A single-cell RNA-sequencing training and analysis suite using the Galaxy framework

Mehmet Tekman¹, Bérénice Batut¹, Alexander Ostrovsky², Christophe Antoniewski^{3

4}, Dave Clements², Fidel Ramirez⁵, Graham J Etherington⁶, Hans-Rudolf Hotz^{7

8}, Jelle Scholtalbers⁹, Jonathan R Manning¹⁰, Lea Bellenger³, Maria A Doyle^{11

12}, Mohammad Heydarian², Ni Huang^{10

13}, Nicola Soranzo⁶, Pablo Moreno¹⁰, Stefan Mautner¹, Irene Papatheodorou¹⁰, Anton Nekrutenko¹⁴, James Taylor², Daniel Blankenberg¹⁵, Rolf Backofen¹, Björn Grüning¹

Affiliations

¹ Department of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany.
² Department of Biology, Johns Hopkins University, Mudd Hall 144, 3400 N. Charles Street, Baltimore, MD 21218, USA.
³ ARTbio, Sorbonne Université, CNRS FR 3631, Inserm US 037, Paris, France.
⁴ Institut de Biologie Paris Seine, 9 Quai Saint-Bernard Université Pierre et Marie Curie, Campus Jussieu, Bâtiments A-B-C, 75005 Paris, France.
⁵ Boehringer Ingelheim International GmbH, Binger Strasse 173, 55216 Ingelheim am Rhein, Biberach, Germany.
⁶ Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK.
⁷ Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland.
⁸ SIB Swiss Institute of Bioinformatics, Maulbeerstrasse 66, 4058 Basel, Switzerland.
⁹ European Molecular Biology Laboratory, Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany.
¹⁰ European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
¹¹ Research Computing Facility, Peter MacCallum Cancer Centre, Melbourne, 305 Grattan Street, Victoria 3000, Australia.
¹² Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Australia.
¹³ Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, UK.
¹⁴ Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA 16802, USA.
¹⁵ Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, NB21 Cleveland, OH 44195, USA.

PMID: 33079170
PMCID: PMC7574357
DOI: 10.1093/gigascience/giaa102

A single-cell RNA-sequencing training and analysis suite using the Galaxy framework

Mehmet Tekman et al. Gigascience. 2020.

. 2020 Oct 20;9(10):giaa102.

doi: 10.1093/gigascience/giaa102.

Authors

Affiliations

¹ Department of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany.
² Department of Biology, Johns Hopkins University, Mudd Hall 144, 3400 N. Charles Street, Baltimore, MD 21218, USA.
³ ARTbio, Sorbonne Université, CNRS FR 3631, Inserm US 037, Paris, France.
⁴ Institut de Biologie Paris Seine, 9 Quai Saint-Bernard Université Pierre et Marie Curie, Campus Jussieu, Bâtiments A-B-C, 75005 Paris, France.
⁵ Boehringer Ingelheim International GmbH, Binger Strasse 173, 55216 Ingelheim am Rhein, Biberach, Germany.
⁶ Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK.
⁷ Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland.
⁸ SIB Swiss Institute of Bioinformatics, Maulbeerstrasse 66, 4058 Basel, Switzerland.
⁹ European Molecular Biology Laboratory, Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany.
¹⁰ European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
¹¹ Research Computing Facility, Peter MacCallum Cancer Centre, Melbourne, 305 Grattan Street, Victoria 3000, Australia.
¹² Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Australia.
¹³ Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, UK.
¹⁴ Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA 16802, USA.
¹⁵ Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, NB21 Cleveland, OH 44195, USA.

PMID: 33079170
PMCID: PMC7574357
DOI: 10.1093/gigascience/giaa102

Abstract

Background: The vast ecosystem of single-cell RNA-sequencing tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatics community leans once more towards the large computing requirements and the statistically driven methods needed to process and understand these ever-growing datasets.

Results: Here we outline several Galaxy workflows and learning resources for single-cell RNA-sequencing, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology. The Galaxy reproducible bioinformatics framework provides tools, workflows, and trainings that not only enable users to perform 1-click 10x preprocessing but also empower them to demultiplex raw sequencing from custom tagged and full-length sequencing protocols. The downstream analysis supports a range of high-quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal, and clustering. The teaching resources cover concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal.

Conclusions: The reproducible and training-oriented Galaxy framework provides a sustainable high-performance computing environment for users to run flexible analyses on both 10x and alternative platforms. The tutorials from the Galaxy Training Network along with the frequent training workshops hosted by the Galaxy community provide a means for users to learn, publish, and teach single-cell RNA-sequencing analysis.

Keywords: 10x; Galaxy; Web; high-performance computing; resources; scRNA; single-cell; training.

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Figures

**Figure 1:**
The main stages of single-cell analysis, separated broadly into the upper and lower stages of pre-processing and downstream analysis, respectively. *Top:* The 2 main routes to generating a count matrix from sequencing data: via 1-click quantification solutions or through manual demultiplexing. *Bottom:* The 4 main stages required to perform cluster-based analysis from the count matrix, through filtering, normalization, confounder removal, and embedding.

**Figure 2:**
Downstream analysis workflows as shown in the Galaxy Workflow Editor for (*top*) RaceID and (*bottom*) ScanPy, each displaying modules symbolizing the 5 main stages of analysis.

**Figure 3:**
Galaxy Training Network hosting a comprehensive suite of tools, trainings, and workflows to perform scRNA-seq analysis.

See this image and copyright information in PMC

References

1. Wagner A, Regev A, Yosef N. Revealing the vectors of cellular identity with single-cell genomics. Nat Biotechnol. 2016;34(11):1145. - PMC - PubMed
1. Rozenblatt-Rosen O, Stubbington MJ, Regev A, et al. The Human Cell Atlas: from vision to reality. Nature. 2017;550(7677):451–3. - PubMed
1. Briggs JA, Weinreb C, Wagner DE, et al. The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution. Science. 2018;360(6392):eaar5780. - PMC - PubMed
1. Camara PG. Methods and challenges in the analysis of single-cell RNA-sequencing data. Curr Opin Syst Biol. 2018;7:47–53.
1. McCarthy DJ, Campbell KR, Lun AT, et al. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics. 2017;33(8):1179–86. - PMC - PubMed

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A single-cell RNA-sequencing training and analysis suite using the Galaxy framework

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A single-cell RNA-sequencing training and analysis suite using the Galaxy framework

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