# ChIP-Seq Workflow Template

## Introduction

The following analyzes the ChIP-Seq data from Kaufman et al. (2010) using for peak calling MACS2 where the uninduced sample serves as input (reference). Prior to running this analysis the corresponding FASTQ files need to be downloaded following the instructions here.

For learning purposes one can use the much smaller toy data set and ChIP-Seq Rmd workflow instance provided by the systemPipeRdata package. This toy workflow instance can be conveniently obtained by running genWorkenvir(workflow='chipseq') (see below). The FASTQ data used for this toy instance are the same as for the RNA-Seq workflow, but the analysis code in the Rmd file is almost identical to the ChIP-Seq workflow below.

For the analysis of the Kaufman et al. (2010) data set (see download here) users want to use the Rmd instance linked from the top right corner of this page. Additional detail about this is provided here.

### 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.

### Workflow environment

NOTE: this section describes how to set up the proper environment (directory structure) for running systemPipeR workflows. After mastering this task the workflow run instructions can be deleted since they are not expected to be included in a final HTML/PDF report of a workflow.

1. If a remote system or cluster is used, then users need to log in to the remote system first. The following applies to an HPC cluster (e.g. HPCC cluster).

A terminal application needs to be used to log in to a user’s cluster account. Next, one can open an interactive session on a computer node with srun --x11. More details about argument settings for srun are available in this HPCC manual or the HPCC section of this website here. Next, load the R version required for running the workflow with module load. Sometimes it may be necessary to first unload an active software version before loading another version, e.g. module unload R.

### 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.

### Workflow environment

NOTE: this section describes how to set up the proper environment (directory structure) for running systemPipeR workflows. After mastering this task the workflow run instructions can be deleted since they are not expected to be included in a final HTML/PDF report of a workflow.

1. If a remote system or cluster is used, then users need to log in to the remote system first. The following applies to an HPC cluster (e.g. HPCC cluster).

A terminal application needs to be used to log in to a user’s cluster account. Next, one can open an interactive session on a computer node with srun --x11. More details about argument settings for srun are available in this HPCC manual or the HPCC section of this website here. Next, load the R version required for running the workflow with module load. Sometimes it may be necessary to first unload an active software version before loading another version, e.g. module unload R.

srun --x11 --partition=gen242 --mem=20gb --cpus-per-task 8 --ntasks 1 --time 20:00:00 --pty bash -l

1. Load a workflow template with the genWorkenvir function. This can be done from the command-line or from within R. However, only one of the two options needs to be used.

From command-line

$Rscript -e "systemPipeRdata::genWorkenvir(workflow='chipseq')"$ cd chipseq


From R

library(systemPipeRdata)
genWorkenvir(workflow = "chipseq")
setwd("chipseq")

1. Optional: if the user wishes to use another Rmd file than the template instance provided by the genWorkenvir function, then it can be copied or downloaded into the root directory of the workflow environment (e.g. with cp or wget).

2. Now one can open from the root directory of the workflow the corresponding R Markdown script (e.g. systemPipeChIPseq.Rmd) using an R IDE, such as nvim-r, ESS or RStudio. Subsequently, the workflow can be run as outlined below. For learning purposes it is recommended to run workflows for the first time interactively. Once all workflow steps are understood and possibly modified to custom needs, one can run the workflow from start to finish with a single command using runWF().

The systemPipeR package needs to be loaded to perform the analysis steps shown in this report (H Backman and Girke 2016). The package allows users to run the entire analysis workflow interactively or with a single command while also generating the corresponding analysis report. For details see systemPipeR's main vignette.

library(systemPipeR)


To apply workflows to custom data, the user needs to modify the targets file and if necessary update the corresponding parameter (.cwl and .yml) files. A collection of pre-generated .cwl and .yml files are provided in the param/cwl subdirectory of each workflow template. They are also viewable in the GitHub repository of systemPipeRdata (see here). For more information of the structure of the targets file, please consult the documentation here. More details about the new parameter files from systemPipeR can be found here.

