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- W2412948894 abstract "In the midst of the important genomic variants associated to the susceptibility and resistance to complex diseases, Copy Number Variations (CNV) has emerged as a prevalent class of structural variation. Following the flood of next-generation sequencing data, numerous tools publicly available have been developed to provide computational strategies to identify CNV at improved accuracy. This review goes beyond scrutinizing the main approaches widely used for structural variants detection in general, including Split-Read, Paired-End Mapping, Read-Depth, and Assembly-based. In this paper, (1) we characterize the relevant technical details around the detection of CNV, which can affect the estimation of breakpoints and number of copies, (2) we pinpoint the most important insights related to GC-content and mappability biases, and (3) we discuss the paramount caveats in the tools evaluation process. The points brought out in this study emphasize common assumptions, a variety of possible limitations, valuable insights, and directions for desirable contributions to the state-of-the-art in CNV detection tools." @default.
- W2412948894 created "2016-06-24" @default.
- W2412948894 creator A5017592309 @default.
- W2412948894 creator A5049869228 @default.
- W2412948894 date "2017-11-01" @default.
- W2412948894 modified "2023-09-27" @default.
- W2412948894 title "Copy Number Variations Detection: Unravelling the Problem in Tangible Aspects" @default.
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- W2412948894 doi "https://doi.org/10.1109/tcbb.2016.2576441" @default.
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