数字农科院2.0

Systematic benchmarking of tools for structural variation detection using short- and long-read sequencing data in pigs

文献类型： 外文期刊

作者： Sang He;Bangmin Song;Yueting Tang;Xiaolu Qu;Xingzheng Li;Xintong Yang;Qi Bao;Lingzhao Fang;Jicai Jiang;Zhonglin Tang;Guoqiang Yi

作者机构：

关键词： （1-1-1）Bioinformatics;Omics;Structural biology

期刊名称： iScience

ISSN： 2589-0042

年卷期： 2025 年 28 卷 3 期

页码：

收录情况： SCIE（2025版）

摘要： Evaluating diverse structural variation (SV) detection-relevant programs leveraging different algorithms has become a pressing need in humans and farm animals. We addressed this by sequencing five genetically diverse pig individuals (breeds) with short- and long-read DNA-sequencing platforms. We created the SV benchmark set for each breed and assessed the performance of 16 SV calling-relevant tools. Results showed that long-read platforms enabled detecting many SVs missed by short-read platforms with similar precision. Benchmark SVs, mainly 200–500 bp insertions/deletions, had high validation rates. The assembly-based SV calling program SVIM-asm showed superior detection performance and resource consumption. The SVs with more supporting reads, sizes under 1 kb, outside simple repeat area, in low GC content and runs of homozygosity regions, had higher detection accuracy. Alignment-based tools performed well even at 5 × depth. Our study provides systematic guidance for an optimal SV calling pipeline in pigs and other farm animals.

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