Abstract
The growing volumes of microbiome studies over the past decade have revealed a wide repertoire of microbial associations under diverse conditions. Microbes produce small molecules to interact with each other as well as to modulate their environments. Their metabolic profiles hold the key to understanding these association patterns for translational applications. Based on this concept, we developed MicrobiomeNet, a comprehensive database that integrates microbial associations with their metabolic profiles for mechanistic insights. It currently contains a total of ∼5.8 million known microbial associations, coupled with >12 400 genome-scale metabolic models (GEMs) covering ∼6000 microbial species. Users can intuitively explore microbial associations and compare their corresponding metabolic profiles. Our case studies show that MicrobiomeNet can provide mechanistic insights that are consistent with the literature. MicrobiomeNet is freely available at https://www.microbiomenet.com/.