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

This article has been updated


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.

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Figure 1: The SEQC (MAQC-III) project and experimental design.
Figure 2: Gene detection and junction discovery.
Figure 3: Sensitivity, specificity and reproducibility of differential expression calls.
Figure 4: Built-in truths for assessing RNA-seq.
Figure 5: Cross-platform agreement of expression levels.
Figure 6: Multiple performance metrics for the quantification of genes and alternative transcripts.

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Gene Expression Omnibus

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  • 09 September 2014

    In the version of this article initially published online, the superscript 95 for the footnote for “these authors contributed equally to this work” was omitted for the first three authors. The error has been corrected for the print, PDF and HTML versions of this article.


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All SEQC (MAQC-III) participants freely donated their time and reagents for the completion and analyses of the project. Many participants contributed to the sometimes-heated discussions on the topic of this paper during numerous e-mail exchanges, teleconferences and face-to-face project meetings. The common conclusions and recommendations reported in this paper evolved from this extended discourse. The authors gratefully acknowledge support by the National Center for Biotechnology Information (NCBI)'s Supercomputing Center, the FDA's Supercomputing Center, China's National Supercomputing Center of Tianjin, the Vienna Scientific Cluster High Performance Computing Facility (VSC), the Vienna Science and Technology Fund (WWTF), Baxter, the Austrian Institute of Technology, and the Austrian Centre of Biopharmaceutical Technology. This work was supported in part by China's Program of Global Experts. This work was supported in part by the US National Institutes of Health (NIH) grants R01CA163256, R01HG006798, R01NS076465, R44HG005297, U54CA119338, PO1HG00205, R24GM102656 and the Intramural Research Program of the NIH, National Library of Medicine, National Institute of Environmental Health Sciences (NIEHS) Z01 ES102345-04, Shriners Research Grant 85500, an Australia National Health and Medical Research Council (NH&MRC) Project grant (1023454) and Victorian State Government Operational Infrastructure Support (Australia), the National 973 Key Basic Research Program of China (2010CB945401), the National Natural Science Foundation of China (31240038 and 31071162), and the Science and Technology Commission of Shanghai Municipality (11DZ2260300). We greatly appreciate SAS Institute, Inc. for kindly hosting several face-to-face meetings of the SEQC (MAQC-III) project.

Author information




Project coordination: US Food and Drug Administration.

Project lead: Weida Tong & Leming Shi.

Manuscript lead: David P. Kreil.

Scientific management: David P. Kreil, Christopher E. Mason, Weida Tong & Leming Shi.

Next-generation sequencing technology lead: Christopher E. Mason.

The following authors contributed to project leadership: 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 Hong, Nadereh 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, Weida Tong, David P. Kreil, Christopher E. Mason & Leming Shi.

The following authors contributed equally to this work: Zhenqiang Su, Paweł P. Łabaj & Sheng Li.

Corresponding authors

Correspondence to David P Kreil or Christopher E Mason or Leming Shi.

Ethics declarations

Competing interests

Some of the SEQC (MAQC-III) Consortium members are employed by companies that provide services or manufacture products or equipment related to gene expression profiling, as can be seen from the affiliations provided by the manuscript authors.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–46, Supplementary Tables 1–15 and Supplementary Notes (PDF 24302 kb)

Supplementary Data 1

RNA-seq read coverage flanking all 250 candidate junctions considered for validation. (TXT 870 kb)

Supplementary Data 2

Employed qPCR primer sequences, qPCR results and expression level estimates, as well as the corresponding RNA-seq expression level estimates for the 173 performed assays. (XLS 121 kb)

Supplementary Data 3

Supplementary Data 3 (ZIP 38371 kb)

Supplementary Protocols (PDF 1467 kb)

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SEQC/MAQC-III Consortium., Su, Z., Łabaj, P. et al. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat Biotechnol 32, 903–914 (2014).

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