iscream is a BED file querying package that can can retrieve records within regions of interest from multiple tabixed BED files. It can make queries like tabix, summarize data within regions and make matrices across input files and regions. All operations can be done in parallel and return R/Bioconductor data structures.
iscream was designed to be memory efficient and fast on large datasets. This
vignette demonstrates iscream’s functions on a small dataset. For more
information on performance and considerations on larger real datasets see
vignette("performance").
iscream may be installed from https://bioconductor.org with
if (!require("BiocManager"))
install.packages("BiocManager")
BiocManager::install("iscream")Although iscream will compile using Rhtslib, it will perform best if compiled
against htslib which has itself been
compiled with libdeflate. See vignette("htslib") for more information.
library(iscream)
set_threads(2)## iscream now using 2 of 4 available threads.On load, iscream will inform the user about the number if threads it is set to
use. This is configurable with either set_threads() or by setting
option("iscream.threads"), which can be done in your .Rprofile to set it on
startup. Once loaded you use the following querying functions.
tabix()tabix is a command-line tool that queries records from compressed BED files. As input it receives the path to a tabixed BED file and one or more genomic regions of interest. It queries and prints the records from the tabixed BED file that fall within the regions of interest. This output may be redirected to a file and read into R.
iscream’s tabix() function works in a similar way but supports multiple files
and multiple input formats all in one function. It receives a vector of paths to
tabixed BED files and regions of interest. The input regions may be a vector of
"chr:start-end" strings, a data frame with "chr", "start", and "end"
columns or a GRanges object. It queries the BED files and returns the records
as a parsed data.table. To get a GRanges object instead, use
tabix_gr.
Using iscream eliminates context switching between the shell and R for tabix
queries since the queries are made in R and the result is an R data structure.
Rsamtools is another R package that can query data from BED
files, but it only supports one file at a time, only accepts GRanges as the
input regions and does not parse the tab-delimited strings. See
vignette("tabix") for a comparison of iscream and Rsamtools.
Using list.files(), you can get the paths to all compressed bed files in a
directory. These files contain small regions from chromosome 1 and Y from the
snmC-seq2 methylation data (Luo et al. 2018).
data_dir <- system.file("extdata", package = "iscream")
(bedfiles <- list.files(
data_dir,
pattern = "cell[1-5].bed.gz$",
full.names = TRUE))## [1] "/tmp/Rtmpj9ILAQ/Rinst11d6c93d88b614/iscream/extdata/cell1.bed.gz"
## [2] "/tmp/Rtmpj9ILAQ/Rinst11d6c93d88b614/iscream/extdata/cell2.bed.gz"
## [3] "/tmp/Rtmpj9ILAQ/Rinst11d6c93d88b614/iscream/extdata/cell3.bed.gz"
## [4] "/tmp/Rtmpj9ILAQ/Rinst11d6c93d88b614/iscream/extdata/cell4.bed.gz"For demonstration, I’m using two regions.
regions <- c("chr1:184577-680065", "chrY:56877780-56882524")Note: in this dataset the chromosome identifier is in a “chr[number]”
format. However, in some datasets, the chromosome ID is just a number or a
letter like ‘1’, or ‘Y’. To see the chromosome ID format across your files you
can use query_chroms() to get all unique chromosomes across all input files:
query_chroms(bedfiles)## [1] "chr1" "chrY"tabix() one file:
