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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Bibliographic Details
Published in:PeerJ (San Francisco, CA) CA), 2017-08, Vol.5, p.e3678-e3678, Article e3678
Main Authors: Desai, Amit P, Razeghin, Mehdi, Meruvia-Pastor, Oscar, Peña-Castillo, Lourdes
Format: Article
Language:English
Subjects:
Acids
Analysis
Bioinformatics
Biology
Computational Biology
Computational Science
Computer applications
Computer programs
Consortia
Data processing
Data visualization
Gene co-expression network analysis (GCNA)
Gene expression
GeNET
Genomics
Internet
Methods
Mycobacterium tuberculosis
Ontology
Proteins
Relational data bases
Researchers
Software
Studies
Transcriptomics
Tuberculosis
User interface
Web applications
Web tool
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Summary: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.
ISSN:2167-8359
2167-8359
DOI:10.7717/peerj.3678