Papers

Top Papers Overall

Citations

as of 10/10/2022

2351

Shi, L., Reid, L.H., Jones, W.D., Shippy, R., Warrington, J.A., Baker, S.C., Collins, P.J., de Longueville, F., Kawasaki, E.S., Lee, K.Y. et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nature Biotechnology, 24(9):1151-1161, 2006.

790

Shi, L., Campbell, G., Jones, W. D., Campagne, F., Wen, Z., Walker, S. J., Su, Z., Chu, T. M., Goodsaid, F. M., Pusztai, L., Tong, W. and Wolfinger, R. D. et al.  The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nature Biotechnology, 28: 827-838, 2010.

739

SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequence Quality Control consortium. Nature Biotechnology. 32, 903–914 (2014).

736

Canales, R.D., Luo, Y., Willey, J.C., Austermiller, B., Barbacioru, C.C., Boysen, C., Hunkapiller, K., Jensen, R.V., Knight, C.R., Lee, K.Y. et al. Evaluation of DNA microarray results with quantitative gene expression platforms. Nature Biotechnology, 24(9):1115-1122, 2006.

589

Patterson, T.A., Lobenhofer, E.K., Fulmer-Smentek, S.B., Collins, P.J., Chu, T.M., Bao, W., Fang, H., Kawasaki, E.S., Hager, J., Tikhonova, I.R. et al. Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project. Nature Biotechnology, 24(9):1140-1150, 2006.

 

2022

2021

Nature Biotechnology Sequencing Quality Control 2 Collection (Sept 9 2021)

 

2020

Haibe-Kains B, Adam GA, Hosny A, Khodakarami F, Waldron L, Wang B, McIntosh C, Goldenberg A, Kundaje A, Greene CS, Broderick T. Transparency and reproducibility in artificial intelligence. Nature. 2020 Oct;586(7829):E14-6.
Chen, W. et al. A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples. Nature Biotechnology. 2020. (also included in Nature Biotechnology 2021 collection)

2017

Shi L, Kusko R, Wolfinger RD, Haibe-Kains B, Fischer M, Sansone SA, Mason CE, Furlanello C, Jones WD, Ning B, Tong W. The international MAQC Society launches to enhance reproducibility of high-throughput technologies. Nature Biotechnology. 2017 Dec;35(12):1127-8.

2014

SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequence Quality Control consortium. Nature Biotechnology. 32, 903–914 (2014).

Wang, C., Gong, B., Bushel, P.R., Thierry-Mieg, J., Thierry-Mieg, D., Xu J., Fang, H., Hong H., Jie Shen, Zhenqiang Su, Joe Meehan, Chao Guo, Xiaojin Li, Lu Yang, Haiqing Li, Paweł P. Łabaj, David P. Kreil, Dalila Megherbi, Caiment Florian, Stan Gaj, Joost van Delft, Jos Kleinjans, Andreas Scherer, Devanarayan Viswanath, 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 Auerbach and Weida Tong, The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance, Nature Biotechnology. 32, 926–932 (2014)

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 and Christopher E Mason, Detecting and correcting systematic variation in large-scale RNA sequencing data, Nature Biotechnology. 32, 888–895 (2014)

Sarah A Munro, Steve Lund, P Scott Pine, Hans Binder, Djork-Arné Clevert, Ana Conesa, Joaquin Dopazo, Mario Fasold, Sepp Hochreiter, Huixiao Hong, Nederah Jafari, David P Kreil, Paweł P Łabaj, Yang Liao, Simon Lin, Joseph Meehan, Christopher E Mason, Javier Santoyo, 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 and Marc Salit, Assessing Technical Performance in RNA-Seq Experiments with External Spike-in RNA Controls, Nature Communications. 5, Article number: 5125 (2014)

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 and Charles Wang, A rat RNA-Seq transcriptomic BodyMap across 11 organs and 4 developmental stages, Nature Communications. 5, Article number: 3230 (2014)

Ying Yu, Chen Zhao, Zhenqiang Su, Charles Wang, James C Fuscoe, Weida Tong and Leming Shi, Comprehensive RNA-Seq transcriptomic profiling across 11 organs, 4 ages, and 2 sexes of Fischer 344 rats, Scientific Data. 1, Article number: 140013 (2014)

