Improved Bayesian phylogenetic inference based on approximate conditional independence

拟议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亲缘关系（亲缘关系）的软件。这项工作将比过去更加准确和迅速。这项工作很重要，因为生物技术有许多有价值的应用。除了它们在研究地球生命史中的核心重要性外，生物遗传学还被研究影响人类健康的病毒（如艾滋病毒和流感）的科学家所使用，被想要预测物种如何对气候变化做出反应的科学家所使用，甚至被科学家在法庭案件中分析法医证据。这项研究旨在改进许多科学家在研究进化历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之一是基于可能性的方法，该方法模拟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，特别是当存在大量物种和长DNA序列时，就像现在常见的那样。拟议的研究包括组建一个团队来设计，实施，测试和分发新的算法和软件，以将标准的当前方法转换为贝叶斯系统发育推断。贝叶斯系统发育推断的当前技术水平涉及使用马尔可夫链蒙特卡罗（MCMC）方法从后验分布中采样系统发育树，后验分布描述了由活体个体中测量的DNA序列所告知的相关物种的进化历史。通过设计，MCMC产生依赖样本，其中顺序采样的每棵树可能与前一棵采样树非常相似，甚至完全相同。需要非常大的样本来获得代表完整后验分布的样本，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，以高精度估计的后验概率的树木的基础上的条件分支分布，而不是简单的样本频率。这个结果的一个后果是获得真正独立的样本的系统发育树的分布，接近真实分布的可能性，并获得正确的推断所需的后验分布通过重要性抽样。其目标是充分开发和测试这种采样方法与有效的算法，将与公众分享，并在新的自由和开放源码软件中实施该方法。

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.