This Web Collection presents the results of the RNA Sequencing Quality Control (SEQC) project that sought to evaluate the reproducibility and comparability of high-throughput sequencing of RNA (RNA-seq). Data from several different laboratories are compared and the performance of different sequencing platforms and data analysis approaches are assessed, together with benchmarking against DNA microarrays. Ultimately, these multi-platform, cross-site studies will enable RNA-seq to be applied more broadly in analyzing large cohorts for discovery research and clinical use. This is the latest phase of a collaboration between government, academic and industry researchers as part of the MicroArray Quality Control (MAQC) consortium.
We are grateful for the support of our sponsors, the FDA's National Center for Toxicological Research (NCTR/FDA) and the State Key Laboratory of Genetic Engineering (SKLGE) at Fudan University. As always, Nature Research carries sole responsibility for all editorial content.
Editorial
Honing our reading skills
doi:10.1038/nbt.3021
Nature Biotechnology 32, 845 (2014)
Studies from the RNA Sequencing Quality Control (SEQC) initiative exemplify the kind of experimental groundwork needed to expand RNA-seq into a broader array of basic and translational applications.
News and Views
The devil in the details of RNA-seq
Anton Kratz & Piero Carninci
doi:10.1038/nbt.3015
Nature Biotechnology 32, 882–884 (2014)
Research
A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium
SEQC/MAQC-III Consortium
doi:10.1038/nbt.2957
Nature Biotechnology 32, 903–914 (2014)
Cross-platform ultradeep transcriptomic profiling of human reference RNA samples by RNA-Seq
Joshua Xu, Zhenqiang Su, Huixiao Hong, Jean Thierry-Mieg, Danielle Thierry-Mieg, David P. Kreil, Christopher E. Mason, Weida Tong & Leming Shi
doi:10.1038/sdata.2014.20
Scientific Data 1, Article number: 140020 (2014)
Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study
Sheng Li, Scott W Tighe, Charles M Nicolet, Deborah Grove, Shawn Levy, William Farmerie, Agnes Viale, Chris Wright, Peter A Schweitzer, Yuan Gao, Dewey Kim, Joe Boland, Belynda Hicks, Ryan Kim, Sagar Chhangawala, Nadereh Jafari, Nalini Raghavachari, Jorge Gandara, Natália Garcia-Reyero, Cynthia Hendrickson, David Roberson, Jeffrey Rosenfeld, Todd Smith, Jason G Underwood, May Wang, Paul Zumbo, Don A Baldwin, George S Grills & Christopher E Mason
doi:10.1038/nbt.2972
Nature Biotechnology 32, 915–925 (2014)
The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance
Charles Wang, Binsheng Gong, Pierre R Bushel, Jean Thierry-Mieg, Danielle Thierry-Mieg, Joshua Xu, Hong Fang, Huixiao Hong, Jie Shen, Zhenqiang Su, Joe Meehan, Xiaojin Li, Lu Yang, Haiqing Li, Pawel P Labaj, David P Kreil, Dalila Megherbi, Stan Gaj, Florian Caiment, Joost van Delft, Jos Kleinjans, Andreas Scherer, Viswanath Devanarayan, Jian Wang, Yong Yang, Hui-Rong Qian, Lee J Lancashire, Marina Bessarabova, Yuri Nikolsky, Cesare Furlanello, Marco Chierici, Davide Albanese, Giuseppe Jurman, Samantha Riccadonna, Michele Filosi, Roberto Visintainer, Ke K Zhang, Jianying Li, Jui-Hua Hsieh, Daniel L Svoboda, James C Fuscoe, Youping Deng, Leming Shi, Richard S Paules, Scott S Auerbach & Weida Tong
doi:10.1038/nbt.3001
Nature Biotechnology 32, 926–932 (2014)
Transcriptomic profiling of rat liver samples in a comprehensive study design by RNA-Seq
Binsheng Gong, Charles Wang, Zhenqiang Su, Huixiao Hong, Jean Thierry-Mieg, Danielle Thierry-Mieg, Leming Shi, Scott S. Auerbach, Weida Tong & Joshua Xu
doi:10.1038/sdata.2014.21
Scientific Data 1, Article number: 140021 (2014)
Detecting and correcting systematic variation in large-scale RNA sequencing data
Sheng Li, Pawel P Labaj, Paul Zumbo, Peter Sykacek, Wei Shi, Leming Shi, John Phan, Po-Yen Wu, May Wang, Charles Wang, Danielle Thierry-Mieg, Jean Thierry-Mieg, David P Kreil & Christopher E Mason
doi:10.1038/nbt.3000
Nature Biotechnology 32, 888–895 (2014)
Normalization of RNA-seq data using factor analysis of control genes or samples
Davide Risso, John Ngai, Terence P Speed & Sandrine Dudoit
doi:10.1038/nbt.2931
Nature Biotechnology 32, 896–902 (2014)
Assessing technical performance in different gene expression experiments with external spike-in RNA control ratio mixtures
Sarah A. Munro, Steven P. Lund, P. Scott Pine, Hans Binder, Djork-Arné Clevert, Ana Conesa, Joaquin Dopazo, Mario Fasold, Sepp Hochreiter, Huixiao Hong, Nadereh Jafari, David P. Kreil, Paweł P. Łabaj, Sheng Li, Yang Liao, Simon M. Lin, Joseph Meehan, Christopher E. Mason, Javier Santoyo-Lopez, Robert A. Setterquist, Leming Shi, Wei Shi, Gordon K. Smyth, Nancy Stralis-Pavese, Zhenqiang Su, Weida Tong, Charles Wang, Jian Wang, Joshua Xu, Zhan Ye, Yong Yang, Ying Yu & Marc Salit
doi:10.1038/ncomms6125
Nature Communications 5, Article number: 5125 (2014)
A rat RNA-Seq transcriptomic BodyMap across 11 organs and 4 developmental stages
Ying Yu, James C. Fuscoe, Chen Zhao, Chao Guo, Meiwen Jia, Tao Qing, Desmond I. Bannon, Lee Lancashire, Wenjun Bao, Tingting Du, Heng Luo, Zhenqiang Su, Wendell D. Jones, Carrie L. Moland, William S. Branham, Feng Qian, Baitang Ning, Yan Li, Huixiao Hong, Lei Guo, Nan Mei, Tieliu Shi, Kevin Y. Wang, Russell D. Wolfinger, Yuri Nikolsky, Stephen J. Walker, Penelope Duerksen-Hughes, Christopher E. Mason, Weida Tong, Jean Thierry-Mieg, Danielle Thierry-Mieg, Leming Shi & Charles Wang
doi:10.1038/ncomms4230
Nature Communications 5, Article number: 3230 (2014)
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