Learning and teaching biological data science in the Bioconductor community
: Drnevich, Jenny; Tan, Frederick J.; Almeida-Silva, Fabricio; Castelo, Robert; Culhane, Aedin C.; Davis, Sean; Doyle, Maria A.; Geistlinger, Ludwig; Ghazi, Andrew R.; Holmes, Susan; Lahti, Leo; Mahmoud, Alexandru; Nishida, Kozo; Ramos, Marcel; Rue-Albrecht, Kevin; Shih, David J. H.; Gatto, Laurent; Soneson, Charlotte
: Ouellette B.F. Francis
Publisher: Public Library of Science (PLoS)
: 2025
: PLoS Computational Biology
: PLOS Computational Biology
: e1012925
: 21
: 4
: 1553-7358
DOI: https://doi.org/10.1371/journal.pcbi.1012925
: https://doi.org/10.1371/journal.pcbi.1012925
: https://research.utu.fi/converis/portal/detail/Publication/491928607
Modern biological research is increasingly data-intensive, leading to a growing demand for effective training in biological data science. In this article, we provide an overview of key resources and best practices available within the Bioconductor project—an open-source software community focused on omics data analysis. This guide serves as a valuable reference for both learners and educators in the field.
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This project has been made possible in part by grants 2021-237919 (to ACC), 2022-311145 (to RC), and 2024-342820 (to ACC) from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation. LL acknowledges funding from the Research Council of Finland (decision 330887) and the European Union's Horizon 2020 research and innovation programme under grant agreement No 952914. SD acknowledges funding from NCI grant 1U24CA289073. AM acknowledges funding from NIH grant 2U24HG004059-17. CS is supported by the Novartis Research Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
