Tophat2
edgeR
DESeq2
## Creating a generic function for 'nchar' from package 'base' in package 'S4Vectors'
Note: the most recent version of this tutorial can be found here and a short overview slide show here.
systemPipeR
provides utilities for building analysis workflows with automated report generation for next generation sequence (NGS) applications such as RNA-Seq, ChIP-Seq, VAR-Seq and many others (Girke 2014). 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
(Heng Li 2013; H Li and Durbin 2009), TopHat2
(Kim et al. 2013) and Bowtie2
(Langmead and Salzberg 2012), as well as the R-based NGS aligners Rsubread
(Liao, Smyth, and Shi 2013) and gsnap (gmapR)
(Wu and Nacu 2010). 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 (Lawrence et al. 2013).
Motivation and advantages of sytemPipeR
environment:
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 tabular 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.
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:
targets.txt
: initial one provided by user; downstream targets_*.txt
files are generated automatically*.param
: defines parameter for input/output file operations, e.g. trim.param
, bwa.param
, vartools.parm
, …*_run.sh
: optional bash script, e.g.: gatk_run.sh
.BatchJobs
: defines type of scheduler for BatchJobs
*.tmpl
: specifies parameters of scheduler used by a system, e.g. Torque, SGE, StarCluster, Slurm, etc.The R software for running systemPipeR
and systemPipeRdata
can be downloaded from CRAN. The systemPipeR
environment can be installed from R using the biocLite
install command.
source("http://bioconductor.org/biocLite.R") # Sources the biocLite.R installation script
biocLite("systemPipeR") # Installs systemPipeR from Bioconductor
biocLite("tgirke/systemPipeRdata", build_vignettes=TRUE, dependencies=TRUE) # From github
library("systemPipeR") # Loads the package
library(help="systemPipeR") # Lists package info
vignette("systemPipeR") # Opens vignette
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 (Howard et al. 2013). 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:
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.
targets
fileThe 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.
targets
file for single end (SE) sampleslibrary(systemPipeR)
targetspath <- system.file("extdata", "targets.txt", package="systemPipeR")
read.delim(targetspath, comment.char = "#")
## FileName SampleName Factor SampleLong Experiment Date
## 1 ./data/SRR446027_1.fastq M1A M1 Mock.1h.A 1 23-Mar-2012
## 2 ./data/SRR446028_1.fastq M1B M1 Mock.1h.B 1 23-Mar-2012
## 3 ./data/SRR446029_1.fastq A1A A1 Avr.1h.A 1 23-Mar-2012
## 4 ./data/SRR446030_1.fastq A1B A1 Avr.1h.B 1 23-Mar-2012
## 5 ./data/SRR446031_1.fastq V1A V1 Vir.1h.A 1 23-Mar-2012
## 6 ./data/SRR446032_1.fastq V1B V1 Vir.1h.B 1 23-Mar-2012
## 7 ./data/SRR446033_1.fastq M6A M6 Mock.6h.A 1 23-Mar-2012
## 8 ./data/SRR446034_1.fastq M6B M6 Mock.6h.B 1 23-Mar-2012
## 9 ./data/SRR446035_1.fastq A6A A6 Avr.6h.A 1 23-Mar-2012
## 10 ./data/SRR446036_1.fastq A6B A6 Avr.6h.B 1 23-Mar-2012
## 11 ./data/SRR446037_1.fastq V6A V6 Vir.6h.A 1 23-Mar-2012
## 12 ./data/SRR446038_1.fastq V6B V6 Vir.6h.B 1 23-Mar-2012
## 13 ./data/SRR446039_1.fastq M12A M12 Mock.12h.A 1 23-Mar-2012
## 14 ./data/SRR446040_1.fastq M12B M12 Mock.12h.B 1 23-Mar-2012
## 15 ./data/SRR446041_1.fastq A12A A12 Avr.12h.A 1 23-Mar-2012
## 16 ./data/SRR446042_1.fastq A12B A12 Avr.12h.B 1 23-Mar-2012
## 17 ./data/SRR446043_1.fastq V12A V12 Vir.12h.A 1 23-Mar-2012
## 18 ./data/SRR446044_1.fastq V12B V12 Vir.12h.B 1 23-Mar-2012
targets
file for paired end (PE) samplestargetspath <- system.file("extdata", "targetsPE.txt", package="systemPipeR")
read.delim(targetspath, comment.char = "#")[1:2,1:6]
## FileName1 FileName2 SampleName Factor SampleLong Experiment
## 1 ./data/SRR446027_1.fastq ./data/SRR446027_2.fastq M1A M1 Mock.1h.A 1
## 2 ./data/SRR446028_1.fastq ./data/SRR446028_2.fastq M1B M1 Mock.1h.B 1
Sample comparisons are defined in the header lines of the targets
file starting with ‘# <CMP>
’.
