functions are included, including enrichment, comparison, P-value calculation, shuffling, trimming, higher-order motifs, and others. biocViews: MotifAnnotation, MotifDiscovery, DataImport, GeneRegulation Author: Benjamin Jean-Marie Tremblay [aut, cre] Maintainer: Benjamin Jean-Marie Tremblay URL: https://github.com/bjmt/universalmotif VignetteBuilder: knitr BugReports: https://github.com/bjmt/universalmotif/issues git_url: https://git.bioconductor.org/packages/universalmotif git_branch: RELEASE_3_10 git_last_commit: 84152c2 git_last_commit_date: 2020-04-14 Date/Publication: 2020-04-14 source.ver: src/contrib/universalmotif_1.4.10.tar.gz win.binary.ver: bin/windows/contrib/3.6/universalmotif_1.4.10.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/universalmotif_1.4.10.tgz vignettes: vignettes/universalmotif/inst/doc/Introduction.pdf, vignettes/universalmotif/inst/doc/IntroductionToSequenceMotifs.pdf, vignettes/universalmotif/inst/doc/MotifComparisonAndPvalues.pdf, vignettes/universalmotif/inst/doc/MotifManipulation.pdf, vignettes/universalmotif/inst/doc/SequenceSearches.pdf vignetteTitles: Introduction to "universalmotif", Introduction to sequence motifs, Motif comparisons and P-values, Motif import,, export,, and manipulation, Sequence manipulation and scanning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/universalmotif/inst/doc/Introduction.R, vignettes/universalmotif/inst/doc/IntroductionToSequenceMotifs.R, vignettes/universalmotif/inst/doc/MotifComparisonAndPvalues.R, vignettes/universalmotif/inst/doc/MotifManipulation.R, vignettes/universalmotif/inst/doc/SequenceSearches.R dependencyCount: 84 Package: uSORT Version: 1.12.0 Depends: R (>= 3.3.0), tcltk Imports: igraph, Matrix, RANN, RSpectra, VGAM, gplots, parallel, plyr, methods, cluster, Biobase, fpc, BiocGenerics, monocle, grDevices, graphics, stats, utils Suggests: knitr, RUnit, testthat, ggplot2 License: Artistic-2.0 MD5sum: 72bb3a24b7c47c25b91811d9e0585c49 NeedsCompilation: no Title: uSORT: A self-refining ordering pipeline for gene selection Description: This package is designed to uncover the intrinsic cell progression path from single-cell RNA-seq data. It incorporates data pre-processing, preliminary PCA gene selection, preliminary cell ordering, feature selection, refined cell ordering, and post-analysis interpretation and visualization. biocViews: ImmunoOncology, RNASeq, GUI, CellBiology, DNASeq Author: Mai Chan Lau, Hao Chen, Jinmiao Chen Maintainer: Hao Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/uSORT git_branch: RELEASE_3_10 git_last_commit: 7e5a96c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/uSORT_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/uSORT_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/uSORT_1.12.0.tgz vignettes: vignettes/uSORT/inst/doc/uSORT_quick_start.html vignetteTitles: Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/uSORT/inst/doc/uSORT_quick_start.R dependencyCount: 118 Package: VanillaICE Version: 1.48.0 Depends: R (>= 3.5.0), BiocGenerics (>= 0.13.6), GenomicRanges (>= 1.27.6), SummarizedExperiment (>= 1.5.3) Imports: Biobase, S4Vectors (>= 0.23.18), IRanges (>= 1.14.0), oligoClasses (>= 1.31.1), foreach, matrixStats, data.table, grid, lattice, methods, GenomeInfoDb (>= 1.11.4), crlmm, tools, stats, utils, BSgenome.Hsapiens.UCSC.hg18 Suggests: RUnit, SNPchip, human610quadv1bCrlmm, ArrayTV Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: LGPL-2 MD5sum: 3810c8befcd5c15bfb1d81d3e141eaf0 NeedsCompilation: yes Title: A Hidden Markov Model for high throughput genotyping arrays Description: Hidden Markov Models for characterizing chromosomal alterations in high throughput SNP arrays. biocViews: CopyNumberVariation Author: Robert Scharpf , Kevin Scharpf, and Ingo Ruczinski Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/VanillaICE git_branch: RELEASE_3_10 git_last_commit: 66aba0a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/VanillaICE_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/VanillaICE_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/VanillaICE_1.48.0.tgz vignettes: vignettes/VanillaICE/inst/doc/crlmmDownstream.pdf, vignettes/VanillaICE/inst/doc/VanillaICE.pdf vignetteTitles: crlmmDownstream, VanillaICE Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VanillaICE/inst/doc/crlmmDownstream.R, vignettes/VanillaICE/inst/doc/VanillaICE.R dependsOnMe: MinimumDistance suggestsMe: oligoClasses dependencyCount: 77 Package: variancePartition Version: 1.16.1 Depends: R (>= 3.5.0), ggplot2, limma, foreach, scales, Biobase, methods Imports: MASS, pbkrtest (>= 0.4-4), lmerTest, iterators, splines, colorRamps, BiocParallel, gplots, progress, reshape2, lme4 (>= 1.1-10), doParallel, grDevices, graphics, utils, stats Suggests: BiocStyle, knitr, pander, rmarkdown, edgeR, dendextend, tximport, tximportData, ballgown, DESeq2, RUnit, BiocGenerics, r2glmm, readr License: GPL (>= 2) MD5sum: 9e6367c118c385935215a22df0915d96 NeedsCompilation: no Title: Quantify and interpret divers of variation in multilevel gene expression experiments Description: Quantify and interpret multiple sources of biological and technical variation in gene expression experiments. Uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. Includes dream differential expression analysis for repeated measures. biocViews: RNASeq, GeneExpression, GeneSetEnrichment, DifferentialExpression, BatchEffect, QualityControl, Regression, Epigenetics, FunctionalGenomics, Transcriptomics, Normalization, Preprocessing, Microarray, ImmunoOncology, Software Author: Gabriel E. Hoffman Maintainer: Gabriel E. Hoffman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/variancePartition git_branch: RELEASE_3_10 git_last_commit: b3f2867 git_last_commit_date: 2020-01-06 Date/Publication: 2020-01-07 source.ver: src/contrib/variancePartition_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/variancePartition_1.16.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/variancePartition_1.16.1.tgz vignettes: vignettes/variancePartition/inst/doc/theory_practice_random_effects.pdf, vignettes/variancePartition/inst/doc/variancePartition.pdf, vignettes/variancePartition/inst/doc/additional_visualization.html, vignettes/variancePartition/inst/doc/dream.html vignetteTitles: 3) Theory and practice of random effects, 1) Tutorial on using variancePartition, 2) Additional visualizations, 4) dream: differential expression testing with repeated measures designs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/variancePartition/inst/doc/additional_visualization.R, vignettes/variancePartition/inst/doc/dream.R, vignettes/variancePartition/inst/doc/theory_practice_random_effects.R, vignettes/variancePartition/inst/doc/variancePartition.R importsMe: BioMM, muscat dependencyCount: 91 Package: VariantAnnotation Version: 1.32.0 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.15.3), GenomeInfoDb (>= 1.15.2), GenomicRanges (>= 1.37.4), SummarizedExperiment (>= 1.9.9), Rsamtools (>= 1.99.0) Imports: utils, DBI, zlibbioc, Biobase, S4Vectors (>= 0.17.24), IRanges (>= 2.13.13), XVector (>= 0.19.7), Biostrings (>= 2.47.6), AnnotationDbi (>= 1.27.9), rtracklayer (>= 1.39.7), BSgenome (>= 1.47.3), GenomicFeatures (>= 1.31.3) LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib Suggests: RUnit, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP.20101109, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, PolyPhen.Hsapiens.dbSNP131, snpStats, ggplot2, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 51e434870b0e3fbf4cbbd9133f3ba962 NeedsCompilation: yes Title: Annotation of Genetic Variants Description: Annotate variants, compute amino acid coding changes, predict coding outcomes. biocViews: DataImport, Sequencing, SNP, Annotation, Genetics, VariantAnnotation Author: Valerie Obenchain [aut, cre], Martin Morgan [aut], Michael Lawrence [aut], Stephanie Gogarten [ctb] Maintainer: Valerie Obenchain SystemRequirements: GNU make Video: https://www.youtube.com/watch?v=Ro0lHQ_J--I&list=UUqaMSQd_h-2EDGsU6WDiX0Q git_url: https://git.bioconductor.org/packages/VariantAnnotation git_branch: RELEASE_3_10 git_last_commit: eda3ea4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/VariantAnnotation_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/VariantAnnotation_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/VariantAnnotation_1.32.0.tgz vignettes: vignettes/VariantAnnotation/inst/doc/filterVcf.pdf, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.pdf vignetteTitles: 2. Using filterVcf to Select Variants from VCF Files, 1. Introduction to VariantAnnotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantAnnotation/inst/doc/filterVcf.R, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.R dependsOnMe: CNVrd2, deepSNV, ensemblVEP, genotypeeval, HelloRanges, HTSeqGenie, MTseeker, myvariant, PureCN, R453Plus1Toolbox, RareVariantVis, Rariant, seqCAT, signeR, SomaticSignatures, StructuralVariantAnnotation, VariantFiltering, VariantTools importsMe: AllelicImbalance, appreci8R, BadRegionFinder, BBCAnalyzer, biovizBase, biscuiteer, CNVfilteR, CopyNumberPlots, customProDB, decompTumor2Sig, DominoEffect, fcScan, FunciSNP, GA4GHclient, genbankr, GenomicFiles, GenVisR, ggbio, GGtools, gmapR, gQTLstats, gwasurvivr, icetea, igvR, karyoploteR, ldblock, MADSEQ, methyAnalysis, MMAPPR2, motifbreakR, MutationalPatterns, PGA, scoreInvHap, SigsPack, SNPhood, systemPipeR, TitanCNA, TVTB, Uniquorn, VCFArray, XCIR, YAPSA