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dc.contributor.authorXi, Ruibin
dc.contributor.authorHadjipanayis, Angela G.
dc.contributor.authorLuquette, Lovelace J.
dc.contributor.authorKim, Tae-Min
dc.contributor.authorLee, Eunjung
dc.contributor.authorZhang, Jianhua
dc.contributor.authorJohnson, Mark D.
dc.contributor.authorMuzny, Donna M.
dc.contributor.authorWheeler, David A.
dc.contributor.authorGibbs, Richard A.
dc.contributor.authorKucherlapati, Raju
dc.contributor.authorPark, Peter J.
dc.date.accessioned2019-10-14T16:05:19Z
dc.date.issued2011
dc.identifier.citationXi, R., A. G. Hadjipanayis, L. J. Luquette, T.-M. Kim, E. Lee, J. Zhang, M. D. Johnson, et al. 2011. “Copy Number Variation Detection in Whole-Genome Sequencing Data Using the Bayesian Information Criterion.” Proceedings of the National Academy of Sciences 108 (46): E1128–36. doi:10.1073/pnas.1110574108.
dc.identifier.issn0027-8424
dc.identifier.issn0744-2831
dc.identifier.issn1091-6490
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:41542993*
dc.description.abstractDNA copy number variations (CNVs) play an important role in the pathogenesis and progression of cancer and confer susceptibility to a variety of human disorders. Array comparative genomic hybridization has been used widely to identify CNVs genome wide, but the next-generation sequencing technology provides an opportunity to characterize CNVs genome wide with unprecedented resolution. In this study, we developed an algorithm to detect CNVs from whole-genome sequencing data and applied it to a newly sequenced glioblastoma genome with a matched control. This read-depth algorithm, called BIC-seq, can accurately and efficiently identify CNVs via minimizing the Bayesian information criterion. Using BIC-seq, we identified hundreds of CNVs as small as 40 bp in the cancer genome sequenced at 10x coverage, whereas we could only detect large CNVs (>15 kb) in the array comparative genomic hybridization profiles for the same genome. Eighty percent (14/16) of the small variants tested (110 bp to 14 kb) were experimentally validated by quantitative PCR, demonstrating high sensitivity and true positive rate of the algorithm. We also extended the algorithm to detect recurrent CNVs in multiple samples as well as deriving error bars for breakpoints using a Gibbs sampling approach. We propose this statistical approach as a principled yet practical and efficient method to estimate CNVs in whole-genome sequencing data.
dc.language.isoen_US
dc.publisherNational Academy of Sciences
dash.licenseLAA
dc.titleCopy number variation detection in whole-genome sequencing data using the Bayesian information criterion
dc.typeJournal Article
dc.description.versionVersion of Record
dc.relation.journalProceedings of the National Academy of Sciences of the United States of America
dash.depositing.authorKucherlapati, Raju::6c40e7e65abec178aadf46af56934a08::600
dc.date.available2019-10-14T16:05:19Z
dash.workflow.comments1Science Serial ID 90656
dc.identifier.doi10.1073/pnas.1110574108
dash.source.volume108;46
dash.source.pageE1128


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