### Import custom functions

Custom functions for the challenge projects can be imported with the source command from a local R script (here challengeProject_Fct.R). Skip this step if such a script is not available. Alternatively, these functions can be loaded from a custom R package.

source("challengeProject_Fct.R")


### Experiment definition provided by targets file

The targets file defines all FASTQ files and sample comparisons of the analysis workflow. If needed the tab separated (TSV) version of this file can be downloaded from here and the corresponding Google Sheet is here.

targetspath <- "targets_chipseq.txt"
targets <- read.delim(targetspath, comment.char = "#")
knitr::kable(targets)

FileName SampleName Factor SampleLong Experiment Date SampleReference
./data/SRR038845_1.fastq.gz AP1_1 AP1 APETALA1 Induced 1 23-Mar-12
./data/SRR038846_1.fastq.gz AP1_2A AP1 APETALA1 Induced 1 23-Mar-12
./data/SRR038847_1.fastq.gz AP1_2B AP1 APETALA1 Induced 1 23-Mar-12
./data/SRR038848_1.fastq.gz C_1A C Control Mock 1 23-Mar-12 AP1_1
./data/SRR038849_1.fastq.gz C_1B C Control Mock 1 23-Mar-12 AP1_1
./data/SRR038850_1.fastq.gz C_2A C Control Mock 1 23-Mar-12 AP1_2A
./data/SRR038851_1.fastq.gz C_2B C Control Mock 1 23-Mar-12 AP1_2B

## Workflow steps

This tutorial will demonstrate how to either build and run the workflow automatically, or in an interactive mode by appending each step with the appendStep method. In both cases the SYSargsList object will be populated with the instructions for running each workflow step, while supporting both command-line steps as well as line-wise R commands defined in the corresponding code chunks of this or any Rmd file that has been properly formatted.

To create a workflow within systemPipeR, we can start by defining an empty SYSargsList container. When restarting an existing workflow one can set resume=TRUE under the SPRproject() function call.

library(systemPipeR)
sal <- SPRproject()

## Creating directory:  /home/tgirke/tmp/GEN242/content/en/tutorials/spchipseq/data
## Creating directory '/home/tgirke/tmp/GEN242/content/en/tutorials/spchipseq/.SPRproject'
## Creating file '/home/tgirke/tmp/GEN242/content/en/tutorials/spchipseq/.SPRproject/SYSargsList.yml'

sal

## Instance of 'SYSargsList':


Next, the importWF function will load the entire workflow into the SYSargsList object (here sal). Subsequently, the runWF() function will run the workflow from start to finish. If needed, specific workflow steps can be executed by assigning their corresponding position numbers within the workflow to the steps argument (see ?runWF). After completion of the workflow one can render a scientific analysis report in HTML format with the renderReport() function that uses R Markdown internally.

sal <- importWF(sal, file_path = "systemPipeChIPseq.Rmd")  ## Import all the Workflow steps
sal
sal <- runWF(sal)  # Runs workflow
sal <- renderReport(sal)  # Renders report
rmarkdown::render("systemPipeChIPseq.Rmd", clean = TRUE, output_format = "BiocStyle::html_document")  # Alternative report rendering


### Required packages and resources

The systemPipeR package needs to be loaded (H Backman and Girke 2016).

appendStep(sal) <- LineWise(code = {
library(systemPipeR)


#### Read quality filtering and trimming

The function preprocessReads allows to apply predefined or custom read preprocessing functions to all FASTQ files referenced in a SYSargsList container, such as quality filtering or adapter trimming routines. The paths to the resulting output FASTQ files are stored in the outfiles slot of the SYSargsList object. The following example performs adapter trimming with the trimLRPatterns function from the Biostrings package. After the trimming step a new targets file is generated (here targets_trim.txt) containing the paths to the trimmed FASTQ files. The new targets file can be used for the next workflow step with an updated SYSargs2 instance, e.g. running the NGS alignments using the trimmed FASTQ files.

Here, we are appending this step to the SYSargsList object created previously. All the parameters are defined on the preprocessReads/preprocessReads-pe.yml and preprocessReads/preprocessReads-pe.cwl files.

appendStep(sal) <- SYSargsList(step_name = "preprocessing", targets = "targets_chipseq.txt",
inputvars = c(FileName = "_FASTQ_PATH1_", SampleName = "_SampleName_"),


After, we can check the trimLRPatterns function in input parameter:

yamlinput(sal, "preprocessing")Fct  ## [1] "'trimLRPatterns(Rpattern=\"GCCCGGGTAA\", subject=fq)'"  After the preprocessing step, the outfiles files can be used to generate the new targets files containing the paths to the trimmed FASTQ files. The new targets information can be used for the next workflow step instance, e.g. running the NGS alignments with the trimmed FASTQ files. The appendStep function is automatically handling this connectivity between steps. Please check the Alignments step for more details. #### 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. appendStep(sal) <- LineWise(code = { fastq <- getColumn(sal, step = "preprocessing", "targetsWF", column = 1) fqlist <- seeFastq(fastq = fastq, batchsize = 10000, klength = 8) pdf("./results/fastqReport.pdf", height = 18, width = 4 * length(fqlist)) seeFastqPlot(fqlist) dev.off() }, step_name = "fastq_report", dependency = "preprocessing")  Figure 1: FASTQ quality report for 7 samples. ### Alignments #### 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 Bowtie2 index are defined in the bowtie2-index.cwl and bowtie2-index.yml files. Building the index: appendStep(sal) <- SYSargsList(step_name = "bowtie2_index", dir = FALSE, targets = NULL, wf_file = "bowtie2/bowtie2-index.cwl", input_file = "bowtie2/bowtie2-index.yml", dir_path = "param/cwl", inputvars = NULL, dependency = c("preprocessing"))  The parameter settings of the aligner are defined in the workflow_bowtie2-se.cwl and workflow_bowtie2-se.yml files. The following shows how to construct the corresponding SYSargsList object. 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. appendStep(sal) <- SYSargsList(step_name = "bowtie2_alignment", dir = TRUE, targets = "targets_chipseq.txt", wf_file = "workflow-bowtie2/workflow_bowtie2-se.cwl", input_file = "workflow-bowtie2/workflow_bowtie2-se.yml", dir_path = "param/cwl", inputvars = c(FileName = "_FASTQ_PATH1_", SampleName = "_SampleName_"), dependency = c("bowtie2_index"))  To double-check the command line for each sample, please use the following: cmdlist(sal, step = "bowtie2_alignment", targets = 1)  ##bowtie2_alignment
## bowtie2_alignmentAP1_1
## bowtie2_alignmentAP1_1$bowtie2 ## [1] "bowtie2 -S ./results/AP1_1.sam -x ./data/tair10.fasta -k 50 --non-deterministic -U ./data/SRR038845_1.fastq.gz -p 4" ## ##$bowtie2_alignment$AP1_1$samtools-view
## [1] "samtools view -bS -o ./results/AP1_1.bam  ./results/AP1_1.sam "
##
## bowtie2_alignmentAP1_1$samtools-sort ## [1] "samtools sort -o ./results/AP1_1.sorted.bam ./results/AP1_1.bam -@ 4" ## ##$bowtie2_alignment$AP1_1$samtools-index
## [1] "samtools index -b results/AP1_1.sorted.bam  results/AP1_1.sorted.bam.bai  ./results/AP1_1.sorted.bam "


The following provides an overview of the number of reads in each sample and how many of them aligned to the reference.

appendStep(sal) <- LineWise(code = {
fqpaths <- getColumn(sal, step = "bowtie2_alignment", "targetsWF",
column = "FileName")
bampaths <- getColumn(sal, step = "bowtie2_alignment", "outfiles",
column = "samtools_sort_bam")
read_statsDF <- alignStats(args = bampaths, fqpaths = fqpaths,
pairEnd = TRUE)
quote = FALSE, sep = "\t")
}, step_name = "align_stats", dependency = "bowtie2_alignment")


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. The following parameter settings will create a subdirectory under ~/.html called somedir of the user account. The user name under urlbase, here <username>, needs to be changed to the corresponding user name of the person running this function.

appendStep(sal) <- LineWise(code = {
bampaths <- getColumn(sal, step = "bowtie2_alignment", "outfiles",
column = "samtools_sort_bam")
bampaths <- setNames(normalizePath(bampaths), names(bampaths))
symLink2bam(sysargs = bampaths, htmldir = c("~/.html/", "somedir/"),
urlfile = "./results/IGVurl.txt")
}, step_name = "bam_IGV", dependency = "bowtie2_alignment", run_step = "optional")


### 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 targets object containing the paths to the merged BAM files as well as to any unmerged files without replicates. The updated targets object can be used to update the SYSargsList object.

