Cargando…

ChainRank, a chain prioritisation method for contextualisation of biological networks

BACKGROUND: Advances in high throughput technologies and growth of biomedical knowledge have contributed to an exponential increase in associative data. These data can be represented in the form of complex networks of biological associations, which are suitable for systems analyses. However, these n...

Descripción completa

Detalles Bibliográficos
Autores principales: Tényi, Ákos, de Atauri, Pedro, Gomez-Cabrero, David, Cano, Isaac, Clarke, Kim, Falciani, Francesco, Cascante, Marta, Roca, Josep, Maier, Dieter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4700624/
https://www.ncbi.nlm.nih.gov/pubmed/26729273
http://dx.doi.org/10.1186/s12859-015-0864-x
_version_ 1782408350746542080
author Tényi, Ákos
de Atauri, Pedro
Gomez-Cabrero, David
Cano, Isaac
Clarke, Kim
Falciani, Francesco
Cascante, Marta
Roca, Josep
Maier, Dieter
author_facet Tényi, Ákos
de Atauri, Pedro
Gomez-Cabrero, David
Cano, Isaac
Clarke, Kim
Falciani, Francesco
Cascante, Marta
Roca, Josep
Maier, Dieter
author_sort Tényi, Ákos
collection PubMed
description BACKGROUND: Advances in high throughput technologies and growth of biomedical knowledge have contributed to an exponential increase in associative data. These data can be represented in the form of complex networks of biological associations, which are suitable for systems analyses. However, these networks usually lack both, context specificity in time and space as well as the distinctive borders, which are usually assigned in the classical pathway view of molecular events (e.g. signal transduction). This complexity and high interconnectedness call for automated techniques that can identify smaller targeted subnetworks specific to a given research context (e.g. a disease scenario). RESULTS: Our method, named ChainRank, finds relevant subnetworks by identifying and scoring chains of interactions that link specific network components. Scores can be generated from integrating multiple general and context specific measures (e.g. experimental molecular data from expression to proteomics and metabolomics, literature evidence, network topology). The performance of the novel ChainRank method was evaluated on recreating selected signalling pathways from a human protein interaction network. Specifically, we recreated skeletal muscle specific signaling networks in healthy and chronic obstructive pulmonary disease (COPD) contexts. The analysis showed that ChainRank can identify main mediators of context specific molecular signalling. An improvement of up to factor 2.5 was shown in the precision of finding proteins of the recreated pathways compared to random simulation. CONCLUSIONS: ChainRank provides a framework, which can integrate several user-defined scores and evaluate their combined effect on ranking interaction chains linking input data sets. It can be used to contextualise networks, identify signaling and regulatory path amongst targeted genes or to analyse synthetic lethality in the context of anticancer therapy. ChainRank is implemented in R programming language and freely available at https://github.com/atenyi/ChainRank. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0864-x) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-4700624
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-47006242016-01-06 ChainRank, a chain prioritisation method for contextualisation of biological networks Tényi, Ákos de Atauri, Pedro Gomez-Cabrero, David Cano, Isaac Clarke, Kim Falciani, Francesco Cascante, Marta Roca, Josep Maier, Dieter BMC Bioinformatics Methodology Article BACKGROUND: Advances in high throughput technologies and growth of biomedical knowledge have contributed to an exponential increase in associative data. These data can be represented in the form of complex networks of biological associations, which are suitable for systems analyses. However, these networks usually lack both, context specificity in time and space as well as the distinctive borders, which are usually assigned in the classical pathway view of molecular events (e.g. signal transduction). This complexity and high interconnectedness call for automated techniques that can identify smaller targeted subnetworks specific to a given research context (e.g. a disease scenario). RESULTS: Our method, named ChainRank, finds relevant subnetworks by identifying and scoring chains of interactions that link specific network components. Scores can be generated from integrating multiple general and context specific measures (e.g. experimental molecular data from expression to proteomics and metabolomics, literature evidence, network topology). The performance of the novel ChainRank method was evaluated on recreating selected signalling pathways from a human protein interaction network. Specifically, we recreated skeletal muscle specific signaling networks in healthy and chronic obstructive pulmonary disease (COPD) contexts. The analysis showed that ChainRank can identify main mediators of context specific molecular signalling. An improvement of up to factor 2.5 was shown in the precision of finding proteins of the recreated pathways compared to random simulation. CONCLUSIONS: ChainRank provides a framework, which can integrate several user-defined scores and evaluate their combined effect on ranking interaction chains linking input data sets. It can be used to contextualise networks, identify signaling and regulatory path amongst targeted genes or to analyse synthetic lethality in the context of anticancer therapy. ChainRank is implemented in R programming language and freely available at https://github.com/atenyi/ChainRank. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0864-x) contains supplementary material, which is available to authorized users. BioMed Central 2016-01-05 /pmc/articles/PMC4700624/ /pubmed/26729273 http://dx.doi.org/10.1186/s12859-015-0864-x Text en © Tényi et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Tényi, Ákos
de Atauri, Pedro
Gomez-Cabrero, David
Cano, Isaac
Clarke, Kim
Falciani, Francesco
Cascante, Marta
Roca, Josep
Maier, Dieter
ChainRank, a chain prioritisation method for contextualisation of biological networks
title ChainRank, a chain prioritisation method for contextualisation of biological networks
title_full ChainRank, a chain prioritisation method for contextualisation of biological networks
title_fullStr ChainRank, a chain prioritisation method for contextualisation of biological networks
title_full_unstemmed ChainRank, a chain prioritisation method for contextualisation of biological networks
title_short ChainRank, a chain prioritisation method for contextualisation of biological networks
title_sort chainrank, a chain prioritisation method for contextualisation of biological networks
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4700624/
https://www.ncbi.nlm.nih.gov/pubmed/26729273
http://dx.doi.org/10.1186/s12859-015-0864-x
work_keys_str_mv AT tenyiakos chainrankachainprioritisationmethodforcontextualisationofbiologicalnetworks
AT deatauripedro chainrankachainprioritisationmethodforcontextualisationofbiologicalnetworks
AT gomezcabrerodavid chainrankachainprioritisationmethodforcontextualisationofbiologicalnetworks
AT canoisaac chainrankachainprioritisationmethodforcontextualisationofbiologicalnetworks
AT clarkekim chainrankachainprioritisationmethodforcontextualisationofbiologicalnetworks
AT falcianifrancesco chainrankachainprioritisationmethodforcontextualisationofbiologicalnetworks
AT cascantemarta chainrankachainprioritisationmethodforcontextualisationofbiologicalnetworks
AT rocajosep chainrankachainprioritisationmethodforcontextualisationofbiologicalnetworks
AT maierdieter chainrankachainprioritisationmethodforcontextualisationofbiologicalnetworks