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 Load packages

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)

2.2 Generate workflow environment

systemPipeRdata package is a helper package to generate a fully populated systemPipeR workflow environment in the current working directory with a single command. All the instruction for generating the workflow template are provide in the systemPipeRdata vignette here.

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/.

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. More information about the targets files from systemPipeR can be found here.

2.3 Run workflow

Now open the R markdown script systemPipeRIBOseq.Rmdin your R IDE (e.g. vim-r or RStudio) and run the workflow as outlined below.

Here pair-end workflow example is provided. Please refer to the main vignette systemPipeR.Rmd for running the workflow with single-end data.

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.

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", "targetsPE_chip.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment.char = "#")
targets[1:4, -c(5, 6)]
##                     FileName1                   FileName2
## 1 ./data/SRR446027_1.fastq.gz ./data/SRR446027_2.fastq.gz
## 2 ./data/SRR446028_1.fastq.gz ./data/SRR446028_2.fastq.gz
## 3 ./data/SRR446029_1.fastq.gz ./data/SRR446029_2.fastq.gz
## 4 ./data/SRR446030_1.fastq.gz ./data/SRR446030_2.fastq.gz
##   SampleName Factor        Date SampleReference
## 1        M1A     M1 23-Mar-2012                
## 2        M1B     M1 23-Mar-2012                
## 3        A1A     A1 23-Mar-2012             M1A
## 4        A1B     A1 23-Mar-2012             M1B

3.2 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-pe",
    package = "systemPipeR")
trim <- loadWF(targets = targetspath, wf_file = "trim-pe.cwl",
    input_file = "trim-pe.yml", dir_path = dir_path)
trim <- renderWF(trim, inputvars = c(FileName1 = "_FASTQ_PATH1_",
    FileName2 = "_FASTQ_PATH2_", SampleName = "_SampleName_"))
trim
output(trim)[1:2]

Next, we execute the code for trimming all the raw data.

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_trimPE.txt", step = 1,
    new_col = c("FileName1", "FileName2"), new_col_output_index = c(1,
        2), 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", "targetsPE_chip.txt", package = "systemPipeR")
    dir_path <- system.file("extdata/cwl/preprocessReads/trim-pe",
        package = "systemPipeR")
    trim <- loadWorkflow(targets = targets, wf_file = "trim-pe.cwl",
        input_file = "trim-pe.yml", dir_path = dir_path)
    trim <- renderWF(trim, inputvars = c(FileName1 = "_FASTQ_PATH1_",
        FileName2 = "_FASTQ_PATH2_", 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()