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

Author

Z. SU, P. LABAJ, S. LI, J. THIERRY-MIEG, D. THIERRY-MIEG, W. SHI, C. WANG, G. SCHROTH, R. SETTERQUIST, J. THOMPSON, W. JONES, W. XIAO, W. XU, R. JENSEN, R. KELLY, J. XU, A. CONESA, C. FURLANELLO, H. GAO, H. HONG, N. JAFARI, S. LETOVSKY, Y. LIAO, F. LU, E. OAKELEY, Z. PENG, C. PRAUL, J. SANTOYO-LOPEZ, A. SCHERER, T. SHI, G. SMYTH, F. STAEDTLER, P. SYKACEK, X. TAN, E. THOMPSON, J. VANDESOMPELE, M. WANG, J. WANG, R. WOLFINGER, J. ZAVADIL, S. AUERBACH, W. BAO, H. BINDER, T. BLOMQUIST, M. BRILLIANT, P. BUSHEL, W. CAIN, J. CATALANO, C. CHANG, T. CHEN, G. CHEN, R. CHEN, M. CHIERICI, T. CHU, D. CLEVERT, Y. DENG, A. DERTI, V. DEVANARAYAN, Z. DONG, J. DOPAZO, T. DU, H. FANG, Y. FANG, M. FASOLD, A. FERNANDEZ, M. FISCHER, P. FURIO-TARI, J. FUSCOE, F. CAIMENT, S. GAJ, J. GANDARA, W. GE, Y. GONDO, B. GONG, M. GONG, Z. GONG, B. GREEN, C. GUO, L. GUO, J. HADFIELD, J. HELLEMANS, S. HOCHREITER, M. JIA, M. JIAN, C. JOHNSON, S. KAY, J. KLEINJANS, S. LABABIDI, S. LEVY, Q. LI, L. LI, P. LI, Y. LI, H. LI, J. LI, S. LIN, F. LOPEZ, X. LU, H. LUO, X. MA, J. MEEHAN, D. MEGHERBI, N. MEI, B. MU, B. NING, A. PANDEY, J. PEREZ-FLORIDO, R. PERKINS, R. PETERS, J. PHAN, M. PIROOZNIA, F. QIAN, T. QING, L. RAINBOW, P. ROCCA-SERRA, L. SAMBOURG, S. SANSONE, S. SCHWARTZ, R. SHAH, J. SHEN, T. SMITH, O. STEGLE, N. STRALIS-PAVESE, E. STUPKA, Y. SUZUKI, L. SZKOTNICKI, M. TINNING, B. TU, J. VAN DEFT, A. VELA-BOZA, E. VENTURINI, S. WALKER, L. WAN, W. WANG, E. WIEBEN, J. WILLEY, P. WU, J. XUAN, Y. YANG, Z. YE, Y. YIN, Y. YU, Y. YUAN, J. ZHANG, K. ZHANG, W. ZHANG, Y. ZHANG, C. ZHAO, Y. ZHENG, Y. ZHOU, P. ZUMBO, W. TONG, D. KREIL, C. MASON, and L. SHI

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.

Published
2014

2. The MicroArray Quality Control (MAQC)-IIII study of common practices for the development and validation of microarray-based predictive models

Author

L. SHI, G. CAMPBELL, W. JONES, F. CAMPAGNE, Z. WEN, S. WALKER, Z. SU, T. CHU, F. GOODSAID, L. PUSZTAI, J. SHAUGHNESSY, A. OBERTHUER, R. THOMAS, R. PAULES, M. FIELDEN, B. BARLOGIE, W. CHEN, P. DU, M. FISCHER, C. FURLANELLO, B. GALLAS, X. GE, D. MEGHERBI, W. SYMMANS, M. WANG, J. ZHANG, H. BITTER, B. BRORS, P. BUSHEL, M. BYLESJO, M. CHEN, J. CHENG, J. CHOU, T. DAVISON, M. DELORENZI, Y. DENG, V. DEVANARAYAN, D. DIX, J. DOPAZO, K. DORFF, F. ELLOUMI, J. FAN, S. FAN, X. FAN, H. FANG, N. GONZALUDO, K. HESS, H. HONG, J. HUAN, R. IRIZARRY, R. JUDSON, D. JURAEVA, S. LABABIDI, C. LAMBERT, L. LI, Y. LI, Z. LI, S. LIN, G. LIU, E. LOBENHOFER, J. LUO, W. LUO, M. MCCALL, Y. NIKOLSKY, G. PENNELLO, R. PERKINS, R. PHILIP, V. POPOVICI, N. PRICE, F. QIAN, A. SCHERER, T. SHI, W. SHI, J. SUNG, D. THIERRY-MIEG, J. THIERRY-MIEG, V. THODIMA, J. TRYGG, L. VISHNUVAJJALA, S. WANG, J. WU, Y. WU, Q. XIE, W. YOUSEF, L. ZHANG, X. ZHANG, S. ZHONG, Y. ZHOU, S. ZHU, D. ARASAPPAN, W. BAO, A. LUCAS, F. BERTHOLD, R. BRENNAN, A. BUNESS, J. CATALANO, C. CHANG, R. CHEN, Y. CHENG, J. CUI, W. CZIKA, F. DEMICHELIS, X. DENG, D. DOSYMBEKOV, R. EILS, Y. FENG, J. FOSTEL, S. FULMER-SMENTEK, J. FUSCOE, L. GATTO, W. GE, D. GOLDSTEIN, L. GUO, D. HALBERT, J. HAN, S. HARRIS, C. HATZIS, D. HERMAN, J. HUANG, R. JENSEN, R. JIANG, C. JOHNSON, G. JURMAN, Y. KAHLERT, S. KHUDER, M. KOHL, J. LI, M. LI, Q. LI, S. LI, J. LIU, Y. LIU, Z. LIU, L. MENG, M. MADERA, F. MARTINEZ-MURILLO, I. MEDINA, J. MEEHAN, K. MICLAUS, R. MOFFITT, D. MONTANER, P. MUKHERJEE, G. MULLIGAN, P. NEVILLE, T. NIKOLSKAYA, B. NING, G. PAGE, J. PARKER, R. PARRY, X. PENG, R. PETERSON, J. PHAN, B. QUANZ, Y. REN, S. RICCADONNA, A. ROTER, F. SAMUELSON, M. SCHUMACHER, J. SHAMBAUGH, Q. SHI, R. SHIPPY, S. SI, A. SMALTER, C. SOTIRIOU, M. SOUKUP, F. STAEDTLER, G. STEINER, T. STOKES, Q. SUN, P. TAN, R. TANG, Z. TEZAK, B. THORN, M. TSYGANOVA, Y. TURPAZ, S. VEGA, R. VISINTAINER, J. VON FRESE, C. WANG, E. WANG, J. WANG, W. WANG, F. WESTERMANN, J. WILLEY, M. WOODS, S. WU, N. XIAO, J. XU, L. XU, L. YANG, X. ZENG, M. ZHANG, C. ZHAO, R. PURI, U. SCHERF, W. TONG, R. WOLFINGER, and MAQC Consortium

Abstract

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Published
2010

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