ChIP-Seq Workflow Template
Last update: 24 November, 2019
Package
systemPipeR 1.20.0
1 Introduction
Users want to provide here background information about the design of their ChIP-Seq project.
1.1 Background and objectives
This report describes the analysis of several ChIP-Seq experiments studying the DNA binding patterns of the transcriptions factors … from organism ….
1.2 Experimental design
Typically, users want to specify here all information relevant for the analysis of their NGS study. This includes detailed descriptions of FASTQ files, experimental design, reference genome, gene annotations, etc.
2 Workflow environment
2.1 Generate workflow environment
Load workflow environment with sample data into your current working directory. The sample data are described here.
library(systemPipeRdata)
genWorkenvir(workflow = "chipseq")
setwd("chipseq")Alternatively, this can be done from the command-line as follows:
Rscript -e "systemPipeRdata::genWorkenvir(workflow='chipseq')"In the workflow environments generated by genWorkenvir all data inputs are stored in
a data/ directory and all analysis results will be written to a separate
results/ directory, while the systemPipeChIPseq.Rmd script and the targets file are expected to be located in the parent directory. The R session is expected to run from this parent directory. Additional parameter files are stored under param/.
To work with real data, users want to organize their own data similarly
and substitute all test data for their own data. To rerun an established
workflow on new data, the initial targets file along with the corresponding
FASTQ files are usually the only inputs the user needs to provide.
2.2 Run workflow
Now open the R markdown script systemPipeChIPseq.Rmd in your R IDE (e.g. vim-r or RStudio) and run the workflow as outlined below.
2.2.1 Run R session on computer node
After opening the Rmd file of this workflow in Vim and attaching a connected
R session via the F2 (or other) key, use the following command sequence to run your R
session on a computer node.
q("no") # closes R session on head nodesrun --x11 --partition=short --mem=2gb --cpus-per-task 4 --ntasks 1 --time 2:00:00 --pty bash -l
module load R/3.6.0
RNow check whether your R session is running on a computer node of the cluster and assess your environment.
system("hostname") # should return name of a compute node starting with i or c
getwd() # checks current working directory of R session
dir() # returns content of current working directoryThe systemPipeR package needs to be loaded to perform the analysis steps shown in
this report (H Backman and Girke 2016).
library(systemPipeR)## Loading required package: Rsamtools## Loading required package: GenomeInfoDb## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall,
## clusterEvalQ, clusterExport, clusterMap,
## parApply, parCapply, parLapply, parLapplyLB,
## parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## Filter, Find, Map, Position, Reduce,
## anyDuplicated, append, as.data.frame, basename,
## cbind, colnames, dirname, do.call, duplicated,
## eval, evalq, get, grep, grepl, intersect,
## is.unsorted, lapply, mapply, match, mget, order,
## paste, pmax, pmax.int, pmin, pmin.int, rank,
## rbind, rownames, sapply, setdiff, sort, table,
## tapply, union, unique, unsplit, which, which.max,
## which.min## Loading required package: S4Vectors## Loading required package: stats4##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:base':
##
## expand.grid## Loading required package: IRanges## Loading required package: GenomicRanges## Loading required package: Biostrings## Loading required package: XVector##
## Attaching package: 'Biostrings'## The following object is masked from 'package:base':
##
## strsplit## Loading required package: ShortRead## Loading required package: BiocParallel## Loading required package: GenomicAlignments## Loading required package: SummarizedExperiment## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view
## with 'browseVignettes()'. To cite Bioconductor,
## see 'citation("Biobase")', and for packages
## 'citation("pkgname")'.## Loading required package: DelayedArray## Loading required package: matrixStats##
## Attaching package: 'matrixStats'## The following objects are masked from 'package:Biobase':
##
## anyMissing, rowMedians##
## Attaching package: 'DelayedArray'## The following objects are masked from 'package:matrixStats':
##
## colMaxs, colMins, colRanges, rowMaxs, rowMins,
## rowRanges## The following objects are masked from 'package:base':
##
## aperm, apply, rowsum## ##
## Attaching package: 'systemPipeR'## The following object is masked from 'package:BiocStyle':
##
## outputIf applicable users can load custom functions not provided by systemPipeR. Skip
this step if this is not the case.