Joshua Xu, Zhenqiang Su, Huixiao Hong, David P Kreil, Christopher E Mason, Weida Tong and Leming Shi, Cross-platform ultradeep transcriptomic profiling of human reference RNA samples by RNA-Seq, Scientific Data. 1, Article number: 140020 (2014)

Binsheng Gong, Charles Wang, Zhenqiang Su, Huixiao Hong, Scott Auerbach, Leming Shi, Weida Tong and Joshua Xu, Transcriptomic profiling of rat liver samples in a comprehensive study design by RNA-Seq, Scientific Data. 1, Article number: 140021 (2014)

Zhenqiang Su, Hong Fang, Huixiao Hong, Leming Shi, Wenqian Zhang, Wenwei Zhang, Yanyan Zhang, Zirui Dong, Lee J Lancashire, Marina Bessarabova, Xi Yang, Baitang Ning, Binsheng Gong, Joe Meehan, Joshua Xu, Weigong Ge, Roger Perkins, Matthias Fischer and Weida Tong, An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era, Genome Biology, 15:3273 (2014)

2010

Chierici, M., Miclaus, K., Vega, S., and Furlanello, C. An interactive effect of batch size and composition contributes to discordant results in GWAS with the CHIAMO genotyping algorithm. Pharmacogenomics Journal, 10: 355-363, 2010.

Fan, X., Lobenhofer, E. K., Chen, M., Shi, W., Huang, J., Luo, J., Zhang, J., Walker, S. J., Chu, T. M., Li, L., Wolfinger, R., Bao, W., Paules, R. S., Bushel, P. R., Li, J., Shi, T., Nikolskaya, T., Nikolsky, Y., Hong, H., Deng, Y., Cheng, Y., Fang, H., Shi, L., and Tong, W. Consistency of predictive signature genes and classifiers generated using different microarray platforms. Pharmacogenomics Journal. 10: 247-257, 2010.

Hong, H., Shi, L., Su, Z., Ge, W., Jones, W. D., Czika, W., Miclaus, K., Lambert, C. G., Vega, S. C., Zhang, J., Ning, B., Liu, J., Green, B., Xu, L., Fang, H., Perkins, R., Lin, S. M., Jafari, N., Park, K., Ahn, T., Chierici, M., Furlanello, C., Zhang, L., Wolfinger, R. D., Goodsaid, F., and Tong, W. Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples. Pharmacogenomics Journal, 10: 364-374, 2010.

Huang, J., Shi, W., Zhang, J., Chou, J. W., Paules, R. S., Gerrish, K., Li, J., Luo, J., Wolfinger, R. D., Bao, W., Chu, T. M., Nikolsky, Y., Nikolskaya, T., Dosymbekov, D., Tsyganova, M. O., Shi, L., Fan, X., Corton, J. C., Chen, M., Cheng, Y., Tong, W., Fang, H., and Bushel, P. R. Genomic indicators in the blood predict drug-induced liver injury. Pharmacogenomics Journal, 10: 267-277, 2010.

Luo, J., Schumacher, M., Scherer, A., Sanoudou, D., Megherbi, D., Davison, T., Shi, T., Tong, W., Shi, L., Hong, H., Zhao, C., Elloumi, F., Shi, W., Thomas, R., Lin, S., Tillinghast, G., Liu, G., Zhou, Y., Herman, D., Li, Y., Deng, Y., Fang, H., Bushel, P., Woods, M., and Zhang, J. A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data. Pharmacogenomics Journal, 10: 278-291, 2010.

MAQC-II: analyze that! Nature Biotechnology, 28: 761, 2010.

Miclaus, K., Chierici, M., Lambert, C., Zhang, L., Vega, S., Hong, H., Yin, S., Furlanello, C., Wolfinger, R., and Goodsaid, F. Variability in GWAS analysis: the impact of genotype calling algorithm inconsistencies. Pharmacogenomics Journal, 10: 324-335, 2010.

Miclaus, K., Wolfinger, R., Vega, S., Chierici, M., Furlanello, C., Lambert, C., Hong, H., Zhang, L., Yin, S., and Goodsaid, F. Batch effects in the BRLMM genotype calling algorithm influence GWAS results for the Affymetrix 500K array. Pharmacogenomics Journal, 10: 336-346, 2010.

