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Log Transformation Improves Dating of Phylogenies

机译:对数转换可改善系统发育历程

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The level of divergence between species represented by sequence data is a function of unknown time and mutation rates. Therefore, sequence data do not reveal exact timing of evolutionary events, and inferred phylogenies often have branch lengths estimated in the unit of the expected number of substitutions. Dating a phylogeny is the process of translating branch lengths from this unit to time unit. Such a process requires soft or hard constraints for the timing of some nodes and infers the divergence times of the remaining nodes. Dating is crucial for understanding evolutionary processes [2] and is necessary in many downstream applications of phylogenetics and phylodynamics. Many methods have been developed for phylogenetic dating, but none is universally accepted [3,5]. While some of these methods assume a parametric model for mutation rates and use maximum likelihood or Bayesian inference (e.g., [1,4,8,9]), other non-parametric methods rely on optimization problems based on assumed properties of the distribution of the rates (e.g., [4,6-8]). Parametric methods tend to work well for correct models but can be sensitive to model misspecification.
机译:序列数据代表的物种之间的差异水平是未知时间和突变率的函数。因此,序列数据不能揭示进化事件的确切时间,并且推断的系统发育通常具有以预期取代数为单位估算的分支长度。建立系统发育的日期是将分支长度从该单位转换为时间单位的过程。这种过程需要对某些节点的时序进行软约束或硬约束,并推断其余节点的发散时间。约会对于理解进化过程至关重要[2],并且在系统发育和系统动力学的许多下游应用中都是必需的。已经开发出许多用于系统发育约会的方法,但是没有一种方法被普遍接受[3,5]。虽然这些方法中的某些方法假设突变率的参数模型并使用最大似然或贝叶斯推断(例如[1,4,8,9]),但其他非参数方法则基于假设分布的特性依赖于优化问题。费率(例如[4,6-8])。参数化方法通常适用于正确的模型,但对模型错误指定很敏感。

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