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      Polishing copy number variant calls on exome sequencing data via deep learning

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      Author(s)
      Özden, Furkan
      Alkan, Can
      Çiçek, A. Ercüment
      Date
      2022-06-13
      Source Title
      Genome research
      Print ISSN
      1088-9051
      Electronic ISSN
      1549-5469
      Publisher
      NLM (Medline)
      Volume
      32
      Issue
      6
      Pages
      1170 - 1182
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      Accurate and efficient detection of copy number variants (CNVs) is of critical importance owing to their significant association with complex genetic diseases. Although algorithms that use whole-genome sequencing (WGS) data provide stable 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 are cost-efficient, compact, and relatively ubiquitous. The bottleneck is primarily due to the noncontiguous nature of the targeted capture: biases in targeted genomic hybridization, GC content, targeting probes, and sample 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 consistently improves the performance independent of (1) sequencing technology, (2) exome capture kit, and (3) 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. © 2022 Özden et al.; Published by Cold Spring Harbor Laboratory Press.
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      http://hdl.handle.net/11693/111856
      Published Version (Please cite this version)
      https://dx.doi.org/10.1101/gr.274845.120
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