Model Choice and Model Averaging in Molecular Phylogenetics

系统发生分析--一种旨在揭示生物体之间系谱关系的奋进--对分子进化是如何发生的做出了强有力的、尽管是可检验的解释。今天，一个典型的系统发育分析是通过首先假设一个特定的分子进化模型，然后找到最好的系统发育树来描述感兴趣的生物体之间的关系。重要的是，系统发育树是以分析的第一步中选择的模型为条件的。 通常，模型是从一小部分候选模型中选择的。在这个项目中，我们将大大扩展分子进化的候选模型集，并设计“自动化”的方法来同时搜索分子进化的最佳模型和最佳遗传学。我们将使用称为马尔可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好地解释他/她的数据的树，而且还能找到最好的进化模型。我们打算探讨两种不同的方法来选择模型。第一种是贝叶斯方法，使用贝叶斯定理计算模型的概率。 第二种方法使用信息准则来找到一个模型，该模型是模型对观测数据的拟合和模型中自由参数的数量之间的折衷。 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.