• systemPipeR is an R package for building end-to-end analysis pipelines with automated report generation for next generation NGS applications (Girke 2014).
• Important features:
  • Support for R and command-line software, such as NGS aligners, peak callers, variant callers, etc.
  • Runs on single machines and compute clusters with schedulers
  • Uniform sample handling and annotation

• Many NGS applications share several analysis routines, such as:
  • Read QC and preprocessing
  • Alignments
  • Quantification
  • Feature annotations
  • Enrichment analysis
• Thus, a common workflow environment has many advantages for improving efficiency, standardization and reproducibility

• Facilitates design of complex NGS workflows involving multiple R/Bioconductor packages (Huber et al. 2015).
• Makes NGS analysis with Bioconductor utilities more accessible to new users
• Simplifies usage of command-line software from within R
• Reduces complexity of using compute clusters for R and command-line software
• Accelerates runtime of workflows via parallelization on computer systems with mutiple CPU cores and/or multiple compute nodes
• Automates generation of analysis reports to improve reproducibility

• Helper package to generate with a single command NGS workflow templates for systemPipeR.
• Includes sample data for testing.
• User can create new workflows or change and extend existing ones.

1. Read preprocessing
   • Quality filtering (trimming)
   • FASTQ quality report
2. Alignments: rsubread, Bowtie2/Tophat2
   • Available soon: alignment free approaches with Kallisto via artemis or sleuth
3. Alignment statistics
4. Read counting per annotation
5. Sample-wise correlation analysis
6. DEG analysis with edgeR or DESeq2
7. Enrichment analysis of GO terms or other annotation types
8. Gene-wise cluster analysis

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

1. Read preprocessing
   • Quality filtering and/or trimming
   • FASTQ quality report
2. Alignments: rsubread, Bowtie2
3. Alignment statistics
4. Genome-wide coverage statistics
5. Peak calling: MACS2, BayesPeak
6. Peak annotation with genomic context
7. Differential binding analysis
8. Enrichment analysis of GO terms or other annotation types
9. Motif analysis

Workflow templates for:

• miRNA-Seq
• BS-Seq

Run command-line tool, here Tophat2, on single machine. Command-line tool needs to be installed for this.

runCommandline(args)

Submit command-line or R processes to a computer cluster with a queueing system.

clusterRun(args, ...) 

The last step requires additional resource allocation arguments. For details please visit the main manual here.

Generate workflow template, e.g. "rnaseq", "varseq" or "chipseq"

genWorkenvir(workflow="varseq", mydirname=NULL)
setwd("varseq")

Command-line alternative for generating workflow environments

$ echo 'library(systemPipeRdata); 
        genWorkenvir(workflow="varseq", mydirname=NULL)' | R --slave

The workflow templates generated by genWorkenvir contain the following preconfigured directory structure:

workflow_name/            # *.Rnw/*.Rmd scripts, targets file, etc.
                param/    # parameter files for command-line software 
                data/     # inputs e.g. FASTQ, reference, annotations
                results/  # analysis result files

The above structure can be customized as needed, but for first-time users it is easier to keep changes to a minimum.

• Next, run from within R the chosen sample workflow by executing the code provided in the corresponding *.Rnw template file (or *.Rmd or *.R versions).
• Alternatively, one can run an entire workflow from start to finish with a single command by executing from the command-line:

$ make -B

• Analysis reports in PDF or HTML format are autogenerated when running a workflow using standard R resources for scientific report generation including knitr and rmarkdown, respectively.

• Integration of ReportingTools is also straightforward.

• Workflow templates with support for both PDF (.Rnw) and HTML (.Rmd) reports
• Workflow templates for additional NGS applications (see here)
• docopt support for generating .param files
• Additional visualization functions
• Streamline support of very complex experimental designs