readLines(targetspath)[1:4]
## [1] "# Project ID: Arabidopsis - Pseudomonas alternative splicing study (SRA: SRP010938; PMID: 24098335)"
## [2] "# The following line(s) allow to specify the contrasts needed for comparative analyses, such as DEG identification. All possible comparisons can be specified with 'CMPset: ALL'."
## [3] "# <CMP> CMPset1: M1-A1, M1-V1, A1-V1, M6-A6, M6-V6, A6-V6, M12-A12, M12-V12, A12-V12"
## [4] "# <CMP> CMPset2: ALL"
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=targetspath, format="vector", delim="-")
## $CMPset1
## [1] "M1-A1" "M1-V1" "A1-V1" "M6-A6" "M6-V6" "A6-V6" "M12-A12" "M12-V12" "A12-V12"
##
## $CMPset2
## [1] "M1-A1" "M1-V1" "M1-M6" "M1-A6" "M1-V6" "M1-M12" "M1-A12" "M1-V12" "A1-V1"
## [10] "A1-M6" "A1-A6" "A1-V6" "A1-M12" "A1-A12" "A1-V12" "V1-M6" "V1-A6" "V1-V6"
## [19] "V1-M12" "V1-A12" "V1-V12" "M6-A6" "M6-V6" "M6-M12" "M6-A12" "M6-V12" "A6-V6"
## [28] "A6-M12" "A6-A12" "A6-V12" "V6-M12" "V6-A12" "V6-V12" "M12-A12" "M12-V12" "A12-V12"
param
file and SYSargs
containerThe param
file defines the parameters of the command-line software. The following shows the format of a sample param
file provided by this package.
parampath <- system.file("extdata", "tophat.param", package="systemPipeR")
read.delim(parampath, comment.char = "#")
## PairSet Name Value
## 1 modules <NA> bowtie2/2.1.0
## 2 modules <NA> tophat/2.0.8b
## 3 software <NA> tophat
## 4 cores -p 4
## 5 other <NA> -g 1 --segment-length 25 -i 30 -I 3000
## 6 outfile1 -o <FileName1>
## 7 outfile1 path ./results/
## 8 outfile1 remove <NA>
## 9 outfile1 append .tophat
## 10 outfile1 outextension .tophat/accepted_hits.bam
## 11 reference <NA> ./data/tair10.fasta
## 12 infile1 <NA> <FileName1>
## 13 infile1 path <NA>
## 14 infile2 <NA> <FileName2>
## 15 infile2 path <NA>
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=parampath, mytargets=targetspath))
args
## An instance of 'SYSargs' for running 'tophat' on 18 samples
Several accessor functions are available that are named after the slot names of the SYSargs
object class.
names(args)
## [1] "targetsin" "targetsout" "targetsheader" "modules" "software" "cores"
## [7] "other" "reference" "results" "infile1" "infile2" "outfile1"
## [13] "sysargs" "outpaths"
modules(args)
## [1] "bowtie2/2.1.0" "tophat/2.0.8b"
cores(args)
## [1] 4
outpaths(args)[1]
## M1A
## "/tmp/RtmpHGUqhZ/Rbuild36ff74ed3696/systemPipeR/vignettes/results/SRR446027_1.fastq.tophat/accepted_hits.bam"
sysargs(args)[1]
## M1A
## "tophat -p 4 -g 1 --segment-length 25 -i 30 -I 3000 -o /tmp/RtmpHGUqhZ/Rbuild36ff74ed3696/systemPipeR/vignettes/results/SRR446027_1.fastq.tophat /tmp/RtmpHGUqhZ/Rbuild36ff74ed3696/systemPipeR/vignettes/data/tair10.fasta ./data/SRR446027_1.fastq ./data/SRR446027_2.fastq"
The content of the param
file can be returned as JSON object as follows (requires rjson
package).
