Mixed-Model Phylogenetic Methods for Evolutionarily Heterogeneous Data

进化异质数据的混合模型系统发育方法

• 批准号: 0516530
• 负责人: Joseph Thornton
• 金额: $ 35.3万
• 依托单位: University of Oregon Eugene
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-10-01 至 2011-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0516530&HistoricalAwards=false
• 关键词: Mixed; Model; Phylogenetic; Methods; Evolutionarily

Phylogenies -- evolutionary trees that represent the historical relationships among species -- provide the framework for comparative analysis in all fields of biology. Most phylogenies are now inferred from DNA or protein sequence data using methods that assume the evolutionary process is largely homogeneous. In reality, however, evolutionary dynamics often differ among sequence sites and among lineages. Our preliminary data indicate that several kinds of heterogeneity can cause current methods to infer the wrong phylogeny. The goal of this proposal is to develop, implement, and validate a new family of mixed-model phylogenetic methods that incorporate evolutionary heterogeneity in a maximum likelihood framework. The accuracy of our method versus current techniques will be evaluated with experiments using both simulated and empirical data sets. We will distribute user-friendly software to the scientific community that implements our method and provides a high-throughput platform for simulating and analyzing heterogeneous sequence data. This project will accelerate the scientific community's success in reconstructing the Tree of Life and improve our ability to interpret genomic, developmental, and physiological data in a comparative framework. More reliable phylogenies are beneficial to society because an understanding of evolutionary relationships is crucial for characterizing biodiversity and developing strategies to preserve it. Sound phylogenetic knowledge is also central to understanding the evolutionary processes that affect agriculture, ecosystem function, and infectious disease. Our software will also provide scientists a tool for high-throughput phylogenetic experimentation and data analysis, a key goal as whole-genome sequence data become available. This project will also provide graduate and undergraduate education and research training in computer science and biology, an important need as biology becomes increasingly information-driven in the 21st Century.

系统发育学--代表物种间历史关系的进化树--为生物学所有领域的比较分析提供了框架。现在，大多数系统发育都是通过DNA或蛋白质序列数据推断的，这些方法假定进化过程在很大程度上是同类的。然而，在现实中，不同的序列位置和不同的谱系之间的进化动态往往不同。我们的初步数据表明，几种异质性可能导致目前的方法推断错误的系统发育。这项建议的目标是开发、实施和验证一种新的混合模式系统发育方法家族，该方法将进化异质性纳入最大似然框架。我们的方法与当前技术的准确性将通过使用模拟和经验数据集的实验进行评估。我们将向实现我们的方法的科学界分发用户友好的软件，并为模拟和分析异类序列数据提供高通量平台。这个项目将加速科学界重建生命之树的成功，并提高我们在比较框架中解释基因组、发育和生理数据的能力。更可靠的系统发育对社会是有益的，因为对进化关系的理解对于描述生物多样性的特征和制定保护它的战略至关重要。良好的系统发育知识也是理解影响农业、生态系统功能和传染病的进化过程的核心。我们的软件还将为科学家提供高通量系统发育实验和数据分析的工具，这是随着全基因组序列数据变得可用而实现的关键目标。该项目还将提供计算机科学和生物学方面的研究生和本科生教育和研究培训，这是21世纪生物学日益信息化的一个重要需求。