VALOR2: characterization of large-scale structural variants using linked-reads
VALOR2: characterization of large-scale structural variants using linked-reads
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DOI：
10.1186/s13059-020-01975-8
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发表时间：
2020-03-19
期刊：
影响因子：
12.3
通讯作者：
Alkan, Can
中科院分区：
文献类型：
--
作者：
Karaoglanoglu, Fatih;Ricketts, Camir;Alkan, Can
大多数现有的用于结构变异检测的方法集中于缺失、插入和移动的元件的发现和基因分型。检测没有基因组区段的获得或损失的平衡结构变体，例如倒位和易位，是一项特别具有挑战性的任务。此外，有很少的算法来预测插入位点的大散布节段重复和表征易位。在这里，我们提出了新的算法，使用连接读段测序数据来表征大型散布的片段重复、倒位、缺失和易位。我们重新设计了以前的算法VALOR，并在一个名为VALOR 2的新软件包中实现了我们的新算法。
Most existing methods for structural variant detection focus on discovery and genotyping of deletions, insertions, and mobile elements. Detection of balanced structural variants with no gain or loss of genomic segments, for example, inversions and translocations, is a particularly challenging task. Furthermore, there are very few algorithms to predict the insertion locus of large interspersed segmental duplications and characterize translocations. Here, we propose novel algorithms to characterize large interspersed segmental duplications, inversions, deletions, and translocations using linked-read sequencing data. We redesign our earlier algorithm, VALOR, and implement our new algorithms in a new software package, called VALOR2.