Computational Studies of Virus-host Interactions Using Metagenomics Data and Applications

使用宏基因组数据和应用进行病毒-宿主相互作用的计算研究

• 批准号: 9312083
• 负责人: Nathan Ahlgren
• 金额: $ 39.59万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-15 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9312083
• 关键词: Affect; Bacteria; Biological; Body Water; Cells; Communities; Computer software; Computing Methodologies; Data; Data Set; Disease; Environment; Environment and Public Health; Environmental Risk Factor; Functional disorder; Genes; Genome; Geographic Locations; Health; Health Status; Human; Human Microbiome; Human body; Liver Cirrhosis; Location; Logistic Regressions; Machine Learning; Marines; Measures; Metagenomics; Methods; Microbe; Network-based; Oceans; Organism; Pattern; Planet Earth; Play; Policies; Public Health; Research Personnel; Role; Sampling; Science; Series; Soil; Technology; Time; Traveler' s diarrhea; Viral; Viral Genome; Virus; Virus Diseases; Visualization software; base; computer studies; computerized tools; design; experimental study; human disease; interest; metagenome; microbial; microbial community; microbiome; novel; particle; statistics; tool; user-friendly; virus host interaction

Computational Studies of Virus-host Interactions Using Metagenomics Data and Applications Summary: Viruses are ubiquitous in almost every ecological environment including the human body, water, soil, etc. They play important roles in the normal function of human microbiome. Many viruses have been shown to be associated with human diseases. However, our understanding of the roles of viruses in ecological communities is very limited. Recent technological and computational advances make it possible to have a deep understanding of the roles of viruses in public health and the environment. Metagenomics studies from various environments including the human microbiome projects (HMP), global ocean, and the earth microbiome projects have generated large amounts of short read data. Viruses are present in most of these metagenomic data sets and their hosts are unknown. In this proposal, the investigators will develop computational approaches for the identification of viral sequences from metagenomic data sets and for the study of virus-host interactions. For the identification of viral sequences from metagenomics samples, novel statistical measures using word patterns will first be developed. Second, a unified naïve Bayesian integrative approach by combining information from word patterns, gene directionality, and gene annotation will be studied. Third, the identified viral sequences from metagenomes will be further assembled to construct complete viral genomes using a novel binning approach to be developed by the investigators. Finally, the remaining reads will be assigned to the corresponding bins. For the study of virus- host interactions, computational methods to estimate the reliability of virus-host interactions from high-throughput experiments will first be developed. Then machine learning approaches will be developed to predict viruses infecting certain hosts. Finally, a network logistic regression approach will be developed to predict virus-host interactions. These computational approaches for the identification of viral sequences and for predicting virus-host interactions will be applied to a public liver cirrhosis and a unique metagenomics data set to understand how metagenomes change with health status, identify viruses and virus-host interactions associated with disease status and accurately predict disease status using bacteria, viruses and virus-host interactions. The developed computational methods will also be used to analyze metageomic data from various locations based on the TARA ocean data and a unique time series data to understand how environmental factors affect virus abundance and virus-host interactions. Some of the predictions will be experimentally validated. Software derived from the proposal will be developed and freely distributed to the scientific community.

利用宏基因组学数据和计算机技术研究病毒-宿主相互作用 应用 摘要：病毒几乎普遍存在于包括人类在内的所有生态环境中 它们在人体微生物组的正常功能中发挥重要作用。 许多病毒已被证明与人类疾病有关。但我们的 对病毒在生态群落中的作用的了解非常有限。最近 技术和计算的进步使人们有可能深入了解 病毒在公共卫生和环境中的作用。来自不同国家的宏基因组学研究 环境，包括人类微生物组项目（HMP），全球海洋和地球 微生物组项目已经产生了大量的短读数据。病毒存在于 这些宏基因组数据集及其宿主中的大多数是未知的。在本提案中， 研究人员将开发用于识别病毒序列的计算方法 从宏基因组数据集和研究病毒-宿主相互作用。用于鉴定 从宏基因组学样本中提取病毒序列，使用单词模式的新统计方法 将首先开发。第二，统一的朴素贝叶斯综合方法， 将研究来自单词模式、基因方向性和基因注释的信息。第三、 从宏基因组鉴定的病毒序列将进一步组装以构建 使用研究人员开发的新的分组方法完成病毒基因组。 最后，剩余的读段将被分配到相应的bin。为了研究病毒- 宿主相互作用，估算病毒-宿主相互作用可靠性的计算方法 高通量实验将首先开发。然后机器学习方法 将被开发来预测感染某些宿主的病毒。最后，网络逻辑回归 将开发一种方法来预测病毒与宿主的相互作用。这些计算方法 用于鉴定病毒序列和预测病毒-宿主相互作用 一个公共的肝硬化和一个独特的宏基因组学数据集，以了解宏基因组如何 随着健康状况的变化，识别与疾病相关的病毒和病毒-宿主相互作用 利用细菌、病毒和病毒与宿主的相互作用， 所开发的计算方法也将用于分析元年龄组数据， 基于塔拉海洋数据和独特的时间序列数据来了解各个位置 环境因素如何影响病毒丰度和病毒与宿主的相互作用。一些 预测将通过实验验证。从提案中派生的软件将 开发并免费分发给科学界。