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systemPipeR: a multipurpose workflow management system for reproducible data analysis.

Le Zhang1, Daniela Cassol1, Brendan Gongol1

  • 1Institute for Integrative Genome Biology, University of California, Riverside, 1207F Genomics Building, Riverside, CA 92521, United States.

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|April 6, 2026
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Summary
This summary is machine-generated.

systemPipeR enhances reproducible biological data analysis by integrating R/Bioconductor with command-line tools. This workflow management system (WMS) streamlines complex data analysis for genomics and other fields.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Workflow management systems (WMS) are crucial for reproducible multi-step data analyses.
  • Existing WMS often lack deep integration with R and Bioconductor, key ecosystems for statistical and scientific analysis.
  • A need exists for unified solutions linking R-based analyses with command-line tools.

Purpose of the Study:

  • To present systemPipeR, an R-based workflow management system (WMS) that bridges R/Bioconductor with command-line software.
  • To introduce a redesigned architecture for streamlined workflow construction, execution, monitoring, and reporting.
  • To facilitate reproducible end-to-end data analysis in genomics and other data-intensive fields.

Main Methods:

  • Developed a unified environment linking R-based analytical steps with command-line tools via standardized workflow specifications.
  • Implemented a redesigned architecture featuring a flexible workflow management class object.
  • Integrated the Common Workflow Language (CWL) for standardized execution of R and command-line steps, including metadata management and automated reporting.

Main Results:

  • systemPipeR now offers a unified environment for designing and executing reproducible workflows on diverse computing systems.
  • The new architecture simplifies workflow management, from construction to reporting.
  • Enhanced integration with CWL and improved metadata handling facilitate complex data analysis.

Conclusions:

  • systemPipeR provides a robust, general-purpose, R-based WMS for reproducible analysis of complex biological data.
  • Its unified approach addresses the gap in integrating R/Bioconductor with command-line tools.
  • The latest version offers significant improvements for efficient and reliable scientific workflows.