标题： 使用分支特异性替换模型和收缩先验检测进化变化点 显示英文标题

标题： Detecting Evolutionary Change-Points with Branch-Specific Substitution Models and Shrinkage Priors

摘要： 分支特异性替换模型被广泛用于检测进化变化点，例如选择压力的转变。 然而，应用此类模型通常需要先验知识来确定系统发育树上的变化点位置，或者在处理大规模数据集时面临可扩展性问题。 为了解决这两个限制，我们将分支特异性替换模型与收缩先验结合，以无需先验知识自动识别变化点，同时为每个分支估计不同的替换参数。 为了在这种高维模型下实现可行的推断，我们开发了一种针对分支特异性替换参数的解析梯度算法，其计算时间与参数数量成线性关系。 我们将该梯度算法应用于灵长类动物BRCA1基因进化中的选择压力动态推断，以及近期mpox疫情中病毒序列的突变动态分析。 我们的新算法提高了推断效率，在最大似然优化中，与中心差分数值梯度方法相比，每次迭代的速度提升了高达90倍；在贝叶斯框架内，与传统单变量随机游走采样器相比，使用哈密顿蒙特卡洛采样器的计算性能提升了高达360倍。 显示英文摘要

摘要： Branch-specific substitution models are popular for detecting evolutionary change-points, such as shifts in selective pressure. However, applying such models typically requires prior knowledge of change-point locations on the phylogeny or faces scalability issues with large data sets. To address both limitations, we integrate branch-specific substitution models with shrinkage priors to automatically identify change-points without prior knowledge, while simultaneously estimating distinct substitution parameters for each branch. To enable tractable inference under this high-dimensional model, we develop an analytical gradient algorithm for the branch-specific substitution parameters where the computation time is linear in the number of parameters. We apply this gradient algorithm to infer selection pressure dynamics in the evolution of the BRCA1 gene in primates and mutational dynamics in viral sequences from the recent mpox epidemic. Our novel algorithm enhances inference efficiency, achieving up to a 90-fold speedup per iteration in maximum-likelihood optimization when compared to central difference numerical gradient method and up to a 360-fold improvement in computational performance within a Bayesian framework using Hamiltonian Monte Carlo sampler compared to conventional univariate random walk sampler.