A computational framework for integrative analysis of large microbial genomics data

The availability of huge amount of genome sequence data from natural microbial consortia enables integrated analysis to resolve the genetic and metabolic potential of microbial communities, to establish how functions are partitioned in and among populations, and to reveal how microbial communities evolve and adapt across multiple environments. In this paper, we propose to analyze comparative microbial genomes using a computational framework. The framework is designed to investigate genome context patterns of microbial diversity. With an application to investigate functions of three environments (human gut, soil, and marine), we demonstrated that the developed computational framework was able to identify functional modules and evaluate the functional roles of those modules in microbial communities as response to environmental change. We found different gene networks among microbial communities living in different environments. We showed that modules identified by our framework can be computationally annotated to study their biological functions.