Background: The use of Gene Ontology (GO) data in protein analyses have largely contributed to the improved\r\noutcomes of these analyses. Several GO semantic similarity measures have been proposed in recent years and provide\r\ntools that allow the integration of biological knowledge embedded in the GO structure into different biological\r\nanalyses. There is a need for a unified tool that provides the scientific community with the opportunity to explore\r\nthese different GO similarity measure approaches and their biological applications.\r\nResults: We have developed DaGO-Fun, an online tool available at http://web.cbio.uct.ac.za/ITGOM, which\r\nincorporates many different GO similarity measures for exploring, analyzing and comparing GO terms and proteins\r\nwithin the context of GO. It uses GO data and UniProt proteins with their GO annotations as provided by the Gene\r\nOntology Annotation (GOA) project to precompute GO term information content (IC), enabling rapid response to user\r\nqueries.\r\nConclusions: The DaGO-Fun online tool presents the advantage of integrating all the relevant IC-based GO similarity\r\nmeasures, including topology- and annotation-based approaches to facilitate effective exploration of these measures,\r\nthus enabling users to choose the most relevant approach for their application. Furthermore, this tool includes several\r\nbiological applications related to GO semantic similarity scores, including the retrieval of genes based on their GO\r\nannotations, the clustering of functionally related genes within a set, and term enrichment analysis
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