FusorSV：一种用于最优组合来自多种结构变异检测方法的数据的算法。
FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods.
机构信息
The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.
出版信息
Genome Biol. 2018 Mar 20;19(1):38. doi: 10.1186/s13059-018-1404-6.
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 .
从下一代测序数据中全面准确地识别结构变异（SV）仍然是一个主要挑战。我们开发了 FusorSV，它使用数据挖掘方法来评估性能，并合并来自一组 SV 调用算法的调用集。它包括一个使用来自 1000 基因组计划的 27 个人类深度覆盖基因组的分析构建的融合模型。我们鉴定了这 27 个样本中 1000 基因组计划未报告的 843 个新的 SV 调用。对这些调用的一部分进行实验验证，得到了 86.7%的验证率。FusorSV 可在 https://github.com/TheJacksonLaboratory/SVE 获得。
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