systemArgs(sysma=parampath, mytargets=targetspath, type="json")
## [1] "{\"modules\":{\"n1\":\"\",\"v2\":\"bowtie2/2.1.0\",\"n1\":\"\",\"v2\":\"tophat/2.0.8b\"},\"software\":{\"n1\":\"\",\"v1\":\"tophat\"},\"cores\":{\"n1\":\"-p\",\"v1\":\"4\"},\"other\":{\"n1\":\"\",\"v1\":\"-g 1 --segment-length 25 -i 30 -I 3000\"},\"outfile1\":{\"n1\":\"-o\",\"v2\":\"<FileName1>\",\"n3\":\"path\",\"v4\":\"./results/\",\"n5\":\"remove\",\"v1\":\"\",\"n2\":\"append\",\"v3\":\".tophat\",\"n4\":\"outextension\",\"v5\":\".tophat/accepted_hits.bam\"},\"reference\":{\"n1\":\"\",\"v1\":\"./data/tair10.fasta\"},\"infile1\":{\"n1\":\"\",\"v2\":\"<FileName1>\",\"n1\":\"path\",\"v2\":\"\"},\"infile2\":{\"n1\":\"\",\"v2\":\"<FileName2>\",\"n1\":\"path\",\"v2\":\"\"}}"
Load packages and generate workflow environment (here for RNA-Seq)
library(systemPipeR)
library(systemPipeRdata)
genWorkenvir(workflow="rnaseq")
setwd("rnaseq")
Construct SYSargs
object from param
and targets
files.
args <- systemArgs(sysma="param/trim.param", mytargets="targets.txt")
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, 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")
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()
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))
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))
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, clusterRun
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
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,]
## FileName Nreads2x Nalign Perc_Aligned Nalign_Primary Perc_Aligned_Primary
## 1 M1A 192918 177961 92.24697 177961 92.24697
## 2 M1B 197484 159378 80.70426 159378 80.70426
## 3 A1A 189870 176055 92.72397 176055 92.72397
## 4 A1B 188854 147768 78.24457 147768 78.24457
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)
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")
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)
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])
Rsubread
(e.g. for RNA-Seq)The following example shows how one can use within the environment the R-based aligner 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)
gsnap
(e.g. for VAR-Seq and RNA-Seq)Another R-based short read aligner is gsnap
from the gmapR
package (Wu and Nacu 2010). 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)
Create txdb
(needs to be done only once)
library(GenomicFeatures)
txdb <- makeTranscriptDbFromGFF(file="data/tair10.gff", format="gff", dataSource="TAIR", species="A. thaliana")
saveDb(txdb, file="./data/tair10.sqlite")
The following performs read counting with summarizeOverlaps
in parallel mode with multiple cores.
library(BiocParallel)
txdb <- loadDb("./data/tair10.sqlite")
eByg <- exonsBy(txdb, by="gene")
bfl <- BamFileList(outpaths(args), yieldSize=50000, index=character())
multicoreParam <- MulticoreParam(workers=4); register(multicoreParam); registered()
counteByg <- bplapply(bfl, function(x) summarizeOverlaps(eByg, x, mode="Union", ignore.strand=TRUE, inter.feature=TRUE, singleEnd=TRUE)) # Note: for strand-specific RNA-Seq set 'ignore.strand=FALSE' and for PE data set 'singleEnd=FALSE'
countDFeByg <- sapply(seq(along=counteByg), function(x) assays(counteByg[[x]])$counts)
rownames(countDFeByg) <- names(rowData(counteByg[[1]])); colnames(countDFeByg) <- names(bfl)
rpkmDFeByg <- apply(countDFeByg, 2, function(x) returnRPKM(counts=x, ranges=eByg))
write.table(countDFeByg, "results/countDFeByg.xls", col.names=NA, quote=FALSE, sep="\t")
write.table(rpkmDFeByg, "results/rpkmDFeByg.xls", col.names=NA, quote=FALSE, sep="\t")
Please note, in addition to read counts this step generates RPKM normalized expression values. For most statistical differential expression or abundance analysis methods, such as edgeR
or DESeq2
, the raw count values should be used as input. The usage of RPKM values should be restricted to specialty applications required by some users, e.g. manually comparing the expression levels of different genes or features.
