Logical Gene Ontology Annotations (GOAL): Exploring Gene Ontology Annotations with OWL

Overview

Journal J Biomed Semantics

Publisher Biomed Central

Specialties Biology
Biomedical Engineering

Date 2012 May 1

PMID 22541594

Citations 9

Authors

Simon Jupp

Robert Stevens

Robert Hoehndorf

Affiliations

Soon will be listed here.

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.

Citing Articles

Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications.

Alshahrani M, Almansour A, Alkhaldi A, A Thafar M, Uludag M, Essack M PeerJ. 2022; 10:e13061.

PMID: 35402106 PMC: 8988936. DOI: 10.7717/peerj.13061.

Automated generation of gene summaries at the Alliance of Genome Resources.

Kishore R, Arnaboldi V, Van Slyke C, Chan J, Nash R, Urbano J Database (Oxford). 2020; 2020.

PMID: 32559296 PMC: 7304461. DOI: 10.1093/database/baaa037.

Formal axioms in biomedical ontologies improve analysis and interpretation of associated data.

Smaili F, Gao X, Hoehndorf R Bioinformatics. 2019; 36(7):2229-2236.

PMID: 31821406 PMC: 7141863. DOI: 10.1093/bioinformatics/btz920.

Aber-OWL: a framework for ontology-based data access in biology.

Hoehndorf R, Slater L, Schofield P, Gkoutos G BMC Bioinformatics. 2015; 16:26.

PMID: 25627673 PMC: 4384359. DOI: 10.1186/s12859-015-0456-9.

Thematic series on biomedical ontologies in JBMS: challenges and new directions.

Hoehndorf R, Haendel M, Stevens R, Rebholz-Schuhmann D J Biomed Semantics. 2014; 5:15.

PMID: 24602198 PMC: 4006457. DOI: 10.1186/2041-1480-5-15.

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