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

Mehmet Tekman (1) , Bérénice Batut (1) , Alexander Ostrovsky (2) , Christophe Antoniewski (3, 4, 5) , Dave Clements (2) , Fidel Ramirez (6) , Graham J Etherington (7) , Hans-Rudolf Hotz (8, 9) , Jelle Scholtalbers (10) , Jonathan R Manning (11) , Lea Bellenger (5, 4) , Maria A Doyle (12, 13) , Mohammad Heydarian (2) , Ni Huang (11, 14) , Nicola Soranzo (7) , Pablo Moreno (11) , Stefan Mautner (1) , Irene Papatheodorou (11) , Anton Nekrutenko (15) , James Taylor (2) , Daniel Blankenberg (16) , Rolf Backofen (1) , Björn Grüning (1)

1 Albert-Ludwigs-Universität Freiburg = University of Freiburg
2 JHU - Johns Hopkins University
3 IBPS - Institut de Biologie Paris Seine
4 IBPS-Artbio - Bioinformatique [IBPS]
5 PASS - Unité Mixte de Service Production et Analyse de données en Sciences de la vie et en Santé
6 Boehringer Ingelheim International GmbH
7 Earlham Institute [Norwich]
8 FMI - Friedrich Miescher Institute for Biomedical Research
9 Swiss Institute of Bioinformatics - Basel
10 EMBL - European Molecular Biology Laboratory [Heidelberg]
11 EMBL-EBI - European Bioinformatics Institute [Hinxton]
12 Peter MacCallum Cancer Centre [Melbourne, Australie]
13 University of Melbourne
14 The Wellcome Trust Sanger Institute [Cambridge]
15 Penn State - Pennsylvania State University
16 Cleveland Clinic

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.

Mots clés: scRNA; Galaxy; resources; high-performance computing; single-cell; 10x; training; Web

Domaines: Bio-Informatique, Biologie Systémique [q-bio.QM]

Mehmet Tekman, Bérénice Batut, Alexander Ostrovsky, Christophe Antoniewski, Dave Clements, et al.. A single-cell RNA-sequencing training and analysis suite using the Galaxy framework. GigaScience, 2023, 9 (10), ⟨10.1093/gigascience/giaa102⟩. ⟨hal-04187533⟩