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: 10538341
• 负责人: Ran Blekhman
• 金额: $ 36.85万
• 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/10538341
• 关键词: Affect; Animal Model; 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; base; bioinformatics tool; community living; data tools; disease diagnosis; falls; fecal microbiome; improved; insight; machine learning model; metagenomic sequencing; microbial; microbial community; microbiome; microbiome analysis; microbiome research; microbiota; 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.

抽象的 越来越多的证据表明，生活在人体内（和体表）的微生物群落在 疾病的病因学。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 来自小型、嘈杂、相互关联且高度可变的微生物组数据集。增强研究人员的能力 生成微生物群与其宿主之间复杂关系的可靠表征将支持 新颖、更可靠的疾病诊断，使该领域更接近寻找潜在的因果关系 基于微生物组的疗法。然而，到目前为止，研究人员还没有获得所需的大量数据。 得出这些结论。尽管来自数十万个样本的微生物组数据可在 NCBI 序列读取存档 (SRA)，这些数据集尚未得到大规模利用。为了弥补这一点 差距，我们将建立一个自动化管道来处理和聚合超过 750,000 个扩增子样本 来自所有公开可用的人类微生物组样本的鸟枪法宏基因组测序数据。我们将建立一个 我们称之为“人类微生物组纲要”的平台，用于编译相关样本的集合 研究人员可以利用它来寻找迄今为止隐藏在噪音中的生态动态。这 纲要将允许用户看到每个样本中微生物类群的相对丰度，这也将是 链接到由新工具生成的 NCBI 元数据和注释，该工具可估算一组统一的描述符 样本类型、身体部位和宿主特征。我们还将使用该纲要来训练机器学习模型 降维，这将通过整合来提高独立微生物组研究的能力 来自该纲要收集的数十万个样本的见解。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，用户可以在其中上传数据 由降维工具实时处理。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，该数据集将用于提供 以前所未有的细节描述人类微生物组的特征。由此产生的纲要将鼓励 使用公开数据并为新的微生物组分析工具提供信息，这将有助于提取重要的 无法通过传统技术获得所需样本量的研究中的关联。 这项研究的结果将成为鉴定疾病和疾病的微生物组生物标志物的起点。 开发新的治疗方法。