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

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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.