cliProfiler 1.4.0
Cross-linking immunoprecipitation (CLIP) is a technique that combines UV cross-linking and immunoprecipitation to analyse protein-RNA interactions or to pinpoint RNA modifications (e.g. m6A). CLIP-based methods, such as iCLIP and eCLIP, allow precise mapping of RNA modification sites or RNA-binding protein (RBP) binding sites on a genome-wide scale. These techniques help us to unravel post-transcriptional regulatory networks. In order to make the visualization of CLIP data easier, we develop cliProfiler package. The cliProfiler includes seven functions which allow users easily make different profile plots.
The cliProfiler package is available at
https://bioconductor.org and can be
installed via BiocManager::install:
if (!require("BiocManager"))
    install.packages("BiocManager")
BiocManager::install("cliProfiler")A package only needs to be installed once. Load the package into an R session with
library(cliProfiler)The input data for using all the functions in cliProfiler should
be the peak calling result or other similar object that represents the RBP
binding sites or RNA modification position. Moreover, these peaks/signals be
stored in the GRanges object. The GRanges is an S4 class which defined
by GenomicRanges. The GRanges class is a container for the
genomic locations and their associated annotations. For more information about
GRanges objects please check GenomicRanges package. An example
of GRanges object is shown below:
testpath <- system.file("extdata", package = "cliProfiler")
## loading the test GRanges object
test <- readRDS(file.path(testpath, "test.rds"))
## Show an example of GRanges object
test## GRanges object with 100 ranges and 0 metadata columns:
##         seqnames              ranges strand
##            <Rle>           <IRanges>  <Rle>
##     [1]    chr17   28748198-28748218      +
##     [2]    chr10 118860137-118860157      -
##     [3]     chr2 148684461-148684481      +
##     [4]     chr2   84602546-84602566      -
##     [5]    chr18     6111874-6111894      -
##     ...      ...                 ...    ...
##    [96]     chr7 127254692-127254712      +
##    [97]     chr2   28833830-28833850      -
##    [98]     chr9   44607255-44607275      +
##    [99]     chr1 133621331-133621351      -
##   [100]     chr4 130316598-130316618      -
##   -------
##   seqinfo: 22 sequences from an unspecified genome; no seqlengthsThe annotation file that required by functions exonProfile,
geneTypeProfile, intronProfile, spliceSiteProfile and metaGeneProfile
should be in the gff3 format and download from
https://www.gencodegenes.org/. In the
cliProfiler package, we include a test gff3 file.
## the path for the test gff3 file
test_gff3 <- file.path(testpath, "annotation_test.gff3")
## the gff3 file can be loaded by import.gff3 function in rtracklayer package
shown_gff3 <- rtracklayer::import.gff3(test_gff3)
## show the test gff3 file
shown_gff3## GRanges object with 3068 ranges and 23 metadata columns:
