Model Choice and Model Averaging in Molecular Phylogenetics

系统发育分析--一项旨在揭示生物体系谱关系的努力--对分子进化是如何发生的做出了强有力的、尽管可以检验的假设。今天，典型的系统发育分析是首先假设一个特定的分子进化模式，然后找到描述感兴趣的生物之间关系的最佳系统发育树。重要的是，系统发育树取决于在第一步分析中选择的模型。在这个项目中，我们将极大地扩展分子进化的候选模型集，并设计出同时搜索最好的分子进化模型和最好的系统发育的“自动化”方法。我们将使用一种称为马尔科夫链蒙特卡罗的方法的变体来在模型和系统发育中进行搜索。重要的是，生物学家不仅能够找到最能解释他/她的数据的树(S)，而且还能够找到最好的进化模型(S)。我们打算探索两种不同的模式选择方法。第一种是贝叶斯方法，它使用贝叶斯定理计算模型的概率。第二种方法使用信息标准来寻找一个在模型与观测数据的拟合和模型中的自由参数数量之间折衷的模型。我们将把这些方法应用于DNA和氨基酸序列的比对。

Phylogenetic analysis--an endeavor aimed at uncovering the genealogical relationships of organisms--makes strong, though testable, assumptionsabout how molecular evolution occurs. Today, a typical phylogenetic analysis is performed by first assuming a particular model of molecular evolution, and then finding the best phylogenetic tree describing the relationshipsamong the organisms of interest. Importantly, the phylogenetic tree isconditioned on the model chosen in the first step of the analysis. Usually, the model is chosen from a small set of candidate models.In this project, we will vastly expand the set of candidate models of molecular evolution and devise 'automated' ways to simultaneously search for the best model of molecular evolution and the best phylogeny. We will use a variant of a method called Markov chain Monte Carlo to search among models and phylogenies. Importantly, the biologist will not only be able to find the tree(s) that best explains his/her data but also find the best model(s) of evolution. We intend to explore two different methods for choosing among models. The first is a Bayesian method that calculates probabilities of models using Bayes' theorem. The second uses information criteria to find a model that is a compromise between the fit of the model to the observed data and the number of free parameters in the model. We will apply the methods to alignments of DNA and amino sequences.