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在法国牛的长短阅读数据集中全面检测结构变异
Maulana Mughitz Naji1, Christophe Klopp2, Camille Eché3
1Université Paris Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy en Josas, France. maulana-mughitz.naji@inrae.fr.
Scientific reports
|November 19, 2025
概括
这项研究通过使用长读 (LR) 和短读 (SR) 全基因组测序数据,增强了法国牛的结构变异 (SV) 检测. 它建立了一个全面的SV参考小组,以改善牛群的基因组分析.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 动物遗传学动物遗传学
背景情况:
- 结构变异 (SV) 大于50bp是关键的基因组特征.
- 长读数 (LR) 测序越来越受青,而不是短读数 (SR) 测序,以准确检测SV.
- 了解牛中的SVs对于育种和遗传改进至关重要.
研究的目的:
- 对14种法国牛品种的SV进行深入分析,特别是大型插入和删除.
- 使用LR和SR全基因组测序 (WGS) 数据评估各种SV检测和基因型化工具的性能.
- 为牛群构建一个最佳的 SV 参考面板.
主要方法:
- 使用不同LR技术 (PacBio HiFi,Oxford ONT,PacBio CLR) 的SV检测工具 (CUTESV,PBSV,SNIFFLES) 的比较分析.
- 基于SR的SV检测 (DELLY,LUMPY,MANTA) 和基因定型 (GRAPHTYPER,SVTYPER,PARAGRAPH,VG工具包) 工具的评估.
- 使用SR数据评估参考小组组成对SV基因型准确性的影响.
- 开发一个最终的SV参考面板,其中有25,191次删除和30118次插入.
主要成果:
- 在不同的LR测序技术中调查了SV检测工具性能方面的偏差.
- 该研究使用SR数据确定了用于SV检测和基因定型的有效工具.
- 构建了一个最佳的SV参考面板,显著改善了SV基因类型.
- 在14个品种的571个个体中对隔离SVs的景观进行了表征.
结论:
- 这项研究提供了一个强大的框架,用于使用WGS数据在牛群中检测和基因定型.
- 开发的SV参考小组是牛基因组研究和应用的宝贵资源.
- 这项工作促进了对法国牛品种结构变化的理解.
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