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对全基因组序列数据结构变异检测工具的全面评估和指导

Cheng Ma^1,2, Xian Shi^1,3, Xuzhen Li^4,5

¹Key Laboratory of Genetic Evolution & Animal Models and Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.

|October 16, 2024

概括

使用真实数据对鸟类基因组的结构变异 (SV) 调用者进行评估至关重要. 性能因工具和SV类型而异,有些调用器在特定变化和读取深度影响准确度方面表现出色.

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.
进行比较基因组学.

背景情况:

结构变异 (SVs) 显著影响基因组进化,疾病和表型多样性.
现有的SV调用器,通常是针对人类基因组进行优化,由于基因组差异,需要对鸟类物种进行验证.
使用真实数据和验证的SV来严格评估SV呼叫者对于准确的基因组分析至关重要.

研究的目的:

综合评估十种广泛使用的SV调用器在鸟类种群基因组数据上的性能.
评估不同类型和大小的SV调用者的准确性,包括插入,删除和副本数量的变化.
为在鸟类研究中检测SV提供实用指导.

主要方法:

使用人口层面的真实基因组数据对10名SV呼叫者的绩效评估.
使用验证的常见类型的VS进行基准测试.
分析了基于SV类型,大小和所需的读取深度的SV检测准确性.

主要成果:

在不同类型和大小的SV中,SV呼叫者的性能差异很大.
GRIDSS,Lumpy,Wham和Manta表现出卓越的检测准确度;Pindel在小SV中表现出色;CNVnator和CNVkit发现了中大副本数量的变化.
由于一致性不佳,不建议使用组合调用策略;对于80%以上的SV检测,需要高读取深度 (≥50×);对所有工具来说,插入检测,特别是>150bp,是具有挑战性的.

结论:

强调用真实测序数据和验证的SV来评估SV调用者的关键需求,而不仅仅是模拟数据.
突出了SV呼叫者的差异性表现,指导特定鸟类基因组研究的工具选择.
为优化鸟类研究中的SV检测策略提供了实际建议.

关键词:

副本数量变化的变化人口基因组序列的排序阅读深度阅读深度在SV呼叫者SV呼叫者结构变化的结构变化.在WGS中,使用的是WGS.

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