This step can be skipped if merging of BAM files is not desired.

appendStep(sal) <- LineWise(code = {
bampaths <- getColumn(sal, step = "bowtie2_alignment", "outfiles",
column = "samtools_sort_bam")
merge_bams <- mergeBamByFactor(args = bampaths, targetsDF = targetsWF(sal)[["bowtie2_alignment"]],
overwrite = TRUE)
updateColumn(sal, step = "merge_bams", position = "targetsWF") <- merge_bams
writeTargets(sal, step = "merge_bams", file = "targets_merge_bams.txt",
overwrite = TRUE)
}, step_name = "merge_bams", dependency = "bowtie2_alignment")


#### Peak calling with 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 with input sample. The input sample 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 sample(s).

appendStep(sal) <- LineWise(code = {
writeTargetsRef(infile = "targets_merge_bams.txt", outfile = "targets_bam_ref.txt",
silent = FALSE, overwrite = TRUE)
}, step_name = "writeTargetsRef", dependency = "merge_bams")

appendStep(sal) <- SYSargsList(step_name = "call_peaks_macs_withref",
targets = "targets_bam_ref.txt", wf_file = "MACS2/macs2-input.cwl",
input_file = "MACS2/macs2-input.yml", dir_path = "param/cwl",
inputvars = c(FileName1 = "_FASTQ_PATH1_", FileName2 = "_FASTQ_PATH2_",
SampleReference = "_SampleName_"), id = "SampleReference",
dependency = c("writeTargetsRef"))


The peak calling results from MACS2 are written for each sample to separate files in the results/call_peaks_macs_withref directory. They are named after the corresponding files with extensions used by MACS2.

### Annotate peaks with genomic context

#### Annotation with ChIPseeker package

The following annotates the identified peaks with genomic context information using the ChIPseeker package (Yu, Wang, and He 2015).

appendStep(sal) <- LineWise(code = {
library(ChIPseeker)
library(GenomicFeatures)
peaks_files <- getColumn(sal, step = "call_peaks_macs_withref",
"outfiles", column = "peaks_xls")
txdb <- suppressWarnings(makeTxDbFromGFF(file = "data/tair10.gff",
format = "gff", dataSource = "TAIR", organism = "Arabidopsis thaliana"))
for (i in seq(along = peaks_files)) {
peakAnno <- annotatePeak(peaks_files[i], TxDb = txdb,
verbose = FALSE)
df <- as.data.frame(peakAnno)
outpaths <- paste0("./results/", names(peaks_files),
"_ChIPseeker_annotated.xls")
names(outpaths) <- names(peaks_files)
write.table(df, outpaths[i], quote = FALSE, row.names = FALSE,
sep = "\t")
}
updateColumn(sal, step = "annotation_ChIPseeker", position = "outfiles") <- data.frame(outpaths)
}, step_name = "annotation_ChIPseeker", dependency = "call_peaks_macs_withref")


The peak annotation results are written to the results directory.

Summary plots provided by the ChIPseeker package. Here applied only to one sample for demonstration purposes.

appendStep(sal) <- LineWise(code = {
peaks_files <- getColumn(sal, step = "call_peaks_macs_withref",
"outfiles", column = "peaks_xls")
pdf("results/peakscoverage.pdf")
covplot(peak, weightCol = "X.log10.pvalue.")
dev.off()
pdf("results/peaksHeatmap.pdf")
peakHeatmap(peaks_files[1], TxDb = txdb, upstream = 1000,
downstream = 1000, color = "red")
dev.off()
pdf("results/peaksProfile.pdf")
plotAvgProf2(peaks_files[1], TxDb = txdb, upstream = 1000,
downstream = 1000, xlab = "Genomic Region (5'->3')",
ylab = "Read Count Frequency", conf = 0.05)
dev.off()
}, step_name = "ChIPseeker_plots", dependency = "annotation_ChIPseeker")


