Article | Published:

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

Nature Biotechnology volume 32, pages 903914 (2014) | Download Citation

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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Change history

  • 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.


Primary accessions

Gene Expression Omnibus


  1. 1.

    et al. Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476 (2008).

  2. 2.

    , , , & Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Methods 5, 621–628 (2008).

  3. 3.

    et al. Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Bioinformatics 27, i383–i391 (2011).

  4. 4.

    , , , & A comparison of RNA-Seq and high-density exon array for detecting differential gene expression between closely related species. Nucleic Acids Res. 39, 578–588 (2011).

  5. 5.

    et al. RNA-seq: technical variability and sampling. BMC Genomics 12, 293 (2011).

  6. 6.

    , , & RNA-sequence analysis of human B-cells. Genome Res. 21, 991–998 (2011).

  7. 7.

    et al. Landscape of transcription in human cells. Nature 489, 101–108 (2012).

  8. 8.

    Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).

  9. 9.

    International Cancer Genome Consortium. International network of cancer genome projects. Nature 464, 993–998 (2010).

  10. 10.

    et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 24, 1151–1161 (2006).

  11. 11.

    et al. Detecting and correcting systematic variation in large-scale RNA sequencing data. Nat. Biotechnol. 10.1038/nbt.3000 (24 August 2014).

  12. 12.

    et al. The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat. Biotechnol. 10.1038/nbt.3001 (24 August 2014).

  13. 13.

    et al. A rat RNA-seq transcriptomic Bodymap across eleven organs and four developmental stages. Nat. Commun. 5, 3230 (2014).

  14. 14.

    et al. The External RNA Controls Consortium: a progress report. Nat. Methods 2, 731–734 (2005).

  15. 15.

    , , & NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy. Nucleic Acids Res. 40, D130–D135 (2012).

  16. 16.

    et al. GENCODE: The reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

  17. 17.

    & AceView: a comprehensive cDNA-supported gene and transcripts. Genome Biol. 7, S12 (2006).

  18. 18.

    et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

  19. 19.

    , & The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).

  20. 20.

    et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

  21. 21.

    et al. Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study. Nat. Biotechnol. 10.1038/nbt.2972 (24 August 2014).

  22. 22.

    et al. Human transcriptome array for high-throughput clinical studies. Proc. Natl. Acad. Sci. USA 108, 3707–3712 (2011).

  23. 23.

    , , , & RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509–1517 (2008).

  24. 24.

    , & Twenty-five years of quantitative PCR for gene expression analysis. Biotechniques 44 (suppl.) 619–626 (2008).

  25. 25.

    et al. Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries. Genome Biol. 12, R18 (2011).

  26. 26.

    et al. Using RNA sample titrations to assess microarray platform performance and normalization techniques. Nat. Biotechnol. 24, 1123–1131 (2006).

  27. 27.

    et al. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol. 31, 46–53 (2013).

  28. 28.

    , , & Noisy splicing drives mRNA isoform diversity in human cells. PLoS Genet. 6, e1001236 (2010).

  29. 29.

    et al. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 33, e175 (2005).

  30. 30.

    et al. Evaluating the impact of sequencing depth on transcriptome profiling in human adipose. PLoS ONE 8, e66883 (2013).

  31. 31.

    et al. Targeted next-generation sequencing of a cancer transcriptome enhances detection of sequence variants and novel fusion transcripts. Genome Biol. 10, R115 (2009).

  32. 32.

    et al. Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays. BMC Genomics 11, 383 (2010).

  33. 33.

    et al. A systematic comparison and evaluation of high density exon arrays and RNA-seq technology used to unravel the peripheral blood transcriptome of sickle cell disease. BMC Med. Genomics 5, 28 (2012).

  34. 34.

    , , & mRNA enrichment protocols determine the quantification characteristics of external RNA spike-in controls in RNA-seq studies. Sci. China Life Sci. 56, 134–142 (2013).

  35. 35.

    & Summarizing and correcting the GC content bias in high-throughput sequencing. Nucleic Acids Res. 40, e72 (2012).

  36. 36.

    & A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

  37. 37.

    in Bioinformatics and Computational Biology Solutions Using R Bioconductor (eds. Gentleman, R., Carey, V.J., Huber, W., Irizarry, R.A. & Dudoit, S.) 397–420 (Springer, New York, 2005).

  38. 38.

    , , , & Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18, S96–S104 (2002).

  39. 39.

    , , , & A model based background adjustment for oligonucleotide expression arrays. J. Amer. Stat. Assoc. 99, 909–917 (2004).

  40. 40.

    , & A new summarization method for affymetrix probe level data. Bioinformatics 22, 943–949 (2006).

  41. 41.

    , & G-stack modulated probe intensities on expression arrays–sequence corrections and signal calibration. BMC Bioinformatics 11, 207 (2010).