##          seqnames              ranges strand |   source            type
##             <Rle>           <IRanges>  <Rle> | <factor>        <factor>
##      [1]     chr1   72159442-72212307      - |   HAVANA     transcript 
##      [2]     chr1   72212017-72212307      - |   HAVANA     exon       
##      [3]     chr1   72212017-72212111      - |   HAVANA     CDS        
##      [4]     chr1   72212109-72212111      - |   HAVANA     start_codon
##      [5]     chr1   72192043-72192202      - |   HAVANA     exon       
##      ...      ...                 ...    ... .      ...             ...
##   [3064]     chrX 153392866-153392868      + |   HAVANA stop_codon     
##   [3065]     chrX 153237748-153238092      + |   HAVANA five_prime_UTR 
##   [3066]     chrX 153308852-153308924      + |   HAVANA five_prime_UTR 
##   [3067]     chrX 153370845-153370846      + |   HAVANA five_prime_UTR 
##   [3068]     chrX 153392869-153396132      + |   HAVANA three_prime_UTR
##              score     phase                     ID               gene_id
##          <numeric> <integer>            <character>           <character>
##      [1]        NA      <NA>   ENSMUST00000048860.8  ENSMUSG00000039395.8
##      [2]        NA      <NA> exon:ENSMUST00000048..  ENSMUSG00000039395.8
##      [3]        NA         0 CDS:ENSMUST000000488..  ENSMUSG00000039395.8
##      [4]        NA         0 start_codon:ENSMUST0..  ENSMUSG00000039395.8
##      [5]        NA      <NA> exon:ENSMUST00000048..  ENSMUSG00000039395.8
##      ...       ...       ...                    ...                   ...
##   [3064]        NA         0 stop_codon:ENSMUST00.. ENSMUSG00000041649.13
##   [3065]        NA      <NA> UTR5:ENSMUST00000112.. ENSMUSG00000041649.13
##   [3066]        NA      <NA> UTR5:ENSMUST00000112.. ENSMUSG00000041649.13
##   [3067]        NA      <NA> UTR5:ENSMUST00000112.. ENSMUSG00000041649.13
##   [3068]        NA      <NA> UTR3:ENSMUST00000112.. ENSMUSG00000041649.13
##               gene_type   gene_name       level      mgi_id
##             <character> <character> <character> <character>
##      [1] protein_coding        Mreg           2 MGI:2151839
##      [2] protein_coding        Mreg           2 MGI:2151839
##      [3] protein_coding        Mreg           2 MGI:2151839
##      [4] protein_coding        Mreg           2 MGI:2151839
##      [5] protein_coding        Mreg           2 MGI:2151839
##      ...            ...         ...         ...         ...
##   [3064] protein_coding        Klf8           2 MGI:2442430
##   [3065] protein_coding        Klf8           2 MGI:2442430
##   [3066] protein_coding        Klf8           2 MGI:2442430
##   [3067] protein_coding        Klf8           2 MGI:2442430
##   [3068] protein_coding        Klf8           2 MGI:2442430
##                   havana_gene               Parent        transcript_id
##                   <character>      <CharacterList>          <character>
##      [1] OTTMUSG00000049069.1 ENSMUSG00000039395.8 ENSMUST00000048860.8
##      [2] OTTMUSG00000049069.1 ENSMUST00000048860.8 ENSMUST00000048860.8
##      [3] OTTMUSG00000049069.1 ENSMUST00000048860.8 ENSMUST00000048860.8
##      [4] OTTMUSG00000049069.1 ENSMUST00000048860.8 ENSMUST00000048860.8
##      [5] OTTMUSG00000049069.1 ENSMUST00000048860.8 ENSMUST00000048860.8
##      ...                  ...                  ...                  ...
##   [3064] OTTMUSG00000019377.5 ENSMUST00000112574.8 ENSMUST00000112574.8
##   [3065] OTTMUSG00000019377.5 ENSMUST00000112574.8 ENSMUST00000112574.8
##   [3066] OTTMUSG00000019377.5 ENSMUST00000112574.8 ENSMUST00000112574.8
##   [3067] OTTMUSG00000019377.5 ENSMUST00000112574.8 ENSMUST00000112574.8
##   [3068] OTTMUSG00000019377.5 ENSMUST00000112574.8 ENSMUST00000112574.8
##          transcript_type transcript_name transcript_support_level
##              <character>     <character>              <character>
##      [1]  protein_coding        Mreg-201                        1
##      [2]  protein_coding        Mreg-201                        1
##      [3]  protein_coding        Mreg-201                        1
##      [4]  protein_coding        Mreg-201                        1
##      [5]  protein_coding        Mreg-201                        1
##      ...             ...             ...                      ...
##   [3064]  protein_coding        Klf8-202                        1
##   [3065]  protein_coding        Klf8-202                        1
##   [3066]  protein_coding        Klf8-202                        1
##   [3067]  protein_coding        Klf8-202                        1
##   [3068]  protein_coding        Klf8-202                        1
##                                                     tag    havana_transcript
##                                         <CharacterList>          <character>
##      [1]                  basic,appris_principal_1,CCDS OTTMUST00000125321.1
##      [2]                  basic,appris_principal_1,CCDS OTTMUST00000125321.1
##      [3]                  basic,appris_principal_1,CCDS OTTMUST00000125321.1
##      [4]                  basic,appris_principal_1,CCDS OTTMUST00000125321.1
##      [5]                  basic,appris_principal_1,CCDS OTTMUST00000125321.1
##      ...                                            ...                  ...
