High-throughput sequencing has provided a tool capable of observing the human microbiome, but characterizing the biological roles and metabolic potential of these microbial communities remains a significant challenge. Increasing evidence points to the functional activity of gene products, rather than community taxonomic composition, as the most robust descriptor of the microflora's relationship with its host and as a potential point of intervention in modulating human health. Existing computational tools for exploring a newly sequenced metagenome rely heavily on sequence homology and do not yet leverage information from the thousands of publicly available functional experimental results. Likewise, no previous methods have provided genome-scale computational tools for biological hypothesis generation regarding specific molecular interactions among the microflora and with a human host. This proposal aims to develop computational methodology to interpret the functional activity of microfloral communities: 1. Integrate functional information from taxonomic, metagenomic, and metatranscriptomic datasets. We will develop methodology to unify these three representations of microbiome composition by incorporating information from large scale functional genomic data collections. 2. Identify genomic predictors of inter-species functional activity, including host/microflora interactions and points of community-wide regulatory feedback. We will computationally screen microbiome assays for molecular interactions and regulatory motifs spanning multiple organisms in the community. 3. Implement these technologies as publicly available, accessible, and interpretable tools. We will provide freely available, open source, downloadable and web-based implementations of this methodology for use by the bioinformatic and biological communities. As high-throughput sequencing becomes more widely used to study microbial communities in the human microbiome and in the environment, computational tools will be necessary to summarize their global functional activity and systems-level regulatory interactions. In the long term, by providing methodology to understand the human microbiome at the molecular level, we hope to enable its future use as a diagnostic indicator and as a point of intervention to improve human health.
DNA sequencing technology has recently allowed us to examine the microorganisms naturally residing in and on the human body, many of which are beneficial and some of which can be harmful. Although we can now gather data on the cellular behavior of these microbes and on their interactions with human beings, computational tools are needed to interpret this information. By developing new software to study these communities of microorganisms, we hope to eventually be able to detect when they may be causing disease and modify their composition to improve human health.
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