Learning and teaching biological data science in the Bioconductor community

Jenny Drnevich; Frederick J. Tan; Fabricio Almeida-Silva; Robert Castelo; Aedin C. Culhane; Sean Davis; Maria A. Doyle; Ludwig Geistlinger; Andrew R. Ghazi; Susan Holmes; Leo Lahti; Alexandru Mahmoud; Kozo Nishida; Marcel Ramos; Kevin Rue-Albrecht; David J.H. Shih; Laurent Gatto; Charlotte Soneson

doi:10.1371/journal.pcbi.1012925

Learning and teaching biological data science in the Bioconductor community

Jenny Drnevich
, Frederick J. Tan
, Fabricio Almeida-Silva
, Robert Castelo
, Aedin C. Culhane
, Sean Davis
, Maria A. Doyle
, Ludwig Geistlinger
, Andrew R. Ghazi
, Susan Holmes
, Leo Lahti
, Alexandru Mahmoud
, Kozo Nishida
, Marcel Ramos
, Kevin Rue-Albrecht
, David J.H. Shih
, Laurent Gatto
, Charlotte Soneson

University of Illinois at Urbana-Champaign
Johns Hopkins University
Ghent University
VIB Center for Plant Systems Biology
Pompeu Fabra University
University of Colorado Anschutz Medical Campus
Harvard University
Stanford University
University of Turku
RIKEN
City University of New York
University of Oxford
The University of Hong Kong
Université catholique de Louvain
Novartis
Swiss Institute of Bioinformatics

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	e1012925
Number of pages	13
Journal	PLoS Computational Biology
Volume	21
Issue number	4 APRIL
DOIs	https://doi.org/10.1371/journal.pcbi.1012925 https://doi.org/10.1371/journal.pcbi.1012925
Publication status	Published - Apr 2025

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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 APRIL), Article e1012925. https://doi.org/10.1371/journal.pcbi.1012925, https://doi.org/10.1371/journal.pcbi.1012925

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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.",

author = "Jenny Drnevich and Tan, \{Frederick J.\} and Fabricio Almeida-Silva and Robert Castelo and Culhane, \{Aedin C.\} and Sean Davis and Doyle, \{Maria A.\} and Ludwig Geistlinger and Ghazi, \{Andrew R.\} and Susan Holmes and Leo Lahti and Alexandru Mahmoud and Kozo Nishida and Marcel Ramos and Kevin Rue-Albrecht and Shih, \{David J.H.\} and Laurent Gatto and Charlotte Soneson",

note = "Publisher Copyright: {\textcopyright} 2025 Drnevich et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.",

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Drnevich, J, Tan, FJ, Almeida-Silva, F, Castelo, R, Culhane, AC, Davis, S, Doyle, MA, Geistlinger, L, Ghazi, AR, Holmes, S, Lahti, L, Mahmoud, A, Nishida, K, Ramos, M, Rue-Albrecht, K, Shih, DJH, Gatto, L & Soneson, C 2025, 'Learning and teaching biological data science in the Bioconductor community', PLoS Computational Biology, vol. 21, no. 4 APRIL, e1012925. https://doi.org/10.1371/journal.pcbi.1012925, https://doi.org/10.1371/journal.pcbi.1012925

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T1 - Learning and teaching biological data science in the Bioconductor community

AU - Drnevich, Jenny

AU - Tan, Frederick J.

AU - Almeida-Silva, Fabricio

AU - Castelo, Robert

AU - Culhane, Aedin C.

AU - Davis, Sean

AU - Doyle, Maria A.

AU - Geistlinger, Ludwig

AU - Ghazi, Andrew R.

AU - Holmes, Susan

AU - Lahti, Leo

AU - Mahmoud, Alexandru

AU - Nishida, Kozo

AU - Ramos, Marcel

AU - Rue-Albrecht, Kevin

AU - Shih, David J.H.

AU - Gatto, Laurent

AU - Soneson, Charlotte

N1 - Publisher Copyright: © 2025 Drnevich et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PY - 2025/4

Y1 - 2025/4

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/105003896973

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DO - 10.1371/journal.pcbi.1012925

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AN - SCOPUS:105003896973

SN - 1553-734X

VL - 21

JO - PLoS Computational Biology

JF - PLoS Computational Biology

IS - 4 APRIL

M1 - e1012925

ER -

Learning and teaching biological data science in the Bioconductor community

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