Abstract
Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret.REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures. Furthermore, REVIGO visualizes this non-redundant GO term set in multiple ways to assist in interpretation: multidimensional scaling and graph-based visualizations accurately render the subdivisions and the semantic relationships in the data, while treemaps and tag clouds are also offered as alternative views. REVIGO is freely available at http://revigo.irb.hr/.
Highlights
Today’s high-throughput experiments measure the expression of thousands of genes simultaneously using microarrays, RNA-Seq or various proteomics approaches
Researchers analyzing annotations of gene products are often faced with long lists of Gene Ontology (GO) terms that are either close in the GO hierarchy or are related by inheritance
REVIGO performs a simple clustering procedure which is in concept similar to the hierarchical clustering methods such as the neighbor joining approach [10]
Summary
Today’s high-throughput experiments measure the expression of thousands of genes simultaneously using microarrays, RNA-Seq or various proteomics approaches. ChIP-on-chip or ChIP-Seq experiments are used to determine the genome-wide DNA binding pattern of a specific protein, which may affect a large number of genes. New genomes are being sequenced at an ever-increasing pace and their genes characterized by homology-based annotation transfer. In order to interpret the results of such experiments, statistical testing for over- and under-representation of gene functional categories is used [1]. The formality and structure, along with extensive manual curation, have made Gene Ontology (GO) [2] the vocabulary of choice in these analyses. A multitude of Web servers exists to assist in this task, including but not limited to: L2L [3], FatiGO [4], GOrilla [5] or agriGO [6]
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