Oberthuer, A., Juraeva, D., Li, L., Kahlert, Y., Westermann, F., Eils, R., Berthold, F., Shi, L., Wolfinger, R. D., Fischer, M., and Brors, B. Comparison of performance of one-color and two-color gene-expression analyses in predicting clinical endpoints of neuroblastoma patients. Pharmacogenomics Journal, 10: 258-266, 2010.

Parry, R. M., Jones, W., Stokes, T. H., Phan, J. H., Moffitt, R. A., Fang, H., Shi, L., Oberthuer, A., Fischer, M., Tong, W., and Wang, M. D. k-Nearest neighbor models for microarray gene expression analysis and clinical outcome prediction. Pharmacogenomics Journal, 10: 292-309, 2010.

Shi, W., Bessarabova, M., Dosymbekov, D., Dezso, Z., Nikolskaya, T., Dudoladova, M., Serebryiskaya, T., Bugrim, A., Guryanov, A., Brennan, R. J., Shah, R., Dopazo, J., Chen, M., Deng, Y., Shi, T., Jurman, G., Furlanello, C., Thomas, R. S., Corton, J. C., Tong, W., Shi, L., and Nikolsky, Y. Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes. Pharmacogenomics Journal, 10: 310-323, 2010.

Shi, L., Campbell, G., Jones, W. D., Campagne, F., Wen, Z., Walker, S. J., Su, Z., Chu, T. M., Goodsaid, F. M., Pusztai, L., Shaughnessy, J. D., Jr., Oberthuer, A., Thomas, R. S., Paules, R. S., Fielden, M., Barlogie, B., Chen, W., Du, P., Fischer, M., Furlanello, C., Gallas, B. D., Ge, X., Megherbi, D. B., Symmans, W. F., Wang, M. D., Zhang, J., Bitter, H., Brors, B., Bushel, P. R., Bylesjo, M., Chen, M., Cheng, J., Chou, J., Davison, T. S., Delorenzi, M., Deng, Y., Devanarayan, V., Dix, D. J., Dopazo, J., Dorff, K. C., Elloumi, F., Fan, J., Fan, S., Fan, X., Fang, H., Gonzaludo, N., Hess, K. R., Hong, H., Huan, J., Irizarry, R. A., Judson, R., Juraeva, D., Lababidi, S., Lambert, C. G., Li, L., Li, Y., Li, Z., Lin, S. M., Liu, G., Lobenhofer, E. K., Luo, J., Luo, W., McCall, M. N., Nikolsky, Y., Pennello, G. A., Perkins, R. G., Philip, R., Popovici, V., Price, N. D., Qian, F., Scherer, A., Shi, T., Shi, W., Sung, J., Thierry-Mieg, D., Thierry-Mieg, J., Thodima, V., Trygg, J., Vishnuvajjala, L., Wang, S. J., Wu, J., Wu, Y., Xie, Q., Yousef, W. A., Zhang, L., Zhang, X., Zhong, S., Zhou, Y., Zhu, S., Arasappan, D., Bao, W., Lucas, A. B., Berthold, F., Brennan, R. J., Buness, A., Catalano, J. G., Chang, C., Chen, R., Cheng, Y., Cui, J., Czika, W., Demichelis, F., Deng, X., Dosymbekov, D., Eils, R., Feng, Y., Fostel, J., Fulmer-Smentek, S., Fuscoe, J. C., Gatto, L., Ge, W., Goldstein, D. R., Guo, L., Halbert, D. N., Han, J., Harris, S. C., Hatzis, C., Herman, D., Huang, J., Jensen, R. V., Jiang, R., Johnson, C. D., Jurman, G., Kahlert, Y., Khuder, S. A., Kohl, M., Li, J., Li, M., Li, Q. Z., Li, S., Liu, J., Liu, Y., Liu, Z., Meng, L., Madera, M., Martinez-Murillo, F., Medina, I., Meehan, J., Miclaus, K., Moffitt, R. A., Montaner, D., Mukherjee, P., Mulligan, G. J., Neville, P., Nikolskaya, T., Ning, B., Page, G. P., Parker, J., Parry, R. M., Peng, X., Peterson, R. L., Phan, J. H., Quanz, B., Ren, Y., Riccadonna, S., Roter, A. H., Samuelson, F. W., Schumacher, M. M., Shambaugh, J. D., Shi, Q., Shippy, R., Si, S., Smalter, A., Sotiriou, C., Soukup, M., Staedtler, F., Steiner, G., Stokes, T. H., Sun, Q., Tan, P. Y., Tang, R., Tezak, Z., Thorn, B., Tsyganova, M., Turpaz, Y., Vega, S. C., Visintainer, R., von Frese, J., Wang, C., Wang, E., Wang, J., Wang, W., Westermann, F., Willey, J. C., Woods, M., Wu, S., Xiao, N., Xu, J., Xu, L., Yang, L., Zeng, X., Zhang, M., Zhao, C., Puri, R. K., Scherf, U., Tong, W. and Wolfinger, R. D. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nature Biotechnology, 28: 827-838, 2010.

