FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods
FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods
复制标题
DOI：
10.1186/s13059-018-1404-6
复制
发表时间：
2018-03-20
期刊：
影响因子：
12.3
通讯作者：
Malhotra, Ankit
中科院分区：
文献类型：
--
作者：
Becker, Timothy;Lee, Wan-Ping;Malhotra, Ankit
从下一代测序数据中全面准确地鉴定结构变异（SV）仍然是一个重大挑战。我们开发FusorSV，它使用数据挖掘的方法来评估性能和合并调用集合从一个合奏的SV调用算法。它包括一个融合模型，该模型是通过分析来自1000个基因组计划的27个深度覆盖的人类基因组构建的。我们确定了843个新的SV调用，没有报告的1000个基因组计划，这27个样本。这些调用的一个子集的实验验证产生86.7%的验证率。FusorSV可在https://github.com/TheJacksonLaboratory/SVE上获得。
Comprehensive and accurate identification of structural variations (SVs) from next generation sequencing data remains a major challenge. We develop FusorSV, which uses a data mining approach to assess performance and merge callsets from an ensemble of SV-calling algorithms. It includes a fusion model built using analysis of 27 deep-coverage human genomes from the 1000 Genomes Project. We identify 843 novel SV calls that were not reported by the 1000 Genomes Project for these 27 samples. Experimental validation of a subset of these calls yields a validation rate of 86.7%. FusorSV is available at https://github.com/TheJacksonLaboratory/SVE.