suggestsMe: AnnotationHub, BiocParallel, cellbaseR, CrispRVariants, GenomicRanges, GenomicScores, gwascat, GWASTools, omicsPrint, podkat, RVS, SeqArray, trackViewer, trio, vtpnet dependencyCount: 84 Package: VariantExperiment Version: 1.0.0 Depends: R (>= 3.6.0), S4Vectors (>= 0.21.24), SummarizedExperiment (>= 1.13.0), GenomicRanges, GDSArray (>= 1.3.0), DelayedDataFrame (>= 1.0.0) Imports: tools, utils, stats, methods, gdsfmt, SNPRelate, SeqArray, SeqVarTools, DelayedArray, Biostrings, IRanges Suggests: testthat, knitr License: GPL-3 MD5sum: c7875a1dde482a6ac2bd4e5cfbd2229f NeedsCompilation: no Title: A RangedSummarizedExperiment Container for VCF/GDS Data with GDS Backend Description: VariantExperiment is a Bioconductor package for saving data in VCF/GDS format into RangedSummarizedExperiment object. The high-throughput genetic/genomic data are saved in GDSArray objects. The annotation data for features/samples are saved in DelayedDataFrame format with mono-dimensional GDSArray in each column. The on-disk representation of both assay data and annotation data achieves on-disk reading and processing and saves memory space significantly. The interface of RangedSummarizedExperiment data format enables easy and common manipulations for high-throughput genetic/genomic data with common SummarizedExperiment metaphor in R and Bioconductor. biocViews: Infrastructure, DataRepresentation, Sequencing, Annotation, GenomeAnnotation, GenotypingArray Author: Qian Liu [aut, cre], Hervé Pagès [aut], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/VariantExperiment VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/VariantExperiment/issues git_url: https://git.bioconductor.org/packages/VariantExperiment git_branch: RELEASE_3_10 git_last_commit: 540e1d8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/VariantExperiment_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/VariantExperiment_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/VariantExperiment_1.0.0.tgz vignettes: vignettes/VariantExperiment/inst/doc/VariantExperiment-class.html, vignettes/VariantExperiment/inst/doc/VariantExperiment-methods.html vignetteTitles: VariantExperiment-class, VariantExperiment-methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantExperiment/inst/doc/VariantExperiment-class.R, vignettes/VariantExperiment/inst/doc/VariantExperiment-methods.R dependencyCount: 74 Package: VariantFiltering Version: 1.22.0 Depends: R (>= 3.0.0), methods, BiocGenerics (>= 0.25.1), VariantAnnotation (>= 1.13.29) Imports: utils, stats, Biobase, S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), RBGL, graph, AnnotationDbi, BiocParallel, Biostrings (>= 2.33.11), GenomeInfoDb (>= 1.3.6), GenomicRanges (>= 1.19.13), SummarizedExperiment, GenomicFeatures, Rsamtools (>= 1.17.8), BSgenome, GenomicScores (>= 1.0.0), Gviz, shiny, shinythemes, shinyjs, DT, shinyTree LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: RUnit, BiocStyle, org.Hs.eg.db, BSgenome.Hsapiens.1000genomes.hs37d5, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, MafDb.1Kgenomes.phase1.hs37d5, phastCons100way.UCSC.hg19, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137 License: Artistic-2.0 Archs: i386, x64 MD5sum: 7be4c283547af57db19391e60fab3efd NeedsCompilation: yes Title: Filtering of coding and non-coding genetic variants Description: Filter genetic variants using different criteria such as inheritance model, amino acid change consequence, minor allele frequencies across human populations, splice site strength, conservation, etc. biocViews: Genetics, Homo_sapiens, Annotation, SNP, Sequencing, HighThroughputSequencing Author: Robert Castelo [aut, cre], Dei Martinez Elurbe [ctb], Pau Puigdevall [ctb], Joan Fernandez [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/VariantFiltering BugReports: https://github.com/rcastelo/VariantFiltering/issues git_url: https://git.bioconductor.org/packages/VariantFiltering git_branch: RELEASE_3_10 git_last_commit: c765147 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/VariantFiltering_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/VariantFiltering_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/VariantFiltering_1.22.0.tgz vignettes: vignettes/VariantFiltering/inst/doc/usingVariantFiltering.pdf vignetteTitles: VariantFiltering: filter coding and non-coding genetic variants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantFiltering/inst/doc/usingVariantFiltering.R dependencyCount: 162 Package: VariantTools Version: 1.28.1 Depends: S4Vectors (>= 0.17.33), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), VariantAnnotation (>= 1.11.16), methods Imports: Rsamtools (>= 1.31.2), BiocGenerics, Biostrings, parallel, GenomicFeatures (>= 1.31.3), Matrix, rtracklayer (>= 1.39.7), BiocParallel, GenomeInfoDb, BSgenome, Biobase Suggests: RUnit, LungCancerLines (>= 0.0.6), RBGL, graph, gmapR (>= 1.21.3) License: Artistic-2.0 MD5sum: 6bff604faccf69eafb0b338d7b7ee8c3 NeedsCompilation: no Title: Tools for Exploratory Analysis of Variant Calls Description: Explore, diagnose, and compare variant calls using filters. biocViews: Genetics, GeneticVariability, Sequencing Author: Michael Lawrence, Jeremiah Degenhardt, Robert Gentleman Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/VariantTools git_branch: RELEASE_3_10 git_last_commit: 589d65e git_last_commit_date: 2020-04-10 Date/Publication: 2020-04-11 source.ver: src/contrib/VariantTools_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/VariantTools_1.28.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/VariantTools_1.28.1.tgz vignettes: vignettes/VariantTools/inst/doc/tutorial.pdf, vignettes/VariantTools/inst/doc/VariantTools.pdf vignetteTitles: tutorial.pdf, Introduction to VariantTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantTools/inst/doc/VariantTools.R importsMe: HTSeqGenie, MMAPPR2, MTseeker dependencyCount: 85 Package: vbmp Version: 1.54.0 Depends: R (>= 2.10) Suggests: Biobase (>= 2.5.5), statmod License: GPL (>= 2) Archs: i386, x64 MD5sum: f8dd6ad948a8664020a905fa6fdca905 NeedsCompilation: no Title: Variational Bayesian Multinomial Probit Regression Description: Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. It estimates class membership posterior probability employing variational and sparse approximation to the full posterior. This software also incorporates feature weighting by means of Automatic Relevance Determination. biocViews: Classification Author: Nicola Lama , Mark Girolami Maintainer: Nicola Lama URL: http://bioinformatics.oxfordjournals.org/cgi/content/short/btm535v1 git_url: https://git.bioconductor.org/packages/vbmp git_branch: RELEASE_3_10 git_last_commit: 345bf3d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/vbmp_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/vbmp_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/vbmp_1.54.0.tgz vignettes: vignettes/vbmp/inst/doc/vbmp.pdf vignetteTitles: vbmp Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vbmp/inst/doc/vbmp.R dependencyCount: 0 Package: VCFArray Version: 1.2.0 Depends: R (>= 3.6), methods, BiocGenerics, DelayedArray (>= 0.7.28) Imports: tools, GenomicRanges, VariantAnnotation (>= 1.29.3), GenomicFiles (>= 1.17.3), S4Vectors (>= 0.19.19), Rsamtools Suggests: SeqArray, BiocStyle, BiocManager, testthat, knitr, rmarkdown License: GPL-3 MD5sum: b2fd2775363bdff281a7e22e911e61e0 NeedsCompilation: no Title: Representing on-disk / remote VCF files as array-like objects Description: VCFArray extends the DelayedArray to represent VCF data entries as array-like objects with on-disk / remote VCF file as backend. Data entries from VCF files, including info fields, FORMAT fields, and the fixed columns (REF, ALT, QUAL, FILTER) could be converted into VCFArray instances with different dimensions. biocViews: Infrastructure, DataRepresentation, Sequencing, VariantAnnotation Author: Qian Liu [aut, cre], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Liubuntu/VCFArray VignetteBuilder: knitr BugReports: https://github.com/Liubuntu/VCFArray/issues git_url: https://git.bioconductor.org/packages/VCFArray git_branch: RELEASE_3_10 git_last_commit: 24476ec git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/VCFArray_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/VCFArray_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/VCFArray_1.2.0.tgz vignettes: vignettes/VCFArray/inst/doc/VCFArray.html vignetteTitles: VCFArray: DelayedArray objects with on-disk/remote VCF backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VCFArray/inst/doc/VCFArray.R dependencyCount: 86 Package: Vega Version: 1.34.