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GeNET: a web application to explore and share Gene Co-expression Network Analysis data

Gene Co-expression Network Analysis (GCNA) is a popular approach to analyze a collection of gene expression profiles. GCNA yields an assignment of genes to gene co-expression modules, a list of gene sets statistically over-represented in these modules, and a gene-to-gene network. There are several c...

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Autores principales: Desai, Amit P., Razeghin, Mehdi, Meruvia-Pastor, Oscar, Peña-Castillo, Lourdes
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5560228/
https://www.ncbi.nlm.nih.gov/pubmed/28828272
http://dx.doi.org/10.7717/peerj.3678
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author Desai, Amit P.
Razeghin, Mehdi
Meruvia-Pastor, Oscar
Peña-Castillo, Lourdes
author_facet Desai, Amit P.
Razeghin, Mehdi
Meruvia-Pastor, Oscar
Peña-Castillo, Lourdes
author_sort Desai, Amit P.
collection PubMed
description Gene Co-expression Network Analysis (GCNA) is a popular approach to analyze a collection of gene expression profiles. GCNA yields an assignment of genes to gene co-expression modules, a list of gene sets statistically over-represented in these modules, and a gene-to-gene network. There are several computer programs for gene-to-gene network visualization, but these programs have limitations in terms of integrating all the data generated by a GCNA and making these data available online. To facilitate sharing and study of GCNA data, we developed GeNET. For researchers interested in sharing their GCNA data, GeNET provides a convenient interface to upload their data and automatically make it accessible to the public through an online server. For researchers interested in exploring GCNA data published by others, GeNET provides an intuitive online tool to interactively explore GCNA data by genes, gene sets or modules. In addition, GeNET allows users to download all or part of the published data for further computational analysis. To demonstrate the applicability of GeNET, we imported three published GCNA datasets, the largest of which consists of roughly 17,000 genes and 200 conditions. GeNET is available at bengi.cs.mun.ca/genet.
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spelling pubmed-55602282017-08-21 GeNET: a web application to explore and share Gene Co-expression Network Analysis data Desai, Amit P. Razeghin, Mehdi Meruvia-Pastor, Oscar Peña-Castillo, Lourdes PeerJ Bioinformatics Gene Co-expression Network Analysis (GCNA) is a popular approach to analyze a collection of gene expression profiles. GCNA yields an assignment of genes to gene co-expression modules, a list of gene sets statistically over-represented in these modules, and a gene-to-gene network. There are several computer programs for gene-to-gene network visualization, but these programs have limitations in terms of integrating all the data generated by a GCNA and making these data available online. To facilitate sharing and study of GCNA data, we developed GeNET. For researchers interested in sharing their GCNA data, GeNET provides a convenient interface to upload their data and automatically make it accessible to the public through an online server. For researchers interested in exploring GCNA data published by others, GeNET provides an intuitive online tool to interactively explore GCNA data by genes, gene sets or modules. In addition, GeNET allows users to download all or part of the published data for further computational analysis. To demonstrate the applicability of GeNET, we imported three published GCNA datasets, the largest of which consists of roughly 17,000 genes and 200 conditions. GeNET is available at bengi.cs.mun.ca/genet. PeerJ Inc. 2017-08-14 /pmc/articles/PMC5560228/ /pubmed/28828272 http://dx.doi.org/10.7717/peerj.3678 Text en ©2017 Desai et al. 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 use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Desai, Amit P.
Razeghin, Mehdi
Meruvia-Pastor, Oscar
Peña-Castillo, Lourdes
GeNET: a web application to explore and share Gene Co-expression Network Analysis data
title GeNET: a web application to explore and share Gene Co-expression Network Analysis data
title_full GeNET: a web application to explore and share Gene Co-expression Network Analysis data
title_fullStr GeNET: a web application to explore and share Gene Co-expression Network Analysis data
title_full_unstemmed GeNET: a web application to explore and share Gene Co-expression Network Analysis data
title_short GeNET: a web application to explore and share Gene Co-expression Network Analysis data
title_sort genet: a web application to explore and share gene co-expression network analysis data
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5560228/
https://www.ncbi.nlm.nih.gov/pubmed/28828272
http://dx.doi.org/10.7717/peerj.3678
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