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

Sayed Mohammad Ebrahim Sahraeian, Marghoob Mohiyuddin, Robert Sebra, Hagen Tilgner, Pegah T. Afshar, Kin Fai Au, Narges Bani Asadi, Mark B. Gerstein, Wing Hung Wong, Michael P. Snyder, Eric Schadt and Hugo Y. K. Lam ()
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Sayed Mohammad Ebrahim Sahraeian: Roche Sequencing Solutions
Marghoob Mohiyuddin: Roche Sequencing Solutions
Robert Sebra: Icahn School of Medicine at Mount Sinai
Hagen Tilgner: Stanford University School of Medicine
Pegah T. Afshar: Stanford University
Kin Fai Au: University of Iowa
Narges Bani Asadi: Roche Sequencing Solutions
Mark B. Gerstein: Computational Biology and Bioinformatics, Yale University
Wing Hung Wong: Statistics; Health Research and Policy, Stanford University
Michael P. Snyder: Stanford University School of Medicine
Eric Schadt: Icahn School of Medicine at Mount Sinai
Hugo Y. K. Lam: Roche Sequencing Solutions

Nature Communications, 2017, vol. 8, issue 1, 1-15

Abstract: 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.

Date: 2017
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DOI: 10.1038/s41467-017-00050-4

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