A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

Zhenqiang Su, Paweł P. Łabaj, Sheng Li, Jean Thierry-Mieg, Danielle Thierry-Mieg, Wei Shi, Charles Wang, Gary P. Schroth, Robert A. Setterquist, John F. Thompson, Wendell D. Jones, Wenzhong Xiao, Weihong Xu, Roderick V. Jensen, Reagan Kelly, Joshua Xu, Ana Conesa, Cesare Furlanello, Hanlin Gao, Huixiao HongNadereh Jafari, Stan Letovsky, Yang Liao, Fei Lu, Edward J. Oakeley, Zhiyu Peng, Craig A. Praul, Javier Santoyo-Lopez, Andreas Scherer, Tieliu Shi, Gordon K. Smyth, Frank Staedtler, Peter Sykacek, Xin Xing Tan, E. Aubrey Thompson, Jo Vandesompele, May D. Wang, Jian Wang, Russell D. Wolfinger, Jiri Zavadil, Scott S. Auerbach, Wenjun Bao, Hans Binder, Thomas Blomquist, Murray H. Brilliant, Pierre R. Bushel, Weimin Cai, Jennifer G. Catalano, Ching Wei Chang, Tao Chen

Research output: Contribution to journalArticlepeer-review

485 Scopus citations

Abstract

We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

Original languageEnglish (US)
Pages (from-to)903-914
Number of pages12
JournalNature biotechnology
Volume32
Issue number9
DOIs
StatePublished - Sep 1 2014

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology
  • Molecular Medicine
  • Biomedical Engineering

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