--- title: "ChIP-Seq Workflow Template" author: "Author: First Last Name" date: "Last update: `r format(Sys.time(), '%d %B, %Y')`" output: BiocStyle::html_document: toc_float: true code_folding: show package: systemPipeR vignette: | %\VignetteEncoding{UTF-8} %\VignetteIndexEntry{WF: ChIP-Seq Workflow Template} %\VignetteEngine{knitr::rmarkdown} fontsize: 14pt bibliography: bibtex.bib editor_options: chunk_output_type: console --- ```{css, echo=FALSE} pre code { white-space: pre !important; overflow-x: scroll !important; word-break: keep-all !important; word-wrap: initial !important; } ``` ```{r style, echo = FALSE, results = 'asis'} BiocStyle::markdown() options(width = 60, max.print = 1000) knitr::opts_chunk$set( eval = as.logical(Sys.getenv("KNITR_EVAL", "TRUE")), cache = as.logical(Sys.getenv("KNITR_CACHE", "TRUE")), tidy.opts = list(width.cutoff = 60), tidy = TRUE) ``` ```{r setup, echo=FALSE, messages=FALSE, warnings=FALSE} suppressPackageStartupMessages({ library(systemPipeR) }) ``` # Introduction Users want to provide here background information about the design of their ChIP-Seq project. ## Background and objectives This report describes the analysis of several ChIP-Seq experiments studying the DNA binding patterns of the transcriptions factors ... from *organism* .... ## 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. # Samples and environment settings ## Environment settings and input data [*systemPipeRdata*](http://bioconductor.org/packages/release/data/experiment/html/systemPipeRdata.html) package is a helper package to generate a fully populated [*systemPipeR*](http://bioconductor.org/packages/release/bioc/html/systemPipeR.html) workflow environment in the current working directory with a single command. All the instruction for generating the workflow are provide in the *systemPipeRdata* vignette [here](http://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeRdata.html#1_Introduction). ```{r genNew_wf, eval=FALSE} systemPipeRdata::genWorkenvir(workflow = "chipseq", mydirname = "chipseq") setwd("chipseq") ``` After building and loading the workflow environment generated by `genWorkenvir` from *systemPipeRdata* 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/`. The chosen data set used by this report [SRP010938](http://www.ncbi.nlm.nih.gov/sra/?term=SRP010938) contains 18 paired-end (PE) read sets from *Arabidposis thaliana* [@Howard2013-fq]. To minimize processing time during testing, each FASTQ file has been subsetted to 90,000-100,000 randomly sampled PE reads that map to the first 100,000 nucleotides of each chromosome of the *A. thaliana* genome. The corresponding reference genome sequence (FASTA) and its GFF annotation files have been truncated accordingly. This way the entire test sample data set is less than 200MB in storage space. A PE read set has been chosen for this test data set for flexibility, because it can be used for testing both types of analysis routines requiring either SE (single end) reads or PE reads. 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. For more details, please consult the documentation [here](http://www.bioconductor.org/packages/release/bioc/vignettes/systemPipeR/inst/doc/systemPipeR.html). More information about the `targets` files from *systemPipeR* can be found [here](http://www.bioconductor.org/packages/release/bioc/vignettes/systemPipeR/inst/doc/systemPipeR.html#42_Structure_of_initial_targets_data). ### Experiment definition provided by `targets` file The `targets` file defines all FASTQ files and sample comparisons of the analysis workflow. ```{r load_targets_file, eval=TRUE} targetspath <- system.file("extdata", "targetsPE_chip.txt", package = "systemPipeR") targets <- read.delim(targetspath, comment.char = "#") targets[1:4,-c(5,6)] ``` To work with custom data, users need to generate a _`targets`_ file containing the paths to their own FASTQ files. # Workflow environment _`systemPipeR`_ workflows can be designed and built from start to finish with a single command, importing from an R Markdown file or stepwise in interactive mode from the R console. This tutorial