In tgirke/systemPipeRdata: systemPipeRdata: Workflow templates and sample data

BiocStyle::markdown()
options(width=100, max.print=1000)
knitr::opts_chunk$set(
    eval=as.logical(Sys.getenv("KNITR_EVAL", "TRUE")),
    cache=as.logical(Sys.getenv("KNITR_CACHE", "TRUE")))

suppressPackageStartupMessages({
    library(systemPipeR)
    library(systemPipeRdata)
    library(BiocParallel)
    library(Biostrings)
    library(Rsamtools)
    library(GenomicRanges)
    library(ggplot2)
    library(GenomicAlignments)
    library(ShortRead)
})

Note: the most recent version of this tutorial can be found here.

Introduction

systemPipeR provides utilities for building \textit{end-to-end} analysis workflows with automated report generation for next generation sequence (NGS) applications such as RNA-Seq, ChIP-Seq, VAR-Seq and many others [@Girke2014-oy]. An important feature is support for running command-line software, such as NGS aligners, on both single machines or compute clusters. This includes both interactive job submissions or batch submissions to queuing systems of clusters. For instance, systemPipeR can be used with most command-line aligners such as BWA [@Li2013-oy; @Li2009-oc], TopHat2 [@Kim2013-vg] and Bowtie2 [@Langmead2012-bs], as well as the R-based NGS aligners Rsubread [@Liao2013-bn] and gsnap (gmapR) [@Wu2010-iq]. Efficient handling of complex sample sets and experimental designs is facilitated by a well-defined sample annotation infrastructure which improves reproducibility and user-friendliness of many typical analysis workflows in the NGS area [@Lawrence2013-kt].

Motivation and advantages of sytemPipeR environment:

1. Facilitates design of complex NGS workflows involving multiple R/Bioconductor packages
2. Simplifies usage of command-line software from within R
3. Reduces complexity of using compute clusters for R and command-line software
4. Accelerates runtime of workflows via parallelzation on computer systems with mutiple CPU cores and/or multiple compute nodes
5. Automates generation of analysis reports to improve reproducibility

A central concept for designing workflows within the sytemPipeR environment is the use of sample management containers called SYSargs (see Figure 1). Instances of this S4 object class are constructed by the systemArgs function from two simple tablular files: a targets file and a param file. The latter is optional for workflow steps lacking command-line software. Typically, a SYSargs instance stores all sample-level inputs as well as the paths to the corresponding outputs generated by command-line- or R-based software generating sample-level output files, such as read preprocessors (trimmed/filtered FASTQ files), aligners (SAM/BAM files), variant callers (VCF/BCF files) or peak callers (BED/WIG files). Each sample level input/outfile operation uses its own SYSargs instance. The outpaths of SYSargs usually define the sample inputs for the next SYSargs instance. This connectivity is established by writing the outpaths with the writeTargetsout function to a new targets file that serves as input to the next systemArgs call. Typically, the user has to provide only the initial targets file. All downstream targets files are generated automatically. By chaining several SYSargs steps together one can construct complex workflows involving many sample-level input/output file operations with any combinaton of command-line or R-based software.

Figure 1: Workflow structure of systemPipeR

The intended way of running sytemPipeR workflows is via *.Rnw or *.Rmd files, which can be executed either line-wise in interactive mode or with a single command from R or the command-line using a Makefile. This way comprehensive and reproducible analysis reports in PDF or HTML format can be generated in a fully automated manner by making use of the highly functional reporting utilities available for R. Templates for setting up custom project reports are provided as *.Rnw files in the vignettes subdirectory of this package. The corresponding PDFs of these report templates are linked here: systemPipeRNAseq, systemPipeChIPseq and systemPipeVARseq. To work with *.Rnw or *.Rmd files efficiently, basic knowledge of Sweave or knitr and Latex or R Markdown v2 is required.

