Abstract Workflow management systems (WMS) are essential for creating and automating multi-step data analyses and ensuring the reproducibility of biological insights. Although numerous WMS solutions exist, few provide deep integration of command-line software with the R and Bioconductor ecosystems, where a substantial portion of statistical modeling and downstream scientific analysis is performed by a large user base. systemPipeR addresses this gap by offering a unified environment that links R-based analytical steps with command-line tools through a standardized workflow specification. It enables the design and execution of reproducible workflows on both local and high-performance computing systems, while allowing users to select the most appropriate R or command-line tool for each analysis step. The latest version introduces a fully redesigned architecture that streamlines workflow construction, execution, monitoring, and reporting. Key enhancements include a flexible workflow management class object, integration of the Common Workflow Language (CWL), formal declaration and standardized execution of both R and command-line steps, utilities for metadata management, and automated generation of scientific and technical reports. Together, these advances establish systemPipeR as a general-purpose R-based WMS for building and executing end-to-end workflows for reproducible analysis of complex data in genomics and other data-intensive fields. The software is distributed as a free open-source Bioconductor package (https://bioconductor.org/packages/systemPipeR).
Zhang et al. (2026) studied this question.