Dynamic bayesian networks for integrating multi-omics time series microbiome data

Abstract

A key challenge in the analysis of longitudinal microbiome data is theinference of temporal interactions between microbial taxa, their genes, the metabolites that they consume and produce, and host genes. To address these challenges,we developed a computational pipeline, a pipeline for the analysis of longitudinalmulti-omics data (PALM), that first aligns multi-omics data and then uses dynamicBayesian networks (DBNs) to reconstruct a unified model. Our approach overcomesdifferences in sampling and progression rates, utilizes a biologically inspired multiomic framework, reduces the large number of entities and parameters in the DBNs,and validates the learned network. Applying PALM to data collected from inflammatory bowel disease patients, we show that it accurately identifies known and novelinteractions. Targeted experimental validations further support a number of the predicted novel metabolite-taxon interactions