Uncovering Evolutionary History using the Topology of Genomic Data with Applications to HIV

利用基因组数据拓扑揭示进化历史并应用于艾滋病毒

• 批准号: 9037791
• 负责人: Raul Rabadan
• 金额: $ 31.72万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-03 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9037791
• 关键词: Address; Area; Biological; Clinical; Communities; Data; Data Analyses; Data Set; Disease Progression; Epidemiology; Evolution; Genetic Recombination; Genomics; HIV; HIV Infections; Methods; Population; Recording of previous events; Research; Research Personnel; Software Tools; Structure; Virus; interest; mathematical methods; public health relevance; simulation; statistics

 DESCRIPTION (provided by applicant): Recombination is a major force in HIV evolution, but it is hard to study. It is particularly challenging to identify intra-host recombination between closey related viruses. The Investigators therefore propose new mathematical methods to study recombination in HIV and reticulate evolution generally. Each method will address biologically relevant questions about HIV infection and epidemiology. This proposal contributes to topological data analysis (TDA), a new quantitative approach to find structure in large datasets. The Investigators recently found that TDA provides rich information about evolving populations and is useful for detecting recombination. Objectives: Aim 1: Create new methods in TDA to characterize recombination; Aim 2: Apply these methods to HIV evolution; Aim 3: Build software tools for the research community. Aims will proceed iteratively, and preliminary results enable Aims 1 & 2 to begin immediately. The high-level strategy for each evolutionary scenario is: (1) Simulate specific scenario, (2) Discover topological statistic(s) corresponding to biological quantities of interest, (3) Validate statistic's power to estimate biological quantity in simulation, (4) Explain/Prove why the statisti is a valid estimator, (5) Apply statistic to biological/clinical questions.

 描述（由申请人提供）：纯化是HIV进化的主要力量，但很难研究。鉴定紧密相关病毒之间的宿主内重组特别具有挑战性。因此，研究人员提出了新的数学方法来研究HIV中的重组和一般的网状进化。每种方法都将解决有关艾滋病毒感染和流行病学的生物学相关问题。这一建议有助于拓扑数据分析（TDA），一种新的定量方法来发现大型数据集的结构。研究人员最近发现，TDA提供了关于进化群体的丰富信息，并可用于检测重组。 目的： 目标1：在TDA中创建表征重组的新方法; 目标2：将这些方法应用于HIV进化; 目标3：为研究社区构建软件工具。 目标将迭代进行，初步结果使目标1和2能够立即开始。每个进化场景的高级策略是：（1）模拟特定场景，（2）发现与感兴趣的生物量相对应的拓扑统计量，（3）验证统计量在模拟中估计生物量的能力，（4）解释/证明为什么统计量是有效的估计量，（5）将统计量应用于生物/临床问题。