III-CTX: Large-Scale Analysis of Collections of Phylogenetic Trees

III-CTX：系统发育树集合的大规模分析

• 批准号: 0713618
• 负责人: Tiffani Williams
• 金额: --
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-15 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0713618&HistoricalAwards=false
• 关键词: III; CTX; Large; Scale; Analysis

Given a collection of organisms (or taxa), the objective of a phylogenetic analysis is to produce an evolutionary tree describing the genealogical relationships between the taxa. Inferring a phylogenetic tree that represents a good hypothesis of the truth is a difficult problem. Phylogenetic heuristics attempt to search a very small fraction of the exponentially-sized tree space for good approximations of the true evolutionary tree. The overall goal of the proposed research project is to develop information-rich methods for analyzing the large collection of trees encountered during a phylogenetic search. A better understanding of search behavior can drive the design of better heuristics, which ultimately lead to more accurate reconstructions of phylogenetic trees. Specific research objectives include: (i) developing a search history repository that contains all of the trees examined by a phylogenetic heuristic; (ii) designing algorithms for exploiting informative patterns in a search history repository; and (iii) developing visualization tools that provide informative views of the data residing in the repository. The novel data analysis and visualization techniques developed for this research project will complement existing NSF-funded efforts to build phylogenies such as "The Tree of Life", the evolutionary history of all known organisms. Moreover, the proposed work will provide mentoring and interdisciplinary training to a diverse group of students.

给定生物体(或分类群)的集合，系统发育分析的目标是产生描述分类群之间的系谱关系的进化树。推断出一个代表真理的良好假说的系统发育树是一个困难的问题。系统发育启发法试图在指数大小的树空间中搜索非常小的一小部分，以找到真正进化树的良好近似值。拟议研究项目的总体目标是开发信息丰富的方法，用于分析在系统发育搜索过程中遇到的大量树木。对搜索行为的更好理解可以推动更好的启发式算法的设计，最终导致更准确的系统发育树重建。具体的研究目标包括：(1)开发一个搜索历史储存库，其中包含由系统发育启发式检验的所有树；(2)设计利用搜索历史储存库中的信息模式的算法；(3)开发可视化工具，提供驻留在储存库中的数据的信息视图。为这一研究项目开发的新数据分析和可视化技术将补充NSF资助的现有努力，以建立系统发展史，如“生命之树”，即所有已知有机体的进化史。此外，拟议的工作将为不同的学生群体提供指导和跨学科培训。