Deciphering the exact breakpoints of structural variations using long sequencing reads with DeBreak.
Deciphering the exact breakpoints of structural variations using long sequencing reads with DeBreak.
复制标题
DOI：
10.1038/s41467-023-35996-1
复制
发表时间：
2023-01-17
影响因子：
16.6
通讯作者：
Chong, Zechen
中科院分区：
文献类型：
--
作者：
Chen, Yu;Wang, Amy Y.;Barkley, Courtney A.;Zhang, Yixin;Zhao, Xinyang;Gao, Min;Edmonds, Mick D.;Chong, Zechen
长读段测序已经证明了表征所有类型的结构变异（SV）的巨大潜力。然而，现有算法的灵敏度和精度不足。为了解决这些局限性，我们提出了DeBreak，一种全面而准确的SV发现的计算方法。基于比对结果，DeBreak采用基于密度的方法来聚类SV候选物，并采用局部从头组装方法来重建长插入。偏序比对算法确保了具有单碱基对分辨率的精确SV断点，并且k均值聚类方法可以报告多等位基因SV事件。DeBreak在来自PacBio和Nanopore平台的模拟和真实的长读段测序数据上的表现优于现有工具。DeBreak的一个重要应用是分析癌症基因组中潜在的肿瘤驱动SV。DeBreak也可用于补充基于全基因组组装的SV发现。长读段测序是一种有前途的结构变异检测方法，它需要高灵敏度和高精度的算法。在这里，作者开发了DeBreak，这是一种在不同平台的长读序测序数据中进行全面准确的SV检测的算法，其性能优于其他SV调用程序。
Long-read sequencing has demonstrated great potential for characterizing all types of structural variations (SVs). However, existing algorithms have insufficient sensitivity and precision. To address these limitations, we present DeBreak, a computational method for comprehensive and accurate SV discovery. Based on alignment results, DeBreak employs a density-based approach for clustering SV candidates together with a local de novo assembly approach for reconstructing long insertions. A partial order alignment algorithm ensures precise SV breakpoints with single base-pair resolution, and a k-means clustering method can report multi-allele SV events. DeBreak outperforms existing tools on both simulated and real long-read sequencing data from both PacBio and Nanopore platforms. An important application of DeBreak is analyzing cancer genomes for potentially tumor-driving SVs. DeBreak can also be used for supplementing whole-genome assembly-based SV discovery. Long-read sequencing is promising for the detection of structural variants (SVs), which requires algorithms with high sensitivity and precision. Here, the authors develop DeBreak, an algorithm for comprehensive and accurate SV detection in long-read sequencing data across different platforms, which outperforms other SV callers.
登录
查看更多内容
影响因子：
7
作者：
Huddleston J;Chaisson MJP;Steinberg KM;Warren W;Hoekzema K;Gordon D;Graves-Lindsay TA;Munson KM;Kronenberg ZN;Vives L;Peluso P;Boitano M;Chin CS;Korlach J;Wilson RK;Eichler EE
通讯作者：
Eichler EE
DOI：
10.1093/bioinformatics/btx346
发表时间：
2017-10-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Gel B;Serra E
通讯作者：
Serra E
影响因子：
168.9
作者：
Chartier-Harlin, MC;Kachergus, J;Destée, A
通讯作者：
Destée, A
DOI：
10.1038/nrg.2015.25
发表时间：
2016-04
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
Carvalho CM;Lupski JR
通讯作者：
Lupski JR
影响因子：
5.8
作者：
Li, Heng
通讯作者：
Li, Heng