source("systemPipeChIPseq_Fct.R")3 Read preprocessing
3.1 Experiment definition provided by targets file
The targets file defines all FASTQ files and sample comparisons of the analysis workflow.
targetspath <- system.file("extdata", "targets_chip.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment.char = "#")
targets[1:4, -c(5, 6)]## FileName SampleName Factor SampleLong
## 1 ./data/SRR446027_1.fastq.gz M1A M1 Mock.1h.A
## 2 ./data/SRR446028_1.fastq.gz M1B M1 Mock.1h.B
## 3 ./data/SRR446029_1.fastq.gz A1A A1 Avr.1h.A
## 4 ./data/SRR446030_1.fastq.gz A1B A1 Avr.1h.B
## SampleReference
## 1
## 2
## 3 M1A
## 4 M1B3.2 Read quality filtering and trimming
The following example shows how one can design a custom read
preprocessing function using utilities provided by the ShortRead package, and then
apply it with preprocessReads in batch mode to all FASTQ samples referenced in the
corresponding SYSargs2 instance (trim object below). More detailed information on
read preprocessing is provided in systemPipeR's main vignette.
First, we construct SYSargs2 object from cwl and yml param and targets files.
dir_path <- system.file("extdata/cwl/preprocessReads/trim-se",
package = "systemPipeR")
trim <- loadWF(targets = targetspath, wf_file = "trim-se.cwl",
input_file = "trim-se.yml", dir_path = dir_path)
trim <- renderWF(trim, inputvars = c(FileName = "_FASTQ_PATH1_",
SampleName = "_SampleName_"))
trim
output(trim)[1:2]Next, we execute the code for trimming all the raw data.
# args <- systemArgs(sysma='param/trim.param',
# mytargets='targets_chip.txt')
filterFct <- function(fq, cutoff = 20, Nexceptions = 0) {
qcount <- rowSums(as(quality(fq), "matrix") <= cutoff, na.rm = TRUE)
fq[qcount <= Nexceptions]
# Retains reads where Phred scores are >= cutoff with N
# exceptions
}
preprocessReads(args = trim, Fct = "filterFct(fq, cutoff=20, Nexceptions=0)",
batchsize = 1e+05)
writeTargetsout(x = trim, file = "targets_chip_trim.txt", step = 1,
new_col = "FileName", new_col_output_index = 1, overwrite = TRUE)3.3 FASTQ quality report
The following seeFastq and seeFastqPlot functions generate and plot a series
of useful quality statistics for a set of FASTQ files including per cycle quality box
plots, base proportions, base-level quality trends, relative k-mer
diversity, length and occurrence distribution of reads, number of reads
above quality cutoffs and mean quality distribution. The results are
written to a PDF file named fastqReport.pdf.
Parallelization of FASTQ quality report via scheduler (e.g. Slurm) across several compute nodes.
library(BiocParallel)
library(batchtools)
f <- function(x) {
library(systemPipeR)
targets <- system.file("extdata", "targets_chip.txt", package = "systemPipeR")
dir_path <- system.file("extdata/cwl/preprocessReads/trim-se",
package = "systemPipeR")
trim <- loadWorkflow(targets = targets, wf_file = "trim-se.cwl",
input_file = "trim-se.yml", dir_path = dir_path)
trim <- renderWF(trim, inputvars = c(FileName = "_FASTQ_PATH1_",
SampleName = "_SampleName_"))
seeFastq(fastq = infile1(trim)[x], batchsize = 1e+05, klength = 8)
}
resources <- list(walltime = 120, ntasks = 1, ncpus = 4, memory = 1024)
param <- BatchtoolsParam(workers = 4, cluster = "slurm", template = "batchtools.slurm.tmpl",
resources = resources)
fqlist <- bplapply(seq(along = trim), f, BPPARAM = param)
pdf("./results/fastqReport.pdf", height = 18, width = 4 * length(fqlist))
seeFastqPlot(unlist(fqlist, recursive = FALSE))
dev.off()
4 Alignments
4.1 Read mapping with Bowtie2
The NGS reads of this project will be aligned with Bowtie2 against the
reference genome sequence (Langmead and Salzberg 2012). The parameter settings of the
aligner are defined in the bowtie2-index.cwl and bowtie2-index.yml files.