Read counting with summarizeOverlaps
using multiple nodes of a cluster
library(BiocParallel)
f <- function(x) {
library(systemPipeR); library(BiocParallel); library(GenomicFeatures)
txdb <- loadDb("./data/tair10.sqlite")
eByg <- exonsBy(txdb, by="gene")
args <- systemArgs(sysma="tophat.param", mytargets="targets.txt")
bfl <- BamFileList(outpaths(args), yieldSize=50000, index=character())
summarizeOverlaps(eByg, bfl[x], mode="Union", ignore.strand=TRUE, inter.feature=TRUE, singleEnd=TRUE)
}
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)
counteByg <- bplapply(seq(along=args), f)
countDFeByg <- sapply(seq(along=counteByg), function(x) assays(counteByg[[x]])$counts)
rownames(countDFeByg) <- names(rowData(counteByg[[1]])); colnames(countDFeByg) <- names(outpaths(args))
Download miRNA genes from miRBase
system("wget ftp://mirbase.org/pub/mirbase/19/genomes/My_species.gff3 -P ./data/")
gff <- import.gff("./data/My_species.gff3", asRangedData=FALSE)
gff <- split(gff, elementMetadata(gff)$ID)
bams <- names(bampaths); names(bams) <- targets$SampleName
bfl <- BamFileList(bams, yieldSize=50000, index=character())
countDFmiR <- summarizeOverlaps(gff, bfl, mode="Union", ignore.strand=FALSE, inter.feature=FALSE) # Note: inter.feature=FALSE important since pre and mature miRNA ranges overlap
rpkmDFmiR <- apply(countDFmiR, 2, function(x) returnRPKM(counts=x, gffsub=gff))
write.table(assays(countDFmiR)$counts, "results/countDFmiR.xls", col.names=NA, quote=FALSE, sep="\t")
write.table(rpkmDFmiR, "results/rpkmDFmiR.xls", col.names=NA, quote=FALSE, sep="\t")
The following computes the sample-wise Spearman correlation coefficients from the rlog
(regularized-logarithm) transformed expression values generated with the DESeq2
package. After transformation to a distance matrix, hierarchical clustering is performed with the hclust
function and the result is plotted as a dendrogram (sample_tree.pdf).
library(DESeq2, warn.conflicts=FALSE, quietly=TRUE); library(ape, warn.conflicts=FALSE)
countDFpath <- system.file("extdata", "countDFeByg.xls", package="systemPipeR")
countDF <- as.matrix(read.table(countDFpath))
colData <- data.frame(row.names=targetsin(args)$SampleName, condition=targetsin(args)$Factor)
dds <- DESeqDataSetFromMatrix(countData = countDF, colData = colData, design = ~ condition)
d <- cor(assay(rlog(dds)), method="spearman")
hc <- hclust(dist(1-d))
plot.phylo(as.phylo(hc), type="p", edge.col=4, edge.width=3, show.node.label=TRUE, no.margin=TRUE)
rlog
values.
