WebGIVI: a web-based gene enrichment analysis and visualization tool

Author(s)Sun, Liang
Author(s)Zhu, Yongnan
Author(s)Mahmood, A. S. M. Ashique
Author(s)Tudor, Catalina O.
Author(s)Ren, Jia
Author(s)Vijay-Shanker, K.
Author(s)Chen, Jian
Author(s)Schmidt, Carl J.
Ordered AuthorLiang Sun, Yongnan Zhu, A. S. M. Ashique Mahmood, Catalina O. Tudor, Jia Ren, K. Vijay-Shanker, Jian Chen and Carl J. Schmidt
UD AuthorSun, Liangen_US
UD AuthorMahmood, A. S. M. Ashiqueen_US
UD AuthorTudor, Catalina O.en_US
UD AuthorRen, Jiaen_US
UD AuthorVijay-Shanker, K.en_US
UD AuthorSchmidt, Carl J.en_US
Date Accessioned2017-09-12T16:50:47Z
Date Available2017-09-12T16:50:47Z
Copyright DateCopyright © The Author(s). 2017en_US
Publication Date2017-05-04
DescriptionPublisher's PDFen_US
AbstractBACKGROUND: A major challenge of high throughput transcriptome studies is presenting the data to researchers in an interpretable format. In many cases, the outputs of such studies are gene lists which are then examined for enriched biological concepts. One approach to help the researcher interpret large gene datasets is to associate genes and informative terms (iTerm) that are obtained from the biomedical literature using the eGIFT text-mining system. However, examining large lists of iTerm and gene pairs is a daunting task. RESULTS: We have developed WebGIVI, an interactive web-based visualization tool (http://raven.anr.udel.edu/webgivi/) to explore gene:iTerm pairs. WebGIVI was built via Cytoscape and Data Driven Document JavaScript libraries and can be used to relate genes to iTerms and then visualize gene and iTerm pairs. WebGIVI can accept a gene list that is used to retrieve the gene symbols and corresponding iTerm list. This list can be submitted to visualize the gene iTerm pairs using two distinct methods: a Concept Map or a Cytoscape Network Map. In addition, WebGIVI also supports uploading and visualization of any two-column tab separated data. CONCLUSIONS: WebGIVI provides an interactive and integrated network graph of gene and iTerms that allows filtering, sorting, and grouping, which can aid biologists in developing hypothesis based on the input gene lists. In addition, WebGIVI can visualize hundreds of nodes and generate a high-resolution image that is important for most of research publications. The source code can be freely downloaded at https://github.com/sunliang3361/WebGIVI. The WebGIVI tutorial is available at http://raven.anr.udel.edu/webgivi/tutorial.php.en_US
DepartmentUniversity of Delaware. Department of Animal and Food Sciences.en_US
DepartmentUniversity of Delaware. Department of Computer and Information Sciences.en_US
DepartmentUniversity of Delaware. Center for Bioinformatics & Computational Biology.en_US
CitationSun et al. BMC Bioinformatics (2017) 18:237 DOI 10.1186/s12859-017-1664-2en_US
DOI10.1186/s12859-017-1664-2en_US
ISSN1471-2105en_US
URLhttp://udspace.udel.edu/handle/19716/21642
Languageen_USen_US
PublisherBioMed Centralen_US
dc.rightsOpen Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en_US
dc.sourceBMC Genomicsen_US
dc.source.urihttps://bmcbioinformatics.biomedcentral.com/en_US
TitleWebGIVI: a web-based gene enrichment analysis and visualization toolen_US
TypeArticleen_US
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