In ChIP-Seq experiments it is usually more appropriate to eliminate reads mapping
to multiple locations. To achieve this, users want to remove the argument setting
-k 50 non-deterministic in the configuration files.
Building the index:
dir_path <- system.file("extdata/cwl/bowtie2/bowtie2-idx", package = "systemPipeR")
idx <- loadWorkflow(targets = NULL, wf_file = "bowtie2-index.cwl",
input_file = "bowtie2-index.yml", dir_path = dir_path)
idx <- renderWF(idx)
idx
cmdlist(idx)
## Run in single machine
runCommandline(idx, make_bam = FALSE)The following submits 18 alignment jobs via a scheduler to a computer cluster.
targets <- system.file("extdata", "targets_chip.txt", package = "systemPipeR")
dir_path <- system.file("extdata/cwl/bowtie2/bowtie2-se", package = "systemPipeR")
args <- loadWF(targets = targets, wf_file = "bowtie2-mapping-se.cwl",
input_file = "bowtie2-mapping-se.yml", dir_path = dir_path)
args <- renderWF(args, inputvars = c(FileName = "_FASTQ_PATH1_",
SampleName = "_SampleName_"))
args
cmdlist(args)[1:2]
output(args)[1:2]moduleload(modules(args)) # Skip if a module system is not used
resources <- list(walltime = 120, ntasks = 1, ncpus = 4, memory = 1024)
reg <- clusterRun(args, FUN = runCommandline, more.args = list(args = args,
dir = FALSE), conffile = ".batchtools.conf.R", template = "batchtools.slurm.tmpl",
Njobs = 18, runid = "01", resourceList = resources)
getStatus(reg = reg)
waitForJobs(reg = reg)
args <- output_update(args, dir = FALSE, replace = TRUE, extension = c(".sam",
".bam")) ## Updates the output(args) to the right location in the subfolders
output(args)Alternatively, one can run the alignments sequentially on a single system.
args <- runCommandline(args, force = F)Check whether all BAM files have been created and write out the new targets file.
writeTargetsout(x = args, file = "targets_bam.txt", step = 1,
new_col = "FileName", new_col_output_index = 1, overwrite = TRUE)
outpaths <- subsetWF(args, slot = "output", subset = 1, index = 1)
file.exists(outpaths)4.2 Read and alignment stats
The following provides an overview of the number of reads in each sample and how many of them aligned to the reference.
read_statsDF <- alignStats(args = args)
write.table(read_statsDF, "results/alignStats.xls", row.names = FALSE,
quote = FALSE, sep = "\t")
read.delim("results/alignStats.xls")4.3 Create symbolic links for viewing BAM files in IGV
The symLink2bam function creates symbolic links to view the BAM alignment files in a
genome browser such as IGV without moving these large files to a local
system. The corresponding URLs are written to a file with a path
specified under urlfile, here IGVurl.txt. Please replace the directory and the user name.
symLink2bam(sysargs = args, htmldir = c("~/.html/", "somedir/"),
urlbase = "http://cluster.hpcc.ucr.edu/~tgirke/", urlfile = "./results/IGVurl.txt")5 Utilities for coverage data
The following introduces several utilities useful for ChIP-Seq data. They are not part of the actual workflow.
5.1 Rle object stores coverage information
library(rtracklayer)
library(GenomicRanges)
library(Rsamtools)
library(GenomicAlignments)
outpaths <- subsetWF(args, slot = "output", subset = 1, index = 1)
aligns <- readGAlignments(outpaths[1])
cov <- coverage(aligns)
cov5.2 Resizing aligned reads
trim(resize(as(aligns, "GRanges"), width = 200))5.3 Naive peak calling
islands <- slice(cov, lower = 15)
islands[[1]]5.4 Plot coverage for defined region
library(ggbio)
myloc <- c("Chr1", 1, 1e+05)
ga <- readGAlignments(outpaths[1], use.names = TRUE, param = ScanBamParam(which = GRanges(myloc[1],
IRanges(as.numeric(myloc[2]), as.numeric(myloc[3])))))
autoplot(ga, aes(color = strand, fill = strand), facets = strand ~
seqnames, stat = "coverage")6 Peak calling with MACS2
6.1 Merge BAM files of replicates prior to peak calling
Merging BAM files of technical and/or biological replicates can improve
the sensitivity of the peak calling by increasing the depth of read
coverage. The mergeBamByFactor function merges BAM files based on grouping information
specified by a factor, here the Factor column of the imported targets file. It
also returns an updated SYSargs2 object containing the paths to the
merged BAM files as well as to any unmerged files without replicates.