Alternatively, the clustering can be performed with RPKM
normalized expression values. In combination with Spearman correlation the results of the two clustering methods are often relatively similar.
rpkmDFeBygpath <- system.file("extdata", "rpkmDFeByg.xls", package="systemPipeR")
rpkmDFeByg <- read.table(rpkmDFeBygpath, check.names=FALSE)
rpkmDFeByg <- rpkmDFeByg[rowMeans(rpkmDFeByg) > 50,]
d <- cor(rpkmDFeByg, method="spearman")
hc <- hclust(as.dist(1-d))
plot.phylo(as.phylo(hc), type="p", edge.col="blue", edge.width=2, show.node.label=TRUE, no.margin=TRUE)
edgeR
The following run_edgeR
function is a convenience wrapper for identifying differentially expressed genes (DEGs) in batch mode with edgeR
’s GML method (Robinson, McCarthy, and Smyth 2010) for any number of pairwise sample comparisons specified under the cmp
argument. Users are strongly encouraged to consult the edgeR
vignette for more detailed information on this topic and how to properly run edgeR
on data sets with more complex experimental designs.
targets <- read.delim(targetspath, comment="#")
cmp <- readComp(file=targetspath, format="matrix", delim="-")
cmp[[1]]
## [,1] [,2]
## [1,] "M1" "A1"
## [2,] "M1" "V1"
## [3,] "A1" "V1"
## [4,] "M6" "A6"
## [5,] "M6" "V6"
## [6,] "A6" "V6"
## [7,] "M12" "A12"
## [8,] "M12" "V12"
## [9,] "A12" "V12"
countDFeBygpath <- system.file("extdata", "countDFeByg.xls", package="systemPipeR")
countDFeByg <- read.delim(countDFeBygpath, row.names=1)
edgeDF <- run_edgeR(countDF=countDFeByg, targets=targets, cmp=cmp[[1]], independent=FALSE, mdsplot="")
## Disp = 0.20653 , BCV = 0.4545
Filter and plot DEG results for up and down regulated genes. Because of the small size of the toy data set used by this vignette, the FDR value has been set to a relatively high threshold (here 10%). More commonly used FDR cutoffs are 1% or 5%. The definition of ‘up’ and ‘down’ is given in the corresponding help file. To open it, type ?filterDEGs
in the R console.
DEG_list <- filterDEGs(degDF=edgeDF, filter=c(Fold=2, FDR=10))
edgeR
.
names(DEG_list)
## [1] "UporDown" "Up" "Down" "Summary"
DEG_list$Summary[1:4,]
## Comparisons Counts_Up_or_Down Counts_Up Counts_Down
## M1-A1 M1-A1 0 0 0
## M1-V1 M1-V1 1 1 0
## A1-V1 A1-V1 1 1 0
## M6-A6 M6-A6 0 0 0
DESeq2
The following run_DESeq2
function is a convenience wrapper for identifying DEGs in batch mode with DESeq2
(Love, Huber, and Anders 2014) for any number of pairwise sample comparisons specified under the cmp
argument. Users are strongly encouraged to consult the DESeq2
vignette for more detailed information on this topic and how to properly run DESeq2
on data sets with more complex experimental designs.
degseqDF <- run_DESeq2(countDF=countDFeByg, targets=targets, cmp=cmp[[1]], independent=FALSE)
Filter and plot DEG results for up and down regulated genes.
DEG_list2 <- filterDEGs(degDF=degseqDF, filter=c(Fold=2, FDR=10))
DESeq2
.
The function overLapper
can compute Venn intersects for large numbers of sample sets (up to 20 or more) and vennPlot
can plot 2-5 way Venn diagrams. A useful feature is the possiblity to combine the counts from several Venn comparisons with the same number of sample sets in a single Venn diagram (here for 4 up and down DEG sets).
vennsetup <- overLapper(DEG_list$Up[6:9], type="vennsets")
vennsetdown <- overLapper(DEG_list$Down[6:9], type="vennsets")
vennPlot(list(vennsetup, vennsetdown), mymain="", mysub="", colmode=2, ccol=c("blue", "red"))
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.
