Extending Bayesian Phylogenetic Analysis

描述(申请人提供)：系统发育在生物学中起着核心作用。它们在进化生物学中产生了革命性的影响，被用来解决诸如宿主和寄生虫之间的同种关系的历史、生命多样化的重大事件的时间以及生态上重要特征的进化等问题。系统发育学也是医学上的一个重要工具。例如，系统发育学在流行病学中被广泛使用，在其他应用中，它们被用来确定艾滋病毒在人类中流行的来源和时间。然而，现代基因组学以其庞大的数量和高质量的遗传数据对系统发育领域提出了重大挑战：如何在系统发育的背景下理解基因组数据，以及如何利用这些数据来解决有趣和重要的问题，如氨基酸位置的功能重要性？系统发育的贝叶斯估计代表了该领域最有希望的最新发展之一。在这项提案中，将开发理论和方法，在几个重要方面扩展遗传数据的贝叶斯分析。首先，将开发能够在正选择下识别站点和分支组合的方法。这些方法将扩展DNA替换的传统密码子模型，以允许在选择类之间进行切换。其次，将开发改进的贝叶斯推理方法来估计大型系统发育树。改进将涉及马尔科夫链蒙特卡罗的变体，它更好地探索树的空间。第三，将开发用于估计分歧时间的贝叶斯方法。当估计预先指定的物种组(或病毒序列)的分化时间时，这些方法将适应系统发育树、模型参数和校准时间中的不确定性。最后，将开发预测RNA二级结构的新方法。该方法将结合来自比较序列分析和吉布自由能的信息。

DESCRIPTION (provided by applicant): Phylogenies play a central role in biology. They have had a revolutionary effect in evolutionary biology where they are used to address questions such as the history of cospeciation between hosts and parasites, the timing of the major events of the diversification of life, and the evolution of ecologically important characters. Phylogenies also serve as an important tool in medicine. For example, phylogenies are widely used in epidemiology, where among other applications they have been used to establish the origin and timing of the HIV epidemic in humans. However, modem genomics, with its enormous quantity and high quality of genetic data, poses significant challenges to the field of phylogenetics: how can one make sense of genomic data in a phylogenetic context and how can these data be used to address interesting and important questions, such as the functional importance of amino acid positions? Bayesian estimation of phylogeny represents one of the most promising recent developments in the field. In this proposal, theory and methods will be developed that will extend Bayesian analysis of genetic data in several important ways. First, methods capable of identifying site and branch combinations under positive selection will be developed. These methods will expand the traditional codon models of DNA substitution to allow switching between selection classes. Second, improved methods of Bayesian inference will be developed for estimating large phylogenetic trees. The improvements will involve variants of Markov chain Monte Carlo that better explore the space of trees. Third, Bayesian methods for estimating divergence times will be developed. These methods will accommodate uncertainty in the phylogenetic tree, model parameters, and calibration times when estimating divergence times of pre-specified groups of species (or viral sequences). Finally, new methods for predicting RNA secondary structure will be developed. The method will combine information from comparative sequence analysis and the Gibb's free energy.