systemPipeRdata: Workflow templates and sample data

Introduction

systemPipeRdata provides data analysis workflow templates compatible with the systemPipeR software package (H Backman and Girke 2016). The latter is a Workflow Management System (WMS) for designing and running end-to-end analysis workflows with automated report generation for a wide range of data analysis applications. Support for running external software is provided by a command-line interface (CLI) that adopts the Common Workflow Language (CWL). How to use systemPipeR is explained in its main vignette here. The workflow templates provided by systemPipeRdata come equipped with sample data and the necessary parameter files required to run a selected workflow. This setup simplifies the learning process of using systemPipeR, facilitates testing of workflows, and serves as a foundation for designing new workflows. The standardized directory structure (Figure 1) utilized by the workflow templates and their sample data is outlined in the Directory Structure section of systemPipeR's main vignette.

Figure 1: Directory structure ofsystemPipeR's workflows. For details, see here.

Getting started

Installation

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

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

Loading package and documentation

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

Overview of workflow templates

An overview table of workflow templates, included in systemPipeRdata, can be returned as shown below. By clicking the URLs in the last column of the below workflow list, users can view the Rmd source file of a workflow, as well as the corresponding HTML report generated after running a workflow on the provided test data. A list of the default data analysis steps included in each workflow is given here. Similar and additional workflow templates are also available from GitHub (see below and here), which can be retrieved directly from their repositories. To create an empty workflow template without any test data included, users want to choose the new template, which includes only the required directory structure and parameter files.

availableWF()
Name Description URL
new Generic Workflow Template Rmd, HTML
rnaseq RNA-Seq Workflow Template Rmd, HTML
riboseq RIBO-Seq Workflow Template Rmd, HTML
chipseq ChIP-Seq Workflow Template Rmd, HTML
varseq VAR-Seq Workflow Template Rmd, HTML
SPblast BLAST Workflow Template Rmd, HTML
SPcheminfo Cheminformatics Drug Similarity Template Rmd, HTML
SPscrna Basic Single-Cell Workflow Template Rmd, HTML

Table 1: Workflow templates

Use workflow templates

Load a workflow

The chosen example below uses the genWorkenvir function from the systemPipeRdata package to create an RNA-Seq workflow environment (selected under workflow="rnaseq") that is fully populated with a small test data set, including FASTQ files, reference genome and annotation data. 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. After this, the user’s R session needs to be directed into the resulting rnaseq directory (here with setwd). The other workflow templates from the above table can be loaded the same way.

library(systemPipeRdata)
genWorkenvir(workflow = "rnaseq")
setwd("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='rnaseq', mydirname='rnaseq')"
$ cd rnaseq

In addition to the templates distributed through systemPipeRdata, similar workflow templates can also be retrieved directly from GitHub repositories using the genWorkenvir_gh function. Details on this are provided below.

Run and visualize workflow

For running and working with systemPipeR workflows, users want to visit systemPipeR’s main vignette. The following gives only a very brief preview on how to run workflows, and create scientific and technical reports.

After a workflow environment (directory) has been created and the corresponding R session directed into the resulting directory (here rnaseq), the workflow can be loaded from the included R Markdown file (Rmd, here systemPipeRNAseq.Rmd). This template provides common data analysis steps that are typical for RNA-Seq workflows. Users have the options to add, remove, modify workflow steps by applying these changes to the sal workflow management container directly, or updating the Rmd file first and then updating sal accordingly.

library(systemPipeR)
sal <- SPRproject()
sal <- importWF(sal, file_path = "systemPipeRNAseq.Rmd", verbose = FALSE)

The default analysis steps of the imported RNA-Seq workflow are listed below. Users can modify the existing steps, add new ones or remove steps as needed.

Default analysis steps in RNA-Seq Workflow

  1. Read preprocessing
    • Quality filtering (trimming)
    • FASTQ quality report
  2. Alignments: HISAT2 (or any other RNA-Seq aligner)
  3. Alignment stats
  4. Read counting
  5. Sample-wise correlation analysis
  6. Analysis of differentially expressed genes (DEGs)
  7. GO term enrichment analysis
  8. Gene-wise clustering

Once the workflow has been loaded into sal, it can be executed from start to finish (or partially) with the runWF command. However, running the workflow will only be possible if all dependent CL software is installed on a user’s system. Their names and availability on a system can be listed with listCmdTools(sal, check_path=TRUE).

sal <- runWF(sal)

Workflows can be visualized as topology graphs using the plotWF function.

plotWF(sal)
Toplogy graph of RNA-Seq workflow.

