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Galaxy Training: A powerful framework for teaching!

. 2023 Jan 9;19(1):e1010752. doi: 10.1371/journal.pcbi.1010752. eCollection 2023 Jan. Galaxy Training: A powerful framework for teaching! Helena Rasche^{1 2 3}, Simon Gladman⁴, Hans-Rudolf Hotz^{5 6}, Delphine Larivière⁷, Daniel Blankenberg⁸, Pratik D Jagtap⁹, Thomas Wollmann¹⁰, Anthony Bretaudeau^{11 12}, Nadia Goué¹³, Timothy J Griffin⁹, Coline Royaux¹⁴, Yvan Le Bras¹⁴, Subina Mehta⁹, Anna Syme⁴, Frederik Coppens^{15 16}, Bert Droesbeke^{15 16}, Nicola Soranzo¹⁷, Wendi Bacon¹⁸, Fotis Psomopoulos¹⁹, Cristóbal Gallardo-Alba², John Davis²⁰, Melanie Christine Föll^{21 22 23}, Matthias Fahrner^{21 23}, Maria A Doyle^{24 25}, Beatriz Serrano-Solano^{2 26 27}, Anne Claire Fouilloux²⁸, Peter van Heusden²⁹, Wolfgang Maier², Dave Clements³⁰, Florian Heyl²; Galaxy Training Network; Björn Grüning², Bérénice Batut²

Affiliations

¹ Clinical Bioinformatics Group, Department of Pathology, Erasmus Medical Center, Rotterdam, the Netherlands.
² Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany.
³ Academie voor de Technologie van Gezondheid en Milieu, Avans Hogeschool, Breda, the Netherlands.
⁴ Melbourne Bioinformatics, University of Melbourne, Parkville, Victoria, Australia.
⁵ Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
⁶ SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
⁷ Eberly College of Science, Pennsylvania State University, State College, Pennsylvania, United States of America.
⁸ Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America.
⁹ Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, United States of America.
¹⁰ Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Heidelberg, Germany.
¹¹ IGEPP, INRAE, Institut Agro, Univ Rennes, Rennes, France.
¹² GenOuest Core Facility, Univ Rennes, Inria, CNRS, IRISA, Rennes, France.
¹³ AuBi, Mesocenter, Clermont Auvergne University, Aubière, France.
¹⁴ PNDB French Biodiversity e-infrastructure, PatriNat, French Museum of Natural History, Concarneau, France.
¹⁵ Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.
¹⁶ VIB Center for Plant Systems Biology, Ghent, Belgium.
¹⁷ Earlham Institute, Norwich Research Park, Norwich, United Kingdom.
¹⁸ School of Life, Health and Chemical Sciences, The Open University, Milton Keynes, United Kingdom.
¹⁹ Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.
²⁰ Department of Biology, Johns Hopkins University, Baltimore, Maryland, United States of America.
²¹ Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany.
²² Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts, United States of America.
²³ German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany.
²⁴ Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
²⁵ Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia.
²⁶ ELIXIR Germany, Institute of Bio- and Geosciences (IBG-5)-Computational Metagenomics, Forschungszentrum Jülich GmbH, Jülich, Germany.
²⁷ Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
²⁸ Simula Research Laboratory, Oslo, Norway.
²⁹ South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa.
³⁰ Anaconda Inc, Austin, Texas, United States of America.

PMID: 36622853
PMCID: PMC9829167
DOI: 10.1371/journal.pcbi.1010752

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Galaxy Training: A powerful framework for teaching!

Saskia Hiltemann et al. PLoS Comput Biol. 2023.

. 2023 Jan 9;19(1):e1010752. doi: 10.1371/journal.pcbi.1010752. eCollection 2023 Jan. Authors Saskia Hiltemann¹, Helena Rasche^{1 2 3}, Simon Gladman⁴, Hans-Rudolf Hotz^{5 6}, Delphine Larivière⁷, Daniel Blankenberg⁸, Pratik D Jagtap⁹, Thomas Wollmann¹⁰, Anthony Bretaudeau^{11 12}, Nadia Goué¹³, Timothy J Griffin⁹, Coline Royaux¹⁴, Yvan Le Bras¹⁴, Subina Mehta⁹, Anna Syme⁴, Frederik Coppens^{15 16}, Bert Droesbeke^{15 16}, Nicola Soranzo¹⁷, Wendi Bacon¹⁸, Fotis Psomopoulos¹⁹, Cristóbal Gallardo-Alba², John Davis²⁰, Melanie Christine Föll^{21 22 23}, Matthias Fahrner^{21 23}, Maria A Doyle^{24 25}, Beatriz Serrano-Solano^{2 26 27}, Anne Claire Fouilloux²⁸, Peter van Heusden²⁹, Wolfgang Maier², Dave Clements³⁰, Florian Heyl²; Galaxy Training Network; Björn Grüning², Bérénice Batut² Affiliations

