Workflow templates and sample data

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

Note: if you use systemPipeR or systemPipeRdata in published research, please cite: Backman, T.W.H and Girke, T. (2016). systemPipeR: Workflow and Report Generation Environment. BMC Bioinformatics, 17: 388. 10.1186/s12859-016-1241-0.

systemPipeRdata package provides a demo sample FASTQ files used in the workflow reporting vignettes. The chosen data set SRP010938 obtains 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 annotation 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.

Getting started


The systemPipeRdata package is available at Bioconductor and can be installed from within R as follows:

if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")

Also, it is possible to install the development version from Bioconductor.

BiocManager::install("systemPipeRdata", version = "devel", build_vignettes = TRUE,
    dependencies = TRUE)  # Installs Devel version from Bioconductor

Loading package and documentation

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

Starting with pre-configured workflow templates

Load one of the available workflows into your current working directory. The following does this for the rnaseq workflow template. The name of the resulting workflow directory can be specified under the mydirname argument. The default NULL uses the name of the chosen workflow. An error is issued if a directory of the same name and path exists already.

Full details of pre-configed workflows are listed on this page

genWorkenvir(workflow = "systemPipeR/SPrnaseq", mydirname = "rnaseq")

On Linux and OS X systems the same can be achieved from the command-line of a terminal with the following commands.

$ Rscript -e "systemPipeRdata::genWorkenvir(workflow='systemPipeR/SPrnaseq', mydirname='rnaseq')"

Build, run and visualize the workflow template

  • Build workflow from RMarkdown file

This template provides some common steps for a RNAseq workflow. One can add, remove, modify workflow steps by operating on the sal object.

sal <- SPRproject()
sal <- importWF(sal, file_path = "systemPipeVARseq.Rmd", verbose = FALSE)
  • Running workflow

Next, we can run the entire workflow from R with one command:

sal <- runWF(sal)
  • Visualize workflow

systemPipeR workflows instances can be visualized with the plotWF function.

  • Report generation

systemPipeR compiles all the workflow execution logs in one central location, making it easier to check any standard output (stdout) or standard error (stderr) for any command-line tools used on the workflow or the R code stdout.

sal <- renderLogs(sal)

Also, the technical report can be generated using renderReport function.

sal <- renderReport(sal)

Workflow templates collection

A collection of workflow templates are available, and it is possible to browse the current availability, as follows:

availableWF(github = TRUE)

This function returns the list of workflow templates available within the package and systemPipeR Organization on GitHub. Each one listed template can be created as described above.

The workflow template choose from Github will be installed as an R package, and also it creates the environment with all the settings and files to run the demo analysis.

genWorkenvir(workflow="systemPipeR/SPrnaseq", mydirname="NULL")

Besides, it is possible to choose different versions of the workflow template, defined through other branches on the GitHub Repository. By default, the master branch is selected, however, it is possible to define a different branch with the ref argument.

genWorkenvir(workflow="systemPipeR/SPrnaseq", ref = "singleMachine")

Download a specific R Markdown file

Also, it is possible to download a specific workflow script for your analysis. The URL can be specified under url argument and the R Markdown file name in the urlname argument. The default NULL copies the current version available in the chose template.

genWorkenvir(workflow="systemPipeR/SPrnaseq", url = "", 
             urlname = "rnaseq_V-cluster.Rmd")

Dynamic generation of workflow template

It is possible to create a new workflow structure from RStudio menu File -> New File -> R Markdown -> From Template -> systemPipeR New WorkFlow. This interactive option creates the same environment as demonstrated above.

Figure 1: Selecting workflow template within RStudio.

Directory Structure

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

  • workflow/ (e.g. rnaseq/)
    • This is the root directory of the R session running the workflow.
    • Run script ( *.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 non-CWL parameter files such as: *.param, *.tmpl and * These files are only required for backwards compatibility to run old workflows using the previous custom command-line interface.
        • param/cwl/: This subdirectory stores all the CWL parameter files. To organize workflows, each can have its own subdirectory, where all CWL param and input.yml files need to be in the same subdirectory.
      • data/
        • FASTQ files
        • FASTA file of reference (e.g. reference genome)
        • Annotation files
        • etc.
      • results/
        • Analysis results are usually written to this directory, including: alignment, variant and peak files (BAM, VCF, BED); tabular result files; and image/plot files
        • Note, the user has the option to organize results files for a given sample and analysis step in a separate subdirectory.

Note: Directory names are indicated in green. Users can change this structure as needed, but need to adjust the code in their workflows accordingly.

Figure 2: systemPipeR’s preconfigured directory structure.

Return paths to sample data

The location of the sample data provided by systemPipeRdata can be returned as a list.

## $targets
## [1] "C:/Users/lz/AppData/Local/R/win-library/4.2/systemPipeRdata/extdata/param/targets.txt"
## $targetsPE
## [1] "C:/Users/lz/AppData/Local/R/win-library/4.2/systemPipeRdata/extdata/param/targetsPE.txt"

Version information

## R version 4.2.0 (2022-04-22 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19044)
## Matrix products: default
## locale:
## [1] LC_COLLATE=English_United States.utf8 
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets 
## [6] methods   base     
## other attached packages:
## [1] systemPipeRdata_2.1.1 BiocStyle_2.24.0     
## loaded via a namespace (and not attached):
##  [1] bslib_0.3.1            compiler_4.2.0        
##  [3] BiocManager_1.30.18    formatR_1.12          
##  [5] jquerylib_0.1.4        GenomeInfoDb_1.32.1   
##  [7] XVector_0.36.0         bitops_1.0-7          
##  [9] remotes_2.4.2          tools_4.2.0           
## [11] zlibbioc_1.42.0        digest_0.6.29         
## [13] jsonlite_1.8.0         evaluate_0.15         
## [15] rlang_1.0.2            cli_3.3.0             
## [17] rstudioapi_0.13        yaml_2.3.5            
## [19] blogdown_1.10.1        xfun_0.31             
## [21] fastmap_1.1.0          GenomeInfoDbData_1.2.8
## [23] stringr_1.4.0          knitr_1.39            
## [25] Biostrings_2.64.0      sass_0.4.1            
## [27] S4Vectors_0.34.0       IRanges_2.30.0        
## [29] stats4_4.2.0           R6_2.5.1              
## [31] rmarkdown_2.14         bookdown_0.26         
## [33] magrittr_2.0.3         codetools_0.2-18      
## [35] htmltools_0.5.2        BiocGenerics_0.42.0   
## [37] stringi_1.7.6          RCurl_1.98-1.6        
## [39] crayon_1.5.1


This project was supported by funds from the National Institutes of Health (NIH) and the National Science Foundation (NSF).


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.

Last modified 2022-06-05: cwl v2 (81370e97)