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: 490ba1074230997cea0b886262dd60c9 NeedsCompilation: yes Title: An R package for copy number data segmentation Description: Vega (Variational Estimator for Genomic Aberrations) is an algorithm that adapts a very popular variational model (Mumford and Shah) used in image segmentation so that chromosomal aberrant regions can be efficiently detected. biocViews: aCGH, CopyNumberVariation Author: Sandro Morganella Maintainer: Sandro Morganella git_url: https://git.bioconductor.org/packages/Vega git_branch: RELEASE_3_10 git_last_commit: eba8a14 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Vega_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Vega_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Vega_1.34.0.tgz vignettes: vignettes/Vega/inst/doc/Vega.pdf vignetteTitles: Vega hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Vega/inst/doc/Vega.R dependencyCount: 0 Package: VegaMC Version: 3.24.0 Depends: R (>= 2.10.0), biomaRt, Biobase Imports: methods, genoset License: GPL-2 MD5sum: cd3c1e18549d7597f66638104e486157 NeedsCompilation: yes Title: VegaMC: A Package Implementing a Variational Piecewise Smooth Model for Identification of Driver Chromosomal Imbalances in Cancer Description: This package enables the detection of driver chromosomal imbalances including loss of heterozygosity (LOH) from array comparative genomic hybridization (aCGH) data. VegaMC performs a joint segmentation of a dataset and uses a statistical framework to distinguish between driver and passenger mutation. VegaMC has been implemented so that it can be immediately integrated with the output produced by PennCNV tool. In addition, VegaMC produces in output two web pages that allows a rapid navigation between both the detected regions and the altered genes. In the web page that summarizes the altered genes, the link to the respective Ensembl gene web page is reported. biocViews: aCGH, CopyNumberVariation Author: S. Morganella and M. Ceccarelli Maintainer: Sandro Morganella git_url: https://git.bioconductor.org/packages/VegaMC git_branch: RELEASE_3_10 git_last_commit: 27f297a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/VegaMC_3.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/VegaMC_3.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/VegaMC_3.24.0.tgz vignettes: vignettes/VegaMC/inst/doc/VegaMC.pdf vignetteTitles: VegaMC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VegaMC/inst/doc/VegaMC.R dependencyCount: 78 Package: VennDetail Version: 1.2.0 Imports: utils, grDevices, stats, methods, dplyr, purrr, tibble, magrittr, ggplot2, UpSetR, VennDiagram, grid, futile.logger Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 03b1b00ccd2a3443b8fd0897cba9e0f8 NeedsCompilation: no Title: A package for visualization and extract details Description: A set of functions to generate high-resolution Venn,Vennpie plot,extract and combine details of these subsets with user datasets in data frame is available. biocViews: DataRepresentation,GraphAndNetwork Author: Kai Guo, Brett McGregor Maintainer: Kai Guo URL: https://github.com/guokai8/VennDetail VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VennDetail git_branch: RELEASE_3_10 git_last_commit: 4b410f8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/VennDetail_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/VennDetail_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/VennDetail_1.2.0.tgz vignettes: vignettes/VennDetail/inst/doc/VennDetail.html vignetteTitles: VennDetail hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VennDetail/inst/doc/VennDetail.R dependencyCount: 67 Package: vidger Version: 1.6.0 Depends: R (>= 3.5) Imports: Biobase, DESeq2, edgeR, GGally, ggplot2, ggrepel, knitr, RColorBrewer, rmarkdown, scales, stats, SummarizedExperiment, tidyr, utils Suggests: BiocStyle, testthat License: GPL-3 | file LICENSE MD5sum: 653dacd78479aa6adadb8d5c39861529 NeedsCompilation: no Title: Create rapid visualizations of RNAseq data in R Description: The aim of vidger is to rapidly generate information-rich visualizations for the interpretation of differential gene expression results from three widely-used tools: Cuffdiff, DESeq2, and edgeR. biocViews: ImmunoOncology, Visualization, RNASeq, DifferentialExpression, GeneExpression, ImmunoOncology Author: Brandon Monier [aut, cre], Adam McDermaid [aut], Jing Zhao [aut], Qin Ma [aut, fnd] Maintainer: Brandon Monier URL: https://github.com/btmonier/vidger, https://bioconductor.org/packages/release/bioc/html/vidger.html VignetteBuilder: knitr BugReports: https://github.com/btmonier/vidger/issues git_url: https://git.bioconductor.org/packages/vidger git_branch: RELEASE_3_10 git_last_commit: b275586 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/vidger_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/vidger_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/vidger_1.6.0.tgz vignettes: vignettes/vidger/inst/doc/vidger.html vignetteTitles: Visualizing RNA-seq data with ViDGER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/vidger/inst/doc/vidger.R dependencyCount: 135 Package: viper Version: 1.20.0 Depends: R (>= 2.14.0), Biobase, methods Imports: mixtools, stats, parallel, e1071, KernSmooth Suggests: bcellViper License: file LICENSE Archs: i386, x64 MD5sum: 5b3b6b06019560ec48da5c1762ef9928 NeedsCompilation: no Title: Virtual Inference of Protein-activity by Enriched Regulon analysis Description: Inference of protein activity from gene expression data, including the VIPER and msVIPER algorithms biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, FunctionalPrediction, GeneRegulation Author: Mariano J Alvarez Maintainer: Mariano J Alvarez git_url: https://git.bioconductor.org/packages/viper git_branch: RELEASE_3_10 git_last_commit: 05add52 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/viper_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/viper_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/viper_1.20.0.tgz vignettes: vignettes/viper/inst/doc/viper.pdf vignetteTitles: Using VIPER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/viper/inst/doc/viper.R dependsOnMe: vulcan importsMe: diggit, RTN dependencyCount: 20 Package: ViSEAGO Version: 1.0.0 Depends: R (>= 3.6) Imports: data.table, AnnotationDbi, AnnotationForge, biomaRt, dendextend, DiagrammeR, DT, dynamicTreeCut, GOSemSim, ggplot2, GO.db, grDevices, heatmaply, htmlwidgets, igraph, methods, plotly, topGO, RColorBrewer, R.utils, scales, stats, UpSetR, utils, webshot Suggests: htmltools, org.Mm.eg.db, limma, Rgraphviz, BiocStyle, knitr, rmarkdown, corrplot, remotes, BiocManager License: GPL-3 Archs: i386, x64 MD5sum: c63ffef44194ebea20b599edfdce8c87 NeedsCompilation: no Title: ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity Description: The main objective of ViSEAGO package is to carry out a data mining of biological functions and establish links between genes involved in the study. We developed ViSEAGO in R to facilitate functional Gene Ontology (GO) analysis of complex experimental design with multiple comparisons of interest. It allows to study large-scale datasets together and visualize GO profiles to capture biological knowledge. The acronym stands for three major concepts of the analysis: Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology. It provides access to the last current GO annotations, which are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot databases for several species. Using available R packages and novel developments, ViSEAGO extends classical functional GO analysis to focus on functional coherence by aggregating closely related biological themes while studying multiple datasets at once. It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility. biocViews: Software, Annotation, GO, GeneSetEnrichment, MultipleComparison, Clustering, Visualization Author: Aurelien Brionne [aut, cre], Amelie Juanchich [aut], Christelle hennequet-antier [aut] Maintainer: Aurelien Brionne URL: https://forgemia.inra.fr/UMR-BOA/ViSEAGO, https://www.bioconductor.org/packages/release/bioc/html/ViSEAGO.html VignetteBuilder: knitr BugReports: https://forgemia.inra.fr/UMR-BOA/ViSEAGO/issues git_url: https://git.bioconductor.org/packages/ViSEAGO git_branch: RELEASE_3_10 git_last_commit: 7c8ae0d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/ViSEAGO_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/ViSEAGO_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/ViSEAGO_1.0.0.tgz vignettes: vignettes/ViSEAGO/inst/doc/mouse_bionconductor.html, vignettes/ViSEAGO/inst/doc/SS_choice.html, vignettes/ViSEAGO/inst/doc/ViSEAGO.html vignetteTitles: 2: mouse_bionconductor, 3: SS_choice, 1: ViSEAGO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ViSEAGO/inst/doc/mouse_bionconductor.R, vignettes/ViSEAGO/inst/doc/SS_choice.R, vignettes/ViSEAGO/inst/doc/ViSEAGO.R dependencyCount: 145 Package: vsn Version: 3.54.0 Depends: R (>= 3.4.0), Biobase Imports: methods, affy, limma, lattice, ggplot2 Suggests: affydata, hgu95av2cdf, BiocStyle, knitr, dplyr, testthat License: Artistic-2.0 MD5sum: 4aa21023b404468ae5246826f49c40ce NeedsCompilation: yes Title: Variance stabilization and calibration for microarray data Description: The package implements a method for normalising microarray intensities, and works for single- and multiple-color arrays. It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of the maximum-likelihood estimator for an additive-multiplicative error model and affine calibration. The model incorporates data calibration step (a.k.a. normalization), a model for the dependence of the variance on the mean intensity and a variance stabilizing data transformation. Differences between transformed intensities are analogous to "normalized log-ratios". However, in contrast to the latter, their variance is independent of the mean, and they are usually more sensitive and specific in detecting differential transcription. