Copy number variation detection using next generation sequencing read counts

dc.contributor.author Wang, Heng
dc.contributor.author Nettleton, Dan
dc.contributor.author Nettleton, Dan
dc.contributor.author Ying, Kai
dc.contributor.department Statistics
dc.date 2019-08-22T08:01:36.000
dc.date.accessioned 2020-07-02T06:57:06Z
dc.date.available 2020-07-02T06:57:06Z
dc.date.issued 2014-01-01
dc.description.abstract <p>Background: A copy number variation (CNV) is a difference between genotypes in the number of copies of a genomic region. Next generation sequencing (NGS) technologies provide sensitive and accurate tools for detecting genomic variations that include CNVs. However, statistical approaches for CNV identification using NGS are limited. We propose a new methodology for detecting CNVs using NGS data. This method (henceforth denoted by m-HMM) is based on a hidden Markov model with emission probabilities that are governed by mixture distributions. We use the Expectation-Maximization (EM) algorithm to estimate the parameters in the model.</p> <p>Results: A simulation study demonstrates that our proposed m-HMM approach has greater power for detecting copy number gains and losses relative to existing methods. Furthermore, application of our m-HMM to DNA sequencing data from the two maize inbred lines B73 and Mo17 to identify CNVs that may play a role in creating phenotypic differences between these inbred lines provides results concordant with previous array-based efforts to identify CNVs.</p> <p>Conclusions: The new m-HMM method is a powerful and practical approach for identifying CNVs from NGS data.</p>
dc.description.comments <p>This article is published as Wang, Heng, Dan Nettleton, and Kai Ying. "Copy number variation detection using next generation sequencing read counts." <em>BMC bioinformatics</em> 15 (2014): 109. doi: <a href="https://doi.org/10.1186/1471-2105-15-109">10.1186/1471-2105-15-109</a>.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/192/
dc.identifier.articleid 1194
dc.identifier.contextkey 14814671
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/192
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/90501
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/192/2014_Nettleton_CopyNumber.pdf|||Fri Jan 14 21:53:32 UTC 2022
dc.source.uri 10.1186/1471-2105-15-109
dc.subject.disciplines Bioinformatics
dc.subject.disciplines Genetics and Genomics
dc.subject.disciplines Plant Breeding and Genetics
dc.subject.disciplines Statistical Models
dc.subject.keywords Count data
dc.subject.keywords Gamma-Poisson mixture
dc.subject.keywords Hidden Markov model
dc.subject.keywords Plant genomics
dc.subject.keywords Poisson mixture model
dc.title Copy number variation detection using next generation sequencing read counts
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 7d86677d-f28f-4ab1-8cf7-70378992f75b
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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