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

Autores
Ruiz Perez, Daniel; Lugo Martinez, Jose; Bourguignon, Natalia; Mathee, Kalai; Lerner, Betiana; Bar Joseph, Ziv; Narasimhan, Giri
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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
Fil: Ruiz Perez, Daniel. Florida International University; Estados Unidos
Fil: Lugo Martinez, Jose. University of Carnegie Mellon; Estados Unidos
Fil: Bourguignon, Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Florida International University; Estados Unidos. Universidad Tecnológica Nacional; Argentina
Fil: Mathee, Kalai. Florida International University; Estados Unidos
Fil: Lerner, Betiana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; Argentina
Fil: Bar Joseph, Ziv. University of Carnegie Mellon; Estados Unidos
Fil: Narasimhan, Giri. Florida International University; Estados Unidos
Materia
Longitudinal microbiome analysis,
Microbial composition prediction,
Dynamic Bayesian networks,
Temporal alignment
Multi-omic integration,
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/155252
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/155252
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Dynamic bayesian networks for integrating multi-omics time series microbiome dataRuiz Perez, DanielLugo Martinez, JoseBourguignon, NataliaMathee, KalaiLerner, BetianaBar Joseph, ZivNarasimhan, GiriLongitudinal microbiome analysis,Microbial composition prediction,Dynamic Bayesian networks,Temporal alignmentMulti-omic integration,https://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1A 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 interactionsFil: Ruiz Perez, Daniel. Florida International University; Estados UnidosFil: Lugo Martinez, Jose. University of Carnegie Mellon; Estados UnidosFil: Bourguignon, Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Florida International University; Estados Unidos. Universidad Tecnológica Nacional; ArgentinaFil: Mathee, Kalai. Florida International University; Estados UnidosFil: Lerner, Betiana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; ArgentinaFil: Bar Joseph, Ziv. University of Carnegie Mellon; Estados UnidosFil: Narasimhan, Giri. Florida International University; Estados UnidosAmerican Society for Microbiology2021-03-30info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/155252Ruiz Perez, Daniel; Lugo Martinez, Jose; Bourguignon, Natalia; Mathee, Kalai; Lerner, Betiana; et al.; Dynamic bayesian networks for integrating multi-omics time series microbiome data; American Society for Microbiology; mSystems; 6; 2; 30-3-2021; 1-170021-91931098-5530CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1128/mSystems.01105-20info:eu-repo/semantics/altIdentifier/url/https://journals.asm.org/doi/10.1128/mSystems.01105-20info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-12T09:47:31Zoai:ri.conicet.gov.ar:11336/155252instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-11-12 09:47:31.941CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Dynamic bayesian networks for integrating multi-omics time series microbiome data
title Dynamic bayesian networks for integrating multi-omics time series microbiome data
spellingShingle Dynamic bayesian networks for integrating multi-omics time series microbiome data
Ruiz Perez, Daniel
Longitudinal microbiome analysis,
Microbial composition prediction,
Dynamic Bayesian networks,
Temporal alignment
Multi-omic integration,
title_short 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_sort Dynamic bayesian networks for integrating multi-omics time series microbiome data
dc.creator.none.fl_str_mv Ruiz Perez, Daniel
Lugo Martinez, Jose
Bourguignon, Natalia
Mathee, Kalai
Lerner, Betiana
Bar Joseph, Ziv
Narasimhan, Giri
author Ruiz Perez, Daniel
author_facet Ruiz Perez, Daniel
Lugo Martinez, Jose
Bourguignon, Natalia
Mathee, Kalai
Lerner, Betiana
Bar Joseph, Ziv
Narasimhan, Giri
author_role author
author2 Lugo Martinez, Jose
Bourguignon, Natalia
Mathee, Kalai
Lerner, Betiana
Bar Joseph, Ziv
Narasimhan, Giri
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Longitudinal microbiome analysis,
Microbial composition prediction,
Dynamic Bayesian networks,
Temporal alignment
Multi-omic integration,
topic Longitudinal microbiome analysis,
Microbial composition prediction,
Dynamic Bayesian networks,
Temporal alignment
Multi-omic integration,
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv 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
Fil: Ruiz Perez, Daniel. Florida International University; Estados Unidos
Fil: Lugo Martinez, Jose. University of Carnegie Mellon; Estados Unidos
Fil: Bourguignon, Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Florida International University; Estados Unidos. Universidad Tecnológica Nacional; Argentina
Fil: Mathee, Kalai. Florida International University; Estados Unidos
Fil: Lerner, Betiana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; Argentina
Fil: Bar Joseph, Ziv. University of Carnegie Mellon; Estados Unidos
Fil: Narasimhan, Giri. Florida International University; Estados Unidos
description 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
publishDate 2021
dc.date.none.fl_str_mv 2021-03-30
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/155252
Ruiz Perez, Daniel; Lugo Martinez, Jose; Bourguignon, Natalia; Mathee, Kalai; Lerner, Betiana; et al.; Dynamic bayesian networks for integrating multi-omics time series microbiome data; American Society for Microbiology; mSystems; 6; 2; 30-3-2021; 1-17
0021-9193
1098-5530
CONICET Digital
CONICET
url http://hdl.handle.net/11336/155252
identifier_str_mv Ruiz Perez, Daniel; Lugo Martinez, Jose; Bourguignon, Natalia; Mathee, Kalai; Lerner, Betiana; et al.; Dynamic bayesian networks for integrating multi-omics time series microbiome data; American Society for Microbiology; mSystems; 6; 2; 30-3-2021; 1-17
0021-9193
1098-5530
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1128/mSystems.01105-20
info:eu-repo/semantics/altIdentifier/url/https://journals.asm.org/doi/10.1128/mSystems.01105-20
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Society for Microbiology
publisher.none.fl_str_mv American Society for Microbiology
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1848597953450606592
score 12.976206

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