Bouckaert Remco R, Drummond Alexei J
Centre for Computational Evolution, University of Auckland, Auckland, New Zealand.
Department of Computer Science, University of Auckland, Auckland, New Zealand.
BMC Evol Biol. 2017 Feb 6;17(1):42. doi: 10.1186/s12862-017-0890-6.
Reconstructing phylogenies through Bayesian methods has many benefits, which include providing a mathematically sound framework, providing realistic estimates of uncertainty and being able to incorporate different sources of information based on formal principles. Bayesian phylogenetic analyses are popular for interpreting nucleotide sequence data, however for such studies one needs to specify a site model and associated substitution model. Often, the parameters of the site model is of no interest and an ad-hoc or additional likelihood based analysis is used to select a single site model.
bModelTest allows for a Bayesian approach to inferring and marginalizing site models in a phylogenetic analysis. It is based on trans-dimensional Markov chain Monte Carlo (MCMC) proposals that allow switching between substitution models as well as estimating the posterior probability for gamma-distributed rate heterogeneity, a proportion of invariable sites and unequal base frequencies. The model can be used with the full set of time-reversible models on nucleotides, but we also introduce and demonstrate the use of two subsets of time-reversible substitution models.
With the new method the site model can be inferred (and marginalized) during the MCMC analysis and does not need to be pre-determined, as is now often the case in practice, by likelihood-based methods. The method is implemented in the bModelTest package of the popular BEAST 2 software, which is open source, licensed under the GNU Lesser General Public License and allows joint site model and tree inference under a wide range of models.
通过贝叶斯方法重建系统发育树有诸多益处,包括提供一个数学上合理的框架、对不确定性进行现实估计以及能够基于形式原则纳入不同的信息来源。贝叶斯系统发育分析在解释核苷酸序列数据方面很受欢迎,然而对于此类研究,需要指定一个位点模型和相关的替换模型。通常,位点模型的参数并无研究价值,因此会采用一种临时的或基于额外似然性的分析来选择单个位点模型。
bModelTest允许采用贝叶斯方法在系统发育分析中推断和边缘化位点模型。它基于跨维度马尔可夫链蒙特卡罗(MCMC)提议,该提议允许在替换模型之间切换,同时估计伽马分布的速率异质性、不变位点比例和不等碱基频率的后验概率。该模型可与核苷酸上的全套时间可逆模型一起使用,但我们还引入并展示了两个时间可逆替换模型子集的使用。
使用这种新方法,位点模型可以在MCMC分析期间进行推断(并边缘化),无需像目前实际中经常采用的那样,通过基于似然性的方法预先确定。该方法在广受欢迎的BEAST 2软件的bModelTest包中实现,该软件是开源的,根据GNU Lesser General Public License许可,并且允许在广泛的模型下联合进行位点模型和树的推断。