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About REViGO: motivation and goal

The Gene Ontology (GO) is a controlled, hierarchically organized vocabulary for describing function of gene products taking part in biological systems. The GO has a tremendous impact on present-day life science - PubMed shows 515 papers published during the year 2008 with „Gene Ontology" in title or abstract. Today's high-throughput experiments measure expression of thousands of genes simultaneously using microarrays or various proteomics approaches. Afterwards, researchers typically focus on genes whose expression differs between e.g. healthy and diseased tissue, with the resulting gene lists being interpreted by statistical testing for over- and under-representation within GO categories, see Rivals et al., Bioinformatics 23/4 (2007).

Such a way of summarizing experimental results may prove inadequate in the future. As the high-throughput techniques become cheaper and more accurate, they will dependably detect even slight changes in gene expression. Consequently, the lists of relevant genes will grow in size, and so will the resulting lists of GO categories. Additionaly, the interpretation of results is made difficult by high redundancy between individual GO categories.

We propose a computational approach that would (a) enable flexible reduction in size for large user-supplied lists of overlapping GO categories, and (b) visualize the remaining GO terms in a two-dimensional space which reflects the terms' semantic interrelations. Our simple clustering-like algorithm relies on previously defined measures of semantic similarity in the GO space, see e.g. Schlicker and Albrecht, Nucl Acids Res 36 (2008). Dimensionality reduction techniques and graph-based visualization approaches will be used to derive highly informative visualizations.

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If you found REVIGO useful in your work, please cite the following reference:
Supek F, Bošnjak M, Škunca N, Šmuc T.
"REVIGO summarizes and visualizes long lists of Gene Ontology terms" PLoS ONE 2011. doi:10.1371/journal.pone.0021800

REViGO is an iProject funded by the Minstry of Science, Education and Sport of Croatia (2008-057) implemented at the Laboratory for information systems, at the Rudjer Boskovic Institute, Croatia.

Other iProjects at the Rudjer Boskovic Institute:

GORBI - function predictions for thousands of poorly characterized bacterial proteins using phylogenetic profiling.

wGMDH - a Java implementation of the powerful "group method of data handling" regression algorithm. Integrates into Weka.