##   [3064] alternative_5_UTR,basic,appris_principal_1,... OTTMUST00000046245.1
##   [3065] alternative_5_UTR,basic,appris_principal_1,... OTTMUST00000046245.1
##   [3066] alternative_5_UTR,basic,appris_principal_1,... OTTMUST00000046245.1
##   [3067] alternative_5_UTR,basic,appris_principal_1,... OTTMUST00000046245.1
##   [3068] alternative_5_UTR,basic,appris_principal_1,... OTTMUST00000046245.1
##                    protein_id      ccdsid   trans_len exon_number
##                   <character> <character> <character> <character>
##      [1] ENSMUSP00000041878.7 CCDS15032.1        2284        <NA>
##      [2] ENSMUSP00000041878.7 CCDS15032.1        2284           1
##      [3] ENSMUSP00000041878.7 CCDS15032.1        2284           1
##      [4] ENSMUSP00000041878.7 CCDS15032.1        2284           1
##      [5] ENSMUSP00000041878.7 CCDS15032.1        2284           2
##      ...                  ...         ...         ...         ...
##   [3064] ENSMUSP00000108193.2 CCDS30481.1        4752           7
##   [3065] ENSMUSP00000108193.2 CCDS30481.1        4752           1
##   [3066] ENSMUSP00000108193.2 CCDS30481.1        4752           2
##   [3067] ENSMUSP00000108193.2 CCDS30481.1        4752           3
##   [3068] ENSMUSP00000108193.2 CCDS30481.1        4752           7
##                       exon_id
##                   <character>
##      [1]                 <NA>
##      [2] ENSMUSE00000600755.2
##      [3] ENSMUSE00000600755.2
##      [4] ENSMUSE00000600755.2
##      [5] ENSMUSE00000262166.1
##      ...                  ...
##   [3064] ENSMUSE00000692289.2
##   [3065] ENSMUSE00000745002.1
##   [3066] ENSMUSE00000692290.1
##   [3067] ENSMUSE00000253395.2
##   [3068] ENSMUSE00000692289.2
##   -------
##   seqinfo: 19 sequences from an unspecified genome; no seqlengthsThe function windowProfile allows users to find out the enrichment of peaks
against the customized annotation file. This customized annotation file should
be stored in the GRanges object.
metaGeneProfile() outputs a meta profile, which shows the location of binding
sites or modification sites ( peaks/signals) along transcript regions
(5’UTR, CDS and 3’UTR). The input of this function should be a GRanges
object.
Besides the GRanges object, a path to the gff3 annotation file which
download from Gencode is required by
metaGeneProfile.
The output of metaGeneProfile is a List objects. The List one contains
the GRanges objects with the calculation result which can be used in different
ways later.
meta <- metaGeneProfile(object = test, annotation = test_gff3)
meta[[1]]## GRanges object with 100 ranges and 5 metadata columns:
##         seqnames              ranges strand |    center    location
##            <Rle>           <IRanges>  <Rle> | <integer> <character>
##     [1]    chr10 118860137-118860157      - | 118860147         CDS
##     [2]     chr2   84602546-84602566      - |  84602556        UTR3
##     [3]    chr18     6111874-6111894      - |   6111884         CDS
##     [4]    chr11   33213145-33213165      - |  33213155        UTR3
##     [5]    chr11   96819422-96819442      - |  96819432         CDS
##     ...      ...                 ...    ... .       ...         ...
##    [96]     chr8   72222842-72222862      + |  72222852          NO
##    [97]    chr18   36648184-36648204      + |  36648194         CDS
##    [98]     chr8 105216021-105216041      + | 105216031        UTR3
##    [99]     chr7 127254692-127254712      + | 127254702        UTR3
##   [100]     chr9   44607255-44607275      + |  44607265        UTR5
##                       Gene_ID         Transcript_ID  Position
##                   <character>           <character> <numeric>
##     [1]  ENSMUSG00000028630.9  ENSMUST00000004281.9  0.674444
##     [2] ENSMUSG00000034101.14  ENSMUST00000067232.9  0.122384
##     [3] ENSMUSG00000041225.16 ENSMUST00000077128.12  0.199836
##     [4] ENSMUSG00000040594.19  ENSMUST00000102815.9  0.159303
##     [5] ENSMUSG00000038615.17  ENSMUST00000107658.7  0.889039
##     ...                   ...                   ...       ...
##    [96]                   Nan                  <NA> 5.0000000
##    [97]  ENSMUSG00000117942.1  ENSMUST00000140061.7 0.1694561
##    [98] ENSMUSG00000031885.14  ENSMUST00000109392.8 0.0457421
##    [99]  ENSMUSG00000054716.4  ENSMUST00000052509.5 0.3978495
##   [100] ENSMUSG00000032097.10  ENSMUST00000217034.1 0.5779817
##   -------
##   seqinfo: 22 sequences from an unspecified genome; no seqlengthsHere is an explanation of the metaData columns of the output GRanges objects:
peak/signal belongs to.peak/signal belongs.peak/signal within the genomic
region. This value close to 0 means this peak located close to the 5’ end of
the genomic feature. The position value close to 1 means the peak close to the
3’ end of the genomic feature. Value 5 means this peaks can not be mapped to
any annotation.The List two is the meta plot which in the ggplot class. The user can use
all the functions from ggplot2 to change the detail of this plot.
library(ggplot2)
## For example if user want to have a new name for the plot
meta[[2]] + ggtitle("Meta Profile 2")For the advance usage, the metaGeneProfile provides two methods to calculate
the relative position. The first method return a relative position of the
peaks/signals in the genomic feature without the introns. The second method
return a relative position value of the peak in the genomic feature with the
introns. With the parameter include_intron we can easily shift between these
two methods. If the data is a polyA plus data, we will recommend you to set
include_intron = FALSE.
meta <- metaGeneProfile(object = test, annotation = test_gff3, 
                        include_intron = TRUE)
meta[[2]]The group option allows user to make a meta plot with multiple conditions.