Relevant workflow parameter files:

1. targets.txt: initial one provided by user; downstream targets_*.txt files are generated automatically
2. *.param: defines parameter for input/output file operations, e.g. trim.param, bwa.param, vartools.parm, ...
3. *_run.sh: optional bash script, e.g.: gatk_run.sh
4. Compute cluster environment (skip on single machine):
   • .BatchJobs: defines type of scheduler for BatchJobs
   • *.tmpl: specifies parameters of scheduler used by a system, e.g. Torque, SGE, StarCluster, Slurm, etc.

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Getting Started

Installation

The R software for running systemPipeR and systemPipeRdata can be downloaded from CRAN. The systemPipeR environment can be installed from R using the install command:

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("systemPipeR") # Installs systemPipeR from Bioconductor 
BiocManager::install("tgirke/systemPipeRdata", build_vignettes=TRUE, dependencies=TRUE) # From github

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Loading package and documentation

library("systemPipeR") # Loads the package
library(help="systemPipeR") # Lists package info
vignette("systemPipeR") # Opens vignette

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Load sample data and workflow templates

The mini sample FASTQ files used by this overview vignette as well as the associated workflow reporting vignettes can be downloaded from here. Preferentially, they should be loaded from the systemPipeRdata package as shown below. The chosen data set 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. thalina genome. The corresponding reference genome sequence (FASTA) and its GFF annotion files (provided in the same download) have been truncated accordingly. This way the entire test sample data set requires less than 200MB disk 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.

Load one of the available NGS workflow templates into your current working directory (here for rnaseq).

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

The working environment of the sample data contains the following preconfigured directory structure:

• workflow/
  • This is the directory of the R session running the workflow.
  • Run script ( *.Rnw or *.Rmd) and sample annotation (targets.txt) files are located here.
  • Note, this directory can have any name (e.g. rnaseq, varseq). Changing its name does not require any modifications in the run script(s).
  • Important subdirectories:
    • param/
      • Stores parameter files such as: *.param, *.tmpl and *_run.sh.
    • data/
      • FASTQ samples
      • Reference FASTA file
      • Annotations
      • etc.
    • results/
      • Alignment, variant and peak files (BAM, VCF, BED)
      • Tabular result files
      • Images and plots
      • etc.

The sample workflows provided by the package are based on the above directory structure, where directory names are indicated in grey. Users can change this structure as needed, but need to adjust the code in their workflows accordingly.

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Structure of targets file

The targets file defines all input files (e.g. FASTQ, BAM, BCF) and sample comparisons of an analysis workflow. The following shows the format of a sample targets file provided by this package. In a target file with a single type of input files, here FASTQ files of single end (SE) reads, the first three columns are mandatory including their column names, while it is four mandatory columns for FASTQ files for PE reads. All subsequent columns are optional and any number of additional columns can be added as needed.

Structure of targets file for single end (PE) samples

library(systemPipeR)
read.delim("targets.txt", comment.char = "#")

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Structure of targets file for paired end (PE) samples

read.delim("targetsPE.txt", comment.char = "#")[1:2,1:6]

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Sample comparisons

Sample comparisons are defined in the header lines of the targets file starting with '# <CMP>'.

readLines("targets.txt")[1:4]

The function readComp imports the comparison and stores them in a list. Alternatively, readComp can obtain the comparison information from the corresponding SYSargs object (see below). Note, the header lines are optional. They are mainly useful for controlling comparative analysis according to certain biological expectations, such as simple pairwise comparisons in RNA-Seq experiments.

readComp(file="targets.txt", format="vector", delim="-")

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Structure of param file and SYSargs container

The param file defines the parameters of the command-line software. The following shows the format of a sample param file provided by this package.

read.delim("param/tophat.param", comment.char = "#")

The systemArgs function imports the definitions of both the param file and the targets file, and stores all relevant information as SYSargs object. To run the pipeline without command-line software, one can assign NULL to sysma instead of a param file. In addition, one can start the systemPipeR workflow with pre-generated BAM files by providing a targets file where the FileName column gives the paths to the BAM files and sysma is assigned NULL.

