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

Amit P Desai; Mehdi Razeghin; Oscar Meruvia-Pastor; Lourdes Peña-Castillo

doi:10.7717/peerj.3678

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

PeerJ. 2017 Aug 14:5:e3678. doi: 10.7717/peerj.3678. eCollection 2017.

Authors

Amit P Desai¹, Mehdi Razeghin¹, Oscar Meruvia-Pastor^{1

2}, Lourdes Peña-Castillo^{1

3}

Affiliations

¹ Department of Computer Science, Memorial University of Newfoundland, St. John's, Canada.
² Office of the Dean of Science, Memorial University of Newfoundland, St. John's, Canada.
³ Department of Biology, Memorial University of Newfoundland, St. John's, Canada.

Abstract

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.

Keywords: Data visualization; GeNET; Gene co-expression network analysis (GCNA); Gene expression; Transcriptomics; Web tool.

Grants and funding

This work was supported by a Discovery Grant (No. 402087-2011) of the Natural Sciences and Engineering Research Council of Canada (NSERC) to LPC. NSERC had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.