Regulatory Snapshots: integrative mining of regulatory modules from expression time series and regulatory networks.

Explaining regulatory mechanisms is crucial to understand complex cellular responses leading to system perturbations. Some strategies reverse engineer regulatory interactions from experimental data, while others identify functional regulatory units (modules) under the assumption that biological syst...

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Main Authors: Joana P Gonçalves, Ricardo S Aires, Alexandre P Francisco, Sara C Madeira
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2012-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC3341384?pdf=render
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spelling doaj-cf164d1d72d346789de416c1eb6a897a2020-11-24T21:52:48ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0175e3597710.1371/journal.pone.0035977Regulatory Snapshots: integrative mining of regulatory modules from expression time series and regulatory networks.Joana P GonçalvesRicardo S AiresAlexandre P FranciscoSara C MadeiraExplaining regulatory mechanisms is crucial to understand complex cellular responses leading to system perturbations. Some strategies reverse engineer regulatory interactions from experimental data, while others identify functional regulatory units (modules) under the assumption that biological systems yield a modular organization. Most modular studies focus on network structure and static properties, ignoring that gene regulation is largely driven by stimulus-response behavior. Expression time series are key to gain insight into dynamics, but have been insufficiently explored by current methods, which often (1) apply generic algorithms unsuited for expression analysis over time, due to inability to maintain the chronology of events or incorporate time dependency; (2) ignore local patterns, abundant in most interesting cases of transcriptional activity; (3) neglect physical binding or lack automatic association of regulators, focusing mainly on expression patterns; or (4) limit the discovery to a predefined number of modules. We propose Regulatory Snapshots, an integrative mining approach to identify regulatory modules over time by combining transcriptional control with response, while overcoming the above challenges. Temporal biclustering is first used to reveal transcriptional modules composed of genes showing coherent expression profiles over time. Personalized ranking is then applied to prioritize prominent regulators targeting the modules at each time point using a network of documented regulatory associations and the expression data. Custom graphics are finally depicted to expose the regulatory activity in a module at consecutive time points (snapshots). Regulatory Snapshots successfully unraveled modules underlying yeast response to heat shock and human epithelial-to-mesenchymal transition, based on regulations documented in the YEASTRACT and JASPAR databases, respectively, and available expression data. Regulatory players involved in functionally enriched processes related to these biological events were identified. Ranking scores further suggested ability to discern the primary role of a gene (target or regulator). Prototype is available at: http://kdbio.inesc-id.pt/software/regulatorysnapshots.http://europepmc.org/articles/PMC3341384?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Joana P Gonçalves
Ricardo S Aires
Alexandre P Francisco
Sara C Madeira
spellingShingle Joana P Gonçalves
Ricardo S Aires
Alexandre P Francisco
Sara C Madeira
Regulatory Snapshots: integrative mining of regulatory modules from expression time series and regulatory networks.
PLoS ONE
author_facet Joana P Gonçalves
Ricardo S Aires
Alexandre P Francisco
Sara C Madeira
author_sort Joana P Gonçalves
title Regulatory Snapshots: integrative mining of regulatory modules from expression time series and regulatory networks.
title_short Regulatory Snapshots: integrative mining of regulatory modules from expression time series and regulatory networks.
title_full Regulatory Snapshots: integrative mining of regulatory modules from expression time series and regulatory networks.
title_fullStr Regulatory Snapshots: integrative mining of regulatory modules from expression time series and regulatory networks.
title_full_unstemmed Regulatory Snapshots: integrative mining of regulatory modules from expression time series and regulatory networks.
title_sort regulatory snapshots: integrative mining of regulatory modules from expression time series and regulatory networks.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2012-01-01
description Explaining regulatory mechanisms is crucial to understand complex cellular responses leading to system perturbations. Some strategies reverse engineer regulatory interactions from experimental data, while others identify functional regulatory units (modules) under the assumption that biological systems yield a modular organization. Most modular studies focus on network structure and static properties, ignoring that gene regulation is largely driven by stimulus-response behavior. Expression time series are key to gain insight into dynamics, but have been insufficiently explored by current methods, which often (1) apply generic algorithms unsuited for expression analysis over time, due to inability to maintain the chronology of events or incorporate time dependency; (2) ignore local patterns, abundant in most interesting cases of transcriptional activity; (3) neglect physical binding or lack automatic association of regulators, focusing mainly on expression patterns; or (4) limit the discovery to a predefined number of modules. We propose Regulatory Snapshots, an integrative mining approach to identify regulatory modules over time by combining transcriptional control with response, while overcoming the above challenges. Temporal biclustering is first used to reveal transcriptional modules composed of genes showing coherent expression profiles over time. Personalized ranking is then applied to prioritize prominent regulators targeting the modules at each time point using a network of documented regulatory associations and the expression data. Custom graphics are finally depicted to expose the regulatory activity in a module at consecutive time points (snapshots). Regulatory Snapshots successfully unraveled modules underlying yeast response to heat shock and human epithelial-to-mesenchymal transition, based on regulations documented in the YEASTRACT and JASPAR databases, respectively, and available expression data. Regulatory players involved in functionally enriched processes related to these biological events were identified. Ranking scores further suggested ability to discern the primary role of a gene (target or regulator). Prototype is available at: http://kdbio.inesc-id.pt/software/regulatorysnapshots.
url http://europepmc.org/articles/PMC3341384?pdf=render
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