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing Author: Wolfgang Huber, with contributions from Anja von Heydebreck. Many comments and suggestions by users are acknowledged, among them Dennis Kostka, David Kreil, Hans-Ulrich Klein, Robert Gentleman, Deepayan Sarkar and Gordon Smyth Maintainer: Wolfgang Huber URL: http://www.r-project.org, http://www.ebi.ac.uk/huber VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/vsn git_branch: RELEASE_3_10 git_last_commit: bac14b8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/vsn_3.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/vsn_3.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/vsn_3.54.0.tgz vignettes: vignettes/vsn/inst/doc/C-likelihoodcomputations.pdf, vignettes/vsn/inst/doc/D-convergence.pdf, vignettes/vsn/inst/doc/A-vsn.html vignetteTitles: Likelihood Calculations for vsn, Verifying and assessing the performance with simulated data, Introduction to vsn (HTML version) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vsn/inst/doc/A-vsn.R, vignettes/vsn/inst/doc/C-likelihoodcomputations.R dependsOnMe: affyPara, cellHTS2, MmPalateMiRNA, webbioc importsMe: arrayQualityMetrics, coexnet, DAPAR, DEP, Doscheda, imageHTS, LVSmiRNA, metaseqR, MSnbase, NormalyzerDE, PowerExplorer, pvca, Ringo, tilingArray suggestsMe: adSplit, beadarray, BiocCaseStudies, DESeq, DESeq2, ggbio, GlobalAncova, globaltest, limma, lumi, PAA, twilight dependencyCount: 63 Package: vtpnet Version: 0.26.0 Depends: R (>= 3.0.0), graph, GenomicRanges, gwascat, doParallel, foreach Suggests: MotifDb, VariantAnnotation, Rgraphviz License: Artistic-2.0 MD5sum: 92c57191cf5e303cd78f566e1e064a04 NeedsCompilation: no Title: variant-transcription factor-phenotype networks Description: variant-transcription factor-phenotype networks, inspired by Maurano et al., Science (2012), PMID 22955828 biocViews: Network Author: VJ Carey Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/vtpnet git_branch: RELEASE_3_10 git_last_commit: fafb285 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/vtpnet_0.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/vtpnet_0.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/vtpnet_0.26.0.tgz vignettes: vignettes/vtpnet/inst/doc/vtpnet.pdf vignetteTitles: vtpnet: variant-transcription factor-network tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vtpnet/inst/doc/vtpnet.R dependencyCount: 96 Package: vulcan Version: 1.8.0 Depends: R (>= 3.4), ChIPpeakAnno,TxDb.Hsapiens.UCSC.hg19.knownGene, zoo, GenomicRanges, S4Vectors, viper, DiffBind, locfit Imports: wordcloud, csaw, gplots, stats, utils, caTools, graphics, DESeq, Biobase Suggests: vulcandata License: LGPL-3 MD5sum: 131c6c0c801f38dec885f212b5536126 NeedsCompilation: no Title: VirtUaL ChIP-Seq data Analysis using Networks Description: Vulcan (VirtUaL ChIP-Seq Analysis through Networks) is a package that interrogates gene regulatory networks to infer cofactors significantly enriched in a differential binding signature coming from ChIP-Seq data. In order to do so, our package combines strategies from different BioConductor packages: DESeq for data normalization, ChIPpeakAnno and DiffBind for annotation and definition of ChIP-Seq genomic peaks, csaw to define optimal peak width and viper for applying a regulatory network over a differential binding signature. biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, ChIPSeq Author: Federico M. Giorgi, Andrew N. Holding, Florian Markowetz Maintainer: Federico M. Giorgi git_url: https://git.bioconductor.org/packages/vulcan git_branch: RELEASE_3_10 git_last_commit: b5638d5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/vulcan_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/vulcan_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/vulcan_1.8.0.tgz vignettes: vignettes/vulcan/inst/doc/vulcan.pdf vignetteTitles: Vulcan: VirtUaL ChIP-Seq Analysis through Networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vulcan/inst/doc/vulcan.R suggestsMe: flowSpecs dependencyCount: 195 Package: waddR Version: 1.0.1 Depends: R (>= 3.6.0) Imports: Rcpp (>= 1.0.1), arm (>= 1.10-1), BiocFileCache, BiocParallel, SingleCellExperiment, parallel, methods, stats LinkingTo: Rcpp, RcppArmadillo, Suggests: knitr, devtools, testthat, roxygen2, rprojroot, rmarkdown, scater License: MIT + file LICENSE MD5sum: b74660c20409c313ae322ad71cfc2062 NeedsCompilation: yes Title: Statistical tests for detecting differential distributions based on the 2-Wasserstein distance Description: The package offers statistical tests based on the 2-Wasserstein distance for detecting and characterizing differences between two distributions given in the form of samples. Functions for calculating the 2-Wasserstein distance and testing for differential distributions are provided, as welll as specifically tailored test for differential expression in single-cell RNA sequencing data. biocViews: Software, StatisticalMethod, SingleCell, DifferentialExpression Author: Roman Schefzik [aut], Julian Flesch [cre] Maintainer: Julian Flesch URL: https://github.com/goncalves-lab/waddR.git VignetteBuilder: knitr BugReports: https://github.com/goncalves-lab/waddR/issues git_url: https://git.bioconductor.org/packages/waddR git_branch: RELEASE_3_10 git_last_commit: d2c2b15 git_last_commit_date: 2020-03-20 Date/Publication: 2020-03-20 source.ver: src/contrib/waddR_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/waddR_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/waddR_1.0.1.tgz vignettes: vignettes/waddR/inst/doc/waddR.html, vignettes/waddR/inst/doc/wasserstein_metric.html, vignettes/waddR/inst/doc/wasserstein_singlecell.html, vignettes/waddR/inst/doc/wasserstein_test.html vignetteTitles: waddR, wasserstein_metric, wasserstein_singlecell, wasserstein_test hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/waddR/inst/doc/waddR.R, vignettes/waddR/inst/doc/wasserstein_metric.R, vignettes/waddR/inst/doc/wasserstein_singlecell.R, vignettes/waddR/inst/doc/wasserstein_test.R dependencyCount: 83 Package: wateRmelon Version: 1.30.0 Depends: R (>= 2.10), Biobase, limma, methods, matrixStats, methylumi, lumi, ROC, IlluminaHumanMethylation450kanno.ilmn12.hg19, illuminaio Imports: Biobase Suggests: RPMM, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICmanifest, irlba Enhances: minfi License: GPL-3 Archs: i386, x64 MD5sum: db3f6738d2d6ca7fa68c59ae93d84f3e NeedsCompilation: no Title: Illumina 450 methylation array normalization and metrics Description: 15 flavours of betas and three performance metrics, with methods for objects produced by methylumi and minfi packages. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl Author: Leonard C Schalkwyk, Ruth Pidsley, Chloe CY Wong, with functions contributed by Nizar Touleimat, Matthieu Defrance, Andrew Teschendorff, Jovana Maksimovic, Tyler Gorrie-Stone, Louis El Khoury Maintainer: Leo git_url: https://git.bioconductor.org/packages/wateRmelon git_branch: RELEASE_3_10 git_last_commit: 66d7579 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/wateRmelon_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/wateRmelon_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/wateRmelon_1.30.0.tgz vignettes: vignettes/wateRmelon/inst/doc/wateRmelon.pdf vignetteTitles: The \Rpackage{wateRmelon} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wateRmelon/inst/doc/wateRmelon.R dependsOnMe: bigmelon, skewr importsMe: ChAMP suggestsMe: RnBeads dependencyCount: 163 Package: wavClusteR Version: 2.20.0 Depends: R (>= 3.2), GenomicRanges (>= 1.31.8), Rsamtools Imports: methods, BiocGenerics, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Biostrings (>= 2.47.6), foreach, GenomicFeatures (>= 1.31.3), ggplot2, Hmisc, mclust, rtracklayer (>= 1.39.7), seqinr, stringr, wmtsa Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 Enhances: doMC License: GPL-2 MD5sum: b255c8ff771174166f2b9102fbfab84a NeedsCompilation: no Title: Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data Description: The package provides an integrated pipeline for the analysis of PAR-CLIP data. PAR-CLIP-induced transitions are first discriminated from sequencing errors, SNPs and additional non-experimental sources by a non- parametric mixture model. The protein binding sites (clusters) are then resolved at high resolution and cluster statistics are estimated using a rigorous Bayesian framework. Post-processing of the results, data export for UCSC genome browser visualization and motif search analysis are provided. In addition, the package allows to integrate RNA-Seq data to estimate the False Discovery Rate of cluster detection. Key functions support parallel multicore computing. Note: while wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other NGS data obtained from experimental procedures that induce nucleotide substitutions (e.g. BisSeq). biocViews: ImmunoOncology, Sequencing, Technology, RIPSeq, RNASeq, Bayesian Author: Federico Comoglio and Cem Sievers Maintainer: Federico Comoglio VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wavClusteR git_branch: RELEASE_3_10 git_last_commit: f791050 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/wavClusteR_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/wavClusteR_2.