GOssTo: a Stand-alone Application and a Web Tool for Calculating Semantic Similarities on the Gene Ontology

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2014 Mar 25

PMID 24659104

Citations 22

Authors

Horacio Caniza

Alfonso E Romero

Samuel Heron

Haixuan Yang

Alessandra Devoto

Marco Frasca

Marco Mesiti

Giorgio Valentini

Alberto Paccanaro

Affiliations

Soon will be listed here.

Abstract

Summary: We present GOssTo, the Gene Ontology semantic similarity Tool, a user-friendly software system for calculating semantic similarities between gene products according to the Gene Ontology. GOssTo is bundled with six semantic similarity measures, including both term- and graph-based measures, and has extension capabilities to allow the user to add new similarities. Importantly, for any measure, GOssTo can also calculate the Random Walk Contribution that has been shown to greatly improve the accuracy of similarity measures. GOssTo is very fast, easy to use, and it allows the calculation of similarities on a genomic scale in a few minutes on a regular desktop machine.

Contact: alberto@cs.rhul.ac.uk

Availability: GOssTo is available both as a stand-alone application running on GNU/Linux, Windows and MacOS from www.paccanarolab.org/gossto and as a web application from www.paccanarolab.org/gosstoweb. The stand-alone application features a simple and concise command line interface for easy integration into high-throughput data processing pipelines.

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References

Ferreira J, Hastings J, Couto F . Exploiting disjointness axioms to improve semantic similarity measures. Bioinformatics. 2013; 29(21):2781-7. DOI: 10.1093/bioinformatics/btt491. View

Ashburner M, Ball C, Blake J, Botstein D, Butler H, Cherry J . Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000; 25(1):25-9. PMC: 3037419. DOI: 10.1038/75556. View

Du Z, Li L, Chen C, Yu P, Wang J . G-SESAME: web tools for GO-term-based gene similarity analysis and knowledge discovery. Nucleic Acids Res. 2009; 37(Web Server issue):W345-9. PMC: 2703883. DOI: 10.1093/nar/gkp463. View

Mazandu G, Mulder N . Information content-based gene ontology semantic similarity approaches: toward a unified framework theory. Biomed Res Int. 2013; 2013:292063. PMC: 3775452. DOI: 10.1155/2013/292063. View

Yang H, Nepusz T, Paccanaro A . Improving GO semantic similarity measures by exploring the ontology beneath the terms and modelling uncertainty. Bioinformatics. 2012; 28(10):1383-9. DOI: 10.1093/bioinformatics/bts129. View