Orchestrating Microbiome Analysis with Bioconductor
Abstract
The expansion of microbiome research has led to the accumulation of interlinked datasets encompassing versatile taxonomic and functional assays. The analysis of increasingly large and heterogeneous multi-modal microbiome data would benefit from unified approaches supporting the design of modular data science workflows through interoperable methods. The Bioconductor project has recently developed an optimized statistical programming framework for multi-assay data integration. Building on this foundation, we introduce a community-developed open source ecosystem for microbiome data science. In contrast to the previous alternatives, the methodology is specifically designed to support joint analysis of hierarchical, interlinked, and heterogeneous multi-table datasets that are increasingly common in modern microbiome research. This data science ecosystem encompasses open data, methods, tutorials, and an active online community. These resources support standardized and reproducible data wrangling, joint analysis, and reporting. We have detailed the functionality and usage in the online book https://microbiome.github.io/OMA , which offers guidance for prospective users and contributors.
Cite
Borman T, Benedetti G, Muluh G, Raulo A, Valderrama B, Sannikov A, et al. Orchestrating Microbiome Analysis with Bioconductor. bioRxiv. 2025. doi:10.1101/2025.10.29.685036
@article{borman_biorxiv_2025,
author = {Borman, Tuomas and Benedetti, Giulio and Muluh, Geraldson and Raulo, Aura and Valderrama, Benjamin and Sannikov, Artur and Peschel, Stefanie and Liu, Yihan and Hindström, Rasmus and {OMA consortium} and Pärnänen, Katariina and Müller, Christian L. and Havulinna, Aki S. and Shetty, Sudarshan and Ramos, Marcel and Braccia, Domenick J. and Bravo, Héctor Corrada and Ernst, Felix M. and Waldron, Levi and Bastiaanssen, Thomaz F. S. and Mallick, Himel and Lahti, Leo},
title = {{Orchestrating Microbiome Analysis with Bioconductor}},
journal = {bioRxiv},
year = {2025},
doi = {10.1101/2025.10.29.685036},
url = {https://doi.org/10.1101/2025.10.29.685036}
}