Toplogy graph of RNA-Seq workflow.

Scientific and technical reports can be generated with the renderReport and renderLogs functions, respectively. Scientific reports can also be generated with the render function of the rmarkdown package. The technical reports are based on log information that systemPipeR collects during workflow runs.

# Scietific report
sal <- renderReport(sal)
rmarkdown::render("systemPipeRNAseq.Rmd", clean = TRUE, output_format = "BiocStyle::html_document")

# Technical (log) report
sal <- renderLogs(sal)

Additional workflow templates on GitHub

In addition to the workflow templates distributed with the systemPipeRdata package, similar templates are available as standalone repositories on GitHub. These workflows include both well-established and experimental analysis pipelines that extend the range of supported applications. A curated list of available workflow repositories is maintained on the systemPipe project website here. Each repository contains a complete workflow environment including parameter files, example input data (when available), and documentation describing the workflow structure and usage.

Workflow environments hosted on GitHub can be initialized directly from within R using the genWorkenvir_gh() function. For example:

genWorkenvir_gh(url = "https://github.com/systemPipeR/sprwf-rnaseq.git",
    mydirname = "sprwf-rnaseq")

This command downloads the workflow repository and creates a local workflow environment that can be executed or customized by the user. After initialization, the R session should be directed to the resulting project directory, and the param and data directories loaded with the getParam_gh and getData_gh functions. Detailed instructions and documentation for each workflow are provided within the corresponding GitHub repositories.

Useful functionalities

Create workflow templates interactively

It is possible to create a new workflow environment from RStudio. This can be done by selecting File -> New File -> R Markdown -> From Template -> systemPipeR New WorkFlow. This option creates a template workflow that has the expected directory structure (see here).

Figure 2: Selecting workflow template within RStudio.

Return paths to sample data

The paths to the sample data provided by the systemPipeRdata package can be returned with the the pathList function.

pathList()[1:2]
## $targets
## [1] "/tmp/Rtmp78NOAO/Rinst163b4dce65ab/systemPipeRdata/extdata/param/targets.txt"
## 
## $targetsPE
## [1] "/tmp/Rtmp78NOAO/Rinst163b4dce65ab/systemPipeRdata/extdata/param/targetsPE.txt"

Analysis steps in selected workflows

The following gives an overview of the default data analysis steps used by selected workflow templates included in the systemPipeRdata package (see Table 1).

Any of the workflows included below can be loaded by assigning their name to the workflow argument of the genWorkenvir function. The workflow names can be looked up under the ‘Name’ column of Table 1.

library(systemPipeRdata)
genWorkenvir(workflow = "...")

Generic template

[ Vignette ]

This Generic workflow (named new) is intended to be used as a template for creating new workflows from scratch where users can add steps by copying and pasting existing R or CL steps as needed, and populate them with their own code. In its current form, this mini workflow will export a test dataset to multiple files, compress/decompress the exported files, import them back into R, and then perform a simple statistical analysis and plot the results.

  1. R step: export tabular data to files
  2. CL step: compress files
  3. CL step: uncompress files
  4. R step: import files and plot summary statistics

RNA-Seq workflow

[ Vignette ]

  1. Read preprocessing
    • Quality filtering (trimming)
    • FASTQ quality report
  2. Alignments: HISAT2 (or any other RNA-Seq aligner)
  3. Alignment stats
  4. Read counting
  5. Sample-wise correlation analysis
  6. Analysis of differentially expressed genes (DEGs)
  7. GO term enrichment analysis
  8. Gene-wise clustering

ChIP-Seq Workflow

[ Vignette ]

  1. Read preprocessing
    • Quality filtering (trimming)
    • FASTQ quality report
  2. Alignments: Bowtie2 or rsubread
  3. Alignment stats
  4. Peak calling: MACS2
  5. Peak annotation with genomic context
  6. Differential binding analysis
  7. GO term enrichment analysis
  8. Motif analysis

VAR-Seq Workflow

[ Vignette ]

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

Ribo-Seq Workflow

[ Vignette ]

  1. Read preprocessing
    • Adaptor trimming and quality filtering
    • FASTQ quality report
  2. Alignments: HISAT2 (or any other RNA-Seq aligner)
  3. Alignment stats
  4. Compute read distribution across genomic features
  5. Adding custom features to workflow (e.g. uORFs)
  6. Genomic read coverage along transcripts
  7. Read counting
  8. Sample-wise correlation analysis
  9. Analysis of differentially expressed genes (DEGs)
  10. GO term enrichment analysis
  11. Gene-wise clustering
  12. Differential ribosome binding (translational efficiency)

scRNA-Seq Workflow

[ Vignette ]