Slikker, W., Jr. Of genomics and bioinformatics. Pharmacogenomics Journal, 10: 245-246, 2010.

Tillinghast, G. W. Microarrays in the clinic. Nature Biotechnology, 28: 810-812, 2010.

Zhang, L., Yin, S., Miclaus, K., Chierici, M., Vega, S., Lambert, C., Hong, H., Wolfinger, R. D., Furlanello, C., and Goodsaid, F. Assessment of variability in GWAS with CRLMM genotyping algorithm on WTCCC coronary artery disease. Pharmacogenomics Journal, 10: 347-354, 2010.

2009

Fan, X., Fang, H., Hong, H., Perkins, R.G., She, L., Tong, W. Correlation analysis of external RNA controls reveals its utility for assessment of microarray assay. Annals of Biochemistry, 385(2):203-207, 2009.

Fan, X., Shi, L., Fang, H., Harris, S., Perkins, R.G., Tong, W. Investigation of reproducibility of differentially expressed genes in DNA microarrays through statistical simulation. BMC Proceedings, 3 Suppl 2:S4, 2009.

2008

Shi, L., Jones, W.D., Jensen, R.V., Harris, S.C., Perkins, R.G., Goodsaid, F.M., Guo, L., Croner, L.J., Boysen, C., Fang, H. et al. The balance of reproducibility, sensitivity, and specificity of lists of differenctially expressed genes in microarray studies. BMC Bioinformatics, 9 Suppl 9:S10, 2008.

Shi, L., Perkins, R.G., Fang, H., Tong, W. Reproducible and reliable microarray results through quality control: good laboratory proficiency and appropriate data analysis practices are essential. Current Opinions on Biotechnology, 19(1):10-18, 2008.

2006

Canales, R.D., Luo, Y., Willey, J.C., Austermiller, B., Barbacioru, C.C., Boysen, C., Hunkapiller, K., Jensen, R.V., Knight, C.R., Lee, K.Y. et al. Evaluation of DNA microarray results with quantitative gene expression platforms. Nature Biotechnology, 24(9):1115-1122, 2006.

Guo, L., Lobenhofer, E., Wang, C., Shippy, R., Harris, S., Zhang, L., Mei, N., Chen, T., Herman, D., Goodsaid, F. Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nature Biotechnology, 24:1162-1169, 2006.

Patterson, T.A., Lobenhofer, E.K., Fulmer-Smentek, S.B., Collins, P.J., Chu, T.M., Bao, W., Fang, H., Kawasaki, E.S., Hager, J., Tikhonova, I.R. et al. Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project. Nature Biotechnology, 24(9):1140-1150, 2006.

Shi, L., Reid, L.H., Jones, W.D., Shippy, R., Warrington, J.A., Baker, S.C., Collins, P.J., de Longueville, F., Kawasaki, E.S., Lee, K.Y. et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nature Biotechnology, 24(9):1151-1161, 2006.

Shippy, R., Fulmer-Smentek, S., Jensen, R.V., Jones, W.D., Wolber, P.K., Johnson, C.D., Pine, P.S., Boysen, C., Guo, X., Chudin, E. et al. Using RNA sample titrations to assess microarray platform performance and normalization techniques. Nature Biotechnology, 24(9):1123-1131, 2006.

Tong, W., Lucas, A.B., Shippy, R., Fan, X., Fang, H., Hong, H., Orr, M.S., Chu, T.M., Guo, X., Collins, P.J. et al. Evaluation of external RNA controls for the assessment of microarray performance. Nature Biotechnology. 24(9):1132-1139, 2006.