Complex genomic structural variants (CGSVs) are abnormalities that present with three or more breakpoints, making their discovery a challenge. The majority of existing algorithms for structural variant detection are only designed to find simple structural variants (SSVs) such as deletions and inversions; they fail to find more complex events such as deletion–inversions or deletion–duplications, for example. In this study, we present an algorithm named CleanBreak that employs a clique partitioning graph-based strategy to identify collections of SSV clusters and then subsequently identifies overlapping SSV clusters to examine the search space of possible CGSVs, choosing the one that is most concordant with local read depth. We evaluated CleanBreak's performance on whole genome simulated data and a real data set from the 1000 Genomes Project. We also compared CleanBreak with another algorithm for CGSV discovery. The results demonstrate CleanBreak's utility as an effective method to discover CGSVs.

中文翻译：

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使用不一致簇归一化的复杂变体发现

复杂的基因组结构变异（CGSV）是存在三个或更多断点的异常现象，这使它们的发现成为一个挑战。现有的大多数用于结构变异检测的算法仅设计用于查找简单的结构变异（SSV），例如缺失和倒位。例如，他们无法找到更复杂的事件，例如删除-倒置或删除-重复。在这项研究中，我们提出了一种名为CleanBreak的算法，该算法采用基于派系分区图的策略来识别SSV群集的集合，然后随后识别重叠的SSV群集以检查可能的CGSV的搜索空间，选择与本地最一致的SVSV。读取深度。我们根据1000个基因组计划的全基因组模拟数据和真实数据集评估了CleanBreak的性能。我们还将CleanBreak与另一个用于CGSV发现的算法进行了比较。结果表明CleanBreak的实用程序是发现CGSV的有效方法。