Galaxy Training: A powerful framework for teaching!

Saskia Hiltemann, Helena Rasche, Simon Gladman, Hans Rudolf Hotz, Delphine Larivière, Daniel Blankenberg, Pratik D. Jagtap, Thomas Wollmann, Anthony Bretaudeau, Nadia Goué, Timothy J. Griffin, Coline Royaux, Yvan Le Bras, Subina Mehta, Anna Syme, Frederik Coppens, Bert Droesbeke, Nicola Soranzo, Wendi Bacon, Fotis PsomopoulosCristóbal Gallardo-Alba, John Davis, Melanie Christine Föll, Matthias Fahrner, Maria A. Doyle, Beatriz Serrano-Solano, Anne Claire Fouilloux, Peter van Heusden, Wolfgang Maier, Dave Clements, Florian Heyl, Björn Grüning, Bérénice Batut

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


AThUe:rePilseaasnecoonngfiormintghaetxaplllhoesaiodninogflesvceilesnartiefircepdraetsaesnetetsdcboerirnegctglye:nerated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://, an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.

Original languageEnglish (US)
Article numbere1010752
JournalPLoS computational biology
Issue number1
StatePublished - Jan 2023

Bibliographical note

Publisher Copyright:
© 2023 This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

PubMed: MeSH publication types

  • Journal Article
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't


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