贝叶斯选方法用于估计新生的适应性突变的适应性效应
bioRxiv : the preprint server for biology
|April 16, 2025
概括
我们开发了BASIL,这是一种分析条形码谱系跟踪数据的新方法,以准确估计有益的突变适应效应. 巴西尔克服了现有方法的局限性,改善了我们对适应动态的理解.
科学领域:
- 进化生物学 进化生物学
- 人口遗传学 人口遗传学
- 分子生物学分子生物学
背景情况:
- 有益突变的适应性效应 (DFE) 的分布对于理解适应至关重要.
- 条形码谱系追踪 (BLT) 允许追踪数百万个谱系以检测新的突变.
- 目前分析BLT数据的方法可能会受到噪音和竞争的影响.
研究的目的:
- 开发一种可靠的方法来分析BLT数据,以准确推断有益突变的适应性影响.
- 为了解决现有的BLT数据分析方法中的偏差.
- 改进从实验进化研究中推断DFE的推理.
主要方法:
- 开发了BASIL (BAyesian Selection Inference for Lineage tracking data),一种用于分析BLT数据的新贝叶斯方法.
- 动态更新基于条形码读数的谱系适应性和大小.
- 使用新的实验数据校准噪声模型,揭示了噪声变异与谱系丰富性的非线性缩放.
主要成果:
- 在模拟和发布的BLT数据集上,BASIL表现出了稳定性和准确性.
- 确定了BLT实验中的噪声变异与谱系丰富性非线性扩展.
- 描述了采样频率和读取覆盖范围对检测适应系的功率的影响.
结论:
- BASIL提供了一个更准确,更可靠的方法来从BLT实验中推断DFE.
- 该方法增强了研究大种群适应动态的能力.
- 这项工作有助于在多种不同的实验设置中系统地推断突变适应效应.
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