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/wavClusteR_2.20.0.tgz vignettes: vignettes/wavClusteR/inst/doc/wavCluster_vignette.html vignetteTitles: wavClusteR: a workflow for PAR-CLIP data analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wavClusteR/inst/doc/wavCluster_vignette.R dependencyCount: 146 Package: waveTiling Version: 1.28.0 Depends: oligo, oligoClasses, Biobase, Biostrings, GenomeGraphs Imports: methods, affy, preprocessCore, GenomicRanges, waveslim, IRanges Suggests: BSgenome, BSgenome.Athaliana.TAIR.TAIR9, waveTilingData, pd.atdschip.tiling, TxDb.Athaliana.BioMart.plantsmart22 License: GPL (>=2) MD5sum: 45a0e8af5b880addc6da959b287ed774 NeedsCompilation: yes Title: Wavelet-Based Models for Tiling Array Transcriptome Analysis Description: This package is designed to conduct transcriptome analysis for tiling arrays based on fast wavelet-based functional models. biocViews: Microarray, DifferentialExpression, TimeCourse, GeneExpression Author: Kristof De Beuf , Peter Pipelers and Lieven Clement Maintainer: Kristof De Beuf URL: https://r-forge.r-project.org/projects/wavetiling/ git_url: https://git.bioconductor.org/packages/waveTiling git_branch: RELEASE_3_10 git_last_commit: 13e6d5c git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/waveTiling_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/waveTiling_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/waveTiling_1.28.0.tgz vignettes: vignettes/waveTiling/inst/doc/waveTiling-vignette.pdf vignetteTitles: The waveTiling package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/waveTiling/inst/doc/waveTiling-vignette.R dependencyCount: 92 Package: weaver Version: 1.52.0 Depends: R (>= 2.5.0), digest, tools, utils, codetools Suggests: codetools License: GPL-2 Archs: i386, x64 MD5sum: 277d4e8fcbe9db578efce401269c174c NeedsCompilation: no Title: Tools and extensions for processing Sweave documents Description: This package provides enhancements on the Sweave() function in the base package. In particular a facility for caching code chunk results is included. biocViews: Infrastructure Author: Seth Falcon Maintainer: Seth Falcon git_url: https://git.bioconductor.org/packages/weaver git_branch: RELEASE_3_10 git_last_commit: a48bdea git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/weaver_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/weaver_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/weaver_1.52.0.tgz vignettes: vignettes/weaver/inst/doc/weaver_howTo.pdf vignetteTitles: Using weaver to process Sweave documents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/weaver/inst/doc/weaver_howTo.R suggestsMe: BiocCaseStudies dependencyCount: 4 Package: webbioc Version: 1.58.0 Depends: R (>= 1.8.0), Biobase, affy, multtest, annaffy, vsn, gcrma, qvalue Imports: multtest, qvalue, stats, utils, BiocManager License: GPL (>= 2) MD5sum: c084feefa58fceada4b6d91229149c6b NeedsCompilation: no Title: Bioconductor Web Interface Description: An integrated web interface for doing microarray analysis using several of the Bioconductor packages. It is intended to be deployed as a centralized bioinformatics resource for use by many users. (Currently only Affymetrix oligonucleotide analysis is supported.) biocViews: Infrastructure, Microarray, OneChannel, DifferentialExpression Author: Colin A. Smith Maintainer: Colin A. Smith URL: http://www.bioconductor.org/ SystemRequirements: Unix, Perl (>= 5.6.0), Netpbm git_url: https://git.bioconductor.org/packages/webbioc git_branch: RELEASE_3_10 git_last_commit: d075720 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/webbioc_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/webbioc_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/webbioc_1.58.0.tgz vignettes: vignettes/webbioc/inst/doc/demoscript.pdf, vignettes/webbioc/inst/doc/webbioc.pdf vignetteTitles: webbioc Demo Script, webbioc Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 89 Package: widgetTools Version: 1.64.0 Depends: R (>= 2.4.0), methods, utils, tcltk Suggests: Biobase License: LGPL MD5sum: 9b3c973d176e258f25a12b7e4cda682b NeedsCompilation: no Title: Creates an interactive tcltk widget Description: This packages contains tools to support the construction of tcltk widgets biocViews: Infrastructure Author: Jianhua Zhang Maintainer: Jianhua Zhang git_url: https://git.bioconductor.org/packages/widgetTools git_branch: RELEASE_3_10 git_last_commit: d0d335f git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/widgetTools_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/widgetTools_1.64.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/widgetTools_1.64.0.tgz vignettes: vignettes/widgetTools/inst/doc/widgetTools.pdf vignetteTitles: widgetTools Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/widgetTools/inst/doc/widgetTools.R dependsOnMe: tkWidgets importsMe: OLINgui suggestsMe: affy dependencyCount: 3 Package: wiggleplotr Version: 1.10.1 Depends: R (>= 3.6) Imports: dplyr, ggplot2 (>= 2.2.0), GenomicRanges, rtracklayer, cowplot, assertthat, purrr, S4Vectors, IRanges, GenomeInfoDb Suggests: knitr, rmarkdown, biomaRt, GenomicFeatures, testthat, ensembldb, EnsDb.Hsapiens.v86, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, AnnotationDbi, AnnotationFilter License: Apache License 2.0 Archs: i386, x64 MD5sum: 726685dc1fd8ce3de1e6ae9dfb828ab0 NeedsCompilation: no Title: Make read coverage plots from BigWig files Description: Tools to visualise read coverage from sequencing experiments together with genomic annotations (genes, transcripts, peaks). Introns of long transcripts can be rescaled to a fixed length for better visualisation of exonic read coverage. biocViews: ImmunoOncology, Coverage, RNASeq, ChIPSeq, Sequencing, Visualization, GeneExpression, Transcription, AlternativeSplicing Author: Kaur Alasoo [aut, cre] Maintainer: Kaur Alasoo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wiggleplotr git_branch: RELEASE_3_10 git_last_commit: ec89346 git_last_commit_date: 2019-11-01 Date/Publication: 2019-11-01 source.ver: src/contrib/wiggleplotr_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/wiggleplotr_1.10.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/wiggleplotr_1.10.1.tgz vignettes: vignettes/wiggleplotr/inst/doc/wiggleplotr.html vignetteTitles: Introduction to wiggleplotr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wiggleplotr/inst/doc/wiggleplotr.R dependencyCount: 88 Package: Wrench Version: 1.4.0 Depends: R (>= 3.5.0) Imports: limma, matrixStats, locfit, stats, graphics Suggests: knitr, rmarkdown, metagenomeSeq, DESeq2, edgeR License: Artistic-2.0 MD5sum: 3f161aa73e7c041cde0995df9a2271b1 NeedsCompilation: no Title: Wrench normalization for sparse count data Description: Wrench is a package for normalization sparse genomic count data, like that arising from 16s metagenomic surveys. biocViews: Normalization, Sequencing, Software Author: Senthil Kumar Muthiah [aut], Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo URL: https://github.com/HCBravoLab/Wrench VignetteBuilder: knitr BugReports: https://github.com/HCBravoLab/Wrench/issues git_url: https://git.bioconductor.org/packages/Wrench git_branch: RELEASE_3_10 git_last_commit: 9f992d5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Wrench_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Wrench_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Wrench_1.4.0.tgz vignettes: vignettes/Wrench/inst/doc/vignette.html vignetteTitles: Wrench hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Wrench/inst/doc/vignette.R importsMe: metagenomeSeq dependencyCount: 10 Package: XBSeq Version: 1.18.0 Depends: DESeq2, R (>= 3.3) Imports: pracma, matrixStats, locfit, ggplot2, methods, Biobase, dplyr, magrittr, roar Suggests: knitr, DESeq, rmarkdown, BiocStyle, testthat License: GPL (>=3) MD5sum: abf59d96145f9b50a8a8bf15f0414a94 NeedsCompilation: no Title: Test for differential expression for RNA-seq data Description: We developed a novel algorithm, XBSeq, where a statistical model was established based on the assumption that observed signals are the convolution of true expression signals and sequencing noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows a Poisson distribution. Given measureable observed and noise signals from RNA-seq data, true expression signals, assuming governed by the negative binomial distribution, can be delineated and thus the accurate detection of differential expressed genes. biocViews: ImmunoOncology, RNASeq, DifferentialExpression, Sequencing, Software, ExperimentalDesign Author: Yuanhang Liu Maintainer: Yuanhang Liu URL: https://github.com/Liuy12/XBSeq VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/XBSeq git_branch: RELEASE_3_10 git_last_commit: a141dbb git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/XBSeq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/XBSeq_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/XBSeq_1.18.0.tgz vignettes: vignettes/XBSeq/inst/doc/XBSeq.html vignetteTitles: Differential expression and apa usage analysis of count data using