¹ Clinical Bioinformatics Group, Department of Pathology, Erasmus Medical Center, Rotterdam, the Netherlands.
² Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany.
³ Academie voor de Technologie van Gezondheid en Milieu, Avans Hogeschool, Breda, the Netherlands.
⁴ Melbourne Bioinformatics, University of Melbourne, Parkville, Victoria, Australia.
⁵ Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
⁶ SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
⁷ Eberly College of Science, Pennsylvania State University, State College, Pennsylvania, United States of America.
⁸ Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America.
⁹ Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, United States of America.
¹⁰ Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Heidelberg, Germany.
¹¹ IGEPP, INRAE, Institut Agro, Univ Rennes, Rennes, France.
¹² GenOuest Core Facility, Univ Rennes, Inria, CNRS, IRISA, Rennes, France.
¹³ AuBi, Mesocenter, Clermont Auvergne University, Aubière, France.
¹⁴ PNDB French Biodiversity e-infrastructure, PatriNat, French Museum of Natural History, Concarneau, France.
¹⁵ Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.
¹⁶ VIB Center for Plant Systems Biology, Ghent, Belgium.
¹⁷ Earlham Institute, Norwich Research Park, Norwich, United Kingdom.
¹⁸ School of Life, Health and Chemical Sciences, The Open University, Milton Keynes, United Kingdom.
¹⁹ Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.
²⁰ Department of Biology, Johns Hopkins University, Baltimore, Maryland, United States of America.
²¹ Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany.
²² Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts, United States of America.
²³ German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany.
²⁴ Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
²⁵ Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia.
²⁶ ELIXIR Germany, Institute of Bio- and Geosciences (IBG-5)-Computational Metagenomics, Forschungszentrum Jülich GmbH, Jülich, Germany.
²⁷ Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
²⁸ Simula Research Laboratory, Oslo, Norway.
²⁹ South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa.
³⁰ Anaconda Inc, Austin, Texas, United States of America.

PMID: 36622853
PMCID: PMC9829167
DOI: 10.1371/journal.pcbi.1010752

Item in Clipboard

Abstract

There is an ongoing explosion of scientific datasets being generated, 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://training.galaxyproject.org), 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.

Copyright: 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 Disclaimer

Conflict of interest statement

We have read the journal’s policy and the authors of this manuscript have the following competing interests: DB has a significant financial interest in GalaxyWorks, a company that may have a commercial interest in the results of this research and technology. This potential conflict of interest has been reviewed and is managed by the Cleveland Clinic. This does not alter our adherence to all the PLOS Computational Biology policies on sharing data and materials.

Figures
Fig 1. Statistics about training events organized…

Fig 1. Statistics about training events organized by the Galaxy community and visits of the…
Fig 1. Statistics about training events organized by the Galaxy community and visits of the GTN website over years.

(A) Number of events supported GTN materials, registered on the Galaxy Community Hub website [22], per year between 2018 and 2021. (B) Number of TIaaS events per year on Galaxy Europe (collected in May 2022). (C) Number of participants to TIaaS events per year on UseGalaxy.* (collected in May 2022). (D) Number of visits per year on the GTN website and per topics, initially tracked by Google Analytics and later with Plausible (collected in May 2022). The latest usage statistics are publicly available from https://plausible.galaxyproject.eu/training.galaxyproject.org .

Fig 2. Content of material available on…

Fig 2. Content of material available on the GTN and feedback from learners.

(A) Evolution…

Fig 2. Content of material available on the GTN and feedback from learners.

(A) Evolution of number of topics, tutorials, and contributors over the months between 2017 and 2022. (B) Number and type of tutorials per topics available on the GTN on April 2022. The latest statistics are publicly available from https://training.galaxyproject.org/stats . (C) Type of supporting materials for tutorials per topics available on the GTN on April 2022. (D) Score of the embedded feedback in the tutorials per topics. Three questions are asked in the form: “How much did you like this tutorial?” (from 1 (bad) to 5 (great)), “What did you like?”, “What could be improved?”. The latest feedback results are publicly available from https://training.galaxyproject.org/feedback .

Fig 3. Structure of a GTN tutorial…

Fig 3. Structure of a GTN tutorial and its features.

(A) Screenshots for parts of…

Fig 3. Structure of a GTN tutorial and its features.

(A) Screenshots for parts of a GTN tutorial to show its structure. Title and authors are listed, followed by an overview box containing metadata about the tutorial (target audience, learning objectives, prerequisites, supporting materials, time estimate, etc). The tutorial content itself is a mix of theory (text) and so-called “hands-on” boxes describing practical steps to be performed in Galaxy. Question and answer boxes may be added at any point in the tutorial to allow students to self-evaluate as they progress. The end of a tutorial provides a box with take-home messages, references, and suggestions for further reading and follow-up tutorials. Additionally, a feedback form is embedded at the end of every tutorial. Access to FAQs, support channels, page view statistics, and translated versions of the materials are provided via the top menu of the webpage. (B) Features of a GTN tutorial grouped in 4 categories: rich metadata, structured content, strong (technical) support, and community oriented.

Fig 4. Typical process to create a…

Fig 4. Typical process to create a new tutorial.

Authors usually start by identifying suitable…

Fig 4. Typical process to create a new tutorial.

Authors usually start by identifying suitable input datasets and developing the workflow in Galaxy. Thus, a workflow can be automatically converted into a tutorial skeleton using the PTDK described in the next section. The tutorial is then tested, reviewed, and finally merged into the GTN.