This step can be skipped if merging of BAM files is not desired.
dir_path <- system.file("extdata/cwl/mergeBamByFactor", package = "systemPipeR")
args <- loadWF(targets = "targets_bam.txt", wf_file = "merge-bam.cwl",
input_file = "merge-bam.yml", dir_path = dir_path)
args <- renderWF(args, inputvars = c(FileName = "_BAM_PATH_",
SampleName = "_SampleName_"))
args_merge <- mergeBamByFactor(args = args, overwrite = TRUE)
writeTargetsout(x = args_merge, file = "targets_mergeBamByFactor.txt",
step = 1, new_col = "FileName", new_col_output_index = 1,
overwrite = TRUE)# Skip if a module system is not used
module("list")
module("unload", "miniconda2")
module("load", "python/2.7.14") # Make sure to set up your enviroment variable for MACS2 6.2 Peak calling without input/reference sample
MACS2 can perform peak calling on ChIP-Seq data with and without input
samples (Zhang et al. 2008). The following performs peak calling without
input on all samples specified in the corresponding args object. Note, due to
the small size of the sample data, MACS2 needs to be run here with the
nomodel setting. For real data sets, users want to remove this parameter
in the corresponding *.param file(s).
dir_path <- system.file("extdata/cwl/MACS2/MACS2-noinput/", package = "systemPipeR")
args <- loadWF(targets = "targets_mergeBamByFactor.txt", wf_file = "macs2.cwl",
input_file = "macs2.yml", dir_path = dir_path)
args <- renderWF(args, inputvars = c(FileName = "_FASTQ_PATH1_",
SampleName = "_SampleName_"))
runCommandline(args, make_bam = FALSE, force = T)
outpaths <- subsetWF(args, slot = "output", subset = 1, index = 1)
file.exists(outpaths)
writeTargetsout(x = args, file = "targets_macs.txt", step = 1,
new_col = "FileName", new_col_output_index = 1, overwrite = TRUE)6.3 Peak calling with input/reference sample
To perform peak calling with input samples, they can be most
conveniently specified in the SampleReference column of the initial
targets file. The writeTargetsRef function uses this information to create a targets
file intermediate for running MACS2 with the corresponding input samples.
writeTargetsRef(infile = "targets_mergeBamByFactor.txt", outfile = "targets_bam_ref.txt",
silent = FALSE, overwrite = TRUE)
dir_path <- system.file("extdata/cwl/MACS2/MACS2-input/", package = "systemPipeR")
args_input <- loadWF(targets = "targets_bam_ref.txt", wf_file = "macs2-input.cwl",
input_file = "macs2.yml", dir_path = dir_path)
args_input <- renderWF(args_input, inputvars = c(FileName1 = "_FASTQ_PATH1_",
FileName2 = "_FASTQ_PATH2_", SampleName = "_SampleName_"))
cmdlist(args_input)[1]
## Run
args_input <- runCommandline(args_input, make_bam = FALSE, force = T)
outpaths_input <- subsetWF(args_input, slot = "output", subset = 1,
index = 1)
file.exists(outpaths_input)
writeTargetsout(x = args_input, file = "targets_macs_input.txt",
step = 1, new_col = "FileName", new_col_output_index = 1,
overwrite = TRUE)The peak calling results from MACS2 are written for each sample to
separate files in the results directory. They are named after the corresponding
files with extensions used by MACS2.
6.4 Identify consensus peaks
The following example shows how one can identify consensus preaks among two peak sets sharing either a minimum absolute overlap and/or minimum relative overlap using the subsetByOverlaps or olRanges functions, respectively. Note, the latter is a custom function imported below by sourcing it.
