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dc.contributor.advisorÇiçek, A. Ercüment
dc.contributor.authorÖzden, Furkan
dc.date.accessioned2021-08-17T08:05:32Z
dc.date.available2021-08-17T08:05:32Z
dc.date.copyright2021-07
dc.date.issued2021-07
dc.date.submitted2021-08-06
dc.identifier.urihttp://hdl.handle.net/11693/76442
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2021.en_US
dc.descriptionIncludes bibliographical references (leaves 30-35).en_US
dc.description.abstractAccurate and efficient detection of copy number variants (CNVs) is of critical importance due to their significant association with complex genetic diseases. Although algorithms that use whole genome sequencing (WGS) data provide sta-ble results with mostly-valid statistical assumptions, copy number detection on whole exome sequencing (WES) data shows comparatively lower accuracy. This is unfortunate as WES data is cost efficient, compact and is relatively ubiquitous. The bottleneck is primarily due to non-contiguous nature of the targeted capture: biases in targeted genomic hybridization, GC content, targeting probes, and sam-ple batching during sequencing. Here, we present a novel deep learning model, DECoNT, which uses the matched WES and WGS data and learns to correct the copy number variations reported by any off-the-shelf WES-based germline CNV caller. We train DECoNT on the 1000 Genomes Project data, and we show that we can efficiently triple the duplication call precision and double the deletion call precision of the state-of-the-art algorithms. We also show that our model con-sistently improves the performance independent from (i) sequencing technology,(ii) exome capture kit and (iii) CNV caller. Using DECoNT as a universal exome CNV call polisher has the potential to improve the reliability of germline CNV detection on WES data sets.en_US
dc.description.statementofresponsibilityby Furkan Özdenen_US
dc.format.extentxiii, 44 leaves : illustrations ; 30 cm.en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCopy number variationen_US
dc.subjectWhole exome sequencingen_US
dc.subjectDeep learningen_US
dc.titlePolishing copy number variant calls on exome sequencing data VIA deep learningen_US
dc.title.alternativeDerin öğrenme ile ekzom dizileme verilerinde gen kopya sayısı analizlerinin geliştirilmesien_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB130114


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