args <- suppressWarnings(systemArgs(sysma="param/tophat.param", mytargets="targets.txt"))
args

Several accessor functions are available that are named after the slot names of the SYSargs object class.

names(args)
modules(args)
cores(args)
outpaths(args)[1]
sysargs(args)[1]

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Workflow overview

Define environment settings and samples

Load package

library(systemPipeR)

Construct SYSargs object from param and targets files.

args <- systemArgs(sysma="param/trim.param", mytargets="targets.txt")

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Read Preprocessing

The function preprocessReads allows to apply predefined or custom read preprocessing functions to all FASTQ files referenced in a SYSargs container, such as quality filtering or adaptor trimming routines. The paths to the resulting output FASTQ files are stored in the outpaths slot of the SYSargs object. 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 adaptor 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 SYSargs instance, \textit{e.g.} running the NGS alignments using the trimmed FASTQ files.

preprocessReads(args=args, Fct="trimLRPatterns(Rpattern='GCCCGGGTAA', subject=fq)", 
                batchsize=100000, overwrite=TRUE, compress=TRUE)
writeTargetsout(x=args, file="targets_trim.txt")

The following example shows how one can design a custom read preprocessing function using utilities provided by the ShortRead package, and then run it in batch mode with the 'preprocessReads' function (here on paired-end reads).

args <- systemArgs(sysma="param/trimPE.param", mytargets="targetsPE.txt")
filterFct <- function(fq, cutoff=20, Nexceptions=0) {
    qcount <- rowSums(as(quality(fq), "matrix") <= cutoff)
    fq[qcount <= Nexceptions] # Retains reads where Phred scores are >= cutoff with N exceptions
}
preprocessReads(args=args, Fct="filterFct(fq, cutoff=20, Nexceptions=0)", batchsize=100000)
writeTargetsout(x=args, file="targets_PEtrim.txt")

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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.

fqlist <- seeFastq(fastq=infile1(args), batchsize=10000, klength=8)
pdf("./results/fastqReport.pdf", height=18, width=4*length(fqlist))
seeFastqPlot(fqlist)
dev.off()

Figure 2: FASTQ quality report

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Parallelization of QC report on single machine with multiple cores

args <- systemArgs(sysma="param/tophat.param", mytargets="targets.txt")
f <- function(x) seeFastq(fastq=infile1(args)[x], batchsize=100000, klength=8)
fqlist <- bplapply(seq(along=args), f, BPPARAM = MulticoreParam(workers=8))
seeFastqPlot(unlist(fqlist, recursive=FALSE))

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Parallelization of QC report via scheduler (e.g. Torque) across several compute nodes

library(BiocParallel); library(BatchJobs)
f <- function(x) {
    library(systemPipeR)
    args <- systemArgs(sysma="param/tophat.param", mytargets="targets.txt")
    seeFastq(fastq=infile1(args)[x], batchsize=100000, klength=8)
}
funs <- makeClusterFunctionsTorque("torque.tmpl")
param <- BatchJobsParam(length(args), resources=list(walltime="20:00:00", nodes="1:ppn=1", memory="6gb"), cluster.functions=funs)
register(param)
fqlist <- bplapply(seq(along=args), f)
seeFastqPlot(unlist(fqlist, recursive=FALSE))

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Alignment with Tophat2

Build Bowtie2 index.

args <- systemArgs(sysma="param/tophat.param", mytargets="targets.txt")
moduleload(modules(args)) # Skip if module system is not available
system("bowtie2-build ./data/tair10.fasta ./data/tair10.fasta")

Execute SYSargs on a single machine without submitting to a queuing system of a compute cluster. This way the input FASTQ files will be processed sequentially. If available, multiple CPU cores can be used for processing each file. The number of CPU cores (here 4) to use for each process is defined in the *.param file. With cores(args) one can return this value from the SYSargs object. Note, if a module system is not installed or used, then the corresponding *.param file needs to be edited accordingly by either providing an empty field in the line(s) starting with module or by deleting these lines.