will demonstrate how to build the workflow in an interactive mode, appending each step. The workflow is constructed by connecting each step via `appendStep` method. Each `SYSargsList` instance contains instructions needed for processing a set of input files with a specific command-line or R software and the paths to the corresponding outfiles generated by a particular tool/step. To create a Workflow within _`systemPipeR`_, we can start by defining an empty container and checking the directory structure: ```{r create_workflow, message=FALSE, eval=FALSE} library(systemPipeR) sal <- SPRproject() sal ``` ## Required packages and resources The `systemPipeR` package needs to be loaded [@H_Backman2016-bt]. ```{r load_SPR, message=FALSE, eval=FALSE, spr=TRUE} cat(crayon::blue$bold("To use this workflow, following R packages are expected:\n")) cat(c("'ggbio", "ChIPseeker", "GenomicFeatures", "GenomicRanges", "Biostrings", "seqLogo", "BCRANK", "readr'\n"), sep = "', '") targetspath <- system.file("extdata", "targetsPE_chip.txt", package = "systemPipeR") ###pre-end appendStep(sal) <- LineWise(code = { library(systemPipeR) }, step_name = "load_SPR") ``` ## Read preprocessing ### 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 png file named `fastqReport.png`. This is the pre-trimming fastq report. Another post-trimming fastq report step is not included in the default. It is recommended to run this step first to decide whether the trimming is needed. Please note that initial targets files are being used here. In this case, it has been added to the first step using the `updateColumn` function, and later, we used the `getColumn` function to extract a named vector. ```{r fastq_report, eval=FALSE, message=FALSE, spr=TRUE} appendStep(sal) <- LineWise( code = { targets <- read.delim(targetspath, comment.char = "#") updateColumn(sal, step = "load_SPR", position = "targetsWF") <- targets fq_files <- getColumn(sal, "load_SPR", "targetsWF", column = 1) fqlist <- seeFastq(fastq = fq_files, batchsize = 10000, klength = 8) png("./results/fastqReport.png", height = 162, width = 288 * length(fqlist)) seeFastqPlot(fqlist) dev.off() }, step_name = "fastq_report", dependency = "load_SPR" ) ``` ![](results/fastqReport.png)
Figure 1: FASTQ quality report for 18 samples

### Preprocessing with `preprocessReads` function 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. Internally, `preprocessReads` uses the `FastqStreamer` function from the `ShortRead` package to stream through large FASTQ files in a memory-efficient manner. The following example performs adapter trimming with the `trimLRPatterns` function from the `Biostrings` package. Here, we are appending this step to the `SYSargsList` object created previously. All the parameters are defined on the `preprocessReads-pe.yml` file. ```{r preprocessing, message=FALSE, eval=FALSE, spr=TRUE} appendStep(sal) <- SYSargsList( step_name = "preprocessing", targets = targetspath, dir = TRUE, wf_file = "preprocessReads/preprocessReads-pe.cwl", input_file = "preprocessReads/preprocessReads-pe.yml", dir_path = system.file("extdata/cwl", package = "systemPipeR"), inputvars = c( FileName1 = "_FASTQ_PATH1_", FileName2 = "_FASTQ_PATH2_", SampleName = "_SampleName_" ), dependency = c("fastq_report") ) ``` 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 next step for more details. The following example shows how one can design a custom read _'preprocessReads'_ function using utilities provided by the `ShortRead` package, and then run it in batch mode with the _'preprocessReads'_ function. Here, it is possible to replace the function used on the `preprocessing` step and modify the `sal` object. Because it is a custom function, it is necessary to save the part in the R object, and internally the `preprocessReads.doc.R` is loading the custom function. If the R object is saved with a different name (here `"param/customFCT.RData"`), please replace that accordingly in the `preprocessReads.doc.R`. Please, note that this step is not added to the workflow, here just for demonstration. First, we defined the custom function in the