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

摘要 越来越多的证据表明，生活在人体内(和身体上)的微生物群落在 疾病病因学。该领域的一个主要障碍是缺乏可靠的方法来提取有意义的信号 来自小的、有噪音的、相互关联的和高度可变的微生物组数据集。提高科研人员的能力 为微生物区系及其宿主之间的复杂关系生成稳健的特征将支持 新的、更可靠的疾病诊断，使该领域离找到潜在的因果联系又近了一步 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海量数据 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序列读取档案(SRA)，这些数据集尚未被大规模利用。为了弥合这一点 GAP，我们将建立一条自动化管道来处理和聚合超过75万个扩增子和 所有可公开获得的人类微生物组样本的鸟枪式元基因组测序数据。我们将建立一个 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 这可以被研究人员用来寻找到目前为止隐藏在噪音中的生态动态。这个 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NCBI元数据和由新工具生成的批注，该工具为 样本类型、身体部位和宿主特征。我们还将使用《纲要》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 从汇编收集的数十万个样本中获得的见解。这些数据和工具将 分布在多个渠道，包括一个Web应用程序，用户可以在该应用程序中上传数据 通过降维工具进行实时处理。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群。由此产生的纲要将鼓励 使用公开可用的数据，并为新的微生物组分析工具提供信息，将有助于提取重要的 在获取传统技术所需的样本量是不切实际的研究中的关联。 这项研究的结果将是鉴定疾病的微生物组生物标志物和 开发新的治疗方法。

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.