Understanding the structure and function of the human gut microbiome is expected to revolutionize healthcare due to its many associations with human disease. A critical step in microbiome analysis involves a clustering stage, where genomic sequences of unknown origin are assigned to latent genomes present in the sample. Current clustering methods rely on mixture-models, yet these fail to correctly model the features of genomic sequences shared across multiple genomes. These sequences are of great importance, often encoding antibiotic resistance genes that drive resistant outbreaks. This project’s goal is to develop a clustering algorithm that will effectively cluster both shared and unique genomic sequences. We have developed two probabilistic models, both based around hierarchical Poisson factorization, that have already produced promising results. The project’s goal will be to refine these models: This will involve robustly evaluating the current models, determining their limitations, and designing new models that improve upon the current. A successful project will enable for the first time, scalable, and comprehensive reconstruction of bacterial genomes. In turn, this will enable a large-scale analysis of antimicrobial resistance in the context of the human gut microbiome. We anticipate a successful project to result in an exciting publication.
