Community-Driven Data Analysis Training for Biology

Bérénice Batut (1) , Saskia Hiltemann (2) , Andrea Bagnacani (3) , Dannon Baker (4) , Vivek Bhardwaj (5) , Clemens Blank (1) , Anthony Bretaudeau (6, 7) , Loraine Brillet-Guéguen (8, 9, 10) , John K. Chilton (11) , Dave Clements (4) , Olivia Doppelt-Azeroual (12) , Anika Erxleben (1) , Mallory Ann Freeberg (13) , Simon Gladman (14) , Youri Hoogstrate (2) , Hans-Rudolf Hotz (15) , Torsten Houwaart (1) , Pratik Jagtap (16) , Delphine Larivière (11) , Gildas Le Corguillé (8, 9, 10) , Thomas Manke (17) , Fabien Mareuil (12) , Fidel Ramírez (17) , Devon Ryan (17) , Florian Christoph Sigloch (1) , Nicola Soranzo (18) , Joachim Wolff (1) , Pavankumar Videm (1) , Markus Wolfien (3) , Aisanjiang Wubuli (19) , Dilmurat Yusuf (1) , James Taylor (4) , Rolf Backofen (1) , Anton Nekrutenko (11) , Bjorn Grüning (1)

1 Bioinformatics Group [Freiburg]
2 Erasmus MC - Erasmus University Medical Center [Rotterdam]
3 Department of Systems Biology and Bioinformatics
4 JHU - Johns Hopkins University
5 Department of Biology [Fribourg]
6 Plateforme bioinformatique GenOuest [Rennes]
7 IGEPP - Institut de Génétique, Environnement et Protection des Plantes
8 ABIMS - ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science
9 FR2424 - Fédération de recherche de Roscoff
10 SBR - Station biologique de Roscoff
11 Penn State - Pennsylvania State University
12 Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB
13 EMBL-EBI - European Bioinformatics Institute [Hinxton]
14 Melbourne Bioinformatics [Australia]
15 FMI - Friedrich Miescher Institute for Biomedical Research
16 University of Minnesota Medical School
17 MPI-IE - Max Planck Institute of Immunobiology and Epigenetics
18 Earlham Institute [Norwich]
19 FBN - Leibniz Institute for Farm Animal Biology

The primary problem with the explosion of biomedical datasets is not the data, not computational resources, and not the required storage space, but the general lack of trained and skilled researchers to manipulate and analyze these data. Eliminating this problem requires development of comprehensive educational resources. Here we present a community-driven framework that enables modern, interactive teaching of data analytics in life sciences and facilitates the development of training materials. The key feature of our system is that it is not a static but a continuously improved collection of tutorials. By coupling tutorials with a web-based analysis framework, biomedical researchers can learn by performing computation themselves through a web browser without the need to install software or search for example datasets. Our ultimate goal is to expand the breadth of training materials to include fundamental statistical and data science topics and to precipitate a complete re-engineering of undergraduate and graduate curricula in life sciences. This project is accessible at https://training.galaxyproject.org.

Keywords: data analysis; genomics; proteomics; next-generation sequencing; training

Domains: Computer Science [cs]; Bioinformatics [q-bio.QM]

Bérénice Batut, Saskia Hiltemann, Andrea Bagnacani, Dannon Baker, Vivek Bhardwaj, et al.. Community-Driven Data Analysis Training for Biology. 2020. ⟨pasteur-02613129⟩