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CoRegNet: reconstruction and integrated analysis of co-regulatory networks

CoRegNet is an R/Bioconductor package to analyze large-scale transcriptomic data by highlighting sets of co-regulators. Based on a transcriptomic dataset, CoRegNet can be used to: reconstruct a large-scale co-regulatory network, integrate regulation evidences such as transcription factor binding sit...

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Detalles Bibliográficos
Autores principales: Nicolle, Rémy, Radvanyi, François, Elati, Mohamed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4565029/
https://www.ncbi.nlm.nih.gov/pubmed/25979476
http://dx.doi.org/10.1093/bioinformatics/btv305
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author Nicolle, Rémy
Radvanyi, François
Elati, Mohamed
author_facet Nicolle, Rémy
Radvanyi, François
Elati, Mohamed
author_sort Nicolle, Rémy
collection PubMed
description CoRegNet is an R/Bioconductor package to analyze large-scale transcriptomic data by highlighting sets of co-regulators. Based on a transcriptomic dataset, CoRegNet can be used to: reconstruct a large-scale co-regulatory network, integrate regulation evidences such as transcription factor binding sites and ChIP data, estimate sample-specific regulator activity, identify cooperative transcription factors and analyze the sample-specific combinations of active regulators through an interactive visualization tool. In this study CoRegNet was used to identify driver regulators of bladder cancer. Availability: CoRegNet is available at http://bioconductor.org/packages/CoRegNet Contact: remy.nicolle@issb.genopole.fr or mohamed.elati@issb.genopole.fr Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-45650292015-09-18 CoRegNet: reconstruction and integrated analysis of co-regulatory networks Nicolle, Rémy Radvanyi, François Elati, Mohamed Bioinformatics Applications Notes CoRegNet is an R/Bioconductor package to analyze large-scale transcriptomic data by highlighting sets of co-regulators. Based on a transcriptomic dataset, CoRegNet can be used to: reconstruct a large-scale co-regulatory network, integrate regulation evidences such as transcription factor binding sites and ChIP data, estimate sample-specific regulator activity, identify cooperative transcription factors and analyze the sample-specific combinations of active regulators through an interactive visualization tool. In this study CoRegNet was used to identify driver regulators of bladder cancer. Availability: CoRegNet is available at http://bioconductor.org/packages/CoRegNet Contact: remy.nicolle@issb.genopole.fr or mohamed.elati@issb.genopole.fr Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2015-09-15 2015-05-14 /pmc/articles/PMC4565029/ /pubmed/25979476 http://dx.doi.org/10.1093/bioinformatics/btv305 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Nicolle, Rémy
Radvanyi, François
Elati, Mohamed
CoRegNet: reconstruction and integrated analysis of co-regulatory networks
title CoRegNet: reconstruction and integrated analysis of co-regulatory networks
title_full CoRegNet: reconstruction and integrated analysis of co-regulatory networks
title_fullStr CoRegNet: reconstruction and integrated analysis of co-regulatory networks
title_full_unstemmed CoRegNet: reconstruction and integrated analysis of co-regulatory networks
title_short CoRegNet: reconstruction and integrated analysis of co-regulatory networks
title_sort coregnet: reconstruction and integrated analysis of co-regulatory networks
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4565029/
https://www.ncbi.nlm.nih.gov/pubmed/25979476
http://dx.doi.org/10.1093/bioinformatics/btv305
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