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

• 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
• 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

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， 几种疾病，包括肥胖症、糖尿病、炎症性肠病和癌症。在PI的推动下， 广泛的微生物组合作在纽约大学Langone健康和建设我们的广泛和 在开发新方法来分析新兴组学数据方面有丰富的经验，我们建议开发两套 新的分析方法，以解决两个计算和分析的挑战，推动微生物组 以充分发挥其临床潜力。在目标1中，我们将采用细粒度的方法深入研究 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 微生物菌种。具体来说，我们假设利用纵向原始宏基因组学 测序数据将产生比现有的更有效和更准确的遗传变异识别方案。 方法，我们将开发一种新的纵向宏基因组测序处理系统，以捕获 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。的 提出的新工具将进一步用于了解微生物菌株如何沿着时间演变，以及如何 将结构变异与宿主特异性性状联系起来。在目标2中，从人类的识别开始， 微生物群作为一个复杂的生态系统，我们将采取全面的方法来开发一套微生物风险评分 捕捉微生物组的多方面特征，并在疾病风险中实施这些评分 预测与其他组学数据相结合。在目标3中，我们将建议的管道应用于两个已完成的 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。 通过广泛的真实的数据分析，我们将验证所提出的方法，说明新的应用， 探索未来的方向。此外，我们将开发、分发给社区，并为以下方面提供支持： 实现这些方法的开源软件包。该提案具有创新性，因为它整合了 总体研究设计，上游生物信息学原始测序处理技术和下游 将临床结果的统计建模转化为简化的分析过程， 纵向研究中微生物组研究的分析工具。拟议的工作将由一名 经验丰富的多学科研究团队。如果这项工作成功，它将有助于理解细菌如何在体内生长。 社区影响人类健康和疾病，并最终导致新的方法来治疗或预防各种 健康状况。