tabix(bedfiles[1], regions)## chr start end V1 V2
## <char> <int> <int> <num> <int>
## 1: chr1 184577 184578 0 1
## 2: chr1 191223 191224 0 1
## 3: chr1 191264 191265 0 1
## 4: chr1 191307 191308 0 1
## 5: chr1 191491 191492 0 1
## 6: chr1 191507 191508 0 1
## 7: chr1 191526 191527 0 1
## 8: chr1 679950 679951 1 1
## 9: chr1 680064 680065 1 1
## 10: chrY 56877780 56877781 0 1
## 11: chrY 56878350 56878351 0 1
## 12: chrY 56878351 56878352 0 1
## 13: chrY 56878431 56878432 0 1
## 14: chrY 56879483 56879484 0 2
## 15: chrY 56879535 56879536 0 2
## 16: chrY 56879539 56879540 1 1
## 17: chrY 56879724 56879725 0 1
## 18: chrY 56879834 56879835 0 1
## 19: chrY 56879863 56879864 1 1
## 20: chrY 56879915 56879916 1 1
## 21: chrY 56879945 56879946 1 1
## 22: chrY 56881130 56881131 1 1
## 23: chrY 56881926 56881927 1 1
## 24: chrY 56882523 56882524 0 1
## chr start end V1 V2With multiple files, the output contains a column for the file name.
tabix(bedfiles, regions)## chr start end V1 V2 file
## <char> <int> <int> <num> <int> <char>
## 1: chr1 184577 184578 0 1 cell1
## 2: chr1 191223 191224 0 1 cell1
## 3: chr1 191264 191265 0 1 cell1
## 4: chr1 191307 191308 0 1 cell1
## 5: chr1 191491 191492 0 1 cell1
## ---
## 70: chrY 56880518 56880519 0 1 cell4
## 71: chrY 56880715 56880716 0 2 cell4
## 72: chrY 56880914 56880915 0 2 cell4
## 73: chrY 56881927 56881928 1 1 cell4
## 74: chrY 56882345 56882346 1 1 cell4Both tabix() and tabix_gr() accept GRanges objects as input regions but
tabix_gr() returns GRanges objects instead of data frames. tabix_gr will
also preserve any input GRanges metadata.
if (!require("GenomicRanges", quietly = TRUE)) {
stop("The 'GenomicRanges' package must be installed for this functionality")
}
gr <- GRanges(regions)
tabix_gr(bedfiles, gr)## GRangesList object of length 4:
## $cell1
## GRanges object with 24 ranges and 3 metadata columns:
## seqnames ranges strand | V1 V2 file
## <Rle> <IRanges> <Rle> | <numeric> <integer> <character>
## [1] chr1 184578 * | 0 1 cell1
## [2] chr1 191224 * | 0 1 cell1
## [3] chr1 191265 * | 0 1 cell1
## [4] chr1 191308 * | 0 1 cell1
## [5] chr1 191492 * | 0 1 cell1
## ... ... ... ... . ... ... ...
## [20] chrY 56879916 * | 1 1 cell1
## [21] chrY 56879946 * | 1 1 cell1
## [22] chrY 56881131 * | 1 1 cell1
## [23] chrY 56881927 * | 1 1 cell1
## [24] chrY 56882524 * | 0 1 cell1
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths
##
## ...
## <3 more elements>You can set the result data frame’s column names or the GRanges mcols with
col.names:
tabix_gr(bedfiles, regions, col.names = c("beta", "coverage"))## GRangesList object of length 4:
## $cell1
## GRanges object with 24 ranges and 3 metadata columns:
## seqnames ranges strand | beta coverage file
## <Rle> <IRanges> <Rle> | <numeric> <integer> <character>
## [1] chr1 184578 * | 0 1 cell1
## [2] chr1 191224 * | 0 1 cell1
## [3] chr1 191265 * | 0 1 cell1
## [4] chr1 191308 * | 0 1 cell1
## [5] chr1 191492 * | 0 1 cell1
## ... ... ... ... . ... ... ...
## [20] chrY 56879916 * | 1 1 cell1
## [21] chrY 56879946 * | 1 1 cell1
## [22] chrY 56881131 * | 1 1 cell1
## [23] chrY 56881927 * | 1 1 cell1
## [24] chrY 56882524 * | 0 1 cell1
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths
##
## ...
## <3 more elements>tabix_raw() will return an unparsed named list like Rsamtools,
but with multi-file support:
tabix_raw(bedfiles, regions)## $cell1
## $cell1$`chr1:184577-680065`
## [1] "chr1\t184577\t184578\t0.000\t1" "chr1\t191223\t191224\t0.000\t1"
## [3] "chr1\t191264\t191265\t0.000\t1" "chr1\t191307\t191308\t0.000\t1"
## [5] "chr1\t191491\t191492\t0.000\t1" "chr1\t191507\t191508\t0.000\t1"
## [7] "chr1\t191526\t191527\t0.000\t1" "chr1\t679950\t679951\t1.000\t1"
## [9] "chr1\t680064\t680065\t1.000\t1"
##
## $cell1$`chrY:56877780-56882524`
## [1] "chrY\t56877780\t56877781\t0.000\t1" "chrY\t56878350\t56878351\t0.000\t1"
## [3] "chrY\t56878351\t56878352\t0.000\t1" "chrY\t56878431\t56878432\t0.000\t1"
## [5] "chrY\t56879483\t56879484\t0.000\t2" "chrY\t56879535\t56879536\t0.000\t2"
## [7] "chrY\t56879539\t56879540\t1.000\t1" "chrY\t56879724\t56879725\t0.000\t1"
## [9] "chrY\t56879834\t56879835\t0.000\t1" "chrY\t56879863\t56879864\t1.000\t1"
## [11] "chrY\t56879915\t56879916\t1.000\t1" "chrY\t56879945\t56879946\t1.000\t1"
## [13] "chrY\t56881130\t56881131\t1.000\t1" "chrY\t56881926\t56881927\t1.000\t1"
## [15] "chrY\t56882523\t56882524\t0.000\t1"
##
##
## $cell2
## $cell2$`chr1:184577-680065`
## character(0)
##
## $cell2$`chrY:56877780-56882524`
## [1] "chrY\t56878351\t56878352\t0.000\t1" "chrY\t56879482\t56879483\t1.000\t1"
## [3] "chrY\t56879483\t56879484\t1.000\t1" "chrY\t56879534\t56879535\t1.000\t1"
## [5] "chrY\t56879538\t56879539\t1.000\t1" "chrY\t56880322\t56880323\t0.000\t1"
## [7] "chrY\t56880404\t56880405\t1.000\t1" "chrY\t56881129\t56881130\t0.000\t1"
## [9] "chrY\t56881285\t56881286\t0.000\t1" "chrY\t56881364\t56881365\t1.000\t1"
## [11] "chrY\t56882344\t56882345\t0.000\t1" "chrY\t56882523\t56882524\t0.000\t1"
##
##
## $cell3
## $cell3$`chr1:184577-680065`
## character(0)
##
## $cell3$`chrY:56877780-56882524`
## [1] "chrY\t56879862\t56879863\t1.000\t1" "chrY\t56879914\t56879915\t0.500\t2"
## [3] "chrY\t56879944\t56879945\t1.000\t1" "chrY\t56879969\t56879970\t1.000\t1"
## [5] "chrY\t56879976\t56879977\t1.000\t1" "chrY\t56880322\t56880323\t0.000\t1"
## [7] "chrY\t56880359\t56880360\t0.000\t1" "chrY\t56881129\t56881130\t0.500\t2"
##
##
## $cell4
## $cell4$`chr1:184577-680065`
## [1] "chr1\t191003\t191004\t1.000\t1" "chr1\t191307\t191308\t1.000\t1"
## [3] "chr1\t191438\t191439\t1.000\t1" "chr1\t191491\t191492\t1.000\t1"
