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This article is part of the supplement: Proceedings of the Bio-Ontologies Special Interest Group 2011

Open Access Proceedings

Logical Gene Ontology Annotations (GOAL): exploring gene ontology annotations with OWL

Simon Jupp1*, Robert Stevens2 and Robert Hoehndorf3

Author Affiliations

1 European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SD, UK

2 School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK

3 Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK

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Journal of Biomedical Semantics 2012, 3(Suppl 1):S3  doi:10.1186/2041-1480-3-S1-S3

Published: 24 April 2012

Abstract

Motivation

Ontologies such as the Gene Ontology (GO) and their use in annotations make cross species comparisons of genes possible, along with a wide range of other analytical activities. The bio-ontologies community, in particular the Open Biomedical Ontologies (OBO) community, have provided many other ontologies and an increasingly large volume of annotations of gene products that can be exploited in query and analysis. As many annotations with different ontologies centre upon gene products, there is a possibility to explore gene products through multiple ontological perspectives at the same time. Questions could be asked that link a gene product’s function, process, cellular location, phenotype and disease. Current tools, such as AmiGO, allow exploration of genes based on their GO annotations, but not through multiple ontological perspectives. In addition, the semantics of these ontology’s representations should be able to, through automated reasoning, afford richer query opportunities of the gene product annotations than is currently possible.

Results

To do this multi-perspective, richer querying of gene product annotations, we have created the Logical Gene Ontology, or GOAL ontology, in OWL that combines the Gene Ontology, Human Disease Ontology and the Mammalian Phenotype Ontology, together with classes that represent the annotations with these ontologies for mouse gene products. Each mouse gene product is represented as a class, with the appropriate relationships to the GO aspects, phenotype and disease with which it has been annotated. We then use defined classes to query these protein classes through automated reasoning, and to build a complex hierarchy of gene products. We have presented this through a Web interface that allows arbitrary queries to be constructed and the results displayed.

Conclusion

This standard use of OWL affords a rich interaction with Gene Ontology, Human Disease Ontology and Mammalian Phenotype Ontology annotations for the mouse, to give a fine partitioning of the gene products in the GOAL ontology. OWL in combination with automated reasoning can be effectively used to query across ontologies to ask biologically rich questions. We have demonstrated that automated reasoning can be used to deliver practical on-line querying support for the ontology annotations available for the mouse.

Availability

The GOAL Web page is to be found at http://owl.cs.manchester.ac.uk/goal webcite.