# source('http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/My_R_Scripts/rangeoverlapper.R')
outpaths <- subsetWF(args, slot = "output", subset = 1, index = 1) ## escolher um dos outputs index
peak_M1A <- outpaths["M1A"]
peak_M1A <- as(read.delim(peak_M1A, comment = "#")[, 1:3], "GRanges")
peak_A1A <- outpaths["A1A"]
peak_A1A <- as(read.delim(peak_A1A, comment = "#")[, 1:3], "GRanges")
(myol1 <- subsetByOverlaps(peak_M1A, peak_A1A, minoverlap = 1))
# Returns any overlap
myol2 <- olRanges(query = peak_M1A, subject = peak_A1A, output = "gr")
# Returns any overlap with OL length information
myol2[values(myol2)["OLpercQ"][, 1] >= 50]
# Returns only query peaks with a minimum overlap of 50%7 Annotate peaks with genomic context
7.1 Annotation with ChIPpeakAnno package
The following annotates the identified peaks with genomic context information using the ChIPpeakAnno and ChIPseeker packages, respectively (Zhu et al. 2010; Yu, Wang, and He 2015).
library(ChIPpeakAnno)
library(GenomicFeatures)
dir_path <- system.file("extdata/cwl/annotate_peaks", package = "systemPipeR")
args <- loadWF(targets = "targets_macs.txt", wf_file = "annotate-peaks.cwl",
input_file = "annotate-peaks.yml", dir_path = dir_path)
args <- renderWF(args, inputvars = c(FileName = "_FASTQ_PATH1_",
SampleName = "_SampleName_"))
txdb <- makeTxDbFromGFF(file = "data/tair10.gff", format = "gff",
dataSource = "TAIR", organism = "Arabidopsis thaliana")
ge <- genes(txdb, columns = c("tx_name", "gene_id", "tx_type"))
for (i in seq(along = args)) {
peaksGR <- as(read.delim(infile1(args)[i], comment = "#"),
"GRanges")
annotatedPeak <- annotatePeakInBatch(peaksGR, AnnotationData = genes(txdb))
df <- data.frame(as.data.frame(annotatedPeak), as.data.frame(values(ge[values(annotatedPeak)$feature,
])))
outpaths <- subsetWF(args, slot = "output", subset = 1, index = 1)
write.table(df, outpaths[i], quote = FALSE, row.names = FALSE,
sep = "\t")
}
writeTargetsout(x = args, file = "targets_peakanno.txt", step = 1,
new_col = "FileName", new_col_output_index = 1, overwrite = TRUE)The peak annotation results are written for each peak set to separate
files in the results directory. They are named after the corresponding peak
files with extensions specified in the annotate_peaks.param file,
here *.peaks.annotated.xls.
7.2 Annotation with ChIPseeker package
Same as in previous step but using the ChIPseeker package for annotating the peaks.
library(ChIPseeker)
for (i in seq(along = args)) {
peakAnno <- annotatePeak(infile1(args)[i], TxDb = txdb, verbose = FALSE)
df <- as.data.frame(peakAnno)
outpaths <- subsetWF(args, slot = "output", subset = 1, index = 1)
write.table(df, outpaths[i], quote = FALSE, row.names = FALSE,
sep = "\t")
}
writeTargetsout(x = args, file = "targets_peakanno.txt", step = 1,
new_col = "FileName", new_col_output_index = 1, overwrite = TRUE)Summary plots provided by the ChIPseeker package. Here applied only to one sample
for demonstration purposes.
peak <- readPeakFile(infile1(args)[1])
covplot(peak, weightCol = "X.log10.pvalue.")
outpaths <- subsetWF(args, slot = "output", subset = 1, index = 1)
peakHeatmap(outpaths[1], TxDb = txdb, upstream = 1000, downstream = 1000,
color = "red")
plotAvgProf2(outpaths[1], TxDb = txdb, upstream = 1000, downstream = 1000,
xlab = "Genomic Region (5'->3')", ylab = "Read Count Frequency")8 Count reads overlapping peaks
The countRangeset function is a convenience wrapper to perform read counting
iteratively over serveral range sets, here peak range sets. Internally,
the read counting is performed with the summarizeOverlaps function from the
GenomicAlignments package. The resulting count tables are directly saved to
files, one for each peak set.
