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Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data
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 analysi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Microbiology
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8546994/ https://www.ncbi.nlm.nih.gov/pubmed/33785573 http://dx.doi.org/10.1128/mSystems.01105-20 |
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author | Ruiz-Perez, Daniel Lugo-Martinez, Jose Bourguignon, Natalia Mathee, Kalai Lerner, Betiana Bar-Joseph, Ziv Narasimhan, Giri |
author_facet | Ruiz-Perez, Daniel Lugo-Martinez, Jose Bourguignon, Natalia Mathee, Kalai Lerner, Betiana Bar-Joseph, Ziv Narasimhan, Giri |
author_sort | Ruiz-Perez, Daniel |
collection | PubMed |
description | 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. IMPORTANCE While a number of large consortia collect and profile 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 that they produce and consume, as well as 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. |
format | Online Article Text |
id | pubmed-8546994 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Society for Microbiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-85469942021-10-27 Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data Ruiz-Perez, Daniel Lugo-Martinez, Jose Bourguignon, Natalia Mathee, Kalai Lerner, Betiana Bar-Joseph, Ziv Narasimhan, Giri mSystems Methods and Protocols 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. IMPORTANCE While a number of large consortia collect and profile 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 that they produce and consume, as well as 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. American Society for Microbiology 2021-03-30 /pmc/articles/PMC8546994/ /pubmed/33785573 http://dx.doi.org/10.1128/mSystems.01105-20 Text en Copyright © 2021 Ruiz-Perez et al. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Methods and Protocols Ruiz-Perez, Daniel Lugo-Martinez, Jose Bourguignon, Natalia Mathee, Kalai Lerner, Betiana Bar-Joseph, Ziv Narasimhan, Giri Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data |
title | Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data |
title_full | Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data |
title_fullStr | Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data |
title_full_unstemmed | Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data |
title_short | Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data |
title_sort | dynamic bayesian networks for integrating multi-omics time series microbiome data |
topic | Methods and Protocols |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8546994/ https://www.ncbi.nlm.nih.gov/pubmed/33785573 http://dx.doi.org/10.1128/mSystems.01105-20 |
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