  1. Import of single cell read count data
  2. Basic stats on input data
  3. QC of cell count data
  4. Cell filtering
  5. Normalization
  6. Identify high variable genes
  7. Scaling
  8. Embedding with tSNE, UMAP, and PCA
  9. Cell clustering and marker gene classification
  10. Cell type classification
  11. Co-visualizatioin of cell types and clusters

BLAST Workflow

[ Vignette ]

  1. Load query sequences
  2. Select and prepare BLASTable databases
  3. Run BLAST against different databases

Cheminformatics Workflow

[ Vignette ]

  1. Import small molecules stored in SDF file
  2. Visualize small molecule structures
  3. Create atom pair and finger print databases for structure similarity searching
  4. Compute all-against-all structural similarities
  5. Hierarchical clustering and PCA of structural similarities
  6. Plot heat map

Version information

sessionInfo()
## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 26.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils    
## [6] datasets  methods   base     
## 
## other attached packages:
##  [1] magrittr_2.0.5              systemPipeRdata_2.16.0     
##  [3] systemPipeR_2.18.0          ShortRead_1.70.0           
##  [5] GenomicAlignments_1.48.0    SummarizedExperiment_1.42.0
##  [7] Biobase_2.72.0              MatrixGenerics_1.24.0      
##  [9] matrixStats_1.5.0           BiocParallel_1.46.0        
## [11] Rsamtools_2.28.0            Biostrings_2.80.2          
## [13] XVector_0.52.0              GenomicRanges_1.64.0       
## [15] IRanges_2.46.0              S4Vectors_0.50.3           
## [17] Seqinfo_1.2.0               BiocGenerics_0.58.1        
## [19] generics_0.1.4              BiocStyle_2.40.0           
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.6        xfun_0.61          
##  [3] bslib_0.12.0        hwriter_1.3.2.1    
##  [5] ggplot2_4.0.3       remotes_2.5.0      
##  [7] htmlwidgets_1.6.4   latticeExtra_0.6-31
##  [9] lattice_0.23-1      vctrs_0.7.3        
## [11] tools_4.6.1         bitops_1.1-0       
## [13] parallel_4.6.1      tibble_3.3.1       
## [15] pkgconfig_2.0.3     Matrix_1.7-6       
## [17] RColorBrewer_1.1-3  S7_0.2.2           
## [19] cigarillo_1.2.1     lifecycle_1.0.5    
## [21] stringr_1.6.0       compiler_4.6.1     
## [23] farver_2.1.2        deldir_2.0-4       
## [25] textshaping_1.0.5   codetools_0.2-20   
## [27] htmltools_0.5.9     sys_3.4.3          
## [29] buildtools_1.0.0    sass_0.4.10        
## [31] yaml_2.3.12         pillar_1.11.1      
## [33] crayon_1.5.3        jquerylib_0.1.4    
## [35] DelayedArray_0.38.2 cachem_1.1.0       
## [37] abind_1.4-8         tidyselect_1.2.1   
## [39] digest_0.6.39       stringi_1.8.9      
## [41] dplyr_1.2.1         maketools_1.3.2    
## [43] fastmap_1.2.0       grid_4.6.1         
## [45] cli_3.6.6           SparseArray_1.12.3 
## [47] S4Arrays_1.12.1     scales_1.4.0       
## [49] rmarkdown_2.32      pwalign_1.8.0      
## [51] jpeg_0.1-11         interp_1.1-6       
## [53] otel_0.2.0          png_0.1-9          
## [55] kableExtra_1.4.1    evaluate_1.0.5     
## [57] knitr_1.52          viridisLite_0.4.3  
## [59] rlang_1.3.0         Rcpp_1.1.2         
## [61] glue_1.8.1          xml2_1.6.0         
## [63] BiocManager_1.30.27 formatR_1.14       
## [65] svglite_2.2.2       rstudioapi_0.19.0  
## [67] jsonlite_2.0.0      R6_2.6.1           
## [69] systemfonts_1.3.2

Funding

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

References

H Backman, Tyler W, and Thomas Girke. 2016. “systemPipeR: NGS workflow and report generation environment.” BMC Bioinformatics 17 (1): 388. https://doi.org/10.1186/s12859-016-1241-0.