COMPUTATIONAL FRAMEWORKS FOR PHAGE DISCOVERY, ECOLOGY, AND DYNAMICS FROM METAGENOMES

项目摘要/摘要 元基因组测序对人类微生物组正变得越来越重要 研究。人类微生物群构成了一个丰富的有益和致病的生态系统 可能影响人类健康的微生物和噬菌体。然而， 由于大多数噬菌体不能被分离，所以对噬菌体的研究仍然很少。 在实验室里。因此，与栽培无关的组学方法，如 元基因组学正在成为直接研究噬菌体的重要工具 来自混血社区。然而，病毒学中的一个重大挑战是相对 缺乏高质量的生物信息学工具来研究噬菌体 元基因组学数据与原核生物可用的此类工具的丰富程度进行了比较。 我们将开发几种新的算法来研究噬菌体和 它们的生态来自元基因组学数据，包括发现新的未培养的 噬菌体，噬菌体群体基因组学，噬菌体分类，噬菌体：宿主和 噬菌体：新陈代谢相互作用，以及整合噬菌体的动力学。我们的 将通过开发和发布开放获取来正式确定方法 基于公平(公平、可访问、可互操作、可重复使用)的数据库和软件 数据原则，这将使对基本问题的调查 主宰人类健康的噬菌体生态学。以展示其实用性和广泛性 我们研究噬菌体的方法的适用性，我们将把它们应用于不同的 一组来自公共来源的人类微生物群的元基因组数据集 可用的数据和几个现有的协作。当我们的方法被设计成 对于从元基因组数据中研究噬菌体，它们也可以被应用 广泛地向所有病毒的研究迈进，包括从转录物中提取的RNA噬菌体 感染真核生物的数据和病毒。成功完成这一项目将 提供可广泛应用于生物信息学研究的可扩展生物信息学方法 来自元基因组数据的噬菌体。

Project Summary/Abstract Metagenomics sequencing is increasingly becoming important for human microbiome research. Human microbiomes comprise a rich ecosystem of beneficial and pathogenic microorganisms and bacteriophages that can influence human health. Yet, bacteriophages remain poorly studied because most bacteriophages cannot be isolated in a laboratory. As a result, cultivation-independent omics approaches such as metagenomics are emerging as important tools for studying bacteriophages directly from mixed communities. However, a significant challenge in virology is the relative absence of high-quality bioinformatics tools to enable the study of bacteriophages from metagenomics data compared to the abundance of such tools available for prokaryotes. We will develop several novel algorithms to enable the study of bacteriophages and their ecology from metagenomics data, including for the discovery of novel uncultivated phages, phage population genomics, phage taxonomy, phage:host and phage:metabolism interactions, and the dynamics of integrated phages. Our approaches will be formalized through the development and release of open access databases and software based on FAIR (Fair, Accessible, Interoperable, Reusable) data principles, which will enable investigation of fundamental questions in bacteriophage ecology governing human health. To demonstrate the utility and wide applicability of our methods to study bacteriophages, we will apply them on a diverse group of metagenome data sets from human microbiomes sourced from publicly available data and several existing collaborations. While our approaches are designed for the study of bacteriophages from metagenome data, they can also be applied broadly towards the study of all viruses including RNA phages from metatranscriptomic data and viruses infecting eukaryotes. Successfully accomplishing this project will provide scalable bioinformatics approaches that can be widely applied to the study of bacteriophages from metagenomics data.