Abstract Markov models of character substitution on phylogenies form the foundation of phylogenetic inference frameworks. Early models made the simplifying assumption that the substitution process is homogeneous over time and across sites in the molecular sequence alignment. While standard practice adopts extensions that accommodate heterogeneity of substitution rates across sites, heterogeneity in the process over time in a site-specific manner remains frequently overlooked. This is problematic, as evolutionary processes that act at the molecular level are highly variable, subjecting different sites to different selective constraints over time, impacting their substitution behavior. We propose incorporating time variability through Markov-modulated models (MMMs), which extend covarion-like models and allow the substitution process (including relative character exchange rates as well as the overall substitution rate) at individual sites to vary across lineages. We implement a general MMM framework in BEAST, a popular Bayesian phylogenetic inference software package, allowing researchers to compose a wide range of MMMs through flexible XML specification. Using examples from bacterial, viral, and plastid genome evolution, we show that MMMs impact phylogenetic tree estimation and can substantially improve model fit compared to standard substitution models. Through simulations, we show that marginal likelihood estimation accurately identifies the generative model and does not systematically prefer the more parameter-rich MMMs. To mitigate the increased computational demands associated with MMMs, our implementation exploits recent developments in BEAGLE, a high-performance computational library for phylogenetic inference. [Bayesian inference; BEAGLE; BEAST; covarion, heterotachy; Markov-modulated models; phylogenetics.]

中文翻译：

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用马尔可夫调制的连续时间马尔可夫链来识别 BEAST 中特定于站点和分支的进化变异

摘要 系统发育特征替换的马尔可夫模型构成了系统发育推理框架的基础。早期的模型做了一个简单的假设，即替换过程随着时间的推移和分子序列比对中的位点是同质的。虽然标准做法采用了适应不同地点替代率异质性的扩展，但随着时间的推移，以特定地点的方式过程中的异质性仍然经常被忽视。这是有问题的，因为在分子水平上起作用的进化过程是高度可变的，随着时间的推移使不同的位点受到不同的选择约束，影响它们的替代行为。我们建议通过马尔可夫调制模型 (MMM) 结合时间可变性，它扩展了类似协变的模型，并允许各个站点的替换过程（包括相对字符交换率以及整体替换率）在不同谱系之间变化。我们在 BEAST 中实现了一个通用的 MMM 框架，BEAST 是一种流行的贝叶斯系统发育推理软件包，允许研究人员通过灵活的 XML 规范来组成范围广泛的 MMM。使用来自细菌、病毒和质体基因组进化的示例，我们表明 MMM 会影响系统发育树估计，并且与标准替代模型相比，可以显着改善模型拟合。通过模拟，我们表明边际似然估计准确地识别了生成模型，并且不会系统地偏爱参数更丰富的 MMM。为了减轻与 MMM 相关的计算需求增加，我们的实现利用了 BEAGLE 的最新发展，这是一个用于系统发育推理的高性能计算库。[贝叶斯推理；比格犬；兽; covariion，heterochy; 马尔可夫调制模型；系统发育学。]