  42. 42.

    , , , & Hybridization thermodynamics of NimbleGen Microarrays. BMC Bioinformatics 11, 35 (2010).

  43. 43.

    et al. The impact of quantitative optimization of hybridization conditions on gene expression analysis. BMC Bioinformatics 12, 73 (2011).

  44. 44.

    et al. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol. 14, R95 (2013).

  45. 45.

    et al. Cross-platform ultradeep transcriptomic profiling of human reference RNA samples by RNA-Seq. Sci. Data (in the press).

  46. 46.

    et al. A comparison of RNA-seq and high-density exon array for detecting differential gene expression between closely related species. Nucleic Acids Res. 39, 578–588 (2011).

  47. 47.

    Nat. Commun. (in the press).

  48. 48.

    , , , & SHRiMP2: Sensitive yet practical short read mapping. Bioinformatics 27, 1011–1012 (2011).

  49. 49.

    , & Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinformatics 28, 1721–1728 (2012).

  50. 50.

    , & featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

  51. 51.

    Gaussian Processes for Machine Learning (MIT Press, 2006).

  52. 52.

    et al. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

  53. 53.

    et al. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief. Bioinform. 14, 671–683 (2013).

  54. 54.

    , & edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

Download references


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


  1. FDA/NCTR, Jefferson, Arkansas, USA.

    • Zhenqiang Su
    • , Reagan Kelly
    • , Joshua Xu
    • , Huixiao Hong
    • , Ching-Wei Chang
    • , Tao Chen
    • , Hong Fang
    • , James C Fuscoe
    • , Weigong Ge
    • , Binsheng Gong
    • , Bridgett Green
    • , Lei Guo
    • , Li-Wu Guo
    • , Yan Li
    • , Joseph Meehan
    • , Nan Mei
    • , Baitang Ning
    • , Roger G Perkins
    • , Feng Qian
    • , Jie Shen
    • , Liqing Wan
    • , Jiekun Xuan
    • , Wenqian Zhang
    • , Weida Tong
    •  & Leming Shi
  2. Chair of Bioinformatics Research Group, Boku University Vienna, Vienna, Austria.

    • Paweł P Łabaj
    • , Peter Sykacek
    • , Nancy Stralis-Pavese
    •  & David P Kreil
  3. Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, USA.

    • Sheng Li
    • , Jorge Gandara
    • , Paul Zumbo
    •  & Christopher E Mason
  4. The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York, USA.

    • Sheng Li
    • , Paul Zumbo
    •  & Christopher E Mason
  5. NIH/NCBI, Bethesda, Maryland, USA.

    • Jean Thierry-Mieg
    •  & Danielle Thierry-Mieg
  6. Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.

    • Wei Shi
    • , Yang Liao
    •  & Gordon K Smyth
  7. Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia.

    • Wei Shi
  8. Center for Genomics and Division of Microbiology & Molecular Genetics, School of Medicine, Loma Linda University, Loma Linda, California, USA.

    • Charles Wang
  9. Illumina Inc., Hayward, California, USA.

    • Gary P Schroth
  10. Life Technologies Corporation, Austin, Texas, USA.

    • Robert A Setterquist
  11. Claritas Genomics, Cambridge, Massachusetts, USA.

    • John F Thompson
  12. Expression Analysis Inc., Durham, North Carolina, USA.

    • Wendell D Jones
  13. Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

    • Wenzhong Xiao
  14. Stanford Genome Technology Center, Palo Alto, California, USA.

    • Wenzhong Xiao
    •  & Weihong Xu
  15. Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia, USA.

    • Roderick V Jensen
  16. Centro de Investigación Príncipe Felipe (CIPF), Computational Genomics Program, Valencia, Spain.

    • Ana Conesa
    • , Joaquin Dopazo
    •  & Pedro Furió-Tari
  17. Fondazione Bruno Kessler (FBK), Trento, Italy.

    • Cesare Furlanello
    •  & Marco Chierici
  18. DNA Sequencing/Solexa Core, Beckman Research Institute, City of Hope Comprehensive Cancer Center, City of Hope National Medical Center, Duarte, California, USA.

    • Hanlin Gao
    •  & Jinhui Wang
  19. Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.

    • Nadereh Jafari
  20. SynapDx Corporation, Lexington, Massachusetts, USA.

    • Stan Letovsky
  21. Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia.

    • Yang Liao
  22. GE Healthcare SeqWright Genomics Services, Houston, Texas, USA.

    • Fei Lu
    • , Xin-Xing Tan
    •  & Lee T Szkotnicki
  23. Novartis Institutes for Biomedical Research (NIBR), Basel, Switzerland.

    • Edward J Oakeley
    • , Frank Staedtler
    •  & Anita Fernandez
  24. BGI-Shenzhen, Bei Shan Industrial Zone, Yantian District, Shenzhen, Guangdong, China.

