Cargando…

RCytoscape: tools for exploratory network analysis

BACKGROUND: Biomolecular pathways and networks are dynamic and complex, and the perturbations to them which cause disease are often multiple, heterogeneous and contingent. Pathway and network visualizations, rendered on a computer or published on paper, however, tend to be static, lacking in detail,...

Descripción completa

Detalles Bibliográficos
Autores principales: Shannon, Paul T, Grimes, Mark, Kutlu, Burak, Bot, Jan J, Galas, David J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3751905/
https://www.ncbi.nlm.nih.gov/pubmed/23837656
http://dx.doi.org/10.1186/1471-2105-14-217
_version_ 1782281699039641600
author Shannon, Paul T
Grimes, Mark
Kutlu, Burak
Bot, Jan J
Galas, David J
author_facet Shannon, Paul T
Grimes, Mark
Kutlu, Burak
Bot, Jan J
Galas, David J
author_sort Shannon, Paul T
collection PubMed
description BACKGROUND: Biomolecular pathways and networks are dynamic and complex, and the perturbations to them which cause disease are often multiple, heterogeneous and contingent. Pathway and network visualizations, rendered on a computer or published on paper, however, tend to be static, lacking in detail, and ill-equipped to explore the variety and quantities of data available today, and the complex causes we seek to understand. RESULTS: RCytoscape integrates R (an open-ended programming environment rich in statistical power and data-handling facilities) and Cytoscape (powerful network visualization and analysis software). RCytoscape extends Cytoscape's functionality beyond what is possible with the Cytoscape graphical user interface. To illustrate the power of RCytoscape, a portion of the Glioblastoma multiforme (GBM) data set from the Cancer Genome Atlas (TCGA) is examined. Network visualization reveals previously unreported patterns in the data suggesting heterogeneous signaling mechanisms active in GBM Proneural tumors, with possible clinical relevance. CONCLUSIONS: Progress in bioinformatics and computational biology depends upon exploratory and confirmatory data analysis, upon inference, and upon modeling. These activities will eventually permit the prediction and control of complex biological systems. Network visualizations -- molecular maps -- created from an open-ended programming environment rich in statistical power and data-handling facilities, such as RCytoscape, will play an essential role in this progression.
format Online
Article
Text
id pubmed-3751905
institution National Center for Biotechnology Information
language English
publishDate 2013
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-37519052013-08-24 RCytoscape: tools for exploratory network analysis Shannon, Paul T Grimes, Mark Kutlu, Burak Bot, Jan J Galas, David J BMC Bioinformatics Software BACKGROUND: Biomolecular pathways and networks are dynamic and complex, and the perturbations to them which cause disease are often multiple, heterogeneous and contingent. Pathway and network visualizations, rendered on a computer or published on paper, however, tend to be static, lacking in detail, and ill-equipped to explore the variety and quantities of data available today, and the complex causes we seek to understand. RESULTS: RCytoscape integrates R (an open-ended programming environment rich in statistical power and data-handling facilities) and Cytoscape (powerful network visualization and analysis software). RCytoscape extends Cytoscape's functionality beyond what is possible with the Cytoscape graphical user interface. To illustrate the power of RCytoscape, a portion of the Glioblastoma multiforme (GBM) data set from the Cancer Genome Atlas (TCGA) is examined. Network visualization reveals previously unreported patterns in the data suggesting heterogeneous signaling mechanisms active in GBM Proneural tumors, with possible clinical relevance. CONCLUSIONS: Progress in bioinformatics and computational biology depends upon exploratory and confirmatory data analysis, upon inference, and upon modeling. These activities will eventually permit the prediction and control of complex biological systems. Network visualizations -- molecular maps -- created from an open-ended programming environment rich in statistical power and data-handling facilities, such as RCytoscape, will play an essential role in this progression. BioMed Central 2013-07-09 /pmc/articles/PMC3751905/ /pubmed/23837656 http://dx.doi.org/10.1186/1471-2105-14-217 Text en Copyright © 2013 Shannon et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software
Shannon, Paul T
Grimes, Mark
Kutlu, Burak
Bot, Jan J
Galas, David J
RCytoscape: tools for exploratory network analysis
title RCytoscape: tools for exploratory network analysis
title_full RCytoscape: tools for exploratory network analysis
title_fullStr RCytoscape: tools for exploratory network analysis
title_full_unstemmed RCytoscape: tools for exploratory network analysis
title_short RCytoscape: tools for exploratory network analysis
title_sort rcytoscape: tools for exploratory network analysis
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3751905/
https://www.ncbi.nlm.nih.gov/pubmed/23837656
http://dx.doi.org/10.1186/1471-2105-14-217
work_keys_str_mv AT shannonpault rcytoscapetoolsforexploratorynetworkanalysis
AT grimesmark rcytoscapetoolsforexploratorynetworkanalysis
AT kutluburak rcytoscapetoolsforexploratorynetworkanalysis
AT botjanj rcytoscapetoolsforexploratorynetworkanalysis
AT galasdavidj rcytoscapetoolsforexploratorynetworkanalysis