ClipSV: improving structural variation detection by read extension, spliced alignment and tree-based decision rules.
ClipSV: improving structural variation detection by read extension, spliced alignment and tree-based decision rules.
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DOI：
10.1093/nargab/lqab003
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发表时间：
2021-03
影响因子：
4.6
通讯作者：
Chong Z
中科院分区：
文献类型：
--
作者：
Xu P;Chen Y;Gao M;Chong Z
结构变异 (SV) 由 50 到数百万个碱基对的基因组变异组成，对人类疾病、复杂性状和进化具有相当大的影响。准确检测SV是表征个体基因组特征的基本步骤。目前，已经提出了几种使用下一代测序（NGS）平台检测SV的方法。然而，由于测序reads长度短和SV内容复杂，SV检测工具仍然受到灵敏度低的限制，特别是对于插入检测。在这项研究中，我们开发了一种新工具 ClipSV 来改进 SV 发现。 ClipSV利用读长扩展和拼接比对方法来克服读长的限制。通过从 SV 相关的短读段重建长序列，ClipSV 可以从长序列比对中发现缺失和短插入。为了全面表征插入，ClipSV 实现了基于树的决策规则，可以有效地利用包含 SV 的读取。基于模拟和真实测序数据的评估，与当前流行的工具相比，ClipSV 表现出整体更好的性能，特别是在插入检测方面。由于 NGS 平台代表了常规基因组应用的主流测序能力，我们预计 ClipSV 将成为未来基因组研究中 SV 表征的重要工具。
Structural variation (SV), which consists of genomic variation from 50 to millions of base pairs, confers considerable impacts on human diseases, complex traits and evolution. Accurately detecting SV is a fundamental step to characterize the features of individual genomes. Currently, several methods have been proposed to detect SVs using the next-generation sequencing (NGS) platform. However, due to the short length of sequencing reads and the complexity of SV content, the SV-detecting tools are still limited by low sensitivity, especially for insertion detection. In this study, we developed a novel tool, ClipSV, to improve SV discovery. ClipSV utilizes a read extension and spliced alignment approach to overcoming the limitation of read length. By reconstructing long sequences from SV-associated short reads, ClipSV discovers deletions and short insertions from the long sequence alignments. To comprehensively characterize insertions, ClipSV implements tree-based decision rules that can efficiently utilize SV-containing reads. Based on the evaluations of both simulated and real sequencing data, ClipSV exhibited an overall better performance compared to currently popular tools, especially for insertion detection. As NGS platform represents the mainstream sequencing capacity for routine genomic applications, we anticipate ClipSV will serve as an important tool for SV characterization in future genomic studies.
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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
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Eichler EE
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Beroukhim R
影响因子：
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Sudmant PH;Rausch T;Gardner EJ;Handsaker RE;Abyzov A;Huddleston J;Zhang Y;Ye K;Jun G;Fritz MH;Konkel MK;Malhotra A;Stütz AM;Shi X;Casale FP;Chen J;Hormozdiari F;Dayama G;Chen K;Malig M;Chaisson MJP;Walter K;Meiers S;Kashin S;Garrison E;Auton A;Lam HYK;Mu XJ;Alkan C;Antaki D;Bae T;Cerveira E;Chines P;Chong Z;Clarke L;Dal E;Ding L;Emery S;Fan X;Gujral M;Kahveci F;Kidd JM;Kong Y;Lameijer EW;McCarthy S;Flicek P;Gibbs RA;Marth G;Mason CE;Menelaou A;Muzny DM;Nelson BJ;Noor A;Parrish NF;Pendleton M;Quitadamo A;Raeder B;Schadt EE;Romanovitch M;Schlattl A;Sebra R;Shabalin AA;Untergasser A;Walker JA;Wang M;Yu F;Zhang C;Zhang J;Zheng-Bradley X;Zhou W;Zichner T;Sebat J;Batzer MA;McCarroll SA;1000 Genomes Project Consortium;Mills RE;Gerstein MB;Bashir A;Stegle O;Devine SE;Lee C;Eichler EE;Korbel JO
通讯作者：
Korbel JO
影响因子：
30.8
作者：
Iafrate, AJ;Feuk, L;Lee, C
通讯作者：
Lee, C
影响因子：
46.9
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Salit M