    • Zhiyu Peng
    • , Zirui Dong
    • , Huan Gao
    • , Meihua Gong
    • , Zhuolin Gong
    • , Min Jian
    • , Shiyong Li
    • , Bimeng Tu
    • , Jun Wang
    • , Ye Yin
    • , Wenqian Zhang
    • , Wenwei Zhang
    •  & Yanyan Zhang
  25. The Pennsylvania State University, University Park, Pennsylvania, USA.

    • Craig A Praul
  26. Medical Genome Project (MGP), Genomics and Bioinformatics Platform of Andalusia (GBPA), Sevilla, Spain.

    • Javier Santoyo-Lopez
    • , Francisco J López
    • , Javier Pérez-Florido
    •  & Alicia Vela-Boza
  27. Edinburgh Genomics, University of Edinburgh, Edinburgh, Scotland, UK.

    • Javier Santoyo-Lopez
  28. Australian Genome Research Facility Ltd., The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.

    • Andreas Scherer
    •  & Matthew Tinning
  29. Spheromics, Kontiolahti, Finland.

    • Andreas Scherer
  30. Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China.

    • Tieliu Shi
    • , Geng Chen
    • , Peng Li
    •  & Chen Zhao
  31. Department of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria, Australia.

    • Gordon K Smyth
  32. Department of Cancer Biology, Mayo Clinic Jacksonville, Jacksonville, Florida, USA.

    • E Aubrey Thompson
  33. Biogazelle, Zwijnaarde, Belgium.

    • Jo Vandesompele
    •  & Jan Hellemans
  34. Department of Biomedical Engineering, GeorgiaTech and Emory University, Atlanta, Georgia, USA.

    • May D Wang
    •  & John H Phan
  35. Research Informatics, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, Indiana, USA.

    • Jian Wang
    •  & Yong Yang
  36. SAS Institute Inc., Cary, North Carolina, USA.

    • Russell D Wolfinger
    • , Wenjun Bao
    • , Tzu-Ming Chu
    •  & Li Li
  37. NYU Genome Technology Center, New York University Langone Medical Center, New York, NewYork, USA.

    • Jiri Zavadil
    •  & Elisa Venturini
  38. NYU Center for Health Informatics and Bioinformatics, Department of Pathology, New York University Langone Medical Center, New York, New York, USA.

    • Jiri Zavadil
  39. Molecular Mechanisms and Biomarkers Group, International Agency for Research on Cancer, Lyon, France.

    • Jiri Zavadil
  40. NIH/NIEHS, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, North Carolina, USA.

    • Scott S Auerbach
    • , Pierre R Bushel
    •  & Jianying Li
  41. Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany.

    • Hans Binder
  42. University of Toledo Health Sciences Campus, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Medical College of Ohio, Toledo, Ohio, USA.

    • Thomas Blomquist
    •  & James C Willey
  43. Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA.

    • Murray H Brilliant
  44. School of Pharmacy, Fudan University, Shanghai, China.

    • Weimin Cai
    • , Tingting Du
    • , Meiwen Jia
    • , Tao Qing
    • , Ying Yu
    •  & Yuanting Zheng
  45. Office of Cellular, Tissue, and Gene Therapies, FDA/CBER, Bethesda, Maryland, USA.

    • Jennifer G Catalano
  46. Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

    • Rong Chen
    •  & Li Li
  47. Institute of Bioinformatics, Johannes Kepler University Linz, Linz, Austria.

    • Djork-Arné Clevert
    •  & Sepp Hochreiter
  48. Department of Internal Medicine, Rush University Cancer Center, Chicago, Illinois, USA.

    • Youping Deng
  49. Novartis Institutes for Biomedical Research, Novartis, Cambridge, Massachusetts, USA.

    • Adnan Derti
  50. AbbVie Inc., Global Pharmaceutical R&D, Souderton, Pennsylvania, USA.

    • Viswanath Devanarayan
  51. CIBER de Enfermedades Raras (CIBERER) and Functional Genomics Node (INB) at CIPF, Valencia, Spain.

    • Joaquin Dopazo
  52. Centre for Genomic Research, University of Liverpool, Liverpool, UK.

    • Yongxiang Fang
    • , Suzanne Kay
    •  & Lucille Rainbow
  53. ecSeq Bioinformatics, Leipzig, Germany.

    • Mario Fasold
  54. Department of Pediatric Oncology and Hematology and Center for Molecular Medicine (CMMC), University of Cologne, Cologne, Germany.

    • Matthias Fischer
  55. Department of Toxicogenomics, Maastricht University, Maastricht, the Netherlands.