Here is an example:
test$Treat <- c(rep("Treatment 1",50), rep("Treatment 2", 50))
meta <- metaGeneProfile(object = test, annotation = test_gff3, 
                        group = "Treat")
meta[[2]]Besides, we provide an annotation filtering option for making the meta plot.
The exlevel and extranscript_support_level could be used for specifying
which level or transcript support level should be excluded. For excluding
the transcript support level NA, user can use 6 instead of NA. About more
information of level and transcript support level you can check the
Gencode data format.
metaGeneProfile(object = test, annotation = test_gff3, exlevel = 3, 
                extranscript_support_level = c(4,5,6))The split option could help to make the meta profile for the peaks/signals
in 5’UTR, CDS and 3’UTR separately. The grey dotted line represents the peaks’s
distribution across all region.
meta <- metaGeneProfile(object = test, annotation = test_gff3, split = TRUE)
meta[[2]]The function intronProfile generates the profile of peaks/signals in the
intronic region. Here is an example for a quick use of intronProfile.
intron <-  intronProfile(test, test_gff3)Similar to metaGeneProfile, the output of intronProfile is a List object
which contains two Lists. List one is the input GRanges objects with the
calculation result.
intron[[1]]## GRanges object with 100 ranges and 7 metadata columns:
##         seqnames              ranges strand |       Treat    center  Intron_S
##            <Rle>           <IRanges>  <Rle> | <character> <integer> <numeric>
##     [1]    chr17   28748198-28748218      + | Treatment 1  28748208         0
##     [2]     chr2 148684461-148684481      + | Treatment 1 148684471         0
##     [3]     chr7     5097955-5097975      + | Treatment 1   5097965         0
##     [4]     chr4 139648373-139648393      + | Treatment 1 139648383 139645102
##     [5]     chr7   27580623-27580643      + | Treatment 1  27580633         0
##     ...      ...                 ...    ... .         ...       ...       ...
##    [96]    chr17   46148089-46148109      - | Treatment 2  46148099         0
##    [97]    chr11   78074094-78074114      - | Treatment 2  78074104         0
##    [98]     chr2   28833830-28833850      - | Treatment 2  28833840         0
##    [99]     chr1 133621331-133621351      - | Treatment 2 133621341         0
##   [100]     chr4 130316598-130316618      - | Treatment 2 130316608         0
##          Intron_E Intron_length Intron_transcript_id Intron_map
##         <numeric>     <numeric>          <character>  <numeric>
##     [1]         0             0                   NO   3.000000
##     [2]         0             0                   NO   3.000000
##     [3]         0             0                   NO   3.000000
##     [4] 139653534          8433 ENSMUST00000178644.1   0.389067
##     [5]         0             0                   NO   3.000000
##     ...       ...           ...                  ...        ...
##    [96]         0             0                   NO          3
##    [97]         0             0                   NO          3
##    [98]         0             0                   NO          3
##    [99]         0             0                   NO          3
##   [100]         0             0                   NO          3
##   -------
##   seqinfo: 22 sequences from an unspecified genome; no seqlengthsThe explanation of meta data in the output of intronProfile list one is
pasted down below:
The List two includes a ggplot object.
intron[[2]]Similar to metaGeneProfile, in intronProfile, we provide options , such as
group, exlevel and extranscript_support_level. The group function could
be used to generate the comparison plot.
intron <-  intronProfile(test, test_gff3, group = "Treat")
intron[[2]]The parameter exlevel and extranscript_support_level could be used for
specifying which level or transcript support level should be excluded.
