RNA-Seq Data Analysis in Galaxy

• Published in: Methods in molecular biology (Clifton, N.J.) Vol. 2284; pp. 367 - 392
• Main Authors: Batut, Bérénice, van den Beek, Marius, Doyle, Maria A., Soranzo, Nicola
• Format: Book Chapter Journal Article
• Language: English
• Published: New York, NY Springer US 01.01.2021
• Series: Methods in Molecular Biology
• Subjects: Animals; Computational Biology - methods; Data Analysis; Datasets as Topic - statistics & numerical data; Differential gene expression; Functional enrichment; Galaxy; Gene Expression Profiling - methods; Gene Expression Profiling - statistics & numerical data; High-Throughput Nucleotide Sequencing - methods; High-Throughput Nucleotide Sequencing - statistics & numerical data; Humans; Quality control; Reproducibility of Results; RNA-Seq - methods; Sequence Analysis, RNA - methods; Sequence Analysis, RNA - statistics & numerical data; Sequence mapping; Software; Visualizations; Whole Exome Sequencing - methods; Whole Exome Sequencing - statistics & numerical data; Workflow; Galaxy; Differential gene expression; Functional enrichment; Quality control; Sequence mapping; Workflow; Visualizations
• ISBN: 9781071613061; 1071613065
• ISSN: 1064-3745; 1940-6029
• DOI: 10.1007/978-1-0716-1307-8_20

A complete RNA-Seq analysis involves the use of several different tools, with substantial software and computational requirements. The Galaxy platform simplifies the execution of such bioinformatics analyses by embedding the needed tools in its web interface, while also providing reproducibility. Here, we describe how to perform a reference-based RNA-Seq analysis using Galaxy, from data upload to visualization and functional enrichment analysis of differentially expressed genes.