library("biomaRt")
listMarts() # To choose BioMart database
m <- useMart("ENSEMBL_MART_PLANT"); listDatasets(m)
m <- useMart("ENSEMBL_MART_PLANT", dataset="athaliana_eg_gene")
listAttributes(m) # Choose data types you want to download
go <- getBM(attributes=c("go_accession", "tair_locus", "go_namespace_1003"), mart=m)
go <- go[go[,3]!="",]; go[,3] <- as.character(go[,3])
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")
Apply the enrichment analysis to the DEG sets obtained in the above differential expression analysis. Note, in the following example the FDR filter is set here to an unreasonably high value, simply because of the small size of the toy data set used in this vignette. Batch enrichment analysis of many gene sets is performed with the GOCluster_Report
function. When method="all"
, it returns all GO terms passing the p-value cutoff specified under the cutoff
arguments. When method="slim"
, it returns only the GO terms specified under the myslimv
argument. The given example shows how one can obtain such a GO slim vector from BioMart for a specific organism.
load("data/GO/catdb.RData")
DEG_list <- filterDEGs(degDF=edgeDF, filter=c(Fold=2, FDR=50), plot=FALSE)
up_down <- DEG_list$UporDown; names(up_down) <- paste(names(up_down), "_up_down", sep="")
up <- DEG_list$Up; names(up) <- paste(names(up), "_up", sep="")
down <- DEG_list$Down; names(down) <- paste(names(down), "_down", sep="")
DEGlist <- c(up_down, up, down)
DEGlist <- DEGlist[sapply(DEGlist, length) > 0]
BatchResult <- GOCluster_Report(catdb=catdb, setlist=DEGlist, method="all", id_type="gene", CLSZ=2, cutoff=0.9, gocats=c("MF", "BP", "CC"), recordSpecGO=NULL)
library("biomaRt"); m <- useMart("ENSEMBL_MART_PLANT", dataset="athaliana_eg_gene")
goslimvec <- as.character(getBM(attributes=c("goslim_goa_accession"), mart=m)[,1])
BatchResultslim <- GOCluster_Report(catdb=catdb, setlist=DEGlist, method="slim", id_type="gene", myslimv=goslimvec, CLSZ=10, cutoff=0.01, gocats=c("MF", "BP", "CC"), recordSpecGO=NULL)
The data.frame
generated by GOCluster_Report
can be plotted with the goBarplot
function. Because of the variable size of the sample sets, it may not always be desirable to show the results from different DEG sets in the same bar plot. Plotting single sample sets is achieved by subsetting the input data frame as shown in the first line of the following example.
gos <- BatchResultslim[grep("M6-V6_up_down", BatchResultslim$CLID), ]
gos <- BatchResultslim
pdf("GOslimbarplotMF.pdf", height=8, width=10); goBarplot(gos, gocat="MF"); dev.off()
goBarplot(gos, gocat="BP")
goBarplot(gos, gocat="CC")
The following example performs hierarchical clustering on the rlog
transformed expression matrix subsetted by the DEGs identified in the above differential expression analysis. It uses a Pearson correlation-based distance measure and complete linkage for cluster joining.
library(pheatmap)
geneids <- unique(as.character(unlist(DEG_list[[1]])))
y <- assay(rlog(dds))[geneids, ]
pdf("heatmap1.pdf")
pheatmap(y, scale="row", clustering_distance_rows="correlation", clustering_distance_cols="correlation")
dev.off()
Load the RNA-Seq sample workflow into your current working directory.
library(systemPipeRdata)
genWorkenvir(workflow="rnaseq")
setwd("rnaseq")
Next, run the chosen sample workflow systemPipeRNAseq
(PDF, Rnw) by executing from the command-line make -B
within the rnaseq
directory. Alternatively, one can run the code from the provided *.Rnw
template file from within R interactively.
Workflow includes following steps:
Tophat2
(or any other RNA-Seq aligner)Load the ChIP-Seq sample workflow into your current working directory.
library(systemPipeRdata)
genWorkenvir(workflow="chipseq")
setwd("chipseq")
Next, run the chosen sample workflow systemPipeChIPseq_single
(PDF, Rnw) by executing from the command-line make -B
within the chipseq
directory. Alternatively, one can run the code from the provided *.Rnw
template file from within R interactively.
Workflow includes following steps:
Bowtie2
or rsubread
MACS2
, BayesPeak
Load the VAR-Seq sample workflow into your current working directory.
library(systemPipeRdata)
genWorkenvir(workflow="varseq")
setwd("varseq")
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.
