Pathway network inference from gene expression data

Autores
Ponzoni, Ignacio; Nueda, María José; Tarazona, Sonia; Götz, Stefan; Montaner, David; Dussaut, Julieta Sol; Dopazo, Joaquín; Conesa, Ana
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Background: The development of high-throughput omics technologies enabled genome-wide measurements of the activity of cellular elements and provides the analytical resources for the progress of the Systems Biology discipline. Analysis and interpretation of gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of biological systems requires a further level of analysis that addresses the characterization of the interaction between functional modules. Results: We present a novel computational methodology to study the functional interconnections among the molecular elements of a biological system. The PANA approach uses high-throughput genomics measurements and a functional annotation scheme to extract an activity profile from each functional block -or pathway- followed by machine-learning methods to infer the relationships between these functional profiles. The result is a global, interconnected network of pathways that represents the functional cross-talk within the molecular system. We have applied this approach to describe the functional transcriptional connections during the yeast cell cycle and to identify pathways that change their connectivity in a disease condition using an Alzheimer example. Conclusions: PANA is a useful tool to deepen in our understanding of the functional interdependences that operate within complex biological systems. We show the approach is algorithmically consistent and the inferred network is well supported by the available functional data. The method allows the dissection of the molecular basis of the functional connections and we describe the different regulatory mechanisms that explain the network's topology obtained for the yeast cell cycle data.
Fil: Ponzoni, Ignacio. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion. Laboratorio de Investigación y Desarrollo en Computacion Cientifica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina
Fil: Nueda, María José. Universidad de Alicante; España
Fil: Tarazona, Sonia. Centro de Investigaciones Principe Felipe; España. Universidad de Valencia; España
Fil: Götz, Stefan. Centro de Investigaciones Principe Felipe; España
Fil: Montaner, David. Centro de Investigaciones Principe Felipe; España
Fil: Dussaut, Julieta Sol. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion. Laboratorio de Investigación y Desarrollo en Computacion Cientifica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Dopazo, Joaquín. Centro de Investigaciones Principe Felipe; España
Fil: Conesa, Ana. Centro de Investigaciones Principe Felipe; España
Materia
Pathways
Pathway Network
Gene Expression
Bioinformatics
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:11336/12460
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/12460
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Pathway network inference from gene expression dataPonzoni, IgnacioNueda, María JoséTarazona, SoniaGötz, StefanMontaner, DavidDussaut, Julieta SolDopazo, JoaquínConesa, AnaPathwaysPathway NetworkGene ExpressionBioinformaticshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Background: The development of high-throughput omics technologies enabled genome-wide measurements of the activity of cellular elements and provides the analytical resources for the progress of the Systems Biology discipline. Analysis and interpretation of gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of biological systems requires a further level of analysis that addresses the characterization of the interaction between functional modules. Results: We present a novel computational methodology to study the functional interconnections among the molecular elements of a biological system. The PANA approach uses high-throughput genomics measurements and a functional annotation scheme to extract an activity profile from each functional block -or pathway- followed by machine-learning methods to infer the relationships between these functional profiles. The result is a global, interconnected network of pathways that represents the functional cross-talk within the molecular system. We have applied this approach to describe the functional transcriptional connections during the yeast cell cycle and to identify pathways that change their connectivity in a disease condition using an Alzheimer example. Conclusions: PANA is a useful tool to deepen in our understanding of the functional interdependences that operate within complex biological systems. We show the approach is algorithmically consistent and the inferred network is well supported by the available functional data. The method allows the dissection of the molecular basis of the functional connections and we describe the different regulatory mechanisms that explain the network's topology obtained for the yeast cell cycle data.Fil: Ponzoni, Ignacio. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion. Laboratorio de Investigación y Desarrollo en Computacion Cientifica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); ArgentinaFil: Nueda, María José. Universidad de Alicante; EspañaFil: Tarazona, Sonia. Centro de Investigaciones Principe Felipe; España. Universidad de Valencia; EspañaFil: Götz, Stefan. Centro de Investigaciones Principe Felipe; EspañaFil: Montaner, David. Centro de Investigaciones Principe Felipe; EspañaFil: Dussaut, Julieta Sol. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion. Laboratorio de Investigación y Desarrollo en Computacion Cientifica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Dopazo, Joaquín. Centro de Investigaciones Principe Felipe; EspañaFil: Conesa, Ana. Centro de Investigaciones Principe Felipe; EspañaBioMed Central2014-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/12460Ponzoni, Ignacio; Nueda, María José; Tarazona, Sonia; Götz, Stefan; Montaner, David; et al.; Pathway network inference from gene expression data; BioMed Central; Bmc Systems Biology; 8; supl. 2; 3-2014; 1-171752-0509enginfo:eu-repo/semantics/altIdentifier/url/http://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S7info:eu-repo/semantics/altIdentifier/doi/10.1186/1752-0509-8-S2-S7info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:40:18Zoai:ri.conicet.gov.ar:11336/12460instacron: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:34982025-09-29 09:40:18.395CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Pathway network inference from gene expression data