For excluding the transcript support level NA, you can use 6. About more
information of level and transcript support level you can check the
Gencode data format.
intronProfile(test, test_gff3, group = "Treat", exlevel = 3, 
    extranscript_support_level = c(4,5,6))## $Peaks
## GRanges object with 100 ranges and 7 metadata columns:
##         seqnames              ranges strand |       Treat    center  Intron_S
##            <Rle>           <IRanges>  <Rle> | <character> <integer> <numeric>
##     [1]    chr17   28748198-28748218      + | Treatment 1  28748208         0
##     [2]     chr2 148684461-148684481      + | Treatment 1 148684471         0
##     [3]     chr7     5097955-5097975      + | Treatment 1   5097965         0
##     [4]     chr4 139648373-139648393      + | Treatment 1 139648383         0
##     [5]     chr7   27580623-27580643      + | Treatment 1  27580633         0
##     ...      ...                 ...    ... .         ...       ...       ...
##    [96]    chr17   46148089-46148109      - | Treatment 2  46148099         0
##    [97]    chr11   78074094-78074114      - | Treatment 2  78074104         0
##    [98]     chr2   28833830-28833850      - | Treatment 2  28833840         0
##    [99]     chr1 133621331-133621351      - | Treatment 2 133621341         0
##   [100]     chr4 130316598-130316618      - | Treatment 2 130316608         0
##          Intron_E Intron_length Intron_transcript_id Intron_map
##         <numeric>     <numeric>          <character>  <numeric>
##     [1]         0             0                   NO          3
##     [2]         0             0                   NO          3
##     [3]         0             0                   NO          3
##     [4]         0             0                   NO          3
##     [5]         0             0                   NO          3
##     ...       ...           ...                  ...        ...
##    [96]         0             0                   NO          3
##    [97]         0             0                   NO          3
##    [98]         0             0                   NO          3
##    [99]         0             0                   NO          3
##   [100]         0             0                   NO          3
##   -------
##   seqinfo: 22 sequences from an unspecified genome; no seqlengths
## 
## $PlotMoreover, in the intronProfile we provide parameters maxLength and
minLength to filter the maximum and minimum length of the intron.
intronProfile(test, test_gff3, group = "Treat", maxLength = 10000,
    minLength = 50)## $Peaks
## GRanges object with 100 ranges and 7 metadata columns:
##         seqnames              ranges strand |       Treat    center  Intron_S
##            <Rle>           <IRanges>  <Rle> | <character> <integer> <numeric>
##     [1]    chr17   28748198-28748218      + | Treatment 1  28748208         0
##     [2]     chr2 148684461-148684481      + | Treatment 1 148684471         0
##     [3]     chr7     5097955-5097975      + | Treatment 1   5097965         0
##     [4]     chr4 139648373-139648393      + | Treatment 1 139648383 139645102
##     [5]     chr7   27580623-27580643      + | Treatment 1  27580633         0
##     ...      ...                 ...    ... .         ...       ...       ...
##    [96]    chr17   46148089-46148109      - | Treatment 2  46148099         0
##    [97]    chr11   78074094-78074114      - | Treatment 2  78074104         0
##    [98]     chr2   28833830-28833850      - | Treatment 2  28833840         0
##    [99]     chr1 133621331-133621351      - | Treatment 2 133621341         0
##   [100]     chr4 130316598-130316618      - | Treatment 2 130316608         0
##          Intron_E Intron_length Intron_transcript_id Intron_map
##         <numeric>     <numeric>          <character>  <numeric>
##     [1]         0             0                   NO   3.000000
##     [2]         0             0                   NO   3.000000
##     [3]         0             0                   NO   3.000000
##     [4] 139653534          8433 ENSMUST00000178644.1   0.389067
##     [5]         0             0                   NO   3.000000
##     ...       ...           ...                  ...        ...
##    [96]         0             0                   NO          3
##    [97]         0             0                   NO          3
##    [98]         0             0                   NO          3
##    [99]         0             0                   NO          3
##   [100]         0             0                   NO          3
##   -------
##   seqinfo: 22 sequences from an unspecified genome; no seqlengths
## 
## $PlotThe exonProfile could help to generate a profile of peaks/signals in the
exons which surrounded by introns. The output of exonProfile is a List
object. The List one is the GRanges objects of input data with the
calculation result.
## Quick use
exon <- exonProfile(test, test_gff3)
exon[[1]]## GRanges object with 100 ranges and 7 metadata columns:
