Markov Chain Monte Carlo Methodology for Phylogenetic Inference from Genetic Data

从遗传数据进行系统发育推断的马尔可夫链蒙特卡罗方法

• 批准号: 9723799
• 负责人: Bret Larget
• 金额: $ 18.56万
• 依托单位: Duquesne University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-15 至 1999-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9723799&HistoricalAwards=false
• 关键词: Markov; Chain; Monte; Carlo; Methodology

Gaining an understanding of the evolutionary history among a group of species is a fundamental problem in biology. The ease with which biologists obtain and disseminate genetic information and the resultant desire to analyze this data necessitates a valid statistical methodology which is computationally feasible for reconstructing large evolutionary trees and assessing the associated uncertainty. Other existing methods for reconstructing trees with may leaves depend on the computationally-intensive bootstrap method of resampling, whose validity in this context has been criticized by several authors. The investigators are developing Bayesian methodology and software implementing a novel Markov chain Monte Carlo alorithm for searching a tree- indexed parametric space, providing evolutionary biologists with a computationally feasible and statistically valid method of assessing uncertainty in reconstructed trees. This methodology is substantially superior on computational and theoretical bases to existing bootstrap methods. This project provides a new sophisticated computational tool for understanding evolutionary relationships of species on the basis of genetic information via a collaboration of the fields of statistics, computer science, and biology. The methodology the investigators are developing uses high performance computing in a novel manner to greatly improve the analysis of genetic data to elucidate evolutionary relationships. The methodology is general and may prove to be useful in wide-spread applications quite unrelated to evolutionary biology. This work is funded by Computational Biology Activities.

了解一群物种的进化史是生物学中的一个基本问题。生物学家获取和传播遗传信息的方便性，以及由此产生的分析这些数据的愿望，需要一种有效的统计方法，这种方法在计算上是可行的，可以用于重建大型进化树和评估相关的不确定性。其他现有的重建多叶树的方法依赖于重采样的计算密集型自举方法，其在这种情况下的有效性受到了一些作者的批评。研究人员正在开发贝叶斯方法和软件，实现一种新的马尔可夫链蒙特卡罗算法，用于搜索树索引的参数空间，为进化生物学家提供一种计算上可行和统计上有效的方法来评估重建树的不确定性。这种方法在计算和理论基础上大大优于现有的自举方法。该项目通过统计学、计算机科学和生物学领域的合作，为理解基于遗传信息的物种进化关系提供了一个新的复杂的计算工具。研究人员正在开发的方法以一种新颖的方式使用高性能计算来极大地改善遗传数据的分析，以阐明进化关系。该方法是通用的，可能被证明在与进化生物学无关的广泛应用中是有用的。这项工作由计算生物学活动资助。