Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data.
dynamic Bayesian networks
longitudinal microbiome analysis
microbial composition prediction
multi-omic integration
temporal alignment
Journal
mSystems
ISSN: 2379-5077
Titre abrégé: mSystems
Pays: United States
ID NLM: 101680636
Informations de publication
Date de publication:
30 03 2021
30 03 2021
Historique:
entrez:
31
3
2021
pubmed:
1
4
2021
medline:
1
4
2021
Statut:
epublish
Résumé
A key challenge in the analysis of longitudinal microbiome data is the inference 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 longitudinal multi-omics data (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-taxon interactions.
Identifiants
pubmed: 33785573
pii: 6/2/e01105-20
doi: 10.1128/mSystems.01105-20
pmc: PMC8546994
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : NIAID NIH HHS
ID : R15 AI128714
Pays : United States
Informations de copyright
Copyright © 2021 Ruiz-Perez et al.
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