DaGO-Fun: tool for Gene Ontology-based functional analysis using term information content measures

Mazandu, Gaston; Mulder, Nicola

DaGO-Fun: tool for Gene Ontology-based functional analysis using term information content measures

Journal Article

2013

Journal Title

BMC Bioinformatics

Link to Journal

http://www.biomedcentral.com/bmcbioinformatics/

Publisher

BioMed Central Ltd

Publisher

University of Cape Town

Department

Institute of Infectious Disease and Molecular Medicine

Faculty

Faculty of Health Sciences

License

http://creativecommons.org/licenses/by/2.0

Abstract

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