Learning and teaching biological data science in the Bioconductor community

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• dc.contributor.author Drnevich, Jenny
• dc.contributor.author Tan, Frederick J.
• dc.contributor.author Almeida-Silva, Fabricio
• dc.contributor.author Castelo Valdueza, Robert
• dc.contributor.author Culhane, Aedín C.
• dc.contributor.author Davis, Sean
• dc.contributor.author Doyle, Maria A.
• dc.contributor.author Geistlinger, Ludwig
• dc.contributor.author Ghazi, Andrew R.
• dc.contributor.author Holmes, Susan
• dc.contributor.author Lahti, Leo
• dc.contributor.author Mahmoud, Alexandru
• dc.contributor.author Nishida, Kozo
• dc.contributor.author Ramos, Marcel
• dc.contributor.author Rue-Albrecht, Kevin
• dc.contributor.author Shih, David J. H.
• dc.contributor.author Gatto, Laurent
• dc.contributor.author Soneson, Charlotte
• dc.date.accessioned 2025-06-03T12:42:39Z
• dc.date.available 2025-06-03T12:42:39Z
• dc.date.issued 2025
• dc.description.abstract 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.
• dc.description.sponsorship 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.
• dc.format.mimetype application/pdf
• dc.identifier.citation Drnevich J, Tan FJ, Almeida-Silva F, Castelo R, Culhane AC, Davis S, et al. Learning and teaching biological data science in the Bioconductor community. PLoS Comput Biol. 2025 Apr 22;21(4):e1012925. DOI: 10.1371/journal.pcbi.1012925
• dc.identifier.doi http://dx.doi.org/10.1371/journal.pcbi.1012925
• dc.identifier.issn 1553-734X
• dc.identifier.uri http://hdl.handle.net/10230/70600
• dc.language.iso eng
• dc.publisher Public Library of Science (PLoS)
• dc.relation.ispartof PLoS Comput Biol. 2025 Apr 22;21(4):e1012925
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/952914
• dc.rights © 2025 Drnevich et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Workshops
• dc.subject.keyword Human learning
• dc.subject.keyword Instructors
• dc.subject.keyword Language
• dc.subject.keyword Ecosystems
• dc.subject.keyword Genome analysis
• dc.subject.keyword Geographic distribution
• dc.subject.keyword Programming languages
• dc.title Learning and teaching biological data science in the Bioconductor community
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion