Bayesian phylogenetic inference using DNA sequences: A Markov Chain Monte Carlo method
Bayesian phylogenetic inference using DNA sequences: A Markov Chain Monte Carlo method
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DOI：
10.1093/oxfordjournals.molbev.a025811
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发表时间：
1997-07-01
影响因子：
10.7
通讯作者：
Rannala, B
中科院分区：
文献类型：
--
作者：
Yang, ZH;Rannala, B
提出了一种改进的贝叶斯方法，用于利用DNA序列数据估计系统发生树。利用物种抽样的生灭过程来确定物种形成和祖先形成时间的先验分布，并利用物种形成的后验概率来估计最大后验概率（MAP）树。蒙特卡洛积分是用来整合特定树木的祖先物种形成时间。一个马尔可夫链蒙特卡罗方法被用来生成一组具有最高后验概率的树。方法描述的经验贝叶斯分析，其中估计的物种形成和灭绝率被用于计算后验概率，和一个分层贝叶斯分析，其中这些参数被删除的模型由一个额外的整合。马尔可夫链蒙特卡罗方法避免了我们早期计算MAP树的方法对所有可能的拓扑求和的要求（该方法将分析中的分类群数量限制为大约5个）。该方法被应用于分析9种灵长类动物的DNA序列，和MAP树，这是相同的拓扑结构的最大似然估计，具有约95%的概率。
An improved Bayesian method is presented for estimating phylogenetic trees using DNA sequence data. The birth-death process with species sampling is used to specify the prior distribution of phylogenies and ancestral speciation times, and the posterior probabilities of phylogenies are used to estimate the maximum posterior probability (MAP) tree. Monte Carlo integration is used to integrate over the ancestral speciation times for particular trees. A Markov Chain Monte Carlo method is used to generate the set of trees with the highest posterior probabilities. Methods are described for an empirical Bayesian analysis, in which estimates of the speciation and extinction rates are used in calculating the posterior probabilities, and a hierarchical Bayesian analysis, in which these parameters are removed from the model by an additional integration. The Markov Chain Monte Carlo method avoids the requirement of our earlier method for calculating MAP trees to sum over all possible topologies (which limited the number of taxa in an analysis to about five). The methods are applied to analyze DNA sequences for nine species of primates, and the MAP tree, which is identical to a maximum-likelihood estimate of topology, has a probability of approximately 95%.