Developing a platform for Bayesian inference of phylogeny

系统发育树可以从许多类型的数据中估计出来，包括从感兴趣的生物体中采样的DNA序列。一种推断系统发育的方法是使用贝叶斯统计，其中推断是基于系统发育树的概率(后验概率)。由于问题的复杂性，树的后验概率分布是数值逼近的，通常使用马尔可夫链蒙特卡罗方法。贝叶斯先生是一个广泛使用的程序，它使用贝叶斯马尔科夫链蒙特卡罗方法来估计系统发育。这笔赠款支持该计划的进一步发展，使生物学家能够探索与系统发育分析有关的许多生物学问题，例如基因组中核苷酸替代过程的可变性。该项目的广泛影响是显著的。成千上万的生物学家使用了贝叶斯先生的计划，并在7500多份出版物中被引用。新版本的程序将以显著的方式扩展程序的能力，使生物学家受益：生物学家可用的系统发育模型的数量将会增加；程序的速度将会提高，马尔可夫链蒙特卡罗算法将得到改进，以更好地探索参数空间；程序将获得一个图形用户界面，应该会使程序的使用变得更容易。

Phylogenetic trees can be estimated from many types of data, including DNA sequences sampled from the organisms of interest. One way to infer phylogeny is to use Bayesian statistics, where inferences are based on the probabilities (posterior probabilities) of phylogenetic trees. Because of the complexity of the problem, the posterior probability distribution of trees is numerically approximated, often using Markov chain Monte Carlo approaches. MrBayes is a widely used program that estimates phylogeny using a Bayesian Markov chain Monte Carlo approach. This grant supports the further development of that program to allow biologists to explore numerous biological problems related to phylogenetic analysis, such as variability in the nucleotide substitution process across a genome.The broader impacts of this project are significant. The MrBayes program is used by thousands of biologists and has been cited in over 7,500 publications. The new version of the program will extend the program's capabilities in significant ways that will benefit biologists: the number of phylogenetic models available to the biologist will expand; the speed of the program will be increased and the Markov chain Monte Carlo algorithms improved to better explore parameter space; and the program will obtain a graphical user interface that should make using the program easier.