Treffer: Galaxy as a gateway to bioinformatics: Multi-Interface Galaxy Hands-on Training Suite (MIGHTS) for scRNA-seq.

Title:
Galaxy as a gateway to bioinformatics: Multi-Interface Galaxy Hands-on Training Suite (MIGHTS) for scRNA-seq.
Authors:
Goclowski CL; Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT, 84112, USA., Jakiela J; School of Chemistry, University of Edinburgh, Edinburgh, EH9 3FJ, UK., Collins T; Department of Computer Science, John Hopkins Medical Institution, Baltimore, MD, 21224, USA., Hiltemann S; Erasmus Medical Center, Rotterdam, Zuid-Holland, 3015 GD, Netherlands., Howells M; School of Computing & Communications, The Open University, Milton Keynes, Buckinghamshire, MK7 6AA, UK., Loach M; School of Life, Health & Chemical Sciences, The Open University, Milton Keynes, Buckinghamshire, MK7 6AA, UK., Manning J; European Bioinformatics Institute, European Molecular Biology Laboratory, Hinxton, CB10 1SD, UK., Moreno P; Early Computational Oncology, AstraZeneca, Cambridge, CB2 0AA, UK., Ostrovsky A; Department of Computer Science, John Hopkins Medical Institution, Baltimore, MD, 21224, USA., Rasche H; Erasmus Medical Center, Rotterdam, Zuid-Holland, 3015 GD, Netherlands., Tekman M; Division of Pharmacology and Toxicology, University of Freiburg, Freiburg im Breisgau, Baden-Württemberg, 79098, Germany., Tyson G; School of Life, Health & Chemical Sciences, The Open University, Milton Keynes, Buckinghamshire, MK7 6AA, UK., Videm P; Department of Computer Science, University of Freiburg, Freiburg im Breisgau,Baden-Württemberg, 79098, Germany., Bacon W; School of Life, Health & Chemical Sciences, The Open University, Milton Keynes, Buckinghamshire, MK7 6AA, UK.
Source:
GigaScience [Gigascience] 2025 Jan 06; Vol. 14.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: United States NLM ID: 101596872 Publication Model: Print Cited Medium: Internet ISSN: 2047-217X (Electronic) Linking ISSN: 2047217X NLM ISO Abbreviation: Gigascience Subsets: MEDLINE
Imprint Name(s):
Publication: 2017- : New York : Oxford University Press
Original Publication: London : BioMed Central
MeSH Terms:
Computational Biology*/methods , Computational Biology*/education , Software* , Single-Cell Analysis*/methods, Humans ; Sequence Analysis, RNA/methods ; RNA-Seq/methods ; User-Computer Interface ; Single-Cell Gene Expression Analysis
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Grant Information:
Engineering and Physical Sciences Research Council
Contributed Indexing:
Keywords: Galaxy project; STEM education; bioinformatics; reproducibility; scRNA-seq; single-cell RNA-seq analysis; sustainability; training
Entry Date(s):
Date Created: 20250108 Date Completed: 20250108 Latest Revision: 20250205
Update Code:
20250205
PubMed Central ID:
PMC11707610
DOI:
10.1093/gigascience/giae107
PMID:
39775842
Database:
MEDLINE

Weitere Informationen

Background: Bioinformatics is fundamental to biomedical sciences, but its mastery presents a steep learning curve for bench biologists and clinicians. Learning to code while analyzing data is difficult. The curve may be flattened by separating these two aspects and providing intermediate steps for budding bioinformaticians. Single-cell analysis is in great demand from biologists and biomedical scientists, as evidenced by the proliferation of training events, materials, and collaborative global efforts like the Human Cell Atlas. However, iterative analyses lacking reinstantiation, coupled with unstandardized pipelines, have made effective single-cell training a moving target.
Findings: To address these challenges, we present a Multi-Interface Galaxy Hands-on Training Suite (MIGHTS) for single-cell RNA sequencing (scRNA-seq) analysis, which offers parallel analytical methods using a graphical interface (buttons) or code. With clear, interoperable materials, MIGHTS facilitates smooth transitions between environments. Bridging the biologist-programmer gap, MIGHTS emphasizes interdisciplinary communication for effective learning at all levels. Real-world data analysis in MIGHTS promotes critical thinking and best practices, while FAIR data principles ensure validation of results. MIGHTS is freely available, hosted on the Galaxy Training Network, and leverages Galaxy interfaces for analyses in both settings. Given the ongoing popularity of Python-based (Scanpy) and R-based (Seurat & Monocle) scRNA-seq analyses, MIGHTS enables analyses using both.
Conclusions: MIGHTS consists of 11 tutorials, including recordings, slide decks, and interactive visualizations, and a demonstrated track record of sustainability via regular updates and community collaborations. Parallel pathways in MIGHTS enable concurrent training of scientists at any programming level, addressing the heterogeneous needs of novice bioinformaticians.
(© The Author(s) 2025. Published by Oxford University Press GigaScience.)