TY - JOUR T1 - A single-cell RNA-seq Training and Analysis Suite using the Galaxy Framework JF - bioRxiv DO - 10.1101/2020.06.06.137570 SP - 2020.06.06.137570 AU - Mehmet Tekman AU - Bérénice Batut AU - Alexander Ostrovsky AU - Christophe Antoniewski AU - Dave Clements AU - Fidel Ramirez AU - Graham J Etherington AU - Hans-Rudolf Hotz AU - Jelle Scholtalbers AU - Jonathan R Manning AU - Lea Bellenger AU - Maria A Doyle AU - Mohammad Heydarian AU - Ni Huang AU - Nicola Soranzo AU - Pablo Moreno AU - Stefan Mautner AU - Irene Papatheodorou AU - Anton Nekrutenko AU - James Taylor AU - Daniel Blankenberg AU - Rolf Backofen AU - Björn Grüning Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/06/17/2020.06.06.137570.abstract N2 - Background The vast ecosystem of single-cell RNA-seq 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 scRNA-seq, 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 one-click 10x preprocessing, but also empowers them to demultiplex raw sequencing data manually. The downstream analysis supports a wide range of high-quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal and clustering. The teaching resources cover an assortment of different 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 HPC environment for users to run flexible analyses on both 10x and alternatively derived datasets. 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 scRNA-seq analysis.Key PointsSingle-cell RNA-seq has stabilised towards 10x Genomics datasets.Galaxy provides rich and reproducible scRNA-seq workflows with a wide range of robust tools.The Galaxy Training Network provides tutorials for the processing of both 10x and non-10x datasets.Competing Interest StatementThe authors have declared no competing interest.List of abbreviationsDOIDigital Object IdentifierGTNGalaxy Training NetworkHDF5Hierarchical Data Format 5HPCHigh Performance ComputingPAGAPartition-based Graph AbstractionPCAPrincipal Component AnalysisscRNASingle-Cell RNAtSNEt-distributed Stochastic Network EmbeddingsUMAPUniform Manifold Approximation and ProjectionUMIUnique Molecular Identifier ER -