A biological/biomedical ontology is a set of computer and human-interpretable terms and relations that represents entities in a biological/biomedical domain and how they relate to each other. Hundreds of biological ontologies have been developed. The most widely used biological ontology is the Gene Ontology (GO), which systematically and semantically represents three major attributed associated with gene products: Biological Processes (BP), Molecular Function (MF), and Cellular Components (CC). One major GO application is GO-based statistical enrichment analyses. The rationale of such an enrichment analysis is that given a group of genes, the co-functioning genes should have a higher or enriched potential to be identified as a relevant group using high throughput technologies (e.g., microarrays and RNA-Seq). Since often hundreds (or even more) of enriched terms are detected, the linear output of enriched terms can be very large and overwhelming, resulting in diluted focus on the analysis of related terms.
To address the ever increasing number of enriched GO terms resulting from high throughput studies, we developed GOfox to support GO enrichment analysis through integrat-ing and extending the features of OntoFox (http://ontofox.hegroup.org) and Ontobee (http://www.ontobee.org). OntoFox is able to fetch ontology terms and axioms. OntoFox includes several semantics algorithms for extracting different levels of in-termediate layer terms between user-selected terms and a top level term of the ontology (Xiang et al., 2010). Ontobee is the default OBO ontology linked data server that facili-tates ontology data sharing, visualization, query, integration, and analysis (Xiang et al., 2011). Ontobee also supports ontology visualization including the hierarchy, definition and annotations. By integrating and extending the features of OntoFox and Ontobee, GOfox is able to represent the enriched GO terms in an interactive hierarchical layout along with term-related information, and it allows users to manually modify the summarized enrichment result. Considering the multiple inheritance strategy used in GO development, GOfox developed a new algorithm to trim down the size of the enriched subset tree of GO. In addition, GOfox retrieves and displays related information such as definition, database cross references and comments, etc. of the selected GO term from Ontobee. This report provides the first time introduction of the GOfox to help researchers better visualize and analyze the results of GO gene enrichment studies.