## [5] "chr1\t191722\t191723\t1.000\t1" "chr1\t191747\t191748\t1.000\t1"
## [7] "chr1\t191776\t191777\t1.000\t1" "chr1\t628458\t628459\t1.000\t1"
## [9] "chr1\t631433\t631434\t1.000\t1" "chr1\t631436\t631437\t1.000\t1"
##
## $cell4$`chrY:56877780-56882524`
## [1] "chrY\t56877780\t56877781\t0.000\t1" "chrY\t56877810\t56877811\t0.000\t1"
## [3] "chrY\t56879834\t56879835\t0.000\t1" "chrY\t56879863\t56879864\t1.000\t1"
## [5] "chrY\t56879915\t56879916\t1.000\t1" "chrY\t56879945\t56879946\t0.000\t1"
## [7] "chrY\t56880045\t56880046\t0.500\t2" "chrY\t56880081\t56880082\t0.000\t1"
## [9] "chrY\t56880092\t56880093\t0.500\t2" "chrY\t56880112\t56880113\t1.000\t1"
## [11] "chrY\t56880137\t56880138\t1.000\t2" "chrY\t56880142\t56880143\t0.500\t2"
## [13] "chrY\t56880323\t56880324\t0.000\t1" "chrY\t56880360\t56880361\t1.000\t1"
## [15] "chrY\t56880405\t56880406\t1.000\t1" "chrY\t56880518\t56880519\t0.000\t1"
## [17] "chrY\t56880715\t56880716\t0.000\t2" "chrY\t56880914\t56880915\t0.000\t2"
## [19] "chrY\t56881927\t56881928\t1.000\t1" "chrY\t56882345\t56882346\t1.000\t1"Since iscream was originally developed to read Whole Genome Bisulfite Sequencing
data, it has the aligner argument for automatically setting column names for
the BISCUIT (Zhou et al. 2024), Bismark (Krueger and Andrews 2011), and BSBolt (Farrell et al. 2021).
tabix_gr(bedfiles, regions, aligner = "biscuit")## GRangesList object of length 4:
## $cell1
## GRanges object with 24 ranges and 3 metadata columns:
## seqnames ranges strand | beta coverage file
## <Rle> <IRanges> <Rle> | <numeric> <integer> <character>
## [1] chr1 184578 * | 0 1 cell1
## [2] chr1 191224 * | 0 1 cell1
## [3] chr1 191265 * | 0 1 cell1
## [4] chr1 191308 * | 0 1 cell1
## [5] chr1 191492 * | 0 1 cell1
## ... ... ... ... . ... ... ...
## [20] chrY 56879916 * | 1 1 cell1
## [21] chrY 56879946 * | 1 1 cell1
## [22] chrY 56881131 * | 1 1 cell1
## [23] chrY 56881927 * | 1 1 cell1
## [24] chrY 56882524 * | 0 1 cell1
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths
##
## ...
## <3 more elements>summarize_regions()Using the same BED files and regions, iscream can also summarize the data within
regions. This requires providing the index of the data columns you want
summarized and the column names for the output. All summarizing functions are
run on each input column by default - see ?summarize_regions for supported
functions.
summarize_regions(
bedfiles,
regions,
columns = c(4, 5),
col_names = c("beta", "coverage")
)## [00:39:53.272479] [iscream::summarize_regions] [info] Summarizing 2 regions from 4 bedfiles
