INVESTIGADORES
LERNER Betiana
artículos
Título:
Dynamic Bayesian networks for integrating multi-omics time-series microbiome data
Autor/es:
DANIEL RUIZ-PEREZ; JOSE LUGO-MARTINEZ; NATALIA BOURGUIGNON; KALAI MATHEE; BETIANA LERNER; ZIV BAR-JOSEPH; GIRI NARASIMHAN
Revista:
mSystems
Editorial:
mSystems
Referencias:
Año: 2021
Resumen:
A key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabo lites 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 theDBNs, and validates the learned network. Applying PALM to data collected from in flammatory bowel disease patients, we show that it accurately identifies known and novel interactions. Targeted experimental validations further support a number of thepredicted 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. Reviewers can view it at https://bit.ly/2O51GIm
