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.

抽象的 越来越多的证据表明，生活在（和）人体中的微生物群落在 疾病病因。该领域的主要障碍是缺乏提取有意义信号的可靠方法 从小型，嘈杂，相互关联和高度可变的微生物组数据集。增强研究人员的能力 为了产生对微生物群和其宿主之间复杂关系的鲁棒特征 新颖的，更可靠的疾病诊断，使该领域更近一步，更接近寻找因果关系。 基于微生物组的治疗剂。但是，到目前为止，研究人员还没有拥有大量数据所需的数据 得出这些结论。尽管来自数十万个样本的微生物组数据在 NCBI序列读取存档（SRA），这些数据集并未大规模利用。桥接这个 差距，我们将建立一条自动管道，以处理和汇总75万以上的amplicon和 来自所有公开可用的人类微生物组样品的shot弹枪宏基因组学测序数据。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品的集合 研究人员可以使用这种方法来找到迄今为止隐藏在噪音中的生态动力学。这 汇编将使用户可以在每个样本中看到微生物类群的相对丰度，这也将是 链接到NCBI元数据和由新工具生成的注释 样本类型，身体部位和主机特征。我们还将使用纲要来训练机器学习模型 减少维度，这将通过合并来提高独立微生物组研究的能力 Compendium收集了数十万个样本的见解。这些数据和工具将是 跨多个渠道分发，包括一个Web应用程序，用户将能够上传数据为 通过缩小维度工具实时处理。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聚合，该数据集将用于提供 人类微生物组的表征以前所未有的细节。由此产生的汇编将鼓励 使用可公开的数据并为新的微生物组分析工具提供信息，以帮助提取重要 在研究中获取常规技术要求的样本量是不切实际的研究的关联。 这项研究的结果将是鉴定疾病微生物组生物标志物和 新型治疗方法的发展。