##         seqnames              ranges strand |       Treat    center    exon_S
##            <Rle>           <IRanges>  <Rle> | <character> <integer> <numeric>
##     [1]    chr17   28748198-28748218      + | Treatment 1  28748208  28746271
##     [2]     chr2 148684461-148684481      + | Treatment 1 148684471 148683594
##     [3]     chr7     5097955-5097975      + | Treatment 1   5097965   5097572
##     [4]     chr4 139648373-139648393      + | Treatment 1 139648383 139648158
##     [5]     chr7   27580623-27580643      + | Treatment 1  27580633  27580337
##     ...      ...                 ...    ... .         ...       ...       ...
##    [96]    chr17   46148089-46148109      - | Treatment 2  46148099         0
##    [97]    chr11   78074094-78074114      - | Treatment 2  78074104         0
##    [98]     chr2   28833830-28833850      - | Treatment 2  28833840  28833550
##    [99]     chr1 133621331-133621351      - | Treatment 2 133621341 133619940
##   [100]     chr4 130316598-130316618      - | Treatment 2 130316608         0
##            exon_E exon_length    exon_transcript_id  exon_map
##         <numeric>   <numeric>           <character> <numeric>
##     [1]  28748406        2136 ENSMUST00000004990.13  0.906835
##     [2] 148684968        1375  ENSMUST00000028928.7  0.637818
##     [3]   5098178         607  ENSMUST00000098845.9  0.647446
##     [4] 139649690        1533  ENSMUST00000039818.9  0.146771
##     [5]  27582099        1763 ENSMUST00000067386.13  0.167896
##     ...       ...         ...                   ...       ...
##    [96]         0           0                    NO  3.000000
##    [97]         0           0                    NO  3.000000
##    [98]  28835373        1824  ENSMUST00000037117.5  0.840461
##    [99] 133621801        1862  ENSMUST00000186476.6  0.247046
##   [100]         0           0                    NO  3.000000
##   -------
##   seqinfo: 22 sequences from an unspecified genome; no seqlengthsHere is the explanation of the meta data column that output from
exonProfile:
The List two is a ggplot object which contains the exon profile.
exon[[2]]The usage of all parameters of exonProfile is similar to intronProfile. For
more detail of parameter usage please check the intronProfile section.
Since the metaGeneProfile, intronProfile and exonProfile require a
annotation file in gff3 format and downloaded from
https://www.gencodegenes.org/. These functions
could only be used for human and mouse. For the user who works on other
species or has a customized annotation file (not in gff3 format), we develop
the function windowProfile.
windowProfile requires GRanges object as input and annotation. And
windowProfile output the relative position of each peak within the given
annotation GRanges. For example, if user would like to make a profile against
all the exons with windowProfile, you just need to first extract all the
exonic region as a GRanges object then run the windowProfile. Here is an
example about using windowProfile to generate the profile.
library(rtracklayer)
## Extract all the exon annotation
test_anno <- rtracklayer::import.gff3(test_gff3)
test_anno <- test_anno[test_anno$type == "exon"]
## Run the windowProfile
window_profile <- windowProfile(test, test_anno)The output of windowProfile is a List objects. In the List one, you will
find the GRanges object of input peaks and calculation result. And the List
two contains the ggplot of windowProfile.
window_profile[[1]]## GRanges object with 100 ranges and 6 metadata columns:
##         seqnames              ranges strand |       Treat    center  window_S
##            <Rle>           <IRanges>  <Rle> | <character> <integer> <numeric>
##     [1]    chr17   28748198-28748218      + | Treatment 1  28748208  28746271
##     [2]     chr2 148684461-148684481      + | Treatment 1 148684471 148683594
##     [3]     chr7     5097955-5097975      + | Treatment 1   5097965   5097572
##     [4]     chr4 139648373-139648393      + | Treatment 1 139648383 139648158
##     [5]     chr7   27580623-27580643      + | Treatment 1  27580633  27580337
##     ...      ...                 ...    ... .         ...       ...       ...
##    [96]    chr17   46148089-46148109      - | Treatment 2  46148099  46147387
##    [97]    chr11   78074094-78074114      - | Treatment 2  78074104  78073376
##    [98]     chr2   28833830-28833850      - | Treatment 2  28833840  28833550
##    [99]     chr1 133621331-133621351      - | Treatment 2 133621341 133619940
##   [100]     chr4 130316598-130316618      - | Treatment 2 130316608 130315383
##          window_E window_length window_map
##         <numeric>     <numeric>  <numeric>
##     [1]  28748406          2136   0.906835
##     [2] 148684968          1375   0.637818
##     [3]   5098178           607   0.647446
##     [4] 139649690          1533   0.146771
##     [5]  27582099          1763   0.167896
##     ...       ...           ...        ...
##    [96]  46148284           898  0.2060134
##    [97]  78074174           799  0.0876095
##    [98]  28835373          1824  0.8404605
##    [99] 133621801          1862  0.2470462
##   [100] 130316808          1426  0.1402525
##   -------
##   seqinfo: 22 sequences from an unspecified genome; no seqlengthsHere is an explanation of the output of windowProfile:
window_profile[[2]]motifProfile generates the motif enrichment profile around the center of the
input peaks. The IUPAC code
could be used for the motif parameter. The IUPAC code includes: A, T, C, G,
R, Y, S, W, K, M, B, D, H, V, N. The parameter flanking represents to the
size of window that user would like to check around the center of peaks. The
parameter fraction could be used to change the y-axis from frequency to
fraction.
