Note: the most recent version of this tutorial can be found here and a short overview slide show here.

1 Introduction Slides

See here.

2 Background

systemPipeR provides utilities for building and running automated end-to-end analysis workflows for a wide range of next generation sequence (NGS) applications such as RNA-Seq, ChIP-Seq, VAR-Seq and Ribo-Seq (Girke 2014). Important features include a uniform workflow interface across different NGS applications, automated report generation, and support for running both R and command-line software, such as NGS aligners or peak/variant callers, on local computers or compute clusters. The latter supports interactive job submissions and 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 (e.g. FASTQ/BAM files) 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:

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

A central concept for designing workflows within the sytemPipeR environment is the use of workflow 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.

Figure 1: Workflow design 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 can be generated in PDF or HTML format 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 by the helper package systemPipeRdata and in the vignettes subdirectory of systemPipeR. The corresponding PDFs of these report templates are available here: systemPipeRNAseq, systemPipeRIBOseq, 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.

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3 Getting Started

3.1 Download latest version of this tutorial

In case there is a newer version of this tutorial, download its systemPipeR_Intro.Rmd source and open it in your R IDE (e.g. vim-r or RStudio).

download.file("", "systemPipeR_Intro.Rmd")

3.2 Installation

The R software for running systemPipeR can be downloaded from CRAN. The systemPipeR environment can be installed from the R console using the biocLite install command. The associated data package systemPipeRdata can be installed the same way. The latter is a helper package for generating systemPipeR workflow environments with a single command containing all parameter files and sample data required to quickly test and run workflows.

source("") # Sources the biocLite.R installation script 
biocLite("systemPipeR") # Installs systemPipeR 
biocLite("systemPipeRdata") # Installs systemPipeRdata
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3.3 Loading package and documentation

library("systemPipeR") # Loads the package
library(help="systemPipeR") # Lists package info
vignette("systemPipeR") # Opens vignette
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3.4 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 loaded via 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.

The following generates a fully populated systemPipeR workflow environment (here for RNA-Seq) in the current working directory of an R session. At this time the package includes workflow templates for RNA-Seq, ChIP-Seq, VAR-Seq and Ribo-Seq. Templates for additional NGS applications will be provided in the future.


The working environment of the sample data loaded in the previous step contains the following preconfigured directory structure. Directory names are indicated in grey. Users can change this structure as needed, but need to adjust the code in their workflows accordingly.

The following parameter files are included in each workflow template:

  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. * optional bash script, e.g.:
  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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3.5 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 included in the package. It also can be viewed and downloaded from systemPipeR’s GitHub repository here. 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 of PE reads. All subsequent columns are optional and any number of additional columns can be added as needed.

3.5.1 Structure of targets file for single end (SE) samples

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

To work with custom data, users need to generate a targets file containing the paths to their own FASTQ files and then provide under targetspath the path to the corresponding targets file.

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3.5.2 Structure of targets file for paired end (PE) samples

targetspath <- 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
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3.5.3 Sample comparisons

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

## [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 information and stores it in a list. Alternatively, readComp can obtain the comparison information from the corresponding SYSargs object (see below). Note, these header lines are optional. They are mainly useful for controlling comparative analyses according to certain biological expectations, such as identifying differentially expressed genes in RNA-Seq experiments based on simple pair-wise comparisons.

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"
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3.6 Structure of param file and SYSargs container

The param file defines the parameters of a chosen 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")

The systemArgs function imports the definitions of both the param file and the targets file, and stores all relevant information in a SYSargs object (S4 class). To run the pipeline without command-line software, one can assign NULL to sysma instead of a param file. In addition, one can start systemPipeR workflows with pre-generated BAM files by providing a targets file where the FileName column provides the paths to the BAM files. Note, in the following example the usage of suppressWarnings() is only relevant for building this vignette. In typical workflows it should be removed.

args <- suppressWarnings(systemArgs(sysma=parampath, mytargets=targetspath))
## An instance of 'SYSargs' for running 'tophat' on 18 samples

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

##  [1] "targetsin"     "targetsout"    "targetsheader" "modules"       "software"      "cores"        
##  [7] "other"         "reference"     "results"       "infile1"       "infile2"       "outfile1"     
## [13] "sysargs"       "outpaths"

Of particular interest is the sysargs() method. It constructs the system commands for running command-lined software as specified by a given param file combined with the paths to the input samples (e.g. FASTQ files) provided by a targets file. The example below shows the sysargs() output for running TopHat2 on the first PE read sample. Evaluating the output of sysargs() can be very helpful for designing and debugging param files of new command-line software or changing the parameter settings of existing ones.

