An online copy number variant detection method for short sequencing reads

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dc.authoridChen, Jie/0000-0003-2151-5276
dc.contributor.authorYigiter, Ayten
dc.contributor.authorChen, Jie
dc.contributor.authorAn, Lingling
dc.contributor.authorDanacioglu, Nazan
dc.date.accessioned2025-03-23T19:35:19Z
dc.date.available2025-03-23T19:35:19Z
dc.date.issued2015
dc.departmentSinop Üniversitesi
dc.description.abstractThe availability of the next generation sequencing (NGS) technology in today's biomedical research has provided new opportunities in scientific discovery of genetic information. The high-throughput NGS technology, especially DNA-seq, is particularly useful in profiling a genome for the analysis of DNA copy number variants (CNVs). The read count (RC) data resulting from NGS technology are massive and information rich. How to exploit the RC data for accurate CNV detection has become a computational and statistical challenge. We provide a statistical online change point method to help detect CNVs in the sequencing RC data in this paper. This method uses the idea of online searching for change point (or breakpoint) with a Markov chain assumption on the breakpoints loci and an iterative computing process via a Bayesian framework. We illustrate that an online change-point detection method is particularly suitable for identifying CNVs in the RC data. The algorithm is applied to the publicly available NCI-H2347 lung cancer cell line sequencing reads data for locating the breakpoints. Extensive simulation studies have been carried out and results show the good behavior of the proposed algorithm. The algorithm is implemented in R and the codes are available upon request.
dc.description.sponsorshipUniversity of Missouri Research Board (UMRB) research grant; National Science Foundation [DMS-1043080, DMS-1222592]; Division Of Mathematical Sciences; Direct For Mathematical & Physical Scien [1222592] Funding Source: National Science Foundation
dc.description.sponsorshipPart of this work was done while J. Chen was on leave from University of Missouri-Kansas City and was a visiting scientist at the Bioinformatics Core of the Stowers Institute for Medical Research. J. Chen was supported in part by a University of Missouri Research Board (UMRB) research grant. L. An was partially supported by the National Science Foundation [DMS-1043080 and DMS-1222592]. The authors thank H. Li for the help on processing the data and the anonymous referees for their valuable comments and suggestions which lead to the improvement of the manuscript.
dc.identifier.doi10.1080/02664763.2014.1001330
dc.identifier.endpage1571
dc.identifier.issn0266-4763
dc.identifier.issn1360-0532
dc.identifier.issue7
dc.identifier.scopus2-s2.0-84928618340
dc.identifier.scopusqualityQ1
dc.identifier.startpage1556
dc.identifier.urihttps://doi.org/10.1080/02664763.2014.1001330
dc.identifier.urihttps://hdl.handle.net/11486/5842
dc.identifier.volume42
dc.identifier.wosWOS:000353506000012
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofJournal of Applied Statistics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250323
dc.subject62-07
dc.subject92-08
dc.subjectDNA copy number variation
dc.subjectchange point (or breakpoint)
dc.subjectnext generation sequencing
dc.subjectBayesian estimate
dc.subjectonline change-point detection method
dc.titleAn online copy number variant detection method for short sequencing reads
dc.typeArticle

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