cliProfiler 1.12.0
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 seqlengths
The 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 seqlengths
The 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 seqlengths
Here 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 seqlengths
The 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
##
## $Plot
Moreover, 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
##
## $Plot
The 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 seqlengths
Here 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 seqlengths
Here 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':
##
## strsplit
##
## Attaching package: 'BiocIO'
## The following object is masked from 'package:rtracklayer':
##
## FileForFormat
The 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 0.03
Each 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 seqlengths
Here is an explanation of the output GRanges object from the
geneTypeProfile
.
geneTP[[2]]
## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
## ℹ The deprecated feature was likely used in the cliProfiler package.
## Please report the issue at <https://github.com/Codezy99/cliProfiler/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
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 seqlengths
Here 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 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.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] BSgenome.Mmusculus.UCSC.mm10_1.4.3 BSgenome_1.74.0
## [3] BiocIO_1.16.0 Biostrings_2.74.0
## [5] XVector_0.46.0 rtracklayer_1.66.0
## [7] GenomicRanges_1.58.0 GenomeInfoDb_1.42.0
## [9] IRanges_2.40.0 ggplot2_3.5.1
## [11] cliProfiler_1.12.0 S4Vectors_0.44.0
## [13] BiocGenerics_0.52.0 BiocStyle_2.34.0
##
## loaded via a namespace (and not attached):
## [1] SummarizedExperiment_1.36.0 gtable_0.3.6
## [3] rjson_0.2.23 xfun_0.48
## [5] bslib_0.8.0 lattice_0.22-6
## [7] Biobase_2.66.0 vctrs_0.6.5
## [9] tools_4.4.1 bitops_1.0-9
## [11] generics_0.1.3 curl_5.2.3
## [13] parallel_4.4.1 tibble_3.2.1
## [15] fansi_1.0.6 highr_0.11
## [17] pkgconfig_2.0.3 Matrix_1.7-1
## [19] lifecycle_1.0.4 GenomeInfoDbData_1.2.13
## [21] farver_2.1.2 compiler_4.4.1
## [23] Rsamtools_2.22.0 tinytex_0.53
## [25] munsell_0.5.1 codetools_0.2-20
## [27] htmltools_0.5.8.1 sass_0.4.9
## [29] RCurl_1.98-1.16 yaml_2.3.10
## [31] pillar_1.9.0 crayon_1.5.3
## [33] jquerylib_0.1.4 BiocParallel_1.40.0
## [35] DelayedArray_0.32.0 cachem_1.1.0
## [37] magick_2.8.5 abind_1.4-8
## [39] tidyselect_1.2.1 digest_0.6.37
## [41] dplyr_1.1.4 restfulr_0.0.15
## [43] bookdown_0.41 labeling_0.4.3
## [45] fastmap_1.2.0 grid_4.4.1
## [47] SparseArray_1.6.0 colorspace_2.1-1
## [49] cli_3.6.3 magrittr_2.0.3
## [51] S4Arrays_1.6.0 XML_3.99-0.17
## [53] utf8_1.2.4 withr_3.0.2
## [55] scales_1.3.0 UCSC.utils_1.2.0
## [57] rmarkdown_2.28 httr_1.4.7
## [59] matrixStats_1.4.1 evaluate_1.0.1
## [61] knitr_1.48 rlang_1.1.4
## [63] Rcpp_1.0.13 glue_1.8.0
## [65] BiocManager_1.30.25 jsonlite_1.8.9
## [67] R6_2.5.1 MatrixGenerics_1.18.0
## [69] GenomicAlignments_1.42.0 zlibbioc_1.52.0