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

Zhenqiang Su, Pawel P. Labaj, 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 Cain, Jennifer G. Catalano, Ching-Wei Chang, Tao Chen, Geng Chen, Rong Chen, Marco Chierici, Tzu-Ming Chu, Djork-Arne Clevert, Youping Deng, Adnan Derti, Viswanath Devanarayan, Zirui Dong, Joaquin Dopazo, Tingting Du, Hong Fang, Yongxiang Fang, Mario Fasold, Anita Fernandez, Matthias Fischer, Pedro Furio-Tari, James C. Fuscoe, Florian Caiment, Stan Gaj, Jorge Gandara, Huan Gao, Weigong Ge, Yoichi Gondo, Binsheng Gong, Meihua Gong, Zhuolin Gong, Bridgett Green, Chao Guo, Lei Guo, Li-Wu Guo, James Hadfield, Jan Hellemans, Sepp Hochreiter, Meiwen Jia, Min Jian, Charles D. Johnson, Suzanne Kay, Jos Kleinjans, Samir Lababidi, Shawn Levy, Quan-Zhen Li, Li Li, Peng Li, Yan Li, Haiqing Li, Jianying Li, Shiyong Li, Simon M. Lin, Francisco J. Lopez, Xin Lu, Heng Luo, Xiwen Ma, Joseph Meehan, Dalila B. Megherbi, Nan Mei, Bing Mu, Baitang Ning, Akhilesh Pandey, Javier Perez-Florido, Roger G. Perkins, Ryan Peters, John H. Phan, Mehdi Pirooznia, Feng Qian, Tao Qing, Lucille Rainbow, Philippe Rocca-Serra, Laure Sambourg, Susanna-Assunta Sansone, Scott Schwartz, Ruchir Shah, Jie Shen, Todd M. Smith, Oliver Stegle, Nancy Stralis-Pavese, Elia Stupka, Yutaka Suzuki, Lee T. Szkotnicki, Matthew Tinning, Bimeng Tu, Joost van Deft, Alicia Vela-Boza, Elisa Venturini, Stephen J. Walker, Liqing Wan, Wei Wang, Jinhui Wang, Jun Wang, Eric D. Wieben, James C. Willey, Po-Yen Wu, Jiekun Xuan, Yong Yang, Zhan Ye, Ye Yin, Ying Yu, Yate-Ching Yuan, John Zhang, Ke K. Zhang, Wenqian Zhang, Wenwei Zhang, Yanyan Zhang, Chen Zhao, Yuanting Zheng, Yiming Zhou, Paul Zumbo, Weida Tong, David P. Kreil*, Christopher E. Mason*, Leming Shi*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

515 Citations (Web of Science)

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
Pages (from-to)903-914
JournalNature Biotechnology
Volume32
Issue number9
DOIs
Publication statusPublished - Sep 2014

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