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树木搜索数量对最大概率推理在族群遗传学中的影响
Chao Liu1,2, Xiaofan Zhou3, Yuanning Li4,5
1Department of Plant Protection, Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Zhejiang University, Hangzhou 310058, China.
Systematic biology
|June 28, 2024
概括
进行多棵树搜索显著提高了最大概率的基因推断. 更多的搜索增加了找到最佳基因树拓的机会,特别是对于不那么复杂的对齐.
科学领域:
- 人类基因组学是什么?
- 计算生物学 计算生物学
- 进化生物学 进化生物学
背景情况:
- 最大概率 (ML) 遗传学推断是遗传学的一个基石.
- 启发式搜索通常会产生低于最佳树的结果,因此需要多个树搜索.
- 多重树搜索对ML推断的确切影响尚未完全理解.
研究的目的:
- 调查ML基因树推断的不同起始树的有效性.
- 确定树查找的数量如何影响ML树推理准确度.
- 评估搜索号对连锁和四重奏基物种树的影响.
主要方法:
- 在ML程序 (IQ-TREE,RAxML-NG,PhyML) 中比较随机,BioNJ和的起始树.
- 从各种类基因组数据集中对19,414个基因对齐进行了100个树查找.
- 根据搜索号码分析了ML基因树,连锁树和ASTRAL物种树的拓差异.
主要成果:
- 生物NJ和储蓄性启动树的性能优于随机启动;IQ-TREE对启动树的敏感性低于RAxML-NG和PhyML.
- 树搜索的数量大大影响了最好的ML基因树拓的恢复.
- 连锁树在≥10次搜索时变得拓相同;在某些数据集中,ASTRAL树在<100次搜索时显示出差异.
- 模拟表明,更容易的基因对齐更有可能产生最好的ML拓.
结论:
- 树搜索的数量是实现强大的ML遗传学推断的关键因素.
- 建议对连接分析进行至少10次搜索,以确保拓稳定性.
- 对于基于四重奏的方法,如ASTRAL,可能需要超过80次搜索才能匹配100次搜索的结果.
- 基因对齐困难影响了找到最佳ML拓学的成功率.
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