Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study (vol 32, pg 915, 2014)

• Type of Material: Article
• Authors: Sheng Li, Weill Cornell Medical College; Scott W. Tighe, University of Vermont; Charles M. Nicolet, University of Southern California, Los Angeles; Deborah Grove, Pennsylvania State University; Shawn Levy, HudsonAlpha Institute for Biotechnology; William Farmerie, University of Florida; Agnes Viale, Memorial Sloan-Kettering Cancer Institute; Chris Wright, University of Illinois; Peter A. Schweitzer, Cornell University; Yuan Gao, Johns Hopkins University; Dewey Kim, Johns Hopkins University; Joe Boland, NIH/NCI/SAIC-Frederick; Belynda Hicks, NIH/NCI/SAIC-Frederick; Ryan Kim, University of California, Davis, Davis; Sagar Chhangawala, Weill Cornell Medical College; Nadereh Jafari, Northwestern University; Nalini Raghavachari, NIH/NHLBI; Jorge Gandara, Weill Cornell Medical College; Natalia Garcia-Reyero, Mississippi State University; Cynthia Hendrickson, HudsonAlpha Institute for Biotechnology; David Roberson, NIH/NCI/SAIC-Frederick; Jeffrey Rosenfeld, University of Medicine and Dentistry of New Jersey; Todd Smith, PerkinElmer Inc.; Jason G. Underwood, University of Washington; Dongmei Wang, Emory University; Paul Zumbo, Weill Cornell Medical College; Don A. Baldwin, Pathonomics LLC; George S. Grills, Cornell University; Christopher E. Mason, Cornell University
• Language: English
• Date: 2014-11-01
• Publisher: Nature Research (part of Springer Nature)
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2015 Nature America, Inc.
• Final Published Version (URL): http://dx.doi.org/10.1038/nbt1114-1166a
• Title of Journal or Parent Work: Nature Biotechnology
• ISSN: 1087-0156
• Volume: 32
• Issue: 11
• Start Page: 1166
• End Page: 1166
• Grant/Funding Information: This work was supported with funding from the National Institutes of Health (NIH), including R01HG006798, R01NS076465, R24RR032341, as well as funds from the Irma T. Hirschl and Monique Weill-Caulier Charitable Trusts and the STARR Consortium (I7-A765).; We are sincerely appreciative of the Association of Biomolecular Resource Facilities (ABRF) for supporting this study and the contributing ABRF Research Groups.; In particular, alphabetically by vendor: Gary Schroth (Illumina); Michael Gallad, Jeff Smith, Tom Bittick and Robert Setterquist (Life Technologies); Jonas Korlach, Steve Turner and Elizabeth Tseng (Pacific Biosciences); and Karin Fredrickson and Clotilde Teiling (Roche Life Sciences).; We would also like to thank the platform vendors, Illumina, Life Technologies, Pacific Biosciences and Roche Life Sciences, for their support of this study, and their distinguished scientists for providing technical expertise and assistance in study designs, protocols, new methods development and significant contributions of reagents and sequencing kits.
• Keywords: Life Sciences & Biomedicine; Biotechnology & Applied Microbiology; Science & Technology
• Research Categories: Biophysics, General; Engineering, Biomedical

Abstract

High-throughput RNA sequencing (RNA-seq) greatly expands the potential for genomics discoveries, but the wide variety of platforms, protocols and performance capabilitites has created the need for comprehensive reference data. Here we describe the Association of Biomolecular Resource Facilities next-generation sequencing (ABRF-NGS) study on RNA-seq. We carried out replicate experiments across 15 laboratory sites using reference RNA standards to test four protocols (poly-A-selected, ribo-depleted, size-selected and degraded) on five sequencing platforms (Illumina HiSeq, Life Technologies PGM and Proton, Pacific Biosciences RS and Roche 454). The results show high intraplatform (Spearman rank R > 0.86) and inter-platform (R > 0.83) concordance for expression measures across the deep-count platforms, but highly variable efficiency and cost for splice junction and variant detection between all platforms. For intact RNA, gene expression profiles from rRNA-depletion and poly-A enrichment are similar. In addition, rRNA depletion enables effective analysis of degraded RNA samples. This study provides a broad foundation for cross-platform standardization, evaluation and improvement of RNA-seq.