    • Florian Caimet
    • , Stan Gaj
    • , Jos Kleinjans
    •  & Joost van Delft
  56. RIKEN BioResource Center, Tsukuba, Ibarak, Japan.

    • Yoichi Gondo
  57. Functional Genomics Core, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, USA.

    • Chao Guo
  58. Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK.

    • James Hadfield
  59. Texas A&M AgriLife Research, College Station, Texas, USA.

    • Charles D Johnson
    •  & Scott Schwartz
  60. FDA/CBER, Rockville, Maryland, USA.

    • Samir Lababidi
  61. HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA.

    • Shawn Levy
  62. University of Texas Southwestern Medical Center (UTSW), Dallas, Texas, USA.

    • Quan-Zhen Li
  63. Bioinformatics Core, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, USA.

    • Haiqing Li
    • , Bing Mu
    •  & Yate-Ching Yuan
  64. The Research Institute, Nationwide Children's Hospital, Columbus, Ohio, USA.

    • Simon M Lin
  65. AbbVie Inc., Global Pharmaceutical R&D, North Chicago, Illinois, USA.

    • Xin Lu
  66. UALR/UAMS Joint Bioinformatics Graduate Program, University of Arkansas at Little Rock, Little Rock, Arkansas, USA.

    • Heng Luo
  67. Discovery Statistics, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, Indiana, USA.

    • Xiwen Ma
  68. CMINDS Research Center, Department of Electrical and Computer Engineering, University of Massachusetts at Lowell, Lowell, Massachusetts, USA.

    • Dalila B Megherbi
  69. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Akhilesh Pandey
  70. Institute of Bioinformatics, Bangalore, India.

    • Akhilesh Pandey
  71. Partek Inc., St. Louis, Missouri, USA.

    • Ryan Peters
  72. School of Medicine, Department of Psychiatry, Johns Hopkins University, Baltimore, Maryland, USA.

    • Mehdi Pirooznia
  73. Oxford e-Research Centre, University of Oxford, Oxford, UK.

    • Philippe Rocca-Serra
    •  & Susanna-Assunta Sansone
  74. UJF-Grenoble 1/CNRS/TIMC-IMAG UMR 5525, Computational and Mathematical Biology (BCM), Grenoble, France.

    • Laure Sambourg
  75. SRA International Inc., Durham, North Carolina, USA.

    • Ruchir Shah
  76. Geospiza Inc., Seattle, Washington, USA.

    • Todd M Smith
  77. European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.

    • Oliver Stegle
  78. San Raffaele Scientific Institute, Center for Translational Genomics and Bioinformatics, Milano, Italy.

    • Elia Stupka
  79. Department of Medical Genome Sciences, The University of Tokyo, Chiba, Japan.

    • Yutaka Suzuki
  80. Wake Forest Institute for Regenerative Medicine, Wake Forest University, Health Sciences, Winston-Salem, North Carolina, USA.

    • Stephen J Walker
  81. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA.

    • Wei Wang
  82. Department of Biology, University of Copenhagen, Copenhagen, Denmark.

    • Jun Wang
  83. King Abdulaziz University, Jeddah, Saudi Arabia.

    • Jun Wang
  84. The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.

    • Jun Wang
  85. Department of Biochemistry and Molecular Biology, Mayo Clinic Rochester, Rochester, Minnesota, USA.

    • Eric D Wieben
  86. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.

    • Po-Yen Wu
  87. Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA.

    • Zhan Ye
  88. Center of Big Data Research, Zhijiang College, Zhejiang University of Technology, Hangzhou, Zhejiang, China.

    • John Zhang
  89. School of Medicine, Department of Pathology, University of North Dakota, Grand Forks, North Dakota, USA.

    • Ke K Zhang
  90. Digomics LLC, Brookline, Massachusetts, USA.

    • Yiming Zhou
  91. University of Warwick, Coventry, UK.

    • David P Kreil
  92. State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Schools of Life Sciences and Pharmacy, Fudan University, Shanghai, China (L.S.'s primary affiliation).

    • Leming Shi
  93. Fudan-Zhangjiang Center for Clinical Genomics, Shanghai, China.

    • Leming Shi
  94. Zhanjiang Center for Translational Medicine, Shanghai, China.

    • Leming Shi


  1. SEQC/MAQC-III Consortium



    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.

    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.

    Corresponding authors

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

    Supplementary information

    PDF files

    1. 1.

      Supplementary Text and Figures

      Supplementary Figures 1–46, Supplementary Tables 1–15 and Supplementary Notes

    2. 2.

      Supplementary Protocols

    Text files

    1. 1.

      Supplementary Data 1

      RNA-seq read coverage flanking all 250 candidate junctions considered for validation.

    Excel files

    1. 1.

      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.

    Zip files

    1. 1.

      Supplementary Data 3

      Supplementary Data 3

    About this article

    Publication history





    Further reading