library(GenomicRanges)
dir_path <- system.file("extdata/cwl/count_rangesets", package = "systemPipeR")
args <- loadWF(targets = "targets_macs.txt", wf_file = "count_rangesets.cwl",
input_file = "count_rangesets.yml", dir_path = dir_path)
args <- renderWF(args, inputvars = c(FileName = "_FASTQ_PATH1_",
SampleName = "_SampleName_"))
## Bam Files
targets <- system.file("extdata", "targets_chip.txt", package = "systemPipeR")
dir_path <- system.file("extdata/cwl/bowtie2/bowtie2-se", package = "systemPipeR")
args_bam <- loadWF(targets = targets, wf_file = "bowtie2-mapping-se.cwl",
input_file = "bowtie2-mapping-se.yml", dir_path = dir_path)
args_bam <- renderWF(args_bam, inputvars = c(FileName = "_FASTQ_PATH1_",
SampleName = "_SampleName_"))
args_bam <- output_update(args_bam, dir = FALSE, replace = TRUE,
extension = c(".sam", ".bam"))
outpaths <- subsetWF(args_bam, slot = "output", subset = 1, index = 1)
bfl <- BamFileList(outpaths, yieldSize = 50000, index = character())
countDFnames <- countRangeset(bfl, args, mode = "Union", ignore.strand = TRUE)
writeTargetsout(x = args, file = "targets_countDF.txt", step = 1,
new_col = "FileName", new_col_output_index = 1, overwrite = TRUE)9 Differential binding analysis
The runDiff function performs differential binding analysis in batch mode for
several count tables using edgeR or DESeq2 (Robinson, McCarthy, and Smyth 2010; Love, Huber, and Anders 2014).
Internally, it calls the functions run_edgeR and run_DESeq2. It also returns
the filtering results and plots from the downstream filterDEGs function using
the fold change and FDR cutoffs provided under the dbrfilter argument.
dir_path <- system.file("extdata/cwl/rundiff", package = "systemPipeR")
args_diff <- loadWF(targets = "targets_countDF.txt", wf_file = "rundiff.cwl",
input_file = "rundiff.yml", dir_path = dir_path)
args_diff <- renderWF(args_diff, inputvars = c(FileName = "_FASTQ_PATH1_",
SampleName = "_SampleName_"))
cmp <- readComp(file = args_bam, format = "matrix")
dbrlist <- runDiff(args = args_diff, diffFct = run_edgeR, targets = targets.as.df(targets(args_bam)),
cmp = cmp[[1]], independent = TRUE, dbrfilter = c(Fold = 2,
FDR = 1))
writeTargetsout(x = args_diff, file = "targets_rundiff.txt",
step = 1, new_col = "FileName", new_col_output_index = 1,
overwrite = TRUE)10 GO term enrichment analysis
The following performs GO term enrichment analysis for each annotated peak set.
dir_path <- system.file("extdata/cwl/annotate_peaks", package = "systemPipeR")
args <- loadWF(targets = "targets_bam_ref.txt", wf_file = "annotate-peaks.cwl",
input_file = "annotate-peaks.yml", dir_path = dir_path)
args <- renderWF(args, inputvars = c(FileName1 = "_FASTQ_PATH1_",
FileName2 = "_FASTQ_PATH2_", SampleName = "_SampleName_"))
args_anno <- loadWF(targets = "targets_macs.txt", wf_file = "annotate-peaks.cwl",
input_file = "annotate-peaks.yml", dir_path = dir_path)
args_anno <- renderWF(args_anno, inputvars = c(FileName = "_FASTQ_PATH1_",
SampleName = "_SampleName_"))
annofiles <- subsetWF(args_anno, slot = "output", subset = 1,
index = 1)
gene_ids <- sapply(names(annofiles), function(x) unique(as.character(read.delim(annofiles[x])[,
"geneId"])), simplify = FALSE)
load("data/GO/catdb.RData")
BatchResult <- GOCluster_Report(catdb = catdb, setlist = gene_ids,
method = "all", id_type = "gene", CLSZ = 2, cutoff = 0.9,
gocats = c("MF", "BP", "CC"), recordSpecGO = NULL)11 Motif analysis
11.1 Parse DNA sequences of peak regions from genome
Enrichment analysis of known DNA binding motifs or de novo discovery
of novel motifs requires the DNA sequences of the identified peak
regions. To parse the corresponding sequences from the reference genome,
the getSeq function from the Biostrings package can be used. The
following example parses the sequences for each peak set and saves the
results to separate FASTA files, one for each peak set. In addition, the
sequences in the FASTA files are ranked (sorted) by increasing p-values
as expected by some motif discovery tools, such as BCRANK.
library(Biostrings)
library(seqLogo)
library(BCRANK)
dir_path <- system.file("extdata/cwl/annotate_peaks", package = "systemPipeR")
args <- loadWF(targets = "targets_macs.txt", wf_file = "annotate-peaks.cwl",
input_file = "annotate-peaks.yml", dir_path = dir_path)
args <- renderWF(args, inputvars = c(FileName = "_FASTQ_PATH1_",
SampleName = "_SampleName_"))
rangefiles <- infile1(args)
for (i in seq(along = rangefiles)) {
df <- read.delim(rangefiles[i], comment = "#")
peaks <- as(df, "GRanges")
names(peaks) <- paste0(as.character(seqnames(peaks)), "_",
start(peaks), "-", end(peaks))
peaks <- peaks[order(values(peaks)$X.log10.pvalue., decreasing = TRUE)]
pseq <- getSeq(FaFile("./data/tair10.fasta"), peaks)
names(pseq) <- names(peaks)
writeXStringSet(pseq, paste0(rangefiles[i], ".fasta"))
}11.2 Motif discovery with BCRANK
The Bioconductor package BCRANK is one of the many tools available for
de novo discovery of DNA binding motifs in peak regions of ChIP-Seq
experiments. The given example applies this method on the first peak
sample set and plots the sequence logo of the highest ranking motif.
set.seed(0)
BCRANKout <- bcrank(paste0(rangefiles[1], ".fasta"), restarts = 25,
use.P1 = TRUE, use.P2 = TRUE)
toptable(BCRANKout)
topMotif <- toptable(BCRANKout, 1)
weightMatrix <- pwm(topMotif, normalize = FALSE)
weightMatrixNormalized <- pwm(topMotif, normalize = TRUE)
pdf("results/seqlogo.pdf")
seqLogo(weightMatrixNormalized)
dev.off()
BCRANK
12 Version Information
sessionInfo()## R version 3.6.1 (2019-07-05)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.10-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.10-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 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 parallel stats graphics grDevices
## [6] utils datasets methods base
##
## other attached packages:
## [1] systemPipeR_1.20.0 ShortRead_1.44.0
## [3] GenomicAlignments_1.22.1 SummarizedExperiment_1.16.0
## [5] DelayedArray_0.12.0 matrixStats_0.55.0
## [7] Biobase_2.46.0 BiocParallel_1.20.0
## [9] Rsamtools_2.2.1 Biostrings_2.54.0
## [11] XVector_0.26.0 GenomicRanges_1.38.0
## [13] GenomeInfoDb_1.22.0 IRanges_2.20.1
## [15] S4Vectors_0.24.0 BiocGenerics_0.32.0
## [17] BiocStyle_2.14.0
##
## loaded via a namespace (and not attached):
## [1] Category_2.52.1 bitops_1.0-6
## [3] bit64_0.9-7 RColorBrewer_1.1-2
## [5] progress_1.2.2 httr_1.4.1
## [7] Rgraphviz_2.30.0 tools_3.6.1
## [9] backports_1.1.5 R6_2.4.1
## [11] DBI_1.0.0 lazyeval_0.2.2
## [13] colorspace_1.4-1 withr_2.1.2
## [15] tidyselect_0.2.5 prettyunits_1.0.2
## [17] bit_1.1-14 curl_4.2
## [19] compiler_3.6.1 graph_1.64.0
## [21] formatR_1.7 rtracklayer_1.46.0
## [23] bookdown_0.16 checkmate_1.9.4
## [25] scales_1.1.0 genefilter_1.68.0
## [27] RBGL_1.62.1 askpass_1.1
## [29] rappdirs_0.3.1 stringr_1.4.0