## [00:39:53.272517] [iscream::summarize_regions] [info] using sum, mean, median, stddev, variance, min, max, range, count
## [00:39:53.272522] [iscream::summarize_regions] [info] with columns 4, 5 as beta, coverage## feature file beta.sum coverage.sum beta.mean coverage.mean
## 1 chr1:184577-680065 cell1 2.0 9 0.2222222 1.000000
## 2 chrY:56877780-56882524 cell1 6.0 17 0.4000000 1.133333
## 3 chr1:184577-680065 cell2 NA NA NA NA
## 4 chrY:56877780-56882524 cell2 6.0 12 0.5000000 1.000000
## 5 chr1:184577-680065 cell3 NA NA NA NA
## 6 chrY:56877780-56882524 cell3 5.0 10 0.6250000 1.250000
## 7 chr1:184577-680065 cell4 10.0 10 1.0000000 1.000000
## 8 chrY:56877780-56882524 cell4 9.5 26 0.4750000 1.300000
## beta.median coverage.median beta.stddev coverage.stddev beta.variance
## 1 0.00 1 0.4409586 0.0000000 0.1944444
## 2 0.00 1 0.5070926 0.3518658 0.2571429
## 3 NA NA NA NA NA
## 4 0.50 1 0.5222330 0.0000000 0.2727273
## 5 NA NA NA NA NA
## 6 0.75 1 0.4432026 0.4629100 0.1964286
## 7 1.00 1 0.0000000 0.0000000 0.0000000
## 8 0.50 1 0.4722566 0.4701623 0.2230263
## coverage.variance beta.min coverage.min beta.max coverage.max beta.range
## 1 0.0000000 0 1 1 1 1
## 2 0.1238095 0 1 1 2 1
## 3 NA NA NA NA NA NA
## 4 0.0000000 0 1 1 1 1
## 5 NA NA NA NA NA NA
## 6 0.2142857 0 1 1 2 1
## 7 0.0000000 1 1 1 1 0
## 8 0.2210526 0 1 1 2 1
## coverage.range count
## 1 0 9
## 2 1 15
## 3 NA NA
## 4 0 12
## 5 NA NA
## 6 1 8
## 7 0 10
## 8 1 20The feature column here contains the genomic region coordinates, but can be
set to something more informational if you have names for the regions. You can
also select the functions applied with fun:
names(regions) <- c("R1", "R2")
summarize_regions(
bedfiles,
regions,
fun = c("mean", "sum"),
columns = 5,
col_names = "coverage"
)## [00:39:53.327425] [iscream::summarize_regions] [info] Summarizing 2 regions from 4 bedfiles
## [00:39:53.327458] [iscream::summarize_regions] [info] using mean, sum
## [00:39:53.327463] [iscream::summarize_regions] [info] with columns 5 as coverage## feature file coverage.mean coverage.sum
## 1 R1 cell1 1.000000 9
## 2 R2 cell1 1.133333 17
## 3 R1 cell2 NA NA
## 4 R2 cell2 1.000000 12
## 5 R1 cell3 NA NA
## 6 R2 cell3 1.250000 10
## 7 R1 cell4 1.000000 10
## 8 R2 cell4 1.300000 26For WGBS data specifically, you can use summarize_meth_regions(), which takes
the aligner argument to correctly parse the columns.
summarize_meth_regions(
bedfiles,
regions,
aligner = "biscuit",
fun = c("mean", "sum")
)## [00:39:53.362221] [iscream::summarize_regions] [info] Summarizing 2 regions from 4 bedfiles
## [00:39:53.362264] [iscream::summarize_regions] [info] using mean, sum
## [00:39:53.362268] [iscream::summarize_regions] [info] with columns 4, 5 as coverage, M## feature file coverage.mean M.mean coverage.sum M.sum
## 1 R1 cell1 1.000000 0.2222222 9 2
## 2 R2 cell1 1.133333 0.4000000 17 6
## 3 R1 cell2 NA NA NA NA
## 4 R2 cell2 1.000000 0.5000000 12 6
## 5 R1 cell3 NA NA NA NA
## 6 R2 cell3 1.250000 0.7500000 10 6
## 7 R1 cell4 1.000000 1.0000000 10 10
## 8 R2 cell4 1.300000 0.6000000 26 12make_mat()make_mat() makes a matrix where every row is a locus found in at least one input
file and the columns are the input files. It returns a named list of the matrix,
coordinates, and file names you can use for analysis or other data structures.
make_mat_se() returns a RangedSummarizedExperiment, and make_mat_gr()
returns a GRanges object.