bampaths <- runCommandline(args=args)

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. To avoid over-subscription of CPU cores on the compute nodes, the value from cores(args) is passed on to the submission command, here nodes in the resources list object. The number of independent parallel cluster processes is defined under the Njobs argument. The following example will run 18 processes in parallel using for each 4 CPU cores. If the resources available on a cluster allow to run all 18 processes at the same time then the shown sample submission will utilize in total 72 CPU cores. Note, runCluster can be used with most queueing systems as it is based on utilities from the BatchJobs 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 conf file (see .BatchJob samples here) and a template file (see *.tmpl samples here) for the queueing available on a system. The following example uses the sample conf and template files for the Torque scheduler provided by this package.

resources <- list(walltime="20:00:00", nodes=paste0("1:ppn=", cores(args)), memory="10gb")
reg <- clusterRun(args, conffile=".BatchJobs.R", template="torque.tmpl", Njobs=18, runid="01", 
                  resourceList=resources)
waitForJobs(reg)

Useful commands for monitoring progress of submitted jobs

showStatus(reg)
file.exists(outpaths(args))
sapply(1:length(args), function(x) loadResult(reg, x)) # Works after job completion

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Read and alignment count stats

Generate table of read and alignment counts for all samples.

read_statsDF <- alignStats(args) 
write.table(read_statsDF, "results/alignStats.xls", row.names=FALSE, quote=FALSE, sep="\t")

The following shows the first four lines of the sample alignment stats file provided by the systemPipeR package. For simplicity the number of PE reads is multiplied here by 2 to approximate proper alignment frequencies where each read in a pair is counted.

read.table(system.file("extdata", "alignStats.xls", package="systemPipeR"), header=TRUE)[1:4,]

Parallelization of read/alignment stats on single machine with multiple cores

f <- function(x) alignStats(args[x])
read_statsList <- bplapply(seq(along=args), f, BPPARAM = MulticoreParam(workers=8))
read_statsDF <- do.call("rbind", read_statsList)

Parallelization of read/alignment stats via scheduler (e.g. Torque) across several compute nodes

library(BiocParallel); library(BatchJobs)
f <- function(x) {
    library(systemPipeR)
    args <- systemArgs(sysma="tophat.param", mytargets="targets.txt")
    alignStats(args[x])
}
funs <- makeClusterFunctionsTorque("torque.tmpl")
param <- BatchJobsParam(length(args), resources=list(walltime="20:00:00", nodes="1:ppn=1", memory="6gb"), cluster.functions=funs)
register(param)
read_statsList <- bplapply(seq(along=args), f)
read_statsDF <- do.call("rbind", read_statsList)

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Create symbolic links for viewing BAM files in IGV

The genome browser IGV supports reading of indexed/sorted BAM files via web URLs. This way it can be avoided to create unnecessary copies of these large files. To enable this approach, an HTML directory with http access needs to be available in the user account (e.g. home/publichtml) of a system. If this is not the case then the BAM files need to be moved or copied to the system where IGV runs. In the following, htmldir defines the path to the HTML directory with http access where the symbolic links to the BAM files will be stored. The corresponding URLs will be written to a text file specified under the _urlfile_ argument.

symLink2bam(sysargs=args, htmldir=c("~/.html/", "somedir/"), 
            urlbase="http://myserver.edu/~username/", 
        urlfile="IGVurl.txt")

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Alternative NGS Aligners

Alignment with Bowtie2 (e.g. for miRNA profiling)

The following example runs Bowtie2 as a single process without submitting it to a cluster.

args <- systemArgs(sysma="bowtieSE.param", mytargets="targets.txt")
moduleload(modules(args)) # Skip if module system is not available
bampaths <- runCommandline(args=args)

Alternatively, submit the job to compute nodes.

resources <- list(walltime="20:00:00", nodes=paste0("1:ppn=", cores(args)), memory="10gb")
reg <- clusterRun(args, conffile=".BatchJobs.R", template="torque.tmpl", Njobs=18, runid="01", 
                  resourceList=resources)
waitForJobs(reg)

