使用松弛时钟和不确定系统发育的分子时间树。

IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in bioinformatics Pub Date : 2023-08-03 eCollection Date: 2023-01-01 DOI:10.3389/fbinf.2023.1225807
Jose Barba-Montoya, Sudip Sharma, Sudhir Kumar
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引用次数: 0

摘要

分子系统学中的一种常见做法是推断系统发育,然后使用放松的时钟方法和校准将其按时间缩放。这种顺序分析实践忽略了系统发育不确定性对分歧时间估计及其置信区间的影响。另一种选择是联合推断系统发育和时间,将系统发育错误纳入分子年代测定中。我们使用计算机模拟和经验数据集比较了这两种替代方案在重建进化时间树方面的性能。我们发现,除了特定情况下的一些节点外,序列和联合分析可以产生相似的分化时间和系统发育关系。当系统发育没有很好地解决时,联合推理表现更好,在这种情况下,联合推理应该是首选的。然而,联合推理对于大型数据集可能是不可行的,因为可用的贝叶斯方法在计算上是繁重的。我们提出了一种联合推断的替代方法,该方法结合了少量自举、最大似然和RelTime方法,用于同时推断进化关系、分歧时间和置信区间,并结合了系统发育的不确定性。新方法减轻了贝叶斯方法带来的高计算负担,同时获得了类似的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Molecular timetrees using relaxed clocks and uncertain phylogenies.

A common practice in molecular systematics is to infer phylogeny and then scale it to time by using a relaxed clock method and calibrations. This sequential analysis practice ignores the effect of phylogenetic uncertainty on divergence time estimates and their confidence/credibility intervals. An alternative is to infer phylogeny and times jointly to incorporate phylogenetic errors into molecular dating. We compared the performance of these two alternatives in reconstructing evolutionary timetrees using computer-simulated and empirical datasets. We found sequential and joint analyses to produce similar divergence times and phylogenetic relationships, except for some nodes in particular cases. The joint inference performed better when the phylogeny was not well resolved, situations in which the joint inference should be preferred. However, joint inference can be infeasible for large datasets because available Bayesian methods are computationally burdensome. We present an alternative approach for joint inference that combines the bag of little bootstraps, maximum likelihood, and RelTime approaches for simultaneously inferring evolutionary relationships, divergence times, and confidence intervals, incorporating phylogeny uncertainty. The new method alleviates the high computational burden imposed by Bayesian methods while achieving a similar result.

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