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.

appendStep(sal) <- LineWise(code = {
library(GenomicRanges)
bam_files <- getColumn(sal, step = "bowtie2_alignment", "outfiles",
column = "samtools_sort_bam")
args <- getColumn(sal, step = "call_peaks_macs_withref",
"outfiles", column = "peaks_xls")
outfiles <- paste0("./results/", names(args), "_countDF.xls")
bfl <- BamFileList(bam_files, yieldSize = 50000, index = character())
countDFnames <- countRangeset(bfl, args, outfiles, mode = "Union",
ignore.strand = TRUE)
updateColumn(sal, step = "count_peak_ranges", position = "outfiles") <- data.frame(countDFnames)
}, step_name = "count_peak_ranges", dependency = "call_peaks_macs_withref",
)


Shows count table generated in previous step (results/AP1_1_countDF.xls). To avoid slowdowns of the load time of this page, ony 200 rows of the source table are imported into the below datatable view .

countDF <- read.delim("results/AP1_1_countDF.xls")[1:200, ]
colnames(countDF)[1] <- "PeakIDs"
library(DT)
datatable(countDF)


### 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.

appendStep(sal) <- LineWise(code = {
countDF_files <- getColumn(sal, step = "count_peak_ranges",
"outfiles")
outfiles <- paste0("./results/", names(countDF_files), "_peaks_edgeR.xls")
names(outfiles) <- names(countDF_files)
format = "matrix")
dbrlist <- runDiff(args = countDF_files, outfiles = outfiles,
diffFct = run_edgeR, targets = targetsWF(sal)[["bowtie2_alignment"]],
cmp = cmp[[1]], independent = TRUE, dbrfilter = c(Fold = 2,
FDR = 1))
}, step_name = "diff_bind_analysis", dependency = "count_peak_ranges",
)


### GO term enrichment analysis

#### Obtain gene-to-GO mappings

The following shows how to obtain gene-to-GO mappings from biomaRt (here for A. thaliana) and how to organize them for the downstream GO term enrichment analysis. Alternatively, the gene-to-GO mappings can be obtained for many organisms from Bioconductor’s *.db genome annotation packages or GO annotation files provided by various genome databases. For each annotation this relatively slow preprocessing step needs to be performed only once. Subsequently, the preprocessed data can be loaded with the load function as shown in the next subsection.

#### GO term enrichment analysis

The following performs GO term enrichment analysis for each annotated peak set. Note: the following assumes that the GO annotation data exists under data/GO/catdb.RData.

appendStep(sal) <- LineWise(code = {
library("biomaRt")
# listMarts() # To choose BioMart database
# listMarts(host='plants.ensembl.org')
m <- useMart("plants_mart", host = "https://plants.ensembl.org")
m <- useMart("plants_mart", dataset = "athaliana_eg_gene",
host = "https://plants.ensembl.org")
go <- getBM(attributes = c("go_id", "tair_locus", "namespace_1003"),
mart = m)
go <- go[go[, 3] != "", ]
go[, 3] <- as.character(go[, 3])
go[go[, 3] == "molecular_function", 3] <- "F"
go[go[, 3] == "biological_process", 3] <- "P"
go[go[, 3] == "cellular_component", 3] <- "C"
go[1:4, ]
if (!dir.exists("./data/GO"))
dir.create("./data/GO")
write.table(go, "data/GO/GOannotationsBiomart_mod.txt", quote = FALSE,
row.names = FALSE, col.names = FALSE, sep = "\t")
catdb <- makeCATdb(myfile = "data/GO/GOannotationsBiomart_mod.txt",
lib = NULL, org = "", colno = c(1, 2, 3), idconv = NULL)
save(catdb, file = "data/GO/catdb.RData")
}, step_name = "get_go_annot", dependency = "annotation_ChIPseeker",
)


#### GO term enrichment analysis

The following performs GO term enrichment analysis for each annotated peak set. Note: the following assumes that the GO annotation data exists under data/GO/catdb.RData.

appendStep(sal) <- LineWise(code = {
annofiles <- getColumn(sal, step = "annotation_ChIPseeker",
"outfiles")
"geneId"])), simplify = FALSE)
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)
write.table(BatchResult, "results/GOBatchAll.xls", quote = FALSE,
row.names = FALSE, sep = "\t")
}, step_name = "go_enrich", dependency = "annotation_ChIPseeker",
)


Shows GO term enrichment results from previous step. The last gene identifier column (10) of this table has been excluded in this viewing instance to minimize the complexity of the result. To avoid slowdowns of the load time of this page, only 50 rows of the source table are imported into the below datatable view .

