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

项目总结/摘要 宏基因组测序对人类微生物组越来越重要 research.人体微生物组包括一个丰富的有益和致病的生态系统 微生物和噬菌体，可以影响人类健康。然而， 噬菌体的研究仍然很少，因为大多数噬菌体不能分离 在实验室里因此，不依赖于培养的组学方法， 宏基因组学正在成为直接研究噬菌体的重要工具 混合社区。然而，病毒学中的一个重大挑战是相对的， 缺乏高质量的生物信息学工具，使噬菌体的研究， 宏基因组学数据与原核生物可用的此类工具的丰度相比。 我们将开发几种新的算法，使噬菌体的研究， 他们的生态学从宏基因组学数据，包括发现新的未开垦的 噬菌体、噬菌体群体基因组学、噬菌体分类学、噬菌体：宿主和 噬菌体：代谢相互作用，以及整合的噬菌体的动力学。我们 通过开发和发布开放获取， 基于FAIR（公平、可扩展、可互操作、可重用）的数据库和软件 数据原则，这将使调查的基本问题， 控制人类健康的噬菌体生态学。为了展示其实用性和广泛性， 我们的方法研究噬菌体的适用性，我们将把它们应用于不同的 一组来自公开来源的人类微生物组的宏基因组数据集 现有的数据和一些现有的合作。虽然我们的方法是为了 对于来自宏基因组数据的噬菌体研究，它们也可以应用于 广泛地朝向所有病毒的研究，包括来自元转录组学的RNA干扰。 数据和病毒感染真核生物。成功完成该项目将 提供可扩展的生物信息学方法，可广泛应用于研究 从宏基因组学数据中提取噬菌体。

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.