Human Microbiome Compendium: large-scale curation and processing of human microbiome datasets

人类微生物组纲要：人类微生物组数据集的大规模管理和处理

• 批准号: 10701823
• 负责人: Ran Blekhman
• 金额: $ 33.59万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10701823
• 关键词: Affect; Automated Annotation; Biological; Biological Markers; Classification; Code; Collection; Complex; Data; Data Set; Databases; Descriptor; Development; Dimensions; Disease; Environment; Etiology; Generations; Genomics; Health; Human; Human Microbiome; Human body; Individual; Internet; Interview; Link; Machine Learning; Meta-Analysis; Metadata; Metagenomics; Methods; Modeling; Noise; Ontology; Output; Pathway interactions; Pattern; Play; Process; Publishing; Research Personnel; Resources; Ribosomal RNA; Running; Sample Size; Sampling; Sequence Read Archive; Shotguns; Signal Transduction; Site; Standardization; Supercomputing; System; Techniques; Therapeutic; Time; Training; Variant; Visualization; Visualization software; Width; Writing; bioinformatics tool; community living; disease diagnosis; falls; fecal microbiome; improved; insight; machine learning model; metagenomic sequencing; microbial; microbial community; microbiome; microbiome analysis; microbiome research; microbiota; model building; model organism; novel; novel therapeutic intervention; sample collection; tool; trait; web app; web services

ABSTRACT Mounting evidence shows the microbial communities living in (and on) the human body play a key role in the etiology of disease. A major obstacle in the field is the dearth of reliable methods for extracting meaningful signals from small, noisy, intercorrelated, and highly variable microbiome datasets. Enhancing the ability of researchers to generate robust characterizations of the complex relationship between microbiota and their hosts will support novel, more reliable diagnosis of disease and bring the field one step closer to finding the causal links underlying microbiome-based therapeutics. Until now, however, researchers have not had the huge volume of data required to draw these conclusions. Although microbiome data from hundreds of thousands of samples is available in the NCBI Sequence Read Archive (SRA), these datasets have not been leveraged at a large scale. To bridge this gap, we will build an automated pipeline to process and aggregate more than 750,000 samples of amplicon and shotgun metagenomics sequencing data from all publicly available human microbiome samples. We will build a platform, which we call "The Human Microbiome Compendium," for compiling collections of relevant samples that can be used by researchers to find ecological dynamics that have until now been hidden in the noise. The compendium will allow users to see relative abundances of microbial taxa in every sample, which will also be linked to NCBI metadata and annotations generated by a new tool that imputes a uniform set of descriptors for sample type, body site, and host traits. We will also use the compendium to train machine learning models for dimensionality reduction, which will improve the power of independent microbiome studies by incorporating insights from the compendium's collection of hundreds of thousands of samples. These data and tools will be distributed across multiple channels, including a web application where users will be able to upload data to be processed in real time by the dimensionality reduction tools. The proposed studies will generate the first comprehensive aggregation of the microbiome datasets available via the SRA, which will be used to provide characterizations of the human microbiome in unprecedented detail. The resulting compendium will encourage the use of publicly available data and inform new microbiome analysis tools that will help extract important associations in studies where it's impractical to acquire the sample sizes required by conventional techniques. Results from this study will be a starting point to identification of microbiome biomarkers for disease and the development of novel therapeutic approaches.

摘要 越来越多的证据表明，生活在人体内（和人体上）的微生物群落在人类疾病中起着关键作用。 疾病的病因。该领域的一个主要障碍是缺乏提取有意义信号的可靠方法 从小的，嘈杂的，相互关联的，高度可变的微生物组数据集。提高研究人员的能力 对微生物群与宿主之间的复杂关系进行强有力的表征， 新的，更可靠的疾病诊断，使该领域更接近于找到潜在的因果联系 基于微生物组的疗法。然而，直到现在，研究人员还没有获得所需的大量数据 得出这些结论。尽管来自数十万样本的微生物组数据可在 NCBI Sequence Read Archive（SRA），这些数据集尚未大规模利用。以弥补这一 gap，我们将建立一个自动化的管道来处理和聚集超过75万个扩增子样本， 鸟枪宏基因组测序数据来自所有公开的人类微生物组样本。新的起点上建设更加富裕 平台，我们称之为“人类微生物组纲要”，用于汇编相关样本的集合 研究人员可以利用它来发现迄今为止一直隐藏在噪音中的生态动态。的 简编将允许用户查看每个样品中微生物分类群的相对丰度，这也将是 链接到NCBI元数据和由一个新工具生成的注释，该工具将一组统一的描述符用于 样本类型、身体部位和宿主特征。我们还将使用汇编来训练机器学习模型， 降维，这将提高独立微生物组研究的能力， 从该汇编收集的数十万样本中获得的见解。这些数据和工具将 分布在多个渠道，包括一个网络应用程序，用户将能够上传数据， 由降维工具进行真实的实时处理。拟议的研究将产生第一个 通过SRA提供的微生物组数据集的全面汇总，将用于提供 以前所未有的细节描述人类微生物组。由此产生的简编将鼓励 使用公开的数据和告知新的微生物组分析工具，这将有助于提取重要的 在研究中，传统技术所需的样本量是不切实际的。 这项研究的结果将是鉴定疾病的微生物组生物标志物的起点， 开发新的治疗方法。