A Galaxy-based training resource for single-cell RNA-sequencing quality control and analyses

Etherington, Graham J., Soranzo, Nicola, Mohammed, Suhaib, Haerty, Wilfried ORCID: https://orcid.org/0000-0003-0111-191X, Davey, Robert P. and Palma, Federica DI (2019) A Galaxy-based training resource for single-cell RNA-sequencing quality control and analyses. GigaScience, 8 (12). ISSN 2047-217X

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Abstract

Background: It is not a trivial step to move from single-cell RNA-sequencing (scRNA-seq) data production to data analysis. There is a lack of intuitive training materials and easy-to-use analysis tools, and researchers can find it difficult to master the basics of scRNA-seq quality control and the later analysis. Results: We have developed a range of practical scripts, together with their corresponding Galaxy wrappers, that make scRNA-seq training and quality control accessible to researchers previously daunted by the prospect of scRNA-seq analysis. We implement a "visualize-filter-visualize" paradigm through simple command line tools that use the Loom format to exchange data between the tools. The point-and-click nature of Galaxy makes it easy to assess, visualize, and filter scRNA-seq data from short-read sequencing data. Conclusion: We have developed a suite of scRNA-seq tools that can be used for both training and more in-depth analyses.

Item Type: Article
Additional Information: Funding Information: This work was strategically funded by the BBSRC Core Strategic Programme Grants BBS/E/T/000PR9817, BBS/E/T/000PR9818, and BBS/E/T/000PR9819 and Core Capability Grant BBS/E/ T/000PR9816 at the Earlham Institute. Publisher Copyright: © 2019 The Author(s) 2019. Published by Oxford University Press.
Uncontrolled Keywords: galaxy,scater,scrna-seq,single cell,training,computer science applications,health informatics ,/dk/atira/pure/subjectarea/asjc/1700/1706
Faculty \ School: Faculty of Science > School of Biological Sciences
Faculty of Medicine and Health Sciences > Norwich Medical School
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Depositing User: LivePure Connector
Date Deposited: 15 Sep 2022 14:30
Last Modified: 29 Sep 2022 19:32
URI: https://ueaeprints.uea.ac.uk/id/eprint/88318
DOI: 10.1093/gigascience/giz144

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