The run time of different methods.

Although various methods have been developed to detect structural variations (SVs) in genomic sequences, few are used to validate these results. Several commonly used SV callers produce many false positive SVs, and existing validation methods are not accurate enough. Therefore, a highly efficient and accurate validation method is essential. In response, we propose SVvalidation—a new method that uses long-read sequencing data for validating SVs with higher accuracy and efficiency. Compared to existing methods, SVvalidation performs better in validating SVs in repeat regions and can determine the homozygosity or heterozygosity of an SV. Additionally, SVvalidation offers the highest recall, precision, and F1-score (improving by 7-16%) across all datasets. Moreover, SVvalidation is suitable for different types of SVs. The program is available at https://github.com/nwpuzhengyan/SVvalidation.

尽管学界已开发出多种用于检测基因组序列中结构变异（structural variations, SVs）的方法，但鲜有方法可用于验证这些检测结果。多款常用的SV调用工具（SV caller）会生成大量假阳性结构变异，而现有验证方法的准确性仍存在不足。因此，开发高效且准确的结构变异验证方法显得尤为必要。为此，我们提出了SVvalidation——一种基于长读长测序数据、可实现高精度与高效率结构变异验证的新方法。相较于现有方法，SVvalidation在重复区域的结构变异验证中表现更出色，且可判定结构变异的纯合性与杂合性。此外，在所有测试数据集上，SVvalidation均能取得最高的召回率、精确率与F1分数，性能提升幅度达7%至16%。同时，SVvalidation适用于多种类型的结构变异。该工具的开源代码可通过https://github.com/nwpuzhengyan/SVvalidation获取。