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，它们被用来解决诸如宿主和寄生虫之间的共同物种形成历史，生命多样化的重大事件的时间以及生态学重要特征的进化等问题。系统发育也是医学上的一个重要工具。例如，遗传学被广泛用于流行病学，其中除其他应用外，它们已被用于确定人类艾滋病毒流行病的起源和时间。然而，现代基因组学，其巨大的数量和高质量的遗传数据，提出了重大的挑战，该领域的遗传学：如何使意义上的基因组数据在系统发育的背景下，这些数据如何可以用来解决有趣的和重要的问题，如功能的重要性氨基酸的位置？贝叶斯估计的遗传学是该领域最有前途的最新发展之一。在这个提议中，理论和方法将被开发，这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鉴定位点和分支组合的方法。这些方法将扩展DNA取代的传统密码子模型，以允许在选择类别之间切换。其次，改进的贝叶斯推断方法将被开发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，更好地探索树的空间。第三，将开发用于估计发散时间的贝叶斯方法。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时，估计预先指定的物种组（或病毒序列）的分歧时间。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的联合收割机信息和吉布斯自由能。

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.