Identifying population-level variation in cross-sectional and longitudinal HMP st

描述（由申请人提供）：在理解人类微生物组结构的内部和人际变异如何影响人类表型的重要性方面，存在根本性的差距。这种差距的持续存在是有问题的，因为它阻碍了将这种变异与宿主健康变化联系起来的能力。部分问题在于过度依赖微生物组范围内的相似性指标，而不是基于种群的指标。这类似于使用微阵列技术比较大肠杆菌基因在指数期和固定期表达的总体差异，而不解决单个基因表达的变化。然而，一个定量的框架，以帮助分析人口数据从横断面和纵向研究是缺乏的。长期目标是了解塑造人类微生物群结构和功能的机制。本提案的目标是开发健壮的计算工具，这些工具经过优化，可以分析大型序列集合，但对于非生物信息学专家的典型研究者来说，这些工具也可以使用。具体来说，这一提议将满足开发计算工具的需求，使hmp科学家能够确定“一个位点上微生物组的变异是否与人类表型（如疾病）有关”。该提案将开发健壮的计算工具，优化分析大型序列集合，但对于不是生物信息学专家的典型研究者来说是可以访问的。该提案的基本原理是即将发布的数据来自一些HMP示范项目，这些项目正在进行横断面和纵向抽样，但已经意识到他们在确定微生物组特定变化与人类表型之间统计上的强大联系方面的能力有限。基于以往广泛的经验和与HMP研究者的互动，目标将通过追求三个具体目标来实现：1)在母软件包中实施和传播计算工具；2)开发工具，将微生物组的主体间变异与健康变异联系起来；3)开发工具，将微生物组的动态与健康变化联系起来。在拟议的研究中开发的每个工具都将使用模拟数据进行验证，并使用hmp生成的序列数据进行评估。这项研究是创新的，因为它建立在一个已经强大的工具集合上，用于描述流行的母亲软件包中的社区“部件列表”，并将创建一套强大的统计工具，用于评估时间变化以及这种变化与健康的关系。拟议的研究意义重大，因为它将通过将社区和人口水平的动态与人类健康的变化联系起来，提高我们推进HMP目标的能力。

DESCRIPTION (provided by applicant): There is a fundamental gap in understanding the significance of how intra- and inter-personal variation in the structure of the human microbiome affects human phenotypes. Continued existence of this gap is problematic because it impedes the ability to relate this variation with changes in host health. Part of the problem is the over-reliance on microbiome-wide metrics of similarity instead of population-based metrics. This is similar to using microarray technology to compare the overall differences of E. coli gene expression in exponential versus stationary phase without addressing the change in expression of individual genes. Yet, a quantitative framework to aid in the analysis of population data from cross- sectional and longitudinal studies is lacking. The long-term goal is to understand the mechanisms that shape the structure and function of the human microbiome. The objective of this proposal is to develop robust computational tools that are optimized to analyze large sequence collections, yet are accessible to the typical investigator that is not an expert in bioinformatics. Specifically, this proposal will fulfill the stated need to develop computational tools that enable HMP-scientists to determine whether "variation in the microbiome at a site can be related to human phenotypes, such as disease." This proposal will develop robust computational tools that are optimized to analyze large sequence collections, yet are accessible to the typical investigator that is not an expert in bioinformatics. The rationale for this proposal is the imminent release of data from a number of HMP Demonstration Projects that are pursuing cross-sectional and longitudinal sampling, but have realized that they are limited in their ability to identify statistically robust linkages between specific changes in the microbiome with human phenotypes. Building upon extensive previous experience and interactions with HMP investigators, the objective will be achieved by pursuing three specific aims: 1) implement and disseminate computational tools in the mothur software package; 2) develop tools to correlate inter- subject variation in the microbiome with variation in health; and 3) develop tools to connect the dynamics of the microbiome with changes in health. Each of the tools developed in the proposed research will be validated using simulated data and evaluated using HMP-generated sequence data. This research is innovative because it builds upon an already strong collection of tools for describing a community's "parts list" within the popular mothur software package and will create a robust set of statistical tools for assessing temporal variation and how that variation is related to health. The proposed research is significant because it will advance our ability to advance the goals of the HMP by relating community and population-level dynamics to changes in human health. PUBLIC HEALTH RELEVANCE: The proposed research is relevant to public health because the generation of tools that allow one to link changes in the microbiome to health will allow scientists to identify microbial populations within the microbiome that are responsible for diseases such as obesity, bacterial vaginosis, irritable bowel disorders, and cancer. Therefore, the proposed research is relevant to the part of the NIH's mission related to fostering innovative research strategies that improve the nation's ability to prevent and treat disease.