Informatics for RNA Sequencing: A Web Resource for Analysis on the Cloud.

Autor: Griffith M; McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri, United States of America; Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri, United States of America; Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America., Walker JR; McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri, United States of America., Spies NC; McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri, United States of America., Ainscough BJ; McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri, United States of America; Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri, United States of America., Griffith OL; McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri, United States of America; Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri, United States of America; Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America; Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2015 Aug 06; Vol. 11 (8), pp. e1004393. Date of Electronic Publication: 2015 Aug 06 (Print Publication: 2015).
DOI: 10.1371/journal.pcbi.1004393
Abstrakt: Massively parallel RNA sequencing (RNA-seq) has rapidly become the assay of choice for interrogating RNA transcript abundance and diversity. This article provides a detailed introduction to fundamental RNA-seq molecular biology and informatics concepts. We make available open-access RNA-seq tutorials that cover cloud computing, tool installation, relevant file formats, reference genomes, transcriptome annotations, quality-control strategies, expression, differential expression, and alternative splicing analysis methods. These tutorials and additional training resources are accompanied by complete analysis pipelines and test datasets made available without encumbrance at www.rnaseq.wiki.
Databáze: MEDLINE