1 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.

2 Getting started

2.1 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")

2.2 Loading package and documentation

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

3 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 final 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. Additional workflow templates are available on this project’s GitHub organization (for details, see below). 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

4 Use workflow templates

4.1 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

4.2 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.

Figure 1: 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)

5 Additional workflow templates

The project’s GitHub Organization hosts a repository of workflow templates, containing both well-established and experimental workflows. Within the R environment, the same availableWF function mentioned earlier can be utilized to retrieve a list of the workflows in this collection.

availableWF(github = TRUE)
Additional Workflow Templates in systemPipeR GitHub Organization:
       Workflow                                     Download URL
1     SPatacseq    https://github.com/systemPipeR/SPatacseq.git
2     SPclipseq    https://github.com/systemPipeR/SPclipseq.git
3      SPdenovo    https://github.com/systemPipeR/SPdenovo.git
4         SPhic    https://github.com/systemPipeR/SPhic.git
5   SPmetatrans    https://github.com/systemPipeR/SPmetatrans.git
6   SPmethylseq    https://github.com/systemPipeR/SPmethylseq.git
7    SPmirnaseq    https://github.com/systemPipeR/SPmirnaseq.git
8 SPpolyriboseq    https://github.com/systemPipeR/SPpolyriboseq.git
9    SPscrnaseq    https://github.com/systemPipeR/SPscrnaseq.git

To download these workflow templates, users can either run the below git clone command from a terminal, or visit the corresponding GitHub page of a chosen workflow via the provided URLs, and then download it as a Zip file and uncompress it. Note, the following lines of code need to be run from a terminal (not R console, e.g. terminal in RStudio) on a system where the git software is installed.

$ git clone <...> # Provide under <...> URL of chosen workflow from table above.
$ cd <Workflow Name>

After a workflow template has been downloaded, one can run it the same way as outlined above.

6 Useful functionalities

6.1 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.

6.2 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/RtmpkDqV6C/Rinst1ff8d01d016eaa/systemPipeRdata/extdata/param/targets.txt"
## 
## $targetsPE
## [1] "/tmp/RtmpkDqV6C/Rinst1ff8d01d016eaa/systemPipeRdata/extdata/param/targetsPE.txt"

7 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). The workflows hosted on this project’s GitHub Organization are not considered here.

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 = "...")

7.1 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

7.2 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

7.3 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

7.4 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

7.5 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)

7.6 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

7.7 BLAST Workflow

[ Vignette ]

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

7.8 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

8 Version information

sessionInfo()
## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [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: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils    
## [6] datasets  methods   base     
## 
## other attached packages:
##  [1] magrittr_2.0.3              systemPipeRdata_2.9.8      
##  [3] systemPipeR_2.11.7          ShortRead_1.63.2           
##  [5] GenomicAlignments_1.41.0    SummarizedExperiment_1.35.5
##  [7] Biobase_2.65.1              MatrixGenerics_1.17.1      
##  [9] matrixStats_1.4.1           BiocParallel_1.39.0        
## [11] Rsamtools_2.21.2            Biostrings_2.73.2          
## [13] XVector_0.45.0              GenomicRanges_1.57.2       
## [15] GenomeInfoDb_1.41.2         IRanges_2.39.2             
## [17] S4Vectors_0.43.2            BiocGenerics_0.51.3        
## [19] BiocStyle_2.33.1           
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1        viridisLite_0.4.2      
##  [3] dplyr_1.1.4             bitops_1.0-9           
##  [5] fastmap_1.2.0           digest_0.6.37          
##  [7] lifecycle_1.0.4         pwalign_1.1.0          
##  [9] compiler_4.5.0          rlang_1.1.4            
## [11] sass_0.4.9              tools_4.5.0            
## [13] utf8_1.2.4              yaml_2.3.10            
## [15] knitr_1.48              S4Arrays_1.5.11        
## [17] htmlwidgets_1.6.4       interp_1.1-6           
## [19] DelayedArray_0.31.14    xml2_1.3.6             
## [21] RColorBrewer_1.1-3      abind_1.4-8            
## [23] hwriter_1.3.2.1         grid_4.5.0             
## [25] fansi_1.0.6             latticeExtra_0.6-30    
## [27] colorspace_2.1-1        ggplot2_3.5.1          
## [29] scales_1.3.0            cli_3.6.3              
## [31] rmarkdown_2.28          crayon_1.5.3           
## [33] generics_0.1.3          remotes_2.5.0          
## [35] rstudioapi_0.17.1       httr_1.4.7             
## [37] cachem_1.1.0            stringr_1.5.1          
## [39] zlibbioc_1.51.2         parallel_4.5.0         
## [41] formatR_1.14            BiocManager_1.30.25    
## [43] vctrs_0.6.5             Matrix_1.7-1           
## [45] jsonlite_1.8.9          bookdown_0.41          
## [47] systemfonts_1.1.0       jpeg_0.1-10            
## [49] jquerylib_0.1.4         glue_1.8.0             
## [51] codetools_0.2-20        stringi_1.8.4          
## [53] gtable_0.3.6            deldir_2.0-4           
## [55] UCSC.utils_1.1.0        munsell_0.5.1          
## [57] tibble_3.2.1            pillar_1.9.0           
## [59] htmltools_0.5.8.1       GenomeInfoDbData_1.2.13
## [61] R6_2.5.1                evaluate_1.0.1         
## [63] kableExtra_1.4.0        lattice_0.22-6         
## [65] highr_0.11              png_0.1-8              
## [67] bslib_0.8.0             Rcpp_1.0.13            
## [69] svglite_2.1.3           SparseArray_1.5.45     
## [71] xfun_0.48               pkgconfig_2.0.3

9 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.