XBSeq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XBSeq/inst/doc/XBSeq.R dependencyCount: 131 Package: XCIR Version: 1.0.0 Depends: methods Imports: stats, utils, data.table, IRanges, VariantAnnotation, seqminer, ggplot2, biomaRt, readxl, S4Vectors Suggests: knitr, rmarkdown License: GPL-2 MD5sum: b26f3f9421fef757d80a49f44d20c7e2 NeedsCompilation: no Title: XCI-inference Description: Models and tools for subject level analysis of X chromosome inactivation (XCI) and XCI-escape inference. biocViews: StatisticalMethod, RNASeq, Sequencing, Coverage Author: Renan Sauteraud, Dajiang Liu Maintainer: Renan Sauteraud URL: https://github.com/SRenan/XCIR VignetteBuilder: knitr BugReports: https://github.com/SRenan/XCIR/issues git_url: https://git.bioconductor.org/packages/XCIR git_branch: RELEASE_3_10 git_last_commit: c20f64b git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/XCIR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/XCIR_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/XCIR_1.0.0.tgz vignettes: vignettes/XCIR/inst/doc/xcir_intro.html vignetteTitles: Introduction to XCIR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XCIR/inst/doc/xcir_intro.R dependencyCount: 117 Package: xcms Version: 3.8.2 Depends: R (>= 2.14.0), methods, Biobase, BiocParallel (>= 1.8.0), MSnbase (>= 2.9.3) Imports: mzR (>= 2.19.5), BiocGenerics, ProtGenerics (>= 1.17.2), lattice, RColorBrewer, plyr, RANN, multtest, MassSpecWavelet (>= 1.5.2), S4Vectors, robustbase, IRanges Suggests: BiocStyle, caTools, knitr (>= 1.1.0), faahKO, msdata (>= 0.25.1), ncdf4, rgl, microbenchmark, testthat, pander, magrittr, MALDIquant, pheatmap Enhances: Rgraphviz, Rmpi, XML License: GPL (>= 2) + file LICENSE MD5sum: f15ac29e2d50f229c392b7cc000a7882 NeedsCompilation: yes Title: LC-MS and GC-MS Data Analysis Description: Framework for processing and visualization of chromatographically separated and single-spectra mass spectral data. Imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. Preprocesses data for high-throughput, untargeted analyte profiling. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Colin A. Smith , Ralf Tautenhahn , Steffen Neumann , Paul Benton , Christopher Conley , Johannes Rainer , Michael Witting Maintainer: Steffen Neumann URL: https://github.com/sneumann/xcms VignetteBuilder: knitr BugReports: https://github.com/sneumann/xcms/issues/new git_url: https://git.bioconductor.org/packages/xcms git_branch: RELEASE_3_10 git_last_commit: dc1d955 git_last_commit_date: 2020-03-04 Date/Publication: 2020-03-04 source.ver: src/contrib/xcms_3.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.6/xcms_3.8.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/xcms_3.8.2.tgz vignettes: vignettes/xcms/inst/doc/new_functionality.html, vignettes/xcms/inst/doc/xcms-direct-injection.html, vignettes/xcms/inst/doc/xcms-lcms-ms.html, vignettes/xcms/inst/doc/xcms.html vignetteTitles: New and modified functionality in xcms, Grouping FTICR-MS data with xcms, LC-MS/MS data analysis with xcms, LCMS data preprocessing and analysis with xcms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/xcms/inst/doc/new_functionality.R, vignettes/xcms/inst/doc/xcms-direct-injection.R, vignettes/xcms/inst/doc/xcms-lcms-ms.R, vignettes/xcms/inst/doc/xcms.R dependsOnMe: CAMERA, flagme, IPO, LOBSTAHS, Metab, metaMS, ncGTW, proFIA importsMe: CAMERA, cliqueMS, cosmiq, Risa suggestsMe: CluMSID, MassSpecWavelet, msPurity, RMassBank dependencyCount: 96 Package: XDE Version: 2.32.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5) Imports: BiocGenerics, genefilter, graphics, grDevices, gtools, MergeMaid, methods, stats, utils, mvtnorm, RColorBrewer, GeneMeta, siggenes Suggests: MASS, RUnit Enhances: coda License: LGPL-2 MD5sum: bb2c861ef0105dc74807eba1ea7de3e2 NeedsCompilation: yes Title: XDE: a Bayesian hierarchical model for cross-study analysis of differential gene expression Description: Multi-level model for cross-study detection of differential gene expression. biocViews: Microarray, DifferentialExpression Author: R.B. Scharpf, G. Parmigiani, A.B. Nobel, and H. Tjelmeland Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/XDE git_branch: RELEASE_3_10 git_last_commit: 4c3c341 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/XDE_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/XDE_2.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/XDE_2.32.0.tgz vignettes: vignettes/XDE/inst/doc/XDE.pdf, vignettes/XDE/inst/doc/XdeParameterClass.pdf vignetteTitles: XDE Vignette, XdeParameterClass Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XDE/inst/doc/XDE.R, vignettes/XDE/inst/doc/XdeParameterClass.R dependencyCount: 47 Package: Xeva Version: 1.2.0 Depends: R (>= 3.6) Imports: methods, stats, utils, BBmisc, Biobase, grDevices, ggplot2, scales, ComplexHeatmap, parallel, doParallel, Rmisc, grid, nlme, PharmacoGx, downloader Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 63214ef9a61ad4750cbc136d6ea14e46 NeedsCompilation: no Title: Analysis of patient-derived xenograft (PDX) data Description: Contains set of functions to perform analysis of patient-derived xenograft (PDX) data. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification Author: Arvind Mer, Benjamin Haibe-Kains Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/Xeva/issues git_url: https://git.bioconductor.org/packages/Xeva git_branch: RELEASE_3_10 git_last_commit: e98ddc6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/Xeva_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/Xeva_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Xeva_1.2.0.tgz vignettes: vignettes/Xeva/inst/doc/Xeva.pdf vignetteTitles: The Xeva User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Xeva/inst/doc/Xeva.R dependencyCount: 132 Package: XINA Version: 1.4.0 Depends: R (>= 3.5), Biobase Imports: mclust, plyr, alluvial, ggplot2, igraph, gridExtra, tools, grDevices, graphics, utils, STRINGdb Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 14eb3ba9d0f5f6635c157977256fb1fb NeedsCompilation: no Title: Multiplexes isobaric mass tagged-based kinetics data for network analysis Description: An intuitive R package simplifies network analyses output from multiplexed high-dimensional proteomics/trascriptomics kinetics data. biocViews: ImmunoOncology, SystemsBiology, Proteomics, RNASeq, Network Author: Lee, Lang Ho and Singh, Sasha A. Maintainer: Lang Ho Lee and Sasha A. Singh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/XINA git_branch: RELEASE_3_10 git_last_commit: 3c704e6 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/XINA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/XINA_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/XINA_1.4.0.tgz vignettes: vignettes/XINA/inst/doc/xina_user_code.html vignetteTitles: xina_user_code hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XINA/inst/doc/xina_user_code.R dependencyCount: 85 Package: xmapbridge Version: 1.44.0 Depends: R (>= 2.0), methods Suggests: RUnit, RColorBrewer License: LGPL-3 Archs: i386, x64 MD5sum: cd0a2825ed4a990d7976db8948cfa4af NeedsCompilation: no Title: Export plotting files to the xmapBridge for visualisation in X:Map Description: xmapBridge can plot graphs in the X:Map genome browser. This package exports plotting files in a suitable format. biocViews: Annotation, ReportWriting, Visualization Author: Tim Yates and Crispin J Miller Maintainer: Chris Wirth URL: http://xmap.picr.man.ac.uk, http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/xmapbridge git_branch: RELEASE_3_10 git_last_commit: 1a2fd9a git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/xmapbridge_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/xmapbridge_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/xmapbridge_1.44.0.tgz vignettes: vignettes/xmapbridge/inst/doc/xmapbridge.pdf vignetteTitles: xmapbridge primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xmapbridge/inst/doc/xmapbridge.R dependencyCount: 1 Package: xps Version: 1.46.0 Depends: R (>= 2.6.0), methods, utils Suggests: tools License: GPL (>= 2.0) MD5sum: bc5bc19a82cb30d2453bd6f7c6c1f9ab NeedsCompilation: yes Title: Processing and Analysis of Affymetrix Oligonucleotide Arrays including Exon Arrays, Whole Genome Arrays and Plate Arrays Description: The package handles pre-processing, normalization, filtering and analysis of Affymetrix GeneChip expression arrays, including exon arrays (Exon 1.0 ST: core, extended, full probesets), gene arrays (Gene 1.0 ST) and plate arrays on computers with 1 GB RAM only. It imports Affymetrix .CDF, .CLF, .PGF and .CEL as well as annotation files, and computes e.g. RMA, MAS5, FARMS, DFW, FIRMA, tRMA, MAS5-calls, DABG-calls, I/NI-calls. It is an R wrapper to XPS (eXpression Profiling System), which is based on ROOT, an object-oriented framework developed at CERN. Thus, the prior installation of ROOT is a prerequisite for the usage of this package, however, no knowledge of ROOT is required. ROOT is licensed under LGPL and can be downloaded from http://root.cern.ch. biocViews: ExonArray, GeneExpression, Microarray, OneChannel, DataImport, Preprocessing, Transcription, DifferentialExpression Author: Christian Stratowa, Vienna, Austria Maintainer: Christian Stratowa SystemRequirements: GNU make, root_v5.34.36 - See README file for installation instructions. git_url: https://git.bioconductor.org/packages/xps git_branch: RELEASE_3_10 git_last_commit: 356498d git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/xps_1.46.