权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Collaborative Research: DMS/NIGMS 2: New statistical methods, theory, and software for microbiome data

合作研究：DMS/NIGMS 2：微生物组数据的新统计方法、理论和软件

基本信息

批准号：
10797410
负责人：
Lingzhou Xue
金额：
$ 29.6万
依托单位：
PENNSYLVANIA STATE UNIVERSITY, THE
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-05 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10797410
关键词：
16S ribosomal RNA sequencing Asthma Biological Clinical Communities Complex Computer software Data Dependence Diabetes Mellitus Diagnostic Disease Foundations Genes Growth Health High-Throughput Nucleotide Sequencing Human Infant Intervention Learning Maps Mediation Metagenomics Methodology Molecular Epidemiology National Institute of General Medical Sciences Nature Obesity Outcome Pattern Phylogenetic Analysis Play Prevention Research Research Personnel Role Sample Size Shotgun Sequencing Statistical Methods Structure Surveys Technology Therapeutic analytical tool autism spectrum disorder biomarker discovery cancer biomarkers combat computerized tools data structure design disease diagnosis genome wide association study high dimensionality improved insight interest metabolomics microbial microbial community microbiome microbiome analysis microbiome research prognostic skin disorder software development theories

项目摘要

Advancement in high-throughput sequencing technology allows the characterization of the microbiome via either marker-gene (e.g., 16S rRNA gene) amplicon sequencing or metagenomics shotgun sequencing. Consequently, the scientific community is increasingly appreciative of the important role that the microbiome community plays in many human health and disease conditions. Despite its popularity, the field of microbiome and metagenomics studies, however, has not yet reached the maturity attained in other established molecular epidemiology fields, such as cancer biomarker discovery and genome-wide association studies for making the leap from omics survey to rational microbiome-based therapeutics. One of the primary limitations to leveraging this large body of microbiome and metagenomics data is computational and statistical challenges. Among these is the technical nature of the data, including high dimensionality, sparse count or compositional data structure, relatively small sample size, and complex dependence/correlation structure such as phylogenetic relatedness. To combat these challenges, this proposal seeks to develop statistical methods, theory, and computational tools to accurately characterize microbial communities within and across large studies while maintaining both statistical rigor and biological relevance. This project develops new statistical methods, theory, and software to characterize microbial communities within and across large studies accurately. Specifically, motivated by biomedical and biological problems encountered in microbiome studies of skin diseases, autism spectrum disorder, and infant growth, the investigators will develop statistical methodology for (1) mapping microbial taxa that influence clinical outcomes of interest in a powerful and robust pattern; (2) learning the correlation structure among microbial taxa to decode the complex networks and interactions among the microbiome community; (3) a new mediation analysis for microbiome studies with high-dimensional microbial profiles and other omics profiles such as metabolomics. Successful completion of this proposal will fill the gap between the burgeoning research interests in microbiome studies and the need for more analytical tools. This proposal will improve the understanding of the underlying microbiome mechanism of many health and disease conditions, which is critical to designing microbiome-based interventions for prognostic, diagnostic, and treatment purposes.

高通量测序技术的进步允许通过以下方式表征微生物组：或者标记基因（例如，16 S rRNA基因）扩增子测序或宏基因组鸟枪测序。因此，科学界越来越认识到，微生物群落在许多人类健康和疾病状况中发挥作用。尽管它很受欢迎，然而，微生物组和宏基因组学研究领域尚未达到成熟，其他已建立的分子流行病学领域，如癌症生物标志物发现和全基因组关联研究，实现从组学调查到合理的基于微生物组的治疗的飞跃。利用大量微生物组和宏基因组数据的主要限制之一是计算和统计挑战。其中包括数据的技术性质，维度、稀疏计数或组合数据结构、相对较小的样本大小和复杂性依赖/相关结构，如系统发育相关性。为了应对这些挑战，该提案旨在开发统计方法、理论和计算工具来准确表征微生物群落在大型研究中，同时保持统计上的严谨性，生物相关性。该项目开发新的统计方法，理论和软件来表征微生物群落在大型研究中的准确性。特别是，出于生物医学的动机，以及在皮肤病、自闭症谱系障碍、和婴儿的生长，研究人员将开发统计方法（1）绘制微生物类群，以强有力的模式影响感兴趣的临床结果;（2）学习相关性微生物分类群之间的结构，以解码微生物组之间的复杂网络和相互作用社区;（3）一种新的中介分析，用于高维微生物谱的微生物组研究和其他组学特征如代谢组学。成功完成此提案将填补差距微生物组研究中新兴的研究兴趣和对更多分析工具的需求之间的关系。该提案将提高对许多健康问题的潜在微生物组机制的理解。和疾病状况，这对于设计基于微生物组的干预措施以进行预后至关重要，诊断和治疗目的。