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Nat Commun. 2017 Jul 5;8(1):59. doi: 10.1038/s41467-017-00050-4.

Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis.

Author information

1
Roche Sequencing Solutions, Belmont, CA, 94002, USA.
2
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
3
Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA.
4
Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA.
5
Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242, USA.
6
Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA.
7
Statistics; Health Research and Policy, Stanford University, Stanford, CA, 94305, USA.
8
Roche Sequencing Solutions, Belmont, CA, 94002, USA. hugo.lam@roche.com.

Abstract

RNA-sequencing (RNA-seq) is an essential technique for transcriptome studies, hundreds of analysis tools have been developed since it was debuted. Although recent efforts have attempted to assess the latest available tools, they have not evaluated the analysis workflows comprehensively to unleash the power within RNA-seq. Here we conduct an extensive study analysing a broad spectrum of RNA-seq workflows. Surpassing the expression analysis scope, our work also includes assessment of RNA variant-calling, RNA editing and RNA fusion detection techniques. Specifically, we examine both short- and long-read RNA-seq technologies, 39 analysis tools resulting in ~120 combinations, and ~490 analyses involving 15 samples with a variety of germline, cancer and stem cell data sets. We report the performance and propose a comprehensive RNA-seq analysis protocol, named RNACocktail, along with a computational pipeline achieving high accuracy. Validation on different samples reveals that our proposed protocol could help researchers extract more biologically relevant predictions by broad analysis of the transcriptome.RNA-seq is widely used for transcriptome analysis. Here, the authors analyse a wide spectrum of RNA-seq workflows and present a comprehensive analysis protocol named RNACocktail as well as a computational pipeline leveraging the widely used tools for accurate RNA-seq analysis.

PMID:
28680106
PMCID:
PMC5498581
DOI:
10.1038/s41467-017-00050-4
[Indexed for MEDLINE]
Free PMC Article

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