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dc.contributor.authorXu, Weihong
dc.contributor.authorSeok, Junhee
dc.contributor.authorMindrinos, Michael N.
dc.contributor.authorSchweitzer, Anthony C.
dc.contributor.authorJiang, Hui
dc.contributor.authorWilhelmy, Julie
dc.contributor.authorClark, Tyson A.
dc.contributor.authorKapur, Karen
dc.contributor.authorXing, Yi
dc.contributor.authorFaham, Malek
dc.contributor.authorStorey, John D.
dc.contributor.authorMoldawer, Lyle L.
dc.contributor.authorMaier, Ronald V.
dc.contributor.authorTompkins, Ronald G.
dc.contributor.authorWong, Wing Hung
dc.contributor.authorDavis, Ronald W.
dc.contributor.authorXiao, Wenzhong
dc.contributor.authorInflammation and Host Response to Injury Large-Scale Collaborative Research Program
dc.date.accessioned2011-10-13T15:17:38Z
dc.date.available2011-10-13T15:17:38Z
dc.date.issued2011-03
dc.date.submitted2010-11
dc.identifier.issn1091-6490
dc.identifier.urihttp://hdl.handle.net/1721.1/66239
dc.description.abstractA 6.9 million-feature oligonucleotide array of the human transcriptome [Glue Grant human transcriptome (GG-H array)] has been developed for high-throughput and cost-effective analyses in clinical studies. This array allows comprehensive examination of gene expression and genome-wide identification of alternative splicing as well as detection of coding SNPs and noncoding transcripts. The performance of the array was examined and compared with mRNA sequencing (RNA-Seq) results over multiple independent replicates of liver and muscle samples. Compared with RNA-Seq of 46 million uniquely mappable reads per replicate, the GG-H array is highly reproducible in estimating gene and exon abundance. Although both platforms detect similar expression changes at the gene level, the GG-H array is more sensitive at the exon level. Deeper sequencing is required to adequately cover low-abundance transcripts. The array has been implemented in a multicenter clinical program and has generated high-quality, reproducible data. Considering the clinical trial requirements of cost, sample availability, and throughput, the GG-H array has a wide range of applications. An emerging approach for large-scale clinical genomic studies is to first use RNA-Seq to the sufficient depth for the discovery of transcriptome elements relevant to the disease process followed by high-throughput and reliable screening of these elements on thousands of patient samples using custom-designed arrays.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant U54GM062119)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant P01HG000205)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant R01HG004634)en_US
dc.language.isoen_US
dc.publisherNational Academy of Sciences (U.S.)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1073/pnas.1019753108en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourcePNASen_US
dc.titleHuman transcriptome array for high-throughput clinical studiesen_US
dc.typeArticleen_US
dc.identifier.citationXu, W. et al. “Human transcriptome array for high-throughput clinical studies.” Proceedings of the National Academy of Sciences 108 (2011): 3707-3712. ©2011 by the National Academy of Sciences.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Clinical Research Centeren_US
dc.contributor.approverTompkins, Ronald G.
dc.contributor.mitauthorTompkins, Ronald G.
dc.relation.journalProceedings of the National Academy of Sciences of the United States of Americaen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsXu, W.; Seok, J.; Mindrinos, M. N.; Schweitzer, A. C.; Jiang, H.; Wilhelmy, J.; Clark, T. A.; Kapur, K.; Xing, Y.; Faham, M.; Storey, J. D.; Moldawer, L. L.; Maier, R. V.; Tompkins, R. G.; Wong, W. H.; Davis, R. W.; Xiao, W.; Toner, M.; Warren, S.; Schoenfeld, D. A.; Rahme, L. G.; McDonald-Smith, G. P.; Hayden, D. L.; Mason, P. H.; Fagan, S.; Yu, Y.-M.; Cobb, J. P.; Remick, D. G.; Mannick, J. A.; Lederer, J. A.; Gamelli, R. L.; Silver, G. M.; West, M. A.; Shapiro, M. B.; Smith, R. D.; Camp, D. G.; Qian, W.; Tibshirani, R.; Lowry, S. F.; Calvano, S. E.; Chaudry, I.; Cohen, M.; Moore, E. E.; Johnson, J. L.; Baker, H. V.; Efron, P. A.; Balis, U. G. J.; Billiar, T. R.; Ochoa, J. B.; Sperry, J.; Miller-Graziano, C. L.; De, A. K.; Bankey, P. E.; Herndon, D. N.; Finnerty, C. C.; Jeschke, M. G.; Minei, J. P.; Arnoldo, B. D.; Hunt, J. L.; Horton, J.; Brownstein, B. H.; Freeman, B.; Nathens, A. B.; Cuschieri, J.; Gibran, N.; Klein, M.; O'Keefe, G.; Altstein, L.; Gao, H.; Harbrecht, B. G.; Hennessy, L.; Honari, S. E.; McKinley, B. A.; Moore, F. A.; Wispelwey, B.en
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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