The R software for running
systemPipeR can be downloaded from CRAN. The
systemPipeR environment can be installed from R using the
biocLite install command. The associated data package
systemPipeRdata can be used to generate
systemPipeR workflow environments with a single command (see below) containing all parameter files and sample data required to quickly test and run workflows.
Loading package and documentation
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 loads one of the available NGS workflow templates (here RNA-Seq) into the user’s current working directory. At the moment, 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 previous step contains the following preconfigured directory structure:
- 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:
- Stores parameter files such as: *.param, *.tmpl and *_run.sh.
- FASTQ samples
- Reference FASTA file
- Alignment, variant and peak files (BAM, VCF, BED)
- Tabular result files
- Images and plots
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.
The following parameter files are included in each workflow template:
targets.txt: initial one provided by user; downstream
targets_*.txt files are generated automatically
*.param: defines parameter for input/output file operations, e.g.
*_run.sh: optional bash script, e.g.:
- Compute cluster environment (skip on single machine):
.BatchJobs: defines type of scheduler for
*.tmpl: specifies parameters of scheduler used by a system, e.g. Torque, SGE, StarCluster, Slurm, etc.
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.
targets file for single end (SE) samples
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 for paired end (PE) samples
Sample comparisons are defined in the header lines of the
targets file starting with ‘
readComp imports the comparison information and stores it in a
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.
param file and
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.
systemArgs function imports the definitions of both the
param file and the
targets file, and stores all relevant information in a
SYSargs S4 class object. To run the pipeline without command-line software, one can assign
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
Several accessor functions are available that are named after the slot names of the
The content of the
param file can also be returned as JSON object as follows (requires