SOFTWARE Open Access WebGIVI: a web-based gene enrichment analysis and visualization tool Liang Sun 1,6 , Yongnan Zhu 2,3 , A. S. M. Ashique Mahmood 4 , Catalina O. Tudor 4 , Jia Ren 5 , K. Vijay-Shanker 4 , Jian Chen 2 and Carl J. Schmidt 1* Abstract Background: 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. Keywords: Visualization, eGIFT, Gene iTerm, Gene enrichment, Web development Background High-throughput technologies provide biologists with large lists of genes or proteins when they compare expression data between two biological states (e.g., nor- mal tissue vs. cancer tissue). Grouping enriched genes to known biological processes and pathways is a common strategy for understanding the biology that underlies the differences between the two states. Approaches include GO enrichment analysis such as DAVID [1, 2], GOEAST [3] and Gorilla [4], and pathway analysis such as KEGG [5] and Reactome [6]. eGIFT eGIFT [7] uses a text-mining method to identify inform- ative terms (iTerms) for individual genes. iTerms are not limited to gene ontology (GO) terms; they also capture more detailed biological knowledge. Consequently, eGIFT provides a finer grained interpretation of gene lists than GO analysis. The current gene analysis results of eGIFT provide users with a list of ranked iTerms and their associated genes in a tabular format. A graphic representation of these gene and iTerm relations would allow biologists to better interpret their input gene lists or gene-iTerm pair lists. This often captures the bio- logical concept enriched in the input data. Visualization tool An effective visualization of large data sets can provide biologists with means to discover buried relationships in complex data sets. Currently, several different visualization * Correspondence: schmidtc@udel.edu 1 Department of Animal and Food Sciences, University of Delaware, Newark, DE, USA Full list of author information is available at the end of the article © The Author(s). 2017 Open 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. Sun et al. BMC Bioinformatics (2017) 18:237 DOI 10.1186/s12859-017-1664-2