##                                                                                                                                                                                                                                                                                    M1A 
## "tophat -p 4 -g 1 --segment-length 25 -i 30 -I 3000 -o /tmp/Rtmpc4JzlC/Rbuild32a024624dd9/systemPipeRdata/vignettes/results/SRR446027_1.fastq.tophat /tmp/Rtmpc4JzlC/Rbuild32a024624dd9/systemPipeRdata/vignettes/data/tair10.fasta ./data/SRR446027_1.fastq ./data/SRR446027_2.fastq"
## [1] "bowtie2/2.2.5" "tophat/2.0.14"
## [1] 4
##                                                                                                               M1A 
## "/tmp/Rtmpc4JzlC/Rbuild32a024624dd9/systemPipeRdata/vignettes/results/SRR446027_1.fastq.tophat/accepted_hits.bam"

The content of the param file can also be returned as JSON object as follows (requires rjson package).

systemArgs(sysma=parampath, mytargets=targetspath, type="json")
## [1] "{\"modules\":{\"n1\":\"\",\"v2\":\"bowtie2/2.2.5\",\"n1\":\"\",\"v2\":\"tophat/2.0.14\"},\"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\":\"\"}}"
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4 More detail

See systemPipeR vignette here.

5 Workflow demo

RIBO-Seq example here

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6 Version information

## R version 3.3.0 (2016-05-03)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 14.04.2 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              
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     
## other attached packages:
##  [1] systemPipeR_1.7.2          ShortRead_1.31.0           GenomicAlignments_1.9.3   
##  [4] SummarizedExperiment_1.3.5 Biobase_2.33.0             BiocParallel_1.7.4        
##  [7] Rsamtools_1.25.0           Biostrings_2.41.4          XVector_0.13.2            
## [10] GenomicRanges_1.25.4       GenomeInfoDb_1.9.1         IRanges_2.7.6             
## [13] S4Vectors_0.11.5           BiocGenerics_0.19.1        BiocStyle_2.1.8           
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.5             lattice_0.20-33         GO.db_3.3.0             digest_0.6.9           
##  [5] plyr_1.8.4              BatchJobs_1.6           backports_1.0.2         RSQLite_1.0.0          
##  [9] evaluate_0.9            ggplot2_2.1.0           zlibbioc_1.19.0         GenomicFeatures_1.25.12
## [13] annotate_1.51.0         Matrix_1.2-6            checkmate_1.8.0         rmarkdown_0.9.6        
## [17] GOstats_2.39.0          splines_3.3.0           stringr_1.0.0           pheatmap_1.0.8         
## [21] RCurl_1.95-4.8          biomaRt_2.29.2          munsell_0.4.3           sendmailR_1.2-1        
## [25] rtracklayer_1.33.7      base64enc_0.1-3         BBmisc_1.9              htmltools_0.3.5        
## [29] fail_1.3                edgeR_3.15.0            codetools_0.2-14        XML_3.98-1.4           
## [33] AnnotationForge_1.15.4  bitops_1.0-6            grid_3.3.0              RBGL_1.49.1            
## [37] xtable_1.8-2            GSEABase_1.35.0         gtable_0.2.0            DBI_0.4-1              
## [41] magrittr_1.5            formatR_1.4             scales_0.4.0            graph_1.51.0           
## [45] stringi_1.1.1           hwriter_1.3.2           genefilter_1.55.2       limma_3.29.10          
## [49] latticeExtra_0.6-28     brew_1.0-6              rjson_0.2.15            RColorBrewer_1.1-2     
## [53] tools_3.3.0             Category_2.39.0         survival_2.39-4         yaml_2.1.13            
## [57] AnnotationDbi_1.35.3    colorspace_1.2-6        knitr_1.13
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Girke, Thomas. 2014. “systemPipeR: NGS Workflow and Report Generation Environment.” UC Riverside.

Howard, Brian E, Qiwen Hu, Ahmet Can Babaoglu, Manan Chandra, Monica Borghi, Xiaoping Tan, Luyan He, et al. 2013. “High-Throughput RNA Sequencing of Pseudomonas-Infected Arabidopsis Reveals Hidden Transcriptome Complexity and Novel Splice Variants.” PLoS One 8 (10): e74183. doi:10.1371/journal.pone.0074183.

Kim, Daehwan, Geo Pertea, Cole Trapnell, Harold Pimentel, Ryan Kelley, and Steven L Salzberg. 2013. “TopHat2: Accurate Alignment of Transcriptomes in the Presence of Insertions, Deletions and Gene Fusions.” Genome Biol. 14 (4): R36. doi:10.1186/gb-2013-14-4-r36.

Langmead, Ben, and Steven L Salzberg. 2012. “Fast Gapped-Read Alignment with Bowtie 2.” Nat. Methods 9 (4). Nature Publishing Group: 357–59. doi:10.1038/nmeth.1923.

Lawrence, Michael, Wolfgang Huber, Hervé Pagès, Patrick Aboyoun, Marc Carlson, Robert Gentleman, Martin T Morgan, and Vincent J Carey. 2013. “Software for Computing and Annotating Genomic Ranges.” PLoS Comput. Biol. 9 (8): e1003118. doi:10.1371/journal.pcbi.1003118.

Li, H, and R Durbin. 2009. “Fast and Accurate Short Read Alignment with Burrows-Wheeler Transform.” Bioinformatics 25 (14): 1754–60. doi:10.1093/bioinformatics/btp324.

Li, Heng. 2013. “Aligning Sequence Reads, Clone Sequences and Assembly Contigs with BWA-MEM.” ArXiv [Q-Bio.GN].

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.

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.