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HiNT:一种基于 Hi-C 数据检测拷贝数变异和易位的计算方法。

HiNT: a computational method for detecting copy number variations and translocations from Hi-C data.

机构信息

Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

出版信息

Genome Biol. 2020 Mar 23;21(1):73. doi: 10.1186/s13059-020-01986-5.

Abstract

The three-dimensional conformation of a genome can be profiled using Hi-C, a technique that combines chromatin conformation capture with high-throughput sequencing. However, structural variations often yield features that can be mistaken for chromosomal interactions. Here, we describe a computational method HiNT (Hi-C for copy Number variation and Translocation detection), which detects copy number variations and interchromosomal translocations within Hi-C data with breakpoints at single base-pair resolution. We demonstrate that HiNT outperforms existing methods on both simulated and real data. We also show that Hi-C can supplement whole-genome sequencing in structure variant detection by locating breakpoints in repetitive regions.

摘要

使用 Hi-C 可以对基因组的三维构象进行分析,该技术将染色质构象捕获与高通量测序相结合。然而,结构变异通常会产生可能被误认为是染色体相互作用的特征。在这里,我们描述了一种计算方法 HiNT(用于拷贝数变异和易位检测的 Hi-C),它可以在 Hi-C 数据中以单碱基分辨率检测到断点处的拷贝数变异和染色体间易位。我们证明,HiNT 在模拟和真实数据上的表现均优于现有方法。我们还表明,通过在重复区域定位断点,Hi-C 可以补充全基因组测序在结构变异检测中的作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cfb/7087379/f4650a283874/13059_2020_1986_Fig1_HTML.jpg

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