systemPipeR 1.24.6
Users want to provide here background information about the design of their ChIP-Seq project.
This report describes the analysis of several ChIP-Seq experiments studying the DNA binding patterns of the transcriptions factors … from organism ….
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.
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)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.
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.
targets fileThe 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 M1BThe 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)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()