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

IF 4.6 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

iScience Pub Date : 2025-02-08 DOI:10.1016/j.isci.2025.111983

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

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Abstract

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

\times

depth. Our study provides systematic guidance for an optimal SV calling pipeline in pigs and other farm animals.

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利用不同的算法评估各种结构变异（SV）检测相关程序已成为人类和农场动物的迫切需要。为此，我们利用短线程和长线程 DNA 测序平台对 5 个不同基因的猪个体（品种）进行了测序。我们为每个品种创建了 SV 基准集，并评估了 16 种 SV 调用相关工具的性能。结果表明，长读取平台能以相似的精度检测出许多被短读取平台遗漏的 SV。基准 SV（主要是 200-500 bp 插入/缺失）的验证率很高。基于汇编的 SV 调用程序 SVIM-asm 在检测性能和资源消耗方面都表现优异。具有更多支持读数、大小在 1 kb 以下、在简单重复区域之外、在低 GC 含量和同源性区域运行的 SV 具有更高的检测准确率。基于比对的工具即使在 5 × 深度时也表现良好。我们的研究为猪和其他农场动物的最佳 SV 调用管道提供了系统指导。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

iScience Multidisciplinary-Multidisciplinary

CiteScore

7.20

自引率

1.70%

发文量

1972

审稿时长

6 weeks

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