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dc.contributor.authorHill, Tom
dc.contributor.authorUnckless, Robert L.
dc.identifier.citationHill, T., & Unckless, R. L. (2019). A Deep Learning Approach for Detecting Copy Number Variation in Next-Generation Sequencing Data. G3 (Bethesda, Md.), 9(11), 3575–3582.
dc.descriptionThis work is licensed under a Creative Commons Attribution 4.0 International License.en_US
dc.description.abstractCopy number variants (CNV) are associated with phenotypic variation in several species. However, properly detecting changes in copy numbers of sequences remains a difficult problem, especially in lower quality or lower coverage next-generation sequencing data. Here, inspired by recent applications of machine learning in genomics, we describe a method to detect duplications and deletions in short-read sequencing data. In low coverage data, machine learning appears to be more powerful in the detection of CNVs than the gold-standard methods of coverage estimation alone, and of equal power in high coverage data. We also demonstrate how replicating training sets allows a more precise detection of CNVs, even identifying novel CNVs in two genomes previously surveyed thoroughly for CNVs using long read data.en_US
dc.publisherGenetics Society of Americaen_US
dc.rights© 2019 Hill, Unckless.en_US
dc.subjectNext-generation sequencingen_US
dc.titleA Deep Learning Approach for Detecting Copy Number Variation in Next-Generation Sequencing Dataen_US
kusw.kuauthorHill, Tom
kusw.kuauthorUnckless, Robert L.
kusw.kudepartmentMolecular Biosciencesen_US
kusw.oanotesPer SHERPA/RoMEO 6/12/2020:

Journal: G3 (ESSN: 2160-1836) RoMEO: This is a RoMEO green journal Listed in: DOAJ as an open access journal Author's Pre-print: green tick author can archive pre-print (ie pre-refereeing) Author's Post-print: green tick author can archive post-print (ie final draft post-refereeing) Publisher's Version/PDF: No information archiving status unknown General Conditions: Creative Commons Attribution License Authors retain copyright Published source must be acknowledged
kusw.oaversionScholarly/refereed, author accepted manuscripten_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US

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© 2019 Hill, Unckless.
Except where otherwise noted, this item's license is described as: © 2019 Hill, Unckless.