Workflow includes following steps:
gsnap
, bwa
VariantTools
, GATK
, BCFtools
VariantTools
and VariantAnnotation
VariantAnnotation
*
not evaluated because required software is not available on all systems.
sessionInfo()
## R version 3.2.2 (2015-08-14)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 14.04.3 LTS
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
## [4] LC_COLLATE=C LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] DESeq2_1.8.1 RcppArmadillo_0.5.500.2.0 Rcpp_0.12.1
## [4] ape_3.3 ggplot2_1.0.1 systemPipeR_1.2.23
## [7] RSQLite_1.0.0 DBI_0.3.1 ShortRead_1.26.0
## [10] GenomicAlignments_1.4.1 BiocParallel_1.2.21 Rsamtools_1.20.4
## [13] Biostrings_2.36.4 XVector_0.8.0 GenomicRanges_1.20.6
## [16] GenomeInfoDb_1.4.2 IRanges_2.2.7 S4Vectors_0.6.5
## [19] BiocGenerics_0.14.0 BiocStyle_1.6.0
##
## loaded via a namespace (and not attached):
## [1] Biobase_2.28.0 edgeR_3.10.2 splines_3.2.2 Formula_1.2-1
## [5] latticeExtra_0.6-26 RBGL_1.44.0 yaml_2.1.13 Category_2.34.2
## [9] lattice_0.20-33 limma_3.24.15 digest_0.6.8 RColorBrewer_1.1-2
## [13] checkmate_1.6.2 colorspace_1.2-6 htmltools_0.2.6 Matrix_1.2-2
## [17] plyr_1.8.3 GSEABase_1.30.2 XML_3.98-1.3 pheatmap_1.0.7
## [21] genefilter_1.50.0 zlibbioc_1.14.0 xtable_1.7-4 GO.db_3.1.2
## [25] scales_0.3.0 brew_1.0-6 annotate_1.46.1 nnet_7.3-11
## [29] proto_0.3-10 survival_2.38-3 magrittr_1.5 evaluate_0.7.2
## [33] fail_1.2 nlme_3.1-122 MASS_7.3-44 foreign_0.8-66
## [37] hwriter_1.3.2 GOstats_2.34.0 graph_1.46.0 tools_3.2.2
## [41] formatR_1.2 BBmisc_1.9 stringr_1.0.0 sendmailR_1.2-1
## [45] locfit_1.5-9.1 munsell_0.4.2 cluster_2.0.3 AnnotationDbi_1.30.1
## [49] lambda.r_1.1.7 futile.logger_1.4.1 grid_3.2.2 rjson_0.2.15
## [53] AnnotationForge_1.10.1 labeling_0.3 bitops_1.0-6 base64enc_0.1-3
## [57] rmarkdown_0.8 gtable_0.1.2 codetools_0.2-14 reshape2_1.4.1
## [61] gridExtra_2.0.0 knitr_1.11 Hmisc_3.16-0 futile.options_1.0.0
## [65] stringi_0.5-5 BatchJobs_1.6 geneplotter_1.46.0 rpart_4.1-10
## [69] acepack_1.3-3.3
Girke, Thomas. 2014. “systemPipeR: NGS Workflow and Report Generation Environment.” UC Riverside. https://github.com/tgirke/systemPipeR.
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Li, Heng. 2013. “Aligning Sequence Reads, Clone Sequences and Assembly Contigs with BWA-MEM.” ArXiv [Q-Bio.GN]. http://arxiv.org/abs/1303.3997.
Liao, Yang, Gordon K Smyth, and Wei Shi. 2013. “The Subread Aligner: Fast, Accurate and Scalable Read Mapping by Seed-and-Vote.” Nucleic Acids Res. 41 (10): e108. doi:10.1093/nar/gkt214.
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. doi:10.1186/s13059-014-0550-8.
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Wu, T D, and S Nacu. 2010. “Fast and SNP-tolerant Detection of Complex Variants and Splicing in Short Reads.” Bioinformatics 26 (7): 873–81. doi:10.1093/bioinformatics/btq057.