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/xps_1.46.0.tgz vignettes: vignettes/xps/inst/doc/APTvsXPS.pdf, vignettes/xps/inst/doc/xps.pdf, vignettes/xps/inst/doc/xpsClasses.pdf, vignettes/xps/inst/doc/xpsPreprocess.pdf vignetteTitles: 3. XPS Vignette: Comparison APT vs XPS, 1. XPS Vignette: Overview, 2. XPS Vignette: Classes, 4. XPS Vignette: Function express() hasREADME: TRUE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/xps/inst/doc/APTvsXPS.R, vignettes/xps/inst/doc/xps.R, vignettes/xps/inst/doc/xpsClasses.R, vignettes/xps/inst/doc/xpsPreprocess.R dependencyCount: 2 Package: XVector Version: 0.26.0 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.19.2), S4Vectors (>= 0.21.13), IRanges (>= 2.15.12) Imports: methods, utils, zlibbioc, BiocGenerics, S4Vectors, IRanges LinkingTo: S4Vectors, IRanges Suggests: Biostrings, drosophila2probe, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 94f1379546344b9a3cadc8eb86888110 NeedsCompilation: yes Title: Foundation of external vector representation and manipulation in Bioconductor Description: Provides memory efficient S4 classes for storing sequences "externally" (e.g. behind an R external pointer, or on disk). biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès and Patrick Aboyoun Maintainer: Hervé Pagès git_url: https://git.bioconductor.org/packages/XVector git_branch: RELEASE_3_10 git_last_commit: 736c949 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/XVector_0.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/XVector_0.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/XVector_0.26.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Biostrings, motifRG, triplex importsMe: BSgenome, ChIPsim, CNEr, compEpiTools, dada2, DECIPHER, gcrma, GenomicFeatures, GenomicRanges, Gviz, HiLDA, IONiseR, IsoformSwitchAnalyzeR, kebabs, MatrixRider, Modstrings, ngsReports, R453Plus1Toolbox, RNAmodR, Rsamtools, rtracklayer, Structstrings, TFBSTools, tracktables, tRNA, tRNAscanImport, VariantAnnotation suggestsMe: IRanges linksToMe: Biostrings, CNEr, DECIPHER, kebabs, MatrixRider, Rsamtools, rtracklayer, ShortRead, triplex, VariantAnnotation, VariantFiltering dependencyCount: 10 Package: yamss Version: 1.12.1 Depends: R (>= 3.3.0), methods, BiocGenerics (>= 0.15.3), SummarizedExperiment Imports: IRanges, stats, S4Vectors, EBImage, Matrix, mzR, data.table, grDevices, limma Suggests: BiocStyle, knitr, rmarkdown, digest, mtbls2, testthat License: Artistic-2.0 MD5sum: 6db2613e6857d73304c046493b21ce4b NeedsCompilation: no Title: Tools for high-throughput metabolomics Description: Tools to analyze and visualize high-throughput metabolomics data aquired using chromatography-mass spectrometry. These tools preprocess data in a way that enables reliable and powerful differential analysis. biocViews: MassSpectrometry, Metabolomics, ImmunoOncology, Software Author: Leslie Myint [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Leslie Myint URL: https://github.com/hansenlab/yamss VignetteBuilder: knitr BugReports: https://github.com/hansenlab/yamss/issues git_url: https://git.bioconductor.org/packages/yamss git_branch: RELEASE_3_10 git_last_commit: 9709243 git_last_commit_date: 2020-03-01 Date/Publication: 2020-03-01 source.ver: src/contrib/yamss_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.6/yamss_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/yamss_1.12.1.tgz vignettes: vignettes/yamss/inst/doc/yamss.html vignetteTitles: yamss User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yamss/inst/doc/yamss.R dependencyCount: 52 Package: YAPSA Version: 1.12.0 Depends: R (>= 3.3.0), GenomicRanges, ggplot2, grid Imports: lsei, SomaticSignatures, VariantAnnotation, GenomeInfoDb, reshape2, gridExtra, corrplot, dendextend, GetoptLong, circlize, gtrellis, PMCMR, ComplexHeatmap, KEGGREST, grDevices Suggests: BSgenome.Hsapiens.UCSC.hg19, testthat, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: d0e9e4317770b6b0469f4a63f6ff57a5 NeedsCompilation: no Title: Yet Another Package for Signature Analysis Description: This package provides functions and routines useful in the analysis of somatic signatures (cf. L. Alexandrov et al., Nature 2013). In particular, functions to perform a signature analysis with known signatures (LCD = linear combination decomposition) and a signature analysis on stratified mutational catalogue (SMC = stratify mutational catalogue) are provided. biocViews: Sequencing, DNASeq, SomaticMutation, Visualization, Clustering, GenomicVariation, StatisticalMethod, BiologicalQuestion Author: Daniel Huebschmann, Zuguang Gu, Matthias Schlesner Maintainer: Daniel Huebschmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/YAPSA git_branch: RELEASE_3_10 git_last_commit: a5b6f83 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/YAPSA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/YAPSA_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/YAPSA_1.12.0.tgz vignettes: vignettes/YAPSA/inst/doc/YAPSA.html vignetteTitles: YAPSA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/YAPSA/inst/doc/YAPSA.R dependencyCount: 180 Package: yaqcaffy Version: 1.46.0 Depends: simpleaffy (>= 2.19.3), methods Imports: stats4 Suggests: MAQCsubsetAFX, affydata, xtable, tcltk2, tcltk License: Artistic-2.0 MD5sum: 4a75b8b145264dbf58328b8914dd5ffe NeedsCompilation: no Title: Affymetrix expression data quality control and reproducibility analysis Description: Quality control of Affymetrix GeneChip expression data and reproducibility analysis of human whole genome chips with the MAQC reference datasets. biocViews: Microarray,OneChannel,QualityControl,ReportWriting Author: Laurent Gatto Maintainer: Laurent Gatto git_url: https://git.bioconductor.org/packages/yaqcaffy git_branch: RELEASE_3_10 git_last_commit: 369e0fa git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/yaqcaffy_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/yaqcaffy_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/yaqcaffy_1.46.0.tgz vignettes: vignettes/yaqcaffy/inst/doc/yaqcaffy.pdf vignetteTitles: yaqcaffy: Affymetrix quality control and MAQC reproducibility hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yaqcaffy/inst/doc/yaqcaffy.R suggestsMe: qcmetrics dependencyCount: 47 Package: yarn Version: 1.12.0 Depends: Biobase Imports: biomaRt, downloader, edgeR, gplots, graphics, limma, matrixStats, preprocessCore, readr, RColorBrewer, stats, quantro Suggests: knitr, rmarkdown, testthat (>= 0.8) License: Artistic-2.0 Archs: i386, x64 MD5sum: 5c11cdd88c75896021d4b5d312977132 NeedsCompilation: no Title: YARN: Robust Multi-Condition RNA-Seq Preprocessing and Normalization Description: Expedite large RNA-Seq analyses using a combination of previously developed tools. YARN is meant to make it easier for the user in performing basic mis-annotation quality control, filtering, and condition-aware normalization. YARN leverages many Bioconductor tools and statistical techniques to account for the large heterogeneity and sparsity found in very large RNA-seq experiments. biocViews: Software, QualityControl, GeneExpression, Sequencing, Preprocessing, Normalization, Annotation, Visualization, Clustering Author: Joseph N Paulson [aut, cre], Cho-Yi Chen [aut], Camila Lopes-Ramos [aut], Marieke Kuijjer [aut], John Platig [aut], Abhijeet Sonawane [aut], Maud Fagny [aut], Kimberly Glass [aut], John Quackenbush [aut] Maintainer: Joseph N Paulson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/yarn git_branch: RELEASE_3_10 git_last_commit: 0f3aef7 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/yarn_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/yarn_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/yarn_1.12.0.tgz vignettes: vignettes/yarn/inst/doc/yarn.pdf vignetteTitles: YARN: Robust Multi-Tissue RNA-Seq Preprocessing and Normalization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yarn/inst/doc/yarn.R dependencyCount: 154 Package: zFPKM Version: 1.8.0 Depends: R (>= 3.4.0) Imports: checkmate, dplyr, ggplot2, tidyr, SummarizedExperiment Suggests: knitr, limma, edgeR, GEOquery, stringr, printr License: GPL-3 | file LICENSE MD5sum: 6b7bb9a994b82266f26fac244c2114b2 NeedsCompilation: no Title: A suite of functions to facilitate zFPKM transformations Description: Perform the zFPKM transform on RNA-seq FPKM data. This algorithm is based on the publication by Hart et al., 2013 (Pubmed ID 24215113). Reference recommends using zFPKM > -3 to select expressed genes. Validated with encode open/closed chromosome data. Works well for gene level data using FPKM or TPM. Does not appear to calibrate well for transcript level data. biocViews: ImmunoOncology, RNASeq, FeatureExtraction, Software, GeneExpression Author: Ron Ammar [aut, cre], John Thompson [aut] Maintainer: Ron Ammar URL: https://github.com/ronammar/zFPKM/ VignetteBuilder: knitr BugReports: https://github.com/ronammar/zFPKM/issues git_url: https://git.bioconductor.org/packages/zFPKM git_branch: RELEASE_3_10 git_last_commit: 