workflow: ```{r custom_preprocessing_function, eval=FALSE} appendStep(sal) <- LineWise( code = { filterFct <- function(fq, cutoff = 20, Nexceptions = 0) { qcount <- rowSums(as(quality(fq), "matrix") <= cutoff, na.rm = TRUE) # Retains reads where Phred scores are >= cutoff with N exceptions fq[qcount <= Nexceptions] } save(list = ls(), file = "param/customFCT.RData") }, step_name = "custom_preprocessing_function", dependency = "preprocessing" ) ``` After, we can edit the input parameter: ```{r editing_preprocessing, message=FALSE, eval=FALSE} yamlinput(sal, "preprocessing")$Fct yamlinput(sal, "preprocessing", "Fct") <- "'filterFct(fq, cutoff=20, Nexceptions=0)'" yamlinput(sal, "preprocessing")$Fct ## check the new function cmdlist(sal, "preprocessing", targets = 1) ## check if the command line was updated with success ``` ## Alignments ### Read mapping with `Bowtie2` The NGS reads of this project will be aligned with `Bowtie2` against the reference genome sequence [@Langmead2012-bs]. The parameter settings of the Bowtie2 index are defined in the `bowtie2-index.cwl` and `bowtie2-index.yml` files. Building the index: ```{r bowtie2_index, eval=FALSE, spr=TRUE} 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 = system.file("extdata/cwl", package = "systemPipeR"), inputvars = NULL, dependency = c("preprocessing") ) ``` The parameter settings of the aligner are defined in the `workflow_bowtie2-pe.cwl` and `workflow_bowtie2-pe.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. ```{r bowtie2_alignment, eval=FALSE, spr=TRUE} appendStep(sal) <- SYSargsList( step_name = "bowtie2_alignment", dir = TRUE, targets = targetspath, wf_file = "workflow-bowtie2/workflow_bowtie2-pe.cwl", input_file = "workflow-bowtie2/workflow_bowtie2-pe.yml", dir_path = system.file("extdata/cwl", package = "systemPipeR"), inputvars = c( FileName1 = "_FASTQ_PATH1_", FileName2 = "_FASTQ_PATH2_", SampleName = "_SampleName_" ), dependency = c("bowtie2_index") ) ``` To double-check the command line for each sample, please use the following: ```{r bowtie2_alignment_check, eval=FALSE} cmdlist(sal, step="bowtie2_alignment", targets=1) ``` ### 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. ```{r align_stats, eval=FALSE, spr=TRUE} appendStep(sal) <- LineWise( code = { fqpaths <- getColumn(sal, step = "bowtie2_alignment", "targetsWF", column = "FileName1") bampaths <- getColumn(sal, step = "bowtie2_alignment", "outfiles", column = "samtools_sort_bam") read_statsDF <- alignStats(args = bampaths, fqpaths = fqpaths, pairEnd = TRUE) write.table(read_statsDF, "results/alignStats.xls", row.names=FALSE, quote=FALSE, sep="\t") }, step_name = "align_stats", dependency = "bowtie2_alignment") ``` ### 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. ```{r bam_IGV, eval=FALSE, spr=TRUE} appendStep(sal) <- LineWise( code = { bampaths <- getColumn(sal, step = "bowtie2_alignment", "outfiles", column = "samtools_sort_bam") symLink2bam( sysargs = bampaths, htmldir = c("~/.html/", "somedir/"), urlbase = "http://cluster.hpcc.ucr.edu/~tgirke/", urlfile = "./results/IGVurl.txt") }, step_name = "bam_IGV", dependency = "bowtie2_alignment", run_step = "optional" ) ``` ## Utilities for coverage data The following introduces several utilities useful for ChIP-Seq data. They are not part of the actual workflow. These utilities can be explored once the workflow is executed. ### Rle object stores coverage information ```{r rle_object, eval=FALSE} bampaths <- getColumn(sal, step = "bowtie2_alignment", "outfiles", column = "samtools_sort_bam") aligns <- readGAlignments(bampaths[1]) cov <- coverage(aligns) cov ``` ### Resizing aligned reads ```{r resize_align, eval=FALSE} trim(resize(as(aligns, "GRanges"), width = 200)) ``` ### Naive peak calling ```{r rle_slice, eval=FALSE} islands <- slice(cov, lower = 15) islands[[1]] ``` ### Plot coverage for defined region ```{r plot_coverage, eval=FALSE} library(ggbio) myloc <- c("Chr1", 1, 100000) ga <- readGAlignments(bampaths[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") ``` ![](results/coverage.png)
Figure 2: Plot coverage for chromosome 1 region.

