Novel Computational Methods for Microbiome Data Analysis in Longitudinal Study

纵向研究中微生物组数据分析的新计算方法

基本信息

批准号：
10660234
负责人：
Huilin Li
金额：
$ 38.14万
依托单位：
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
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：
2023-04-05 至 2027-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10660234
关键词：
Address Affect Bioconductor Bioinformatics COVID-19 COVID-19 patient Cardiovascular Diseases Cessation of life Characteristics Clinical Code Collaborations Communities Companions Complex Computer software Computing Methodologies Critical Illness Data Data Analyses Diabetes Mellitus Disease Disease Progression Ecosystem Environmental Exposure Future Galaxy Genes Genetic Genetic Models Genetic Variation Growth Health High-Throughput Nucleotide Sequencing Human Human Characteristics Human Microbiome Infant Inflammatory Bowel Diseases Link Longitudinal Studies Malignant Neoplasms Mechanical ventilation Mediating Metagenomics Methods Microbe Nature New York Obesity Organ Outcome Pathway interactions Performance Phenotype Phylogenetic Analysis Population Genetics Process Property Research Design Research Methodology Research Personnel Risk Role Sampling Scheme Source Statistical Models Structure System Systems Analysis Taxonomy Techniques Technology Time Trees Universities Variant Work analytical method analytical tool bacterial community bioinformatics tool cohort design disorder risk experience gene function genetic variant genome-wide holistic approach human microbiota improved innovation insight metagenomic sequencing microbial microbiome microbiome analysis microbiome research microbiome signature multidisciplinary novel novel strategies novel therapeutics open source population based prevent repository respiratory microbiome risk prediction software development tool trait web based interface

项目摘要

With the steady growth of longitudinal microbiome studies, microbiomes are now on the cusp of clinical utility for several diseases, including obesity, diabetes, inflammatory bowel disease, and cancer. Motivated by the PI’s broad microbiome collaborations at New York University Langone Health and building upon our extensive and rich experience in developing novel methods to analyze emerging omics data, we propose to develop two sets of novel analytic methods to address two computational and analytical challenges in pushing microbiome research to reach its full clinical potential. In Aim 1, we will take a granular approach to dive into the raw metagenomics sequencing data and investigate how to analytically detect and differentiate closely related microbial strains within species. Specifically, we hypothesize that utilizing longitudinal raw metagenomics sequencing data will produce a more efficient and accurate genetic variants calling scheme than existing approaches, and we will develop a novel longitudinal metagenomics sequencing processing system to capture genomic variants, identify primary and secondary strains, and quantify strain proportions within species. The proposed new tool will be further used to understand how the microbial strains evolve along the time and how to link the structure variations with host-specific traits. In Aim 2, starting from the recognition of the human microbiota as a complex ecosystem, we will take a holistic approach to develop a suite of microbial risk scores to capture the multifaceted characteristics of the microbiome and implement these scores in disease risk prediction in combination with other omics data. In Aim 3, we will apply the proposed pipelines to two finished longitudinal microbiome studies and five on-going large scale population-based cancer microbiome studies. Through the extensive real data analyses, we will validate the proposed methods, illustrate new applications, and explore future directions. In addition, we will develop, distribute to the community, and provide support for open-source software packages implementing these methods. The proposal is innovative because it integrates the overall study design, upstream bioinformatics raw sequencing processing techniques and downstream statistical modeling with clinical outcomes into a streamlined analytic process to produce unbiased and efficient analytic tools for microbiome research in longitudinal studies. The proposed work will be conducted by an experienced multidisciplinary study team. If this work succeeds, it will facilitate the understanding of how bacterial communities affect human health and disease, and ultimately lead to new approaches to treat or prevent a variety of health conditions.

随着纵向微生物组研究的稳步增长，微生物组现在正处于临床应用的风口浪尖 PI 引发的多种疾病，包括肥胖、糖尿病、炎症性肠病和癌症。纽约大学朗格健康中心广泛的微生物组合作，并建立在我们广泛和在开发分析新兴组学数据的新方法方面拥有丰富的经验，我们建议开发两套方法新颖的分析方法来解决推动微生物组发展的两个计算和分析挑战研究以充分发挥其临床潜力在目标 1 中，我们将采取精细的方法深入研究原始数据。宏基因组测序数据并研究如何分析检测和区分密切相关的具体来说，我们利用原始宏基因组学捕获了该纵向数据。测序数据将产生比现有的更有效、更准确的遗传变异识别方案方法，我们将开发一种新颖的纵向宏基因组测序处理系统来捕获基因组变异，识别主要和次要菌株，并量化物种内的菌株比例。提出的新工具将进一步用于了解微生物菌株如何随时间演变以及如何在目标 2 中，从人类的识别开始，将结构变异与宿主特异性联系起来。微生物群作为一个复杂的生态系统，我们将采取整体方法来制定一套微生物风险评分捕捉微生物组的多方面特征并将这些评分应用于疾病风险在目标 3 中，我们将结合其他组学数据进行预测。纵向微生物组研究和五项正在进行的大规模基于人群的癌症微生物组研究。通过广泛的实际数据分析，我们将验证所提出的方法，说明新的应用，此外，我们还将开发、分发给社区并提供支持。该提案具有创新性，因为它集成了这些方法。总体研究设计、上游生物信息学原始测序处理技术和下游将临床结果的统计模型转化为简化的分析过程，以产生公正且高效的结果纵向研究中微生物组研究的分析工具。拟议的工作将由一个人进行。经验丰富的多学科研究团队如果这项工作成功，将有助于了解细菌是如何运作的。社区影响人类健康和疾病，并最终导致治疗或预防各种疾病的新方法的健康状况。