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

Ruiz-Perez, D.; Lugo-Martinez, J.; Bourguignon, N.; Mathee, K.; Lerner, B.; Bar-Joseph, Z.; Narasimhan, G.

doi:10.1101/835124

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bioRxiv · 10.1101/835124

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

Ruiz-Perez, D.·Lugo-Martinez, J.·Bourguignon, N.·Mathee, K.·Lerner, B.·Bar-Joseph, Z.·Narasimhan, G.

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Abstract

A key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites they consume and produce, and host genes. To address these challenges we developed a computational pipeline, PALM, that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to reconstruct a unified model. Our approach overcomes differences in sampling and progression rates, utilizes a biologically-inspired multi-omic 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 novel interactions. Targeted experimental validations further support a number of the predicted novel metabolite-taxa interactions. Source code and data will be freely available after publication under the MIT Open Source license agreement on our GitHub page. IMPORTANCEWhile a number of large consortia are collecting and profiling several different types of microbiome and genomic time series data, very few methods exist for joint modeling of multi-omics data sets. We developed a new computational pipeline, PALM, which uses Dynamic Bayesian Networks (DBNs) and is designed to integrate multi-omics data from longitudinal microbiome studies. When used to integrate sequence, expression, and metabolomics data from microbiome samples along with host expression data, the resulting models identify interactions between taxa, their genes and the metabolites they produce and consume, and their impact on host expression. We tested the models both by using them to predict future changes in microbiome levels, and by comparing the learned interactions to known interactions in the literature. Finally, we performed experimental validations for a few of the predicted interactions to demonstrate the ability of the method to identify novel relationships and their impact.

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Ruiz-Perez, D., Lugo-Martinez, J., Bourguignon, N., Mathee, K., Lerner, B., Bar-Joseph, Z., Narasimhan, G.. 2019-11-08. Dynamic Bayesian networks for integrating multi-omics time-series microbiome data. https://doi.org/10.1101/835124

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