For using the motifProtile, the BSgenome with the sequence information of
the species that you are working with is required.
## Example for running the motifProfile
## The working species is mouse with mm10 annotation.
## Therefore the package 'BSgenome.Mmusculus.UCSC.mm10' need to be installed in 
## advance.
motif <- motifProfile(test, motif = "DRACH",
    genome = "BSgenome.Mmusculus.UCSC.mm10",
    fraction = TRUE, title = "Motif Profile",
    flanking = 10)## 
## Attaching package: 'Biostrings'## The following object is masked from 'package:base':
## 
##     strsplitThe output of motifProfile is a List object. List 1 contains the
data.frame of the motif enrichment information for each position around the
center of the input peaks/signals. List 2 is the ggplot object of the
plot of motif enrichment.
motif[[1]]##    Position Fraction
## 5       -10     0.02
## 6        -9     0.04
## 7        -8     0.04
## 8        -7     0.02
## 9        -6     0.01
## 10       -5     0.01
## 11       -4     0.00
## 12       -3     0.00
## 13       -2     0.94
## 14       -1     0.00
## 15        0     0.00
## 16        1     0.00
## 17        2     0.06
## 18        3     0.02
## 19        4     0.03
## 20        5     0.02
## 21        6     0.01
## 22        7     0.02
## 23        8     0.00
## 24        9     0.03
## 25       10     0.03Each bar in the plot of motifProfile represents for the start site of the
motif that is defined by the user.
motif[[2]]The geneTypeProfile could give users the information of the gene type of the
input peaks. The input peaks for geneTypeProfile should be stored in the
GRanges objects. The annotation file should be a gff3 file and downloaded
from https://www.gencodegenes.org/.
## Quick use of geneTypeProfile
geneTP <- geneTypeProfile(test, test_gff3)The output of geneTypeProfile is an List object. List one includes a
GRanges object with the input peaks and the assignment information.
geneTP[[1]]## GRanges object with 100 ranges and 4 metadata columns:
##         seqnames              ranges strand |       Treat    center
##            <Rle>           <IRanges>  <Rle> | <character> <integer>
##     [1]    chr17   28748198-28748218      + | Treatment 1  28748208
##     [2]    chr10 118860137-118860157      - | Treatment 1 118860147
##     [3]     chr2 148684461-148684481      + | Treatment 1 148684471
##     [4]     chr2   84602546-84602566      - | Treatment 1  84602556
##     [5]    chr18     6111874-6111894      - | Treatment 1   6111884
##     ...      ...                 ...    ... .         ...       ...
##    [96]     chr7 127254692-127254712      + | Treatment 2 127254702
##    [97]     chr2   28833830-28833850      - | Treatment 2  28833840
##    [98]     chr9   44607255-44607275      + | Treatment 2  44607265
##    [99]     chr1 133621331-133621351      - | Treatment 2 133621341
##   [100]     chr4 130316598-130316618      - | Treatment 2 130316608
##               geneType               Gene_ID
##            <character>           <character>
##     [1] protein_coding ENSMUSG00000053436.15
##     [2] protein_coding  ENSMUSG00000028630.9
##     [3] protein_coding  ENSMUSG00000027439.9
##     [4] protein_coding ENSMUSG00000034101.14
##     [5] protein_coding ENSMUSG00000041225.16
##     ...            ...                   ...
##    [96] protein_coding  ENSMUSG00000054716.4
##    [97] protein_coding ENSMUSG00000035666.14
##    [98] protein_coding ENSMUSG00000032097.10
##    [99] protein_coding  ENSMUSG00000094410.7
##   [100] protein_coding ENSMUSG00000028772.19
##   -------
##   seqinfo: 22 sequences from an unspecified genome; no seqlengthsHere is an explanation of the output GRanges object from the
geneTypeProfile.
geneTP[[2]]The spliceSiteProfile gives users the information of the enrichment of peaks
around the 5’ and 3’ splice site (SS) in the absolute distance.
SSprofile <- spliceSiteProfile(test, test_gff3, flanking=200, bin=40)The output of spliceSiteProfile is a List object. The List one includes
the GRanges object of input peaks and the position information of this peak
around the SS.
