Characterization of methods for mechanistic inference of the gut microbiome in disease

Abstract

MotivationKnowledge graphs have found broad biomedical applications, providing useful representations of complex knowledge. Although plentiful evidence exists linking the gut microbiome to disease, mechanistic understanding of those relationships remains generally elusive. A structured analysis of existing resources is necessary to characterize the resources and methodologies needed to facilitate mechanistic inference. ResultsHere we demonstrate the potential of knowledge graphs to hypothesize plausible mechanistic accounts of host-microbe interactions in disease and define the need for semantic constraint in doing so. We constructed a knowledge graph of linked microbes, genes and metabolites called MGMLink, and one of microbial traits, environments, and human pheno-types called KG-microbe-phenio. Using a shortest path search and a pattern based semantically constrained path search through the graphs, we highlight the need for a microbiome-disease resource and semantically informed search methods to enable mechanistic inference. AvailabilityThe software to create MGMLink is openly available at https://github.com/bsantan/MGMLink, and KG-microbe is available at https://github.com/Knowledge-Graph-Hub/kg-microbe and KG-phenio is available at https://github.com/Knowledge-Graph-Hub/kg-phenio. Contactbrook.santangelo@cuanschutz.edu