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(申请人提供)：重组是HIV进化的主要力量，但很难研究。识别Closey相关病毒之间的宿主内重组尤其具有挑战性。因此，研究人员提出了新的数学方法来研究艾滋病毒中的重组和一般的网状进化。每种方法都将解决与艾滋病毒感染和流行病学有关的生物学问题。这一建议有助于拓扑数据分析(TDA)，这是一种在大数据集中发现结构的新的定量方法。研究人员最近发现，TDA提供了关于进化种群的丰富信息，并有助于检测重组。 目标： 目的1：在TDA中创造新的方法来表征重组； 目的2：将这些方法应用于HIV的进化； 目标3：为研究社区构建软件工具。 AIMS将迭代进行，初步结果使AIMS 1和AIMS 2能够立即开始。每个进化情景的高层策略是：(1)模拟特定情景，(2)发现与感兴趣的生物量相对应的拓扑统计量(S)，(3)在模拟中验证统计量估计生物量的能力，(4)解释/证明统计量为什么是有效的估计量，(5)将统计量应用于生物/临床问题。