Deletion Detection Method Using the Distribution of Insert Size and a Precise Alignment Strategy
Deletion Detection Method Using the Distribution of Insert Size and a Precise Alignment Strategy
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利用插入片段大小分布和精确比对策略的缺失检测方法
DOI：
10.1109/tcbb.2019.2934407
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发表时间：
2019-08
期刊：
影响因子：
--
通讯作者：
Jianxin Wang
中科院分区：
文献类型：
--
作者：
Zhen Zhang;Junwei Luo;Juan Shang;Mi Li;Fang-xiang Wu;Yi Pan;Jianxin Wang
纯合缺失和杂合缺失在人类基因组中普遍存在。对于当前的结构变异检测工具而言，确定一个缺失是纯合的还是杂合的具有重要意义。然而，测序错误、微（此处似乎文本不完整）
Homozygous and heterozygous deletions commonly exist in the human genome. For current structural variation detection tools, it is significant to determine whether a deletion is homozygous or heterozygous. However, the problems of sequencing errors, micro-homologies, and micro-insertions prohibit common alignment tools from identifying accurate breakpoint locations, and often result in detecting false structural variations. In this study, we present a novel deletion detection tool called Sprites2. Comparing with Sprites, Sprites2 makes the following modifications: (1) The distribution of insert size is used in Sprites2, which can identify the type of deletions and improve the accuracy of deletion calls. (2) A precise alignment method based on AGE (one algorithm simultaneously aligning 5’ and 3’ ends between two sequences) is adopted in Sprites2 to identify breakpoints, which is helpful to resolve the problems introduced by sequencing errors, micro-homologies, and micro-insertions. In order to test and verify the performance of Sprites2, some simulated and real datasets are adopted in our experiments, and Sprites2 is compared with five popular tools. The experimental results show that Sprites2 can improve the performance of deletion detection. Sprites2 can be downloaded from https://github.com/zhangzhen/sprites2.
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