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bioRxiv · 10.64898/2025.12.22.695980

R2G2: A Python-R Framework for Seamless Integration of R/Bioconductor Tools into Galaxy

joshi, j.·cumbo, f.·Blankenberg, D.
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Abstract

R is widely used in statistical computing, data analysis, and bioinformatics. A key contributor to its success in bioinformatics and computational biology is the open-source project Bioconductor. As of its latest release (3.20), the Bioconductor community offers 2,289 software packages for biomedical research, including genomic, transcriptomic, and proteomic analyses. Given Rs growing importance, integrating R and Bioconductor tools into platforms like Galaxy enhances accessibility, reproducibility, and scalability in bioinformatics workflows. The Galaxy Toolshed provides multiple tools leveraging R and Bioconductor packages. Additionally, various open-source public Galaxy servers, such as usegalaxy.org and usegalaxy.eu, already host several R and Bioconductor-based tools, highlighting the importance of past integration efforts. However, given the vast number of available packages, the full potential of R and Bioconductor within the Galaxy ecosystem remains underutilized. Galaxys web-based interface makes these powerful tools more accessible to researchers without programming expertise, fostering broader collaboration. Despite its advantages, integrating R packages into Galaxy can be complex. It requires XML wrappers to define inputs, outputs, and parameters, which can be time-consuming. Managing dependencies from CRAN and Bioconductor, resolving installation issues, and ensuring compatibility across different package versions further complicates the process. Many tools also require custom scripting, creating a steep learning curve for non-programmers. To address these challenges, we have developed a tool that automates the generation of Galaxy wrappers for R packages. This eliminates the need for manual XML writing, reduces complexity, and saves time. Our tool provides an intuitive interface for creating Galaxy-compatible tools without programming expertise and automates dependency management for seamless execution. Bioconductor has revolutionized bioinformatics, with thousands of researchers relying on its tools. Automating its integration into Galaxy removes technical barriers, democratizing access to advanced bioinformatics tools and workflows. Our solution bridges the gap between R-based analysis and user-friendly, scalable tools, ultimately advancing research accessibility and scientific discovery.

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joshi, j., cumbo, f., Blankenberg, D.. 2025-12-24. R2G2: A Python-R Framework for Seamless Integration of R/Bioconductor Tools into Galaxy. https://doi.org/10.64898/2025.12.22.695980

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