title Pathway network inference from gene expression data
spellingShingle Pathway network inference from gene expression data
Ponzoni, Ignacio
Pathways
Pathway Network
Gene Expression
Bioinformatics
title_short Pathway network inference from gene expression data
title_full Pathway network inference from gene expression data
title_fullStr Pathway network inference from gene expression data
title_full_unstemmed Pathway network inference from gene expression data
title_sort Pathway network inference from gene expression data
dc.creator.none.fl_str_mv Ponzoni, Ignacio
Nueda, María José
Tarazona, Sonia
Götz, Stefan
Montaner, David
Dussaut, Julieta Sol
Dopazo, Joaquín
Conesa, Ana
author Ponzoni, Ignacio
author_facet Ponzoni, Ignacio
Nueda, María José
Tarazona, Sonia
Götz, Stefan
Montaner, David
Dussaut, Julieta Sol
Dopazo, Joaquín
Conesa, Ana
author_role author
author2 Nueda, María José
Tarazona, Sonia
Götz, Stefan
Montaner, David
Dussaut, Julieta Sol
Dopazo, Joaquín
Conesa, Ana
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Pathways
Pathway Network
Gene Expression
Bioinformatics
topic Pathways
Pathway Network
Gene Expression
Bioinformatics
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Background: The development of high-throughput omics technologies enabled genome-wide measurements of the activity of cellular elements and provides the analytical resources for the progress of the Systems Biology discipline. Analysis and interpretation of gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of biological systems requires a further level of analysis that addresses the characterization of the interaction between functional modules. Results: We present a novel computational methodology to study the functional interconnections among the molecular elements of a biological system. The PANA approach uses high-throughput genomics measurements and a functional annotation scheme to extract an activity profile from each functional block -or pathway- followed by machine-learning methods to infer the relationships between these functional profiles. The result is a global, interconnected network of pathways that represents the functional cross-talk within the molecular system. We have applied this approach to describe the functional transcriptional connections during the yeast cell cycle and to identify pathways that change their connectivity in a disease condition using an Alzheimer example. Conclusions: PANA is a useful tool to deepen in our understanding of the functional interdependences that operate within complex biological systems. We show the approach is algorithmically consistent and the inferred network is well supported by the available functional data. The method allows the dissection of the molecular basis of the functional connections and we describe the different regulatory mechanisms that explain the network's topology obtained for the yeast cell cycle data.
Fil: Ponzoni, Ignacio. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion. Laboratorio de Investigación y Desarrollo en Computacion Cientifica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina
Fil: Nueda, María José. Universidad de Alicante; España
Fil: Tarazona, Sonia. Centro de Investigaciones Principe Felipe; España. Universidad de Valencia; España
Fil: Götz, Stefan. Centro de Investigaciones Principe Felipe; España
Fil: Montaner, David. Centro de Investigaciones Principe Felipe; España
Fil: Dussaut, Julieta Sol. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion. Laboratorio de Investigación y Desarrollo en Computacion Cientifica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Dopazo, Joaquín. Centro de Investigaciones Principe Felipe; España
Fil: Conesa, Ana. Centro de Investigaciones Principe Felipe; España
description Background: The development of high-throughput omics technologies enabled genome-wide measurements of the activity of cellular elements and provides the analytical resources for the progress of the Systems Biology discipline. Analysis and interpretation of gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of biological systems requires a further level of analysis that addresses the characterization of the interaction between functional modules. Results: We present a novel computational methodology to study the functional interconnections among the molecular elements of a biological system. The PANA approach uses high-throughput genomics measurements and a functional annotation scheme to extract an activity profile from each functional block -or pathway- followed by machine-learning methods to infer the relationships between these functional profiles. The result is a global, interconnected network of pathways that represents the functional cross-talk within the molecular system. We have applied this approach to describe the functional transcriptional connections during the yeast cell cycle and to identify pathways that change their connectivity in a disease condition using an Alzheimer example. Conclusions: PANA is a useful tool to deepen in our understanding of the functional interdependences that operate within complex biological systems. We show the approach is algorithmically consistent and the inferred network is well supported by the available functional data. The method allows the dissection of the molecular basis of the functional connections and we describe the different regulatory mechanisms that explain the network's topology obtained for the yeast cell cycle data.
publishDate 2014
dc.date.none.fl_str_mv 2014-03
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/11336/12460
Ponzoni, Ignacio; Nueda, María José; Tarazona, Sonia; Götz, Stefan; Montaner, David; et al.; Pathway network inference from gene expression data; BioMed Central; Bmc Systems Biology; 8; supl. 2; 3-2014; 1-17
1752-0509
url http://hdl.handle.net/11336/12460
identifier_str_mv Ponzoni, Ignacio; Nueda, María José; Tarazona, Sonia; Götz, Stefan; Montaner, David; et al.; Pathway network inference from gene expression data; BioMed Central; Bmc Systems Biology; 8; supl. 2; 3-2014; 1-17
1752-0509
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S7
info:eu-repo/semantics/altIdentifier/doi/10.1186/1752-0509-8-S2-S7
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 BioMed Central
publisher.none.fl_str_mv BioMed Central
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
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score 13.070432

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