Copy Number Variations Detection: Unravelling the Problem in Tangible Aspects
Copy Number Variations Detection: Unravelling the Problem in Tangible Aspects
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DOI：
10.1109/tcbb.2016.2576441
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发表时间：
2017-11-01
影响因子：
4.5
通讯作者：
Guimaraes, Katia S.
中科院分区：
文献类型：
--
作者：
do Nascimento, Francisco, Jr.;Guimaraes, Katia S.
在与复杂疾病的易感性和抗性相关的重要基因组变异中，拷贝数变异（CNV）已成为一类普遍的结构变异。随着下一代测序数据的大量涌现，已经开发了许多公开可用的工具来提供计算策略，以提高准确性来鉴定CNV。本文综述了广泛用于结构变异检测的主要方法，包括Split-Read，Paired-End Mapping，Read-Depth和Assembly-based。在本文中，（1）我们描述了CNV检测的相关技术细节，这可能会影响断点和拷贝数的估计，（2）我们指出了与GC含量和可映射性偏差相关的最重要的见解，（3）我们讨论了工具评估过程中的最重要的注意事项。在这项研究中提出的观点强调共同的假设，各种可能的局限性，有价值的见解，并在CNV检测工具的国家的最先进的理想贡献的方向。
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.