## Peak calling with MACS2 ### 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. ```{r merge_bams, eval=FALSE, spr=TRUE} 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"]], out_dir = file.path("results", "merge_bam") ,overwrite=TRUE) updateColumn(sal, step = "merge_bams", position = "targetsWF") <- merge_bams }, step_name = "merge_bams", dependency = "bowtie2_alignment" ) ``` ### Peak calling without input/reference sample MACS2 can perform peak calling on ChIP-Seq data with and without input samples [@Zhang2008-pc]. The following performs peak calling without input on all samples specified in the corresponding `targets` 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). ```{r call_peaks_macs_noref, eval=FALSE, spr=TRUE} cat("Running preprocessing for call_peaks_macs_noref\n") # Previous Linewise step is not run at workflow building time, # but we need the output as input for this sysArgs step. So # we use some preprocess code to predict the output paths # to update the output targets of merge_bams, and then # them into this next step during workflow building phase. mergebam_out_dir = file.path("results", "merge_bam") # make sure this is the same output directory used in merge_bams targets_merge_bam <- targetsWF(sal)$bowtie2_alignment targets_merge_bam <- targets_merge_bam[, -which(colnames(targets_merge_bam) %in% c("FileName1", "FileName2", "FileName"))] targets_merge_bam <- targets_merge_bam[!duplicated(targets_merge_bam$Factor), ] targets_merge_bam <- cbind(FileName = file.path(mergebam_out_dir, paste0(targets_merge_bam$Factor, "_merged.bam")), targets_merge_bam) updateColumn(sal, step = "merge_bams", position = "targetsWF") <- targets_merge_bam # write it out as backup, so you do not need to use preprocess code above again writeTargets(sal, step = "merge_bams", file = "targets_merge_bams.txt", overwrite = TRUE) ###pre-end appendStep(sal) <- SYSargsList( step_name = "call_peaks_macs_noref", targets = "targets_merge_bams.txt", wf_file = "MACS2/macs2-noinput.cwl", input_file = "MACS2/macs2-noinput.yml", dir_path = system.file("extdata/cwl", package = "systemPipeR"), inputvars = c( FileName = "_FASTQ_PATH1_", SampleName = "_SampleName_" ), dependency = c("merge_bams") ) ``` ### 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. ```{r call_peaks_macs_withref, eval=FALSE, spr=TRUE} cat("Running preprocessing for call_peaks_macs_withref\n") # To generate the reference targets file for the next step, use `writeTargetsRef`, # this file needs to be present at workflow building time # Use following preprocess code to do so: writeTargetsRef(infile = "targets_merge_bams.txt", outfile = "targets_bam_ref.txt", silent = FALSE, overwrite = TRUE) ###pre-end 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 = system.file("extdata/cwl", package = "systemPipeR"), inputvars = c( FileName1 = "_FASTQ_PATH1_", FileName2 = "_FASTQ_PATH2_", SampleName = "_SampleName_" ), dependency = c("merge_bams") ) ``` 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. ### Identify consensus peaks The following example shows how one can identify consensus peaks 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. ```{r consensus_peaks, eval=FALSE, spr=TRUE} appendStep(sal) <- LineWise( code = { peaks_files <- getColumn(sal, step = "call_peaks_macs_noref", "outfiles", column = "peaks_xls") peak_M1A <- peaks_files["M1A"] peak_M1A <- as(read.delim(peak_M1A, comment="#")[,1:3], "GRanges") peak_A1A <- peaks_files["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% }, step_name = "consensus_peaks", dependency = "call_peaks_macs_noref" ) ``` ## Annotate peaks with genomic context ### Annotation with `ChIPseeker` package The following annotates the identified peaks with genomic context information using the `ChIPseeker` package [@Yu2015-xu]. ```{r annotation_ChIPseeker, eval=FALSE, spr=TRUE} appendStep(sal) <- LineWise( code = { library(ChIPseeker); library(GenomicFeatures) peaks_files <- getColumn(sal, step = "call_peaks_macs_noref", "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_noref" ) ``` The peak annotation results are written for each peak set to separate files in the `results/` directory. Summary plots provided by the `ChIPseeker` package. Here applied only to one sample for demonstration purposes. ```{r ChIPseeker_plots, eval=FALSE, spr=TRUE} appendStep(sal) <- LineWise( code = { peaks_files <- getColumn(sal, step = "call_peaks_macs_noref", "outfiles", column = "peaks_xls") peak <- readPeakFile(peaks_files[1]) png("results/peakscoverage.png") covplot(peak, weightCol="X.log10.pvalue.") dev.off() png("results/peaksHeatmap.png") peakHeatmap(peaks_files[1], TxDb=txdb, upstream=1000, downstream=1000, color="red") dev.off() png("results/peaksProfile.png") 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" ) ``` ### Annotation with `ChIPpeakAnno` package Same as in previous step but using the `ChIPpeakAnno` package [@Zhu2010-zo] for annotating the peaks. ```{r annotation_ChIPpeakAnno, eval=FALSE, spr=TRUE} appendStep(sal) <- LineWise( code = { library(ChIPpeakAnno); library(GenomicFeatures) peaks_files <- getColumn(sal, step = "call_peaks_macs_noref", "outfiles", column = "peaks_xls") txdb <- suppressWarnings(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=peaks_files)) { peaksGR <- as(read.delim(peaks_files[i], comment="#"), "GRanges") annotatedPeak <- annotatePeakInBatch(peaksGR, AnnotationData=genes(txdb)) df <- data.frame(as.data.frame(annotatedPeak), as.data.frame(values(ge[values(annotatedPeak)$feature,]))) df$tx_name <- as.character(lapply(df$tx_name, function(x) paste(unlist(x), sep='', collapse=', '))) df$tx_type <- as.character(lapply(df$tx_type, function(x) paste(unlist(x), sep='', collapse=', '))) outpaths <- paste0("./results/", names(peaks_files), "_ChIPpeakAnno_annotated.xls") names(outpaths) <- names(peaks_files) write.table(df, outpaths[i], quote=FALSE, row.names=FALSE, sep="\t") } }, step_name = "annotation_ChIPpeakAnno", dependency = "call_peaks_macs_noref", run_step = "optional" ) ``` The peak annotation results are written for each peak set to separate files in the `results/` directory. ## Count reads overlapping peaks The `countRangeset` function is a convenience wrapper to perform read counting iteratively over several 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. ```{r count_peak_ranges, eval=FALSE, spr=TRUE} 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_noref", "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_noref", ) ``` ## Differential binding analysis The `runDiff` function performs differential binding analysis in batch mode for several count tables using `edgeR` or `DESeq2` [@Robinson2010-uk; @Love2014-sh]. 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. ```{r diff_bind_analysis, eval=FALSE, spr=TRUE} 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) cmp <- readComp(file =stepsWF(sal)[["bowtie2_alignment"]], 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 The following performs GO term enrichment analysis for each annotated peak set. ```{r go_enrich, eval=FALSE, spr=TRUE} appendStep(sal) <- LineWise( code = { annofiles <- getColumn(sal, step = "annotation_ChIPseeker", "outfiles") gene_ids <- sapply(annofiles, function(x) unique(as.character (read.delim(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) write.table(BatchResult, "results/GOBatchAll.xls", quote=FALSE, row.names=FALSE, sep="\t") }, step_name = "go_enrich", dependency = "annotation_ChIPseeker", ) ``` ## 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`. ```{r parse_peak_sequences, eval=FALSE, spr=TRUE} appendStep(sal) <- LineWise( code = { library(Biostrings); library(seqLogo); library(BCRANK) rangefiles <- getColumn(sal, step = "call_peaks_macs_noref", "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_noref", ) ``` ### 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. ```{r bcrank_enrich, eval=FALSE, spr=TRUE} appendStep(sal) <- LineWise( code = { library(Biostrings); library(seqLogo); library(BCRANK) rangefiles <- getColumn(sal, step = "call_peaks_macs_noref", "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) png("results/seqlogo.png") seqLogo(weightMatrixNormalized) dev.off() }, step_name = "bcrank_enrich", dependency = "call_peaks_macs_noref", ) ``` ![](results/seqlogo.png)
Figure 3: One of the motifs identified by `BCRANK`

## Version Information ```{r sessionInfo, eval=FALSE, spr=TRUE} 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. ```{r runWF, eval=FALSE} 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](https://mllg.github.io/batchtools/)) and a `template` file (see _`*.tmpl`_ samples [here](https://github.com/mllg/batchtools/tree/master/inst/templates)) 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: ```{r runWF_cluster, eval=FALSE} # wall time in mins, memory in MB resources <- list(conffile=".batchtools.conf.R", template="batchtools.slurm.tmpl", Njobs=18, walltime=120, ntasks=1, ncpus=4, memory=1024, 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. ```{r plotWF, eval=FALSE} plotWF(sal, rstudio = TRUE) ``` ## Checking workflow status To check the summary of the workflow, we can use: ```{r statusWF, eval=FALSE} sal statusWF(sal) ``` ## Accessing logs 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. ```{r logsWF, eval=FALSE} sal <- renderLogs(sal) ``` If you are running on a single machine, use following code as an example to check if some tools used in this workflow are in your environment **PATH**. No warning message should be shown if all tools are installed. ## Tools used To check command-line tools used in this workflow, use `listCmdTools`, and use `listCmdModules` to check if you have a modular system. The following code will print out tools required in your custom SPR project in the report. In case you are running the workflow for the first and do not have a project yet, or you just want to browser this workflow, following code displays the tools required by default. ```{r list_tools} if(file.exists(file.path(".SPRproject", "SYSargsList.yml"))) { local({ sal <- systemPipeR::SPRproject(resume = TRUE) systemPipeR::listCmdTools(sal) systemPipeR::listCmdModules(sal) }) } else { cat(crayon::blue$bold("Tools and modules required by this workflow are:\n")) cat(c("BLAST 2.14.0+"), sep = "\n") } ``` ## Session Info This is the session information for rendering this report. To access the session information of workflow running, check HTML report of `renderLogs`. ```{r report_session_info, eval=TRUE} sessionInfo() ``` # Funding This project was supported by funds from the National Institutes of Health (NIH) and the National Science Foundation (NSF). # References