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.