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在全基因组测序中,由于自身多重合体,重复元素和重复,存在广泛的异构性异构性模式
Xavier Dallaire1,2, Raphael Bouchard1,3, Philippe Hénault1,3
1Institut de biologie intégrative et des systèmes, Université Laval, Québec, Canada.
Genome biology and evolution
|December 12, 2023
概括
偏差单核酸多态 (SNP) 在全基因组测序数据中很常见,特别是在重复区域. 排除这些SNP可以改善种群基因组分析和参数估计.
科学领域:
- 人口基因组学 人口基因组学
- 基因组复杂性 基因组复杂性
- 生物信息学是一种生物信息学.
背景情况:
- 准确的单核酸多态化 (SNP) 调用对于种群基因组工具至关重要.
- 基因组复杂性来自结构变异,对应物和重复的挑战,参考基因组组装和SNP调用.
- 短读重序可以导致错误映射和偏差的SNP,违反孟德尔假设.
研究的目的:
- 在四种物种的全基因组测序 (WGS) 数据中识别偏差SNP.
- 评估异常SNP的流行率和基因组分布.
- 评估偏差SNP对人口遗传参数估计的影响.
主要方法:
- 使用ngsParalog软件来识别偏差的SNP.
- 分析了WGS数据,对北极,湖白鱼,大西洋和美国的覆盖范围低至中等 (1.5×4.8×).
- 研究了偏差SNP与重复元素,重复化区域和覆盖峰值的关联.
主要成果:
- 偏差SNP占鱼类SNP的22%~62%和美国鱼SNP的11%.
- 偏差的SNP集中在重复元素和重复复合的区域.
- 覆盖率高峰,通常与转位子和并列重复相关,包括大多数偏差的SNP.
- 包括偏差的SNP扭曲了位点频谱,低估了FST,高估了核酸多样性.
结论:
- 偏差SNP广泛存在,并源于各种基因组特征.
- 排除偏差SNP对于准确的种群基因组推断至关重要.
- ngsParalog工具提供了一种有效的方法,用于识别跨多种类型和测序深度的异常SNP.
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