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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Copy Number Variations Detection: Unravelling the Problem in Tangible Aspects
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Copy Number Variations Detection: Unravelling the Problem in Tangible Aspects

机译：拷贝数变化检测：从有形方面揭示问题

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摘要

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.
机译：在与易感性和对复杂疾病的抵抗力相关的重要基因组变异中，拷贝数变异（CNV）已成为结构变异的普遍类别。随着下一代测序数据的泛滥，已经开发出许多公共可用的工具，以提供计算策略来以更高的准确性识别CNV。这篇综述不仅详细研究了广泛用于结构变异检测的主要方法，包括拆分阅读，配对末端映射，阅读深度和基于装配的方法。在本文中，（1）我们描述了有关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）我们找出与GC含量和可映射性偏差有关的最重要见解，以及（3）我们讨论了工具评估过程中最重要的警告。这项研究提出的要点强调了共同的假设，各种可能的局限性，宝贵的见识以及为CNV检测工具的最新发展做出可取的贡献的方向。

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