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OPPL-Galaxy, a Galaxy tool for enhancing ontology exploitation as part of bioinformatics workflows.

Aranguren ME, Fernández-Breis JT, Mungall C, Antezana E, González AR, Wilkinson MD - J Biomed Semantics (2013)

Bottom Line: Use cases are provided in order to demonstrate OPPL-Galaxy's capability for enriching, modifying and querying biomedical ontologies.Coupling OPPL-Galaxy with other bioinformatics tools of the Galaxy framework results in a system that is more than the sum of its parts.OPPL-Galaxy opens a new dimension of analyses and exploitation of biomedical ontologies, including automated reasoning, paving the way towards advanced biological data analyses.

View Article: PubMed Central - HTML - PubMed

Affiliation: Ontology Engineering Group, School of Computer Science, Technical University of Madrid (UPM), Boadilla del Monte, 28660, Spain. mikel.egana.aranguren@upm.es.

ABSTRACT

Background: Biomedical ontologies are key elements for building up the Life Sciences Semantic Web. Reusing and building biomedical ontologies requires flexible and versatile tools to manipulate them efficiently, in particular for enriching their axiomatic content. The Ontology Pre Processor Language (OPPL) is an OWL-based language for automating the changes to be performed in an ontology. OPPL augments the ontologists' toolbox by providing a more efficient, and less error-prone, mechanism for enriching a biomedical ontology than that obtained by a manual treatment.

Results: We present OPPL-Galaxy, a wrapper for using OPPL within Galaxy. The functionality delivered by OPPL (i.e. automated ontology manipulation) can be combined with the tools and workflows devised within the Galaxy framework, resulting in an enhancement of OPPL. Use cases are provided in order to demonstrate OPPL-Galaxy's capability for enriching, modifying and querying biomedical ontologies.

Conclusions: Coupling OPPL-Galaxy with other bioinformatics tools of the Galaxy framework results in a system that is more than the sum of its parts. OPPL-Galaxy opens a new dimension of analyses and exploitation of biomedical ontologies, including automated reasoning, paving the way towards advanced biological data analyses.

No MeSH data available.


Related in: MedlinePlus

Toy ontology for OWL rendering convention. Toy ontology to illustrate the convention for representing abstract OWL structures in Figures depicting use cases. Above, the ontology is rendered using MOS; below, the ontology is rendered with the same convention as in Figures 2, 5, 6, 10 and 14. In those Figures, however, names of OWL entities are not included in the ontologies, since OPPL scripts act on absract structures (any axiomatic pattern that matches the query). Solid circle: named class; dotted circle: anonymous class; dot: named individual; solid arrow: subClassOf axiom; dotted arrow: triple (relation between individuals); line ending in circle: restriction (the small circle points to the filler class; there is no distinction between necessary and necessary/sufficient conditions)a.
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Figure 1: Toy ontology for OWL rendering convention. Toy ontology to illustrate the convention for representing abstract OWL structures in Figures depicting use cases. Above, the ontology is rendered using MOS; below, the ontology is rendered with the same convention as in Figures 2, 5, 6, 10 and 14. In those Figures, however, names of OWL entities are not included in the ontologies, since OPPL scripts act on absract structures (any axiomatic pattern that matches the query). Solid circle: named class; dotted circle: anonymous class; dot: named individual; solid arrow: subClassOf axiom; dotted arrow: triple (relation between individuals); line ending in circle: restriction (the small circle points to the filler class; there is no distinction between necessary and necessary/sufficient conditions)a.

Mentions: The actions are based on the addition or removal of axioms of any complexity to/from entities retrieved by the query (OWL classes, properties, or instances). Once an OPPL script has been defined, the OPPL engine is passed this script and the ontology to be modified. The OPPL engine, in turn, modifies the ontology according to the changes defined in the OPPL script, generating a new ontology (Figures 1 and 2).


OPPL-Galaxy, a Galaxy tool for enhancing ontology exploitation as part of bioinformatics workflows.

Aranguren ME, Fernández-Breis JT, Mungall C, Antezana E, González AR, Wilkinson MD - J Biomed Semantics (2013)

Toy ontology for OWL rendering convention. Toy ontology to illustrate the convention for representing abstract OWL structures in Figures depicting use cases. Above, the ontology is rendered using MOS; below, the ontology is rendered with the same convention as in Figures 2, 5, 6, 10 and 14. In those Figures, however, names of OWL entities are not included in the ontologies, since OPPL scripts act on absract structures (any axiomatic pattern that matches the query). Solid circle: named class; dotted circle: anonymous class; dot: named individual; solid arrow: subClassOf axiom; dotted arrow: triple (relation between individuals); line ending in circle: restriction (the small circle points to the filler class; there is no distinction between necessary and necessary/sufficient conditions)a.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3643862&req=5

Figure 1: Toy ontology for OWL rendering convention. Toy ontology to illustrate the convention for representing abstract OWL structures in Figures depicting use cases. Above, the ontology is rendered using MOS; below, the ontology is rendered with the same convention as in Figures 2, 5, 6, 10 and 14. In those Figures, however, names of OWL entities are not included in the ontologies, since OPPL scripts act on absract structures (any axiomatic pattern that matches the query). Solid circle: named class; dotted circle: anonymous class; dot: named individual; solid arrow: subClassOf axiom; dotted arrow: triple (relation between individuals); line ending in circle: restriction (the small circle points to the filler class; there is no distinction between necessary and necessary/sufficient conditions)a.
Mentions: The