Learning and teaching biological data science in the Bioconductor community
(2025) PLoS Computational Biology — Vol. 21, n° 4, p. e1012925 (2025)
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- Authors
Drnevich, Jenny 
Castelo, Robert Soneson, Charlotte
- Abstract
- <jats:p>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.</jats:p>
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Citations
Drnevich, J., Tan, F. J., Almeida-Silva, F., Castelo, R., Culhane, A. C., Davis, S., Doyle, M. A., Geistlinger, L., Ghazi, A. R., Holmes, S., Lahti, L., Mahmoud, A., Nishida, K., Ramos, M., Rue-Albrecht, K., Shih, D. J. H., Gatto, L., & Soneson, C. (2025). Learning and teaching biological data science in the Bioconductor community. PLoS Computational Biology, 21(4), e1012925. https://doi.org/10.1371/journal.pcbi.1012925 (Original work published 2025)