## [31] digest_0.6.23 rmarkdown_1.17
## [33] AnnotationForge_1.28.0 pkgconfig_2.0.3
## [35] htmltools_0.4.0 BSgenome_1.54.0
## [37] dbplyr_1.4.2 limma_3.42.0
## [39] rlang_0.4.2 RSQLite_2.1.2
## [41] GOstats_2.52.0 hwriter_1.3.2
## [43] dplyr_0.8.3 VariantAnnotation_1.32.0
## [45] RCurl_1.95-4.12 magrittr_1.5
## [47] GO.db_3.10.0 GenomeInfoDbData_1.2.2
## [49] Matrix_1.2-17 Rcpp_1.0.3
## [51] munsell_0.5.0 lifecycle_0.1.0
## [53] stringi_1.4.3 yaml_2.2.0
## [55] edgeR_3.28.0 debugme_1.1.0
## [57] zlibbioc_1.32.0 BiocFileCache_1.10.2
## [59] grid_3.6.1 blob_1.2.0
## [61] crayon_1.3.4 lattice_0.20-38
## [63] splines_3.6.1 GenomicFeatures_1.38.0
## [65] annotate_1.64.0 hms_0.5.2
## [67] batchtools_0.9.11 locfit_1.5-9.1
## [69] zeallot_0.1.0 knitr_1.26
## [71] pillar_1.4.2 rjson_0.2.20
## [73] base64url_1.4 codetools_0.2-16
## [75] biomaRt_2.42.0 XML_3.98-1.20
## [77] glue_1.3.1 evaluate_0.14
## [79] latticeExtra_0.6-28 data.table_1.12.6
## [81] BiocManager_1.30.10 vctrs_0.2.0
## [83] gtable_0.3.0 openssl_1.4.1
## [85] purrr_0.3.3 assertthat_0.2.1
## [87] ggplot2_3.2.1 xfun_0.11
## [89] xtable_1.8-4 survival_3.1-7
## [91] pheatmap_1.0.12 tibble_2.1.3
## [93] AnnotationDbi_1.48.0 memoise_1.1.0
## [95] brew_1.0-6 GSEABase_1.48.013 Funding
This project was supported by funds from the National Institutes of Health (NIH) and the National Science Foundation (NSF).
References
H Backman, Tyler W, and Thomas Girke. 2016. “systemPipeR: NGS workflow and report generation environment.” BMC Bioinformatics 17 (1):388. https://doi.org/10.1186/s12859-016-1241-0.
Langmead, Ben, and Steven L Salzberg. 2012. “Fast Gapped-Read Alignment with Bowtie 2.” Nat. Methods 9 (4). Nature Publishing Group:357–59. https://doi.org/10.1038/nmeth.1923.
Love, Michael, Wolfgang Huber, and Simon Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA-seq Data with DESeq2.” Genome Biol. 15 (12):550. https://doi.org/10.1186/s13059-014-0550-8.
Robinson, M D, D J McCarthy, and G K Smyth. 2010. “EdgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data.” Bioinformatics 26 (1):139–40. https://doi.org/10.1093/bioinformatics/btp616.
Yu, Guangchuang, Li-Gen Wang, and Qing-Yu He. 2015. “ChIPseeker: An R/Bioconductor Package for ChIP Peak Annotation, Comparison and Visualization.” Bioinformatics 31 (14):2382–3. https://doi.org/10.1093/bioinformatics/btv145.
Zhang, Y, T Liu, C A Meyer, J Eeckhoute, D S Johnson, B E Bernstein, C Nussbaum, et al. 2008. “Model-Based Analysis of ChIP-Seq (MACS).” Genome Biol. 9 (9). https://doi.org/10.1186/gb-2008-9-9-r137.
Zhu, Lihua J, Claude Gazin, Nathan D Lawson, Hervé Pagès, Simon M Lin, David S Lapointe, and Michael R Green. 2010. “ChIPpeakAnno: A Bioconductor Package to Annotate ChIP-seq and ChIP-chip Data.” BMC Bioinformatics 11 (11~may):237. https://doi.org/10.1186/1471-2105-11-237.