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Nat Biotechnol. 2014 Sep;32(9):903-14. doi: 10.1038/nbt.2957. Epub 2014 Aug 24.

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

Collaborators (162)

Su Z, Łabaj PP, Li S, Thierry-Mieg J, Thierry-Mieg D, Shi W, Wang C, Schroth GP, Setterquist RA, Thompson JF, Jones WD, Xiao W, Xu W, Jensen RV, Kelly R, Xu J, Conesa A, Furlanello C, Gao H, Hong H, Jafari N, Letovsky S, Liao Y, Lu F, Oakeley EJ, Peng Z, Praul CA, Santoyo-Lopez J, Scherer A, Shi T, Smyth GK, Staedtler F, Sykacek P, Tan XX, Thompson EA, Vandesompele J, Wang MD, Wang J, Wolfinger RD, Zavadil J, Auerbach SS, Bao W, Binder H, Blomquist T, Brilliant MH, Bushel PR, Cai W, Catalano JG, Chang CW, Chen T, Chen G, Chen R, Chierici M, Chu TM, Clevert DA, Deng Y, Derti A, Devanarayan V, Dong Z, Dopazo J, Du T, Fang H, Fang Y, Fasold M, Fernandez A, Fischer M, Furió-Tari P, Fuscoe JC, Caimet F, Gaj S, Gandara J, Gao H, Ge W, Gondo Y, Gong B, Gong M, Gong Z, Green B, Guo C, Guo L, Guo LW, Hadfield J, Hellemans J, Hochreiter S, Jia M, Jian M, Johnson CD, Kay S, Kleinjans J, Lababidi S, Levy S, Li QZ, Li L, Li L, Li P, Li Y, Li H, Li J, Li S, Lin SM, López FJ, Lu X, Luo H, Ma X, Meehan J, Megherbi DB, Mei N, Mu B, Ning B, Pandey A, Pérez-Florido J, Perkins RG, Peters R, Phan JH, Pirooznia M, Qian F, Qing T, Rainbow L, Rocca-Serra P, Sambourg L, Sansone SA, Schwartz S, Shah R, Shen J, Smith TM, Stegle O, Stralis-Pavese N, Stupka E, Suzuki Y, Szkotnicki LT, Tinning M, Tu B, van Delft J, Vela-Boza A, Venturini E, Walker SJ, Wan L, Wang W, Wang J, Wang J, Wieben ED, Willey JC, Wu PY, Xuan J, Yang Y, Ye Z, Yin Y, Yu Y, Yuan YC, Zhang J, Zhang KK, Zhang W, Zhang W, Zhang Y, Zhao C, Zheng Y, Zhou Y, Zumbo P, Tong W, Kreil DP, Mason CE, Shi L.


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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