0019ad8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/zFPKM_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/zFPKM_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/zFPKM_1.8.0.tgz vignettes: vignettes/zFPKM/inst/doc/zFPKM.html vignetteTitles: Introduction to zFPKM Transformation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/zFPKM/inst/doc/zFPKM.R dependencyCount: 84 Package: zinbwave Version: 1.8.0 Depends: R (>= 3.4), methods, SummarizedExperiment, SingleCellExperiment Imports: BiocParallel, softImpute, stats, genefilter, edgeR, Matrix Suggests: knitr, rmarkdown, testthat, matrixStats, magrittr, scRNAseq, ggplot2, biomaRt, BiocStyle, Rtsne, DESeq2, Seurat License: Artistic-2.0 MD5sum: f3e82c508c5b31c12e90abdc46bc2c47 NeedsCompilation: no Title: Zero-Inflated Negative Binomial Model for RNA-Seq Data Description: Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data. biocViews: ImmunoOncology, DimensionReduction, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Davide Risso [aut, cre, cph], Svetlana Gribkova [aut], Fanny Perraudeau [aut], Jean-Philippe Vert [aut], Clara Bagatin [aut] Maintainer: Davide Risso VignetteBuilder: knitr BugReports: https://github.com/drisso/zinbwave/issues git_url: https://git.bioconductor.org/packages/zinbwave git_branch: RELEASE_3_10 git_last_commit: 2aad7b5 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/zinbwave_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/zinbwave_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/zinbwave_1.8.0.tgz vignettes: vignettes/zinbwave/inst/doc/intro.html vignetteTitles: zinbwave Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/zinbwave/inst/doc/intro.R importsMe: clusterExperiment, scBFA suggestsMe: splatter dependencyCount: 58 Package: zlibbioc Version: 1.32.0 License: Artistic-2.0 + file LICENSE MD5sum: ef55d0f4357db2f0be8c2a280f610d92 NeedsCompilation: yes Title: An R packaged zlib-1.2.5 Description: This package uses the source code of zlib-1.2.5 to create libraries for systems that do not have these available via other means (most Linux and Mac users should have system-level access to zlib, and no direct need for this package). See the vignette for instructions on use. biocViews: Infrastructure Author: Martin Morgan Maintainer: Bioconductor Package Maintainer URL: http://bioconductor.org/packages/release/bioc/html/Zlibbioc.html git_url: https://git.bioconductor.org/packages/zlibbioc git_branch: RELEASE_3_10 git_last_commit: 2e5b107 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 source.ver: src/contrib/zlibbioc_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.6/zlibbioc_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/zlibbioc_1.32.0.tgz vignettes: vignettes/zlibbioc/inst/doc/UsingZlibbioc.pdf vignetteTitles: Using zlibbioc C libraries hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: affy, affyio, affyPLM, bamsignals, ChemmineOB, LVSmiRNA, MADSEQ, makecdfenv, oligo, polyester, qckitfastq, Rhtslib, Rsamtools, rtracklayer, ShortRead, snpStats, Starr, TransView, VariantAnnotation, XVector linksToMe: bamsignals, csaw, diffHic, methylKit, mzR, Rhtslib, scPipe, seqTools dependencyCount: 0 Package: birte Version: 1.22.0 Depends: R(>= 3.0.0), RcppArmadillo (>= 0.3.6.1), Rcpp Imports: MASS, limma(>= 3.22.0), glmnet, Biobase, nem, graphics, stats, utils LinkingTo: RcppArmadillo, Rcpp Suggests: knitr Enhances: Rgraphviz License: GPL (>= 2) NeedsCompilation: yes Title: Bayesian Inference of Regulatory Influence on Expression (biRte) Description: Expression levels of mRNA molecules are regulated by different processes, comprising inhibition or activation by transcription factors and post-transcriptional degradation by microRNAs. biRte uses regulatory networks of TFs, miRNAs and possibly other factors, together with mRNA, miRNA and other available expression data to predict the relative influence of a regulator on the expression of its target genes. Inference is done in a Bayesian modeling framework using Markov-Chain-Monte-Carlo. A special feature is the possibility for follow-up network reverse engineering between active regulators. biocViews: Microarray, Sequencing, GeneExpression, Transcription, Network, Bayesian, Regression, NetworkInference Author: Holger Froehlich, contributions by Benedikt Zacher Maintainer: Holger Froehlich SystemRequirements: BLAS, LAPACK VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/birte git_branch: RELEASE_3_10 git_last_commit: cadde5e git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 win.binary.ver: bin/windows/contrib/3.6/birte_1.22.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: dSimer Version: 1.12.0 Depends: R (>= 3.3.0), igraph (>= 1.0.1) Imports: stats, Rcpp (>= 0.11.3), ggplot2, reshape2, GO.db, org.Hs.eg.db, AnnotationDbi, graphics LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle License: GPL (>= 2) NeedsCompilation: yes Title: Integration of Disease Similarity Methods Description: dSimer is an R package which provides computation of nine methods for measuring disease-disease similarity, including a standard cosine similarity measure and eight function-based methods. The disease similarity matrix obtained from these nine methods can be visualized through heatmap and network. Biological data widely used in disease-disease associations study are also provided by dSimer. biocViews: Software, Visualization, Network Author: Min Li , Peng Ni with contributions from Zhihui Fei and Ping Huang. Maintainer: Peng Ni VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/dSimer git_branch: RELEASE_3_10 git_last_commit: 34f87d4 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 win.binary.ver: bin/windows/contrib/3.6/dSimer_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/dSimer_1.12.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: flipflop Version: 1.24.0 Depends: R (>= 2.10.0) Imports: methods, Matrix, IRanges, GenomicRanges, parallel Suggests: GenomicFeatures License: GPL-3 NeedsCompilation: yes Title: Fast lasso-based isoform prediction as a flow problem Description: Flipflop discovers which isoforms of a gene are expressed in a given sample together with their abundances, based on RNA-Seq read data. It takes an alignment file in SAM format as input. It can also discover transcripts from several samples simultaneously, increasing statistical power. biocViews: RNASeq, RNASeqData, AlternativeSplicing, Regression Author: Elsa Bernard, Laurent Jacob, Julien Mairal and Jean-Philippe Vert Maintainer: Elsa Bernard URL: http://cbio.ensmp.fr/flipflop SystemRequirements: GNU make PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/flipflop git_branch: RELEASE_3_10 git_last_commit: a968570 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 win.binary.ver: bin/windows/contrib/3.6/flipflop_1.24.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Rchemcpp Version: 2.24.0 Depends: R (>= 2.15.0) Imports: Rcpp (>= 0.11.1), methods, ChemmineR LinkingTo: Rcpp Suggests: apcluster, kernlab License: GPL (>= 2.1) Archs: i386, x64 NeedsCompilation: yes Title: Similarity measures for chemical compounds Description: The Rchemcpp package implements the marginalized graph kernel and extensions, Tanimoto kernels, graph kernels, pharmacophore and 3D kernels suggested for measuring the similarity of molecules. biocViews: ImmunoOncology, Bioinformatics, CellBasedAssays, Clustering, DataImport, Infrastructure, MicrotitrePlateAssay, Proteomics, Software, Visualization Author: Michael Mahr, Guenter Klambauer Maintainer: Guenter Klambauer URL: http://www.bioinf.jku.at/software/Rchemcpp SystemRequirements: GNU make PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/Rchemcpp git_branch: RELEASE_3_10 git_last_commit: 8ecf085 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 win.binary.ver: bin/windows/contrib/3.6/Rchemcpp_2.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/Rchemcpp_2.24.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: gpuMagic Version: 1.2.0 Depends: R (>= 3.6.0), methods, utils Imports: Deriv, DescTools, digest, pryr, stringr, BiocGenerics LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 8c76ff430175e6e45bbbf01361d73355 NeedsCompilation: yes Title: An openCL compiler with the capacity to compile R functions and run the code on GPU Description: The package aims to help users write openCL code with little or no effort. It is able to compile an user-defined R function and run it on a device such as a CPU or a GPU. The user can also write and run their openCL code directly by calling .kernel function. biocViews: Infrastructure Author: Jiefei Wang Maintainer: Jiefei Wang SystemRequirements: 1. C++11, 2. a graphic driver or a CPU SDK. 3. ICD loader For windows user, an ICD loader is required at C:/windows/system32/OpenCL.dll (Usually it is installed by the graphic driver). For linux user (Except mac): ocl-icd-opencl-dev package is required. For mac user, no action is needed for the system has installed the dependency. VignetteBuilder: knitr BugReports: https://github.com/Jiefei-Wang/gpuMagic/issues git_url: https://git.bioconductor.org/packages/gpuMagic git_branch: RELEASE_3_10 git_last_commit: 55f5cf8 git_last_commit_date: 2019-10-29 Date/Publication: 2019-10-29 mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.6/gpuMagic_1.2.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE