A2 Vertaisarvioitu katsausartikkeli tieteellisessä lehdessä
Learning and teaching biological data science in the Bioconductor community
Tekijät: 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
Toimittaja: Ouellette B.F. Francis
Kustantaja: Public Library of Science (PLoS)
Julkaisuvuosi: 2025
Journal: PLoS Computational Biology
Tietokannassa oleva lehden nimi: PLOS Computational Biology
Artikkelin numero: e1012925
Vuosikerta: 21
Numero: 4
eISSN: 1553-7358
DOI: https://doi.org/10.1371/journal.pcbi.1012925
Verkko-osoite: https://doi.org/10.1371/journal.pcbi.1012925
Rinnakkaistallenteen osoite: 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.
Ladattava julkaisu This is an electronic reprint of the original article. |
Julkaisussa olevat rahoitustiedot:
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.