Extending Bayesian Phylogenetic Analysis

描述（由申请人提供）：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，在进化生物学中，它们被用来解决诸如宿主和寄生虫共生的历史、生命多样化主要事件的时间安排以及生态重要特征的进化等问题。系统发生学也是医学研究的重要工具。例如，系统发育学在流行病学中得到广泛应用，在其他应用中，系统发育学已被用于确定人类艾滋病毒流行的起源和时间。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巨大的挑战：如何在系统发育背景下理解基因组数据，如何使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或重排事件的历史，从而产生基因组？系统发育的贝叶斯估计是该领域最有前途的最新发展之一。在本提案中，将发展理论和方法，将在几个重要的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位点和分支组合的方法。这些方法将扩展DNA替代的传统密码子模型，以允许在选择类之间切换。其次，改进的贝叶斯推理方法将用于估计大型系统发育树。改进将涉及马尔可夫链蒙特卡罗的变体，它可以更好地探索树的空间。第三，将开发估计散度时间的贝叶斯方法。这些方法将适应系统发育树、模型参数和校准时间的不确定性，当估计预先指定的物种群（或病毒序列）的分化时间时。最后，将开发新的基因组变化建模方法，如基因复制、缺失、倒置和基因横向转移。

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 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, modern genomics, with its enormous quantity and high quality of genetic data, poses enormous 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 or the history of rearrangement events that gave rise to a genome? 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 prespecified groups of species (or viral sequences). Finally, new methods for modeling genomic changes, such as gene duplications, deletions, inversions, and lateral gene transfer, will be developed.