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Alignment with BWA-MEM (e.g. for VAR-Seq)

The following example runs BWA-MEM as a single process without submitting it to a cluster.

args <- systemArgs(sysma="param/bwa.param", mytargets="targets.txt")
moduleload(modules(args)) # Skip if module system is not available
system("bwa index -a bwtsw ./data/tair10.fasta") # Indexes reference genome
bampaths <- runCommandline(args=args[1:2])

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Alignment with Rsubread (e.g. for RNA-Seq)

The following example shows how one can use within the \Rpackage{systemPipeR} environment the R-based aligner \Rpackage{Rsubread} or other R-based functions that read from input files and write to output files.

library(Rsubread)
args <- systemArgs(sysma="param/rsubread.param", mytargets="targets.txt")
buildindex(basename=reference(args), reference=reference(args)) # Build indexed reference genome
align(index=reference(args), readfile1=infile1(args)[1:4], input_format="FASTQ", 
      output_file=outfile1(args)[1:4], output_format="SAM", nthreads=8, indels=1, TH1=2)
for(i in seq(along=outfile1(args))) asBam(file=outfile1(args)[i], destination=gsub(".sam", "", outfile1(args)[i]), overwrite=TRUE, indexDestination=TRUE)

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Alignment with gsnap

Another R-based short read aligner is gsnap from the gmapR package [@Wu2010-iq]. The code sample below introduces how to run this aligner on multiple nodes of a compute cluster.

library(gmapR); library(BiocParallel); library(BatchJobs)
args <- systemArgs(sysma="param/gsnap.param", mytargets="targetsPE.txt")
gmapGenome <- GmapGenome(reference(args), directory="data", name="gmap_tair10chr/", create=TRUE)
f <- function(x) {
    library(gmapR); library(systemPipeR)
    args <- systemArgs(sysma="gsnap.param", mytargets="targetsPE.txt")
    gmapGenome <- GmapGenome(reference(args), directory="data", name="gmap_tair10chr/", create=FALSE)
    p <- GsnapParam(genome=gmapGenome, unique_only=TRUE, molecule="DNA", max_mismatches=3)
    o <- gsnap(input_a=infile1(args)[x], input_b=infile2(args)[x], params=p, output=outfile1(args)[x])
}
funs <- makeClusterFunctionsTorque("torque.tmpl")
param <- BatchJobsParam(length(args), resources=list(walltime="20:00:00", nodes="1:ppn=1", memory="6gb"), cluster.functions=funs)
register(param)
d <- bplapply(seq(along=args), f)

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VAR-Seq workflow for single machine

Generate workflow template

Load one of the available NGS workflows into your current working directory (here for varseq).

setwd("../")
genWorkenvir(workflow="varseq")
setwd("varseq")

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Run workflow

Next, run the chosen sample workflow systemPipeVARseq_single (PDF, Rnw) by executing from the command-line make -B within the varseq directory. Alternatively, one can run the code from the provided *.Rnw template file from within R interactively. Much more detailed information is available in systemPipeR's overview and workflow vignettes available here.

Workflow includes following steps:

1. Read preprocessing
   • Quality filtering (trimming)
   • FASTQ quality report
2. Alignments: gsnap, bwa*
3. Variant calling: VariantTools, GATK*, BCFtools*
4. Variant filtering: VariantTools and VariantAnnotation
5. Variant annotation: VariantAnnotation
6. Combine results from many samples
7. Summary statistics of samples

*not evaluated because required software is not available on all systems.

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VAR-Seq workflow for computer cluster (demo)

This demonstration will run the above VAR-Seq workflow in parallel on multiple computer nodes of a HPC system using Torque and Maui as scheduler and load balancing system. The workflow template provided for this step is called systemPipeVARseq.Rnw (PDF, Rnw) .

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sessionInfo()

sessionInfo()

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References

tgirke/systemPipeRdata documentation built on Oct. 24, 2024, 9:49 p.m.