Improved Bayesian phylogenetic inference based on approximate conditional independence

基于近似条件独立性的改进贝叶斯系统发育推断

• 批准号: 1354675
• 负责人: Bret Larget
• 金额: $ 27.98万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1354675&HistoricalAwards=false
• 关键词: Improved; Bayesian; phylogenetic; inference; based

The proposed research will use a new mathematical method, together with DNA sequence data, to develop software for determining genealogical relationships (phylogeny) among species of organisms. This will be done with greater accuracy and speed than has been possible in the past. This work is important because phylogenies have many valuable applications. In addition to their central importance in studying the history of life on Earth, phylogenies are used by scientists who study viruses that affect human health, such as HIV and influenza, by scientists who want to predict how species will react to a changing climate, and even by scientists analyzing forensic evidence in court cases. This research intends to improve a broad class of computational tools that many scientists use when they study evolutionary history and mechanisms.Among the most accurate statistical methods for reconstructing evolutionary relationships are likelihood-based methods which model the random process by which DNA sequences evolve over time. These methods, however, are highly computationally intensive, especially when there are both large numbers of species and long DNA sequences, as is now common. The proposed research includes the formation of a team to design, implement, test, and distribute novel algorithms and software for the purpose of transforming the standard current approaches to Bayesian phylogenetic inference. The current state of the art for Bayesian phylogenetic inference involves the use of Markov chain Monte Carlo (MCMC) methods to sample phylogenetic trees from a posterior distribution that describes evolutionary histories of related species informed by DNA sequences measured in living individuals. By design, MCMC produces dependent samples where each tree sampled in sequence is likely to be very similar, or even exactly the same, as the previous sampled tree. Extremely large samples are needed to obtain samples representative of the full posterior distribution, and the computational burden becomes prohibitive. This research will exploit a recent discovery of a new method to estimate with high accuracy the posterior probabilities of trees on the basis of conditional clade distributions rather than simple sample frequencies. A consequence of this result is the possibility of obtaining truly independent samples of phylogenetic trees from a distribution that approximates the true distribution closely, and to obtain correct inference from the desired posterior distribution via importance sampling. The objectives are to fully develop and test this sampling approach with efficient algorithms that will be shared with the public and to implement the approach in new free and open source software.

拟议中的研究将使用一种新的数学方法，结合DNA序列数据，开发用于确定生物物种之间的谱系关系（系统发育）的软件。这将以比过去可能的更高的准确性和速度完成。这项工作很重要，因为系统发育学有许多有价值的应用。系统发生学除了在研究地球生命史方面具有核心重要性外，还被研究影响人类健康的病毒（如艾滋病毒和流感）的科学家使用，被想要预测物种对气候变化的反应的科学家使用，甚至被分析法庭案件中的法医证据的科学家使用。这项研究旨在改进许多科学家在研究进化史和机制时使用的广泛的计算工具。重建进化关系的最准确的统计方法是基于可能性的方法，它模拟DNA序列随时间进化的随机过程。然而，这些方法是高度计算密集型的，特别是当有大量的物种和长DNA序列时，这是现在常见的。提议的研究包括组建一个团队来设计、实现、测试和分发新的算法和软件，目的是将标准的当前方法转化为贝叶斯系统发育推断。贝叶斯系统发育推断的最新技术包括使用马尔可夫链蒙特卡罗（MCMC）方法从描述相关物种进化史的后验分布中取样系统发育树，该分布通过在活着的个体中测量的DNA序列来获得信息。通过设计，MCMC产生依赖样本，其中按顺序采样的每棵树可能与之前采样的树非常相似，甚至完全相同。需要非常大的样本来获得代表完全后验分布的样本，并且计算负担变得令人望而却步。这项研究将利用最近发现的一种新方法，根据条件进化枝分布而不是简单的样本频率，高精度地估计树的后验概率。这一结果的一个结果是，有可能从接近真实分布的分布中获得真正独立的系统发育树样本，并通过重要抽样从期望的后验分布中获得正确的推断。目标是充分开发和测试这种抽样方法，并使用将与公众共享的有效算法，并在新的自由和开源软件中实现该方法。