if (!require("SummarizedExperiment", quietly = TRUE)) {
stop("The 'SummarizedExperiment' package must be installed for this functionality")
}
(mat <- make_mat_se(bedfiles, regions, column = 4, mat_name = "beta"))## [00:39:53.396108] [iscream::query_all] [info] Querying 2 regions from 4 bedfiles
##
## [00:39:53.396657] [iscream::query_all] [info] Creating metadata vectors
## [00:39:53.396713] [iscream::query_all] [info] 62 loci found - 9938 extra rows allocated with 0 resizes
## [00:39:53.396719] [iscream::query_all] [info] Creating dense matrix## class: RangedSummarizedExperiment
## dim: 62 4
## metadata(0):
## assays(1): beta
## rownames: NULL
## rowData names(0):
## colnames(4): cell1 cell2 cell3 cell4
## colData names(0):head(assay(mat), 10)## cell1 cell2 cell3 cell4
## [1,] 0 0 1.0 0
## [2,] 0 0 0.5 0
## [3,] 0 0 1.0 0
## [4,] 0 0 1.0 0
## [5,] 0 0 1.0 0
## [6,] 0 0 0.0 0
## [7,] 0 0 0.0 0
## [8,] 0 0 0.5 0
## [9,] 0 0 0.0 0
## [10,] 0 0 0.0 0If you have sparse data, you can save memory with sparse = TRUE, but only for
make_mat() and make_mat_se().
mat <- make_mat(bedfiles, regions, column = 4, mat_name = "beta", sparse = TRUE)## [00:39:53.489790] [iscream::query_all] [info] Querying 2 regions from 4 bedfiles
##
## [00:39:53.490422] [iscream::query_all] [info] Creating metadata vectors
## [00:39:53.490477] [iscream::query_all] [info] 62 loci found - 9938 extra rows allocated with 0 resizes
## [00:39:53.490501] [iscream::query_all] [info] Creating sparse matrixhead(mat$beta, 10)## 10 x 4 sparse Matrix of class "dgCMatrix"
## cell1 cell2 cell3 cell4
## [1,] . . 1.0 .
## [2,] . . 0.5 .
## [3,] . . 1.0 .
## [4,] . . 1.0 .
## [5,] . . 1.0 .
## [6,] . . . .
## [7,] . . . .
## [8,] . . 0.5 .
## [9,] . . . .
## [10,] . . . .For a BSseq compatible object, use make_meth_mat which returns
a list of BSseq inputs. To make the BSseq object run
if (require("bsseq", quietly = TRUE)) {
meth_mat <- make_mat_bsseq(bedfiles, regions)
do.call(BSseq, meth_mat)
}## [00:39:53.569466] [iscream::query_all] [info] Querying 2 regions from 4 bedfiles
##
## [00:39:53.570324] [iscream::query_all] [info] Creating metadata vectors
## [00:39:53.570375] [iscream::query_all] [info] 62 loci found - 9938 extra rows allocated with 0 resizes
## [00:39:53.570392] [iscream::query_all] [info] Creating dense matrix## An object of type 'BSseq' with
## 62 methylation loci
## 4 samples
## has not been smoothed
## All assays are in-memoryWhile this vignette used a toy dataset, vignette("performance") has tips on
getting the best performance from iscream using a real and larger dataset with
100 files. vignette("TSS") is an example of iscream’s application to examine
methylation around transcription start site on another real dataset. For more
information on the supported Bioconductor data structures and conversions see
vignette("data_structures").
sessionInfo()## R version 4.5.1 Patched (2025-08-23 r88802)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] ggplot2_4.0.0 bsseq_1.46.0
## [3] SummarizedExperiment_1.40.0 Biobase_2.70.0
## [5] MatrixGenerics_1.22.0 matrixStats_1.5.0
## [7] GenomicRanges_1.62.0 Seqinfo_1.0.0
## [9] IRanges_2.44.0 S4Vectors_0.48.0
## [11] BiocGenerics_0.56.0 generics_0.1.4
## [13] iscream_1.0.0 BiocStyle_2.38.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 dplyr_1.1.4
## [3] farver_2.1.2 S7_0.2.0
## [5] R.utils_2.13.0 Biostrings_2.78.0
## [7] bitops_1.0-9 fastmap_1.2.0
## [9] RCurl_1.98-1.17 GenomicAlignments_1.46.0
## [11] stringfish_0.17.0 XML_3.99-0.19
## [13] digest_0.6.37 lifecycle_1.0.4
## [15] statmod_1.5.1 magrittr_2.0.4
## [17] compiler_4.5.1 rlang_1.1.6
## [19] sass_0.4.10 tools_4.5.1
## [21] yaml_2.3.10 data.table_1.17.8
## [23] rtracklayer_1.70.0 knitr_1.50
## [25] labeling_0.4.3 S4Arrays_1.10.0
## [27] curl_7.0.0 DelayedArray_0.36.0
## [29] RColorBrewer_1.1-3 abind_1.4-8
## [31] BiocParallel_1.44.0 HDF5Array_1.38.0
## [33] withr_3.0.2 R.oo_1.27.1
## [35] grid_4.5.1 beachmat_2.26.0
## [37] Rhdf5lib_1.32.0 scales_1.4.0
## [39] gtools_3.9.5 tinytex_0.57
## [41] dichromat_2.0-0.1 cli_3.6.5
## [43] rmarkdown_2.30 crayon_1.5.3
## [45] RcppParallel_5.1.11-1 httr_1.4.7
## [47] rjson_0.2.23 DelayedMatrixStats_1.32.0
## [49] pbapply_1.7-4 cachem_1.1.0
## [51] rhdf5_2.54.0 parallel_4.5.1
## [53] BiocManager_1.30.26 XVector_0.50.0
## [55] restfulr_0.0.16 vctrs_0.6.5
## [57] Matrix_1.7-4 jsonlite_2.0.0
## [59] bookdown_0.45 magick_2.9.0
## [61] h5mread_1.2.0 locfit_1.5-9.12
## [63] limma_3.66.0 jquerylib_0.1.4
## [65] glue_1.8.0 parallelly_1.45.1
## [67] codetools_0.2-20 gtable_0.3.6
## [69] BiocIO_1.20.0 tibble_3.3.0
## [71] pillar_1.11.1 htmltools_0.5.8.1
## [73] rhdf5filters_1.22.0 BSgenome_1.78.0
## [75] R6_2.6.1 sparseMatrixStats_1.22.0
## [77] evaluate_1.0.5 lattice_0.22-7
## [79] R.methodsS3_1.8.2 Rsamtools_2.26.0
## [81] cigarillo_1.0.0 bslib_0.9.0
## [83] Rcpp_1.1.0 SparseArray_1.10.0
## [85] permute_0.9-8 xfun_0.53
## [87] pkgconfig_2.0.3Farrell, Colin, Michael Thompson, Anela Tosevska, Adewale Oyetunde, and Matteo Pellegrini. 2021. “BiSulfite Bolt: A Bisulfite Sequencing Analysis Platform.” Gigascience 10 (5): giab033. https://doi.org/10.1093/gigascience/giab033.
Krueger, Felix, and Simon R. Andrews. 2011. “Bismark: A Flexible Aligner and Methylation Caller for Bisulfite-Seq Applications.” Bioinformatics 27 (11): 1571–2. https://doi.org/10.1093/bioinformatics/btr167.
Luo, Chongyuan, Angeline Rivkin, Jingtian Zhou, Justin P. Sandoval, Laurie Kurihara, Jacinta Lucero, Rosa Castanon, et al. 2018. “Robust Single-Cell DNA Methylome Profiling with snmC-seq2.” Nat Commun 9 (1): 3824. https://doi.org/10.1038/s41467-018-06355-2.
Zhou, Wanding, Benjamin K Johnson, Jacob Morrison, Ian Beddows, James Eapen, Efrat Katsman, Ayush Semwal, et al. 2024. “BISCUIT: An Efficient, Standards-Compliant Tool Suite for Simultaneous Genetic and Epigenetic Inference in Bulk and Single-Cell Studies.” Nucleic Acids Research 52 (6): gkae097. https://doi.org/10.1093/nar/gkae097.