Identifying microbial genomic factors that underlie important phenotypes is a key goal of microbiological and microbiome research. Current approaches for this goal, however, cannot tap into the wealth of genomic information in a systematic manner, particularly in microbes that are not well-characterized. I will present a new conceptual and methodological approach for analyzing microbial communities using multi-sample sequence graphs. Our results demonstrate that this approach captures sequence and variant information more accurately than traditional approaches, provides graphs that are more suitable for comparative analyses, and is computationally tractable. I will end by demonstrating an application for predicting gut colonization trajectories of Vancomycin-resistant Enterococcus. Overall, our results underscore the value of graph-based frameworks for comparative metagenomic analyses.