SSprofile[[1]]## GRanges object with 100 ranges and 4 metadata columns:
##         seqnames              ranges strand |       Treat    center Position5SS
##            <Rle>           <IRanges>  <Rle> | <character> <integer>   <integer>
##     [1]    chr10 118860137-118860157      - | Treatment 1 118860147        <NA>
##     [2]     chr2   84602546-84602566      - | Treatment 1  84602556        <NA>
##     [3]    chr18     6111874-6111894      - | Treatment 1   6111884        -200
##     [4]    chr11   33213145-33213165      - | Treatment 1  33213155        <NA>
##     [5]    chr11   96819422-96819442      - | Treatment 1  96819432        <NA>
##     ...      ...                 ...    ... .         ...       ...         ...
##    [96]     chr8   72222842-72222862      + | Treatment 2  72222852        <NA>
##    [97]    chr18   36648184-36648204      + | Treatment 2  36648194        <NA>
##    [98]     chr8 105216021-105216041      + | Treatment 2 105216031        <NA>
##    [99]     chr7 127254692-127254712      + | Treatment 2 127254702        <NA>
##   [100]     chr9   44607255-44607275      + | Treatment 2  44607265        <NA>
##         Position3SS
##           <integer>
##     [1]        <NA>
##     [2]        <NA>
##     [3]        <NA>
##     [4]        <NA>
##     [5]        <NA>
##     ...         ...
##    [96]        <NA>
##    [97]        <NA>
##    [98]        <NA>
##    [99]        <NA>
##   [100]        <NA>
##   -------
##   seqinfo: 22 sequences from an unspecified genome; no seqlengthsHere is an explanation of output of spliceSiteProfile.
SSprofile[[2]]Similar to metaProfile, The parameter exlevel and
extranscript_support_level could be used for
specifying which level or transcript support level should be excluded.
For excluding the transcript support level NA, you can use 6. About more
information of level and transcript support level you can check the
Gencode data format.
spliceSiteProfile(test, test_gff3, flanking=200, bin=40, exlevel=3,
                        extranscript_support_level = 6,
                        title = "Splice Site Profile")The following is the session info that generated this vignette:
sessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
## 
## 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       
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] BSgenome.Mmusculus.UCSC.mm10_1.4.3 BSgenome_1.66.0                   
##  [3] Biostrings_2.66.0                  XVector_0.38.0                    
##  [5] rtracklayer_1.58.0                 GenomicRanges_1.50.0              
##  [7] GenomeInfoDb_1.34.0                IRanges_2.32.0                    
##  [9] ggplot2_3.3.6                      cliProfiler_1.4.0                 
## [11] S4Vectors_0.36.0                   BiocGenerics_0.44.0               
## [13] BiocStyle_2.26.0                  
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.9                  lattice_0.20-45            
##  [3] Rsamtools_2.14.0            assertthat_0.2.1           
##  [5] digest_0.6.30               utf8_1.2.2                 
##  [7] R6_2.5.1                    evaluate_0.17              
##  [9] highr_0.9                   pillar_1.8.1               
## [11] zlibbioc_1.44.0             rlang_1.0.6                
## [13] jquerylib_0.1.4             magick_2.7.3               
## [15] Matrix_1.5-1                rmarkdown_2.17             
## [17] labeling_0.4.2              BiocParallel_1.32.0        
## [19] stringr_1.4.1               RCurl_1.98-1.9             
## [21] munsell_0.5.0               DelayedArray_0.24.0        
## [23] compiler_4.2.1              xfun_0.34                  
## [25] pkgconfig_2.0.3             htmltools_0.5.3            
## [27] tidyselect_1.2.0            SummarizedExperiment_1.28.0
## [29] tibble_3.1.8                GenomeInfoDbData_1.2.9     
## [31] bookdown_0.29               codetools_0.2-18           
## [33] matrixStats_0.62.0          XML_3.99-0.12              
## [35] fansi_1.0.3                 withr_2.5.0                
## [37] crayon_1.5.2                dplyr_1.0.10               
## [39] GenomicAlignments_1.34.0    bitops_1.0-7               
## [41] grid_4.2.1                  jsonlite_1.8.3             
## [43] gtable_0.3.1                lifecycle_1.0.3            
## [45] DBI_1.1.3                   magrittr_2.0.3             
## [47] scales_1.2.1                cli_3.4.1                  
## [49] stringi_1.7.8               cachem_1.0.6               
## [51] farver_2.1.1                bslib_0.4.0                
## [53] generics_0.1.3              vctrs_0.5.0                
## [55] rjson_0.2.21                restfulr_0.0.15            
## [57] tools_4.2.1                 Biobase_2.58.0             
## [59] glue_1.6.2                  MatrixGenerics_1.10.0      
## [61] parallel_4.2.1              fastmap_1.1.0              
## [63] yaml_2.3.6                  colorspace_2.0-3           
## [65] BiocManager_1.30.19         knitr_1.40                 
## [67] sass_0.4.2                  BiocIO_1.8.0