BatchResult <- read.delim("results/GOBatchAll.xls")[1:50, ]
library(DT)
datatable(BatchResult[, -10], options = list(scrollX = TRUE,
autoWidth = TRUE))


### Motif analysis

#### 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.

appendStep(sal) <- LineWise(code = {
library(Biostrings)
library(seqLogo)
library(BCRANK)
rangefiles <- getColumn(sal, step = "call_peaks_macs_withref",
"outfiles")
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"))
}
}, step_name = "parse_peak_sequences", dependency = "call_peaks_macs_withref")


#### 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.

appendStep(sal) <- LineWise(code = {
library(Biostrings)
library(seqLogo)
library(BCRANK)
rangefiles <- getColumn(sal, step = "call_peaks_macs_withref",
"outfiles")
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()
}, step_name = "bcrank_enrich", dependency = "call_peaks_macs_withref")


Figure 2: One of the motifs identified by BCRANK

### Version Information

appendStep(sal) <- LineWise(code = {
sessionInfo()
}, step_name = "sessionInfo", dependency = "bcrank_enrich")


## Running workflow

### Interactive job submissions in a single machine

For running the workflow, runWF function will execute all the steps store in the workflow container. The execution will be on a single machine without submitting to a queuing system of a computer cluster.

sal <- runWF(sal)


### Parallelization on clusters

Alternatively, the computation can be greatly accelerated by processing many files in parallel using several compute nodes of a cluster, where a scheduling/queuing system is used for load balancing.

The resources list object provides the number of independent parallel cluster processes defined under the Njobs element in the list. The following example will run 18 processes in parallel using each 4 CPU cores. If the resources available on a cluster allow running all 18 processes at the same time, then the shown sample submission will utilize in a total of 72 CPU cores.

Note, runWF can be used with most queueing systems as it is based on utilities from the batchtools package, which supports the use of template files (*.tmpl) for defining the run parameters of different schedulers. To run the following code, one needs to have both a conffile (see .batchtools.conf.R samples here) and a template file (see *.tmpl samples here) for the queueing available on a system. The following example uses the sample conffile and template files for the Slurm scheduler provided by this package.

The resources can be appended when the step is generated, or it is possible to add these resources later, as the following example using the addResources function:

resources <- list(conffile=".batchtools.conf.R",
template="batchtools.slurm.tmpl",
Njobs=8,
walltime=120, ## minutes
ncpus=4,
memory=1024, ## Mb
partition = "short"
)
sal <- addResources(sal, c("bowtie2_alignment"), resources = resources)
sal <- runWF(sal)


### Visualize workflow

systemPipeR workflows instances can be visualized with the plotWF function.

plotWF(sal)


### Checking workflow status

To check the summary of the workflow, we can use:

sal
statusWF(sal)


## Technical report

systemPipeR compiles all the workflow execution logs in one central location, making it easier to check any standard output (stdout) or standard error (stderr) for any command-line tools used on the workflow or the R code stdout.

sal <- renderLogs(sal)


## Scientific report

systemPipeR auto-generates scientific analysis reports in HTML format.

sal <- renderReport(sal)


Alternatively, scientific reports can be rendered with the render function from rmarkdown.

rmarkdown::render("systemPipeChIPseq.Rmd", clean = TRUE, output_format = "BiocStyle::html_document")


## Funding

This project was supported by funds from the National Institutes of Health (NIH) and the National Science Foundation (NSF).

## References

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Langmead, Ben, and Steven L Salzberg. 2012. “Fast Gapped-Read Alignment with Bowtie 2.” Nat. Methods 9 (4): 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.

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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.