序列进化的模拟:它们是多么 (不) 现实的,以及为什么
Johanna Trost1, Julia Haag2, Dimitri Höhler2
1Biometry and Evolutionary Biology Laboratory (LBBE), University Claude Bernard Lyon 1, Lyon, France.
Molecular biology and evolution
|December 21, 2023
概括
目前的模型很难模拟现实的多重序列对齐 (MSA). 机器学习可以区分模拟和实证MSA,揭示进化模型的局限性.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 进化生物学 进化生物学
背景情况:
- 序列进化的概率模型对于评估遗传学推断工具和开发用于遗传学重建的机器学习 (ML) 方法至关重要.
- 对多个序列对齐 (MSA) 的现实模拟对于评估基因组学工具在实证数据上的性能至关重要,并确保ML模型能够很好地概括.
研究的目的:
- 在各种进化模型下使用最先进的方法模拟DNA和蛋白质MSA,并没有插入/删除 (indel) 事件.
- 通过量化监督学习方法的准确性来评估模拟的MSA的现实性,将其与实证MSA区分开来.
主要方法:
- 在逐渐复杂的进化模型下使用最先进的序列模拟器模拟DNA和蛋白质MSA.
- 应用两个不同的,独立开发的监督学习分类方法来区分模拟与实证MSAs.
主要成果:
- 通过使用这两种分类方法,在所有测试的进化模型中,在实证和模拟的MSA之间进行区分的准确性很高.
- 研究结果表明,当前的进化模型不足以捕捉实证MSA的关键特征,例如地点智能的进化速率和核酸/氨基酸组成.
结论:
- 现有的序列演变概率模型不能完全复制经验序列数据的复杂性.
- 监督学习方法可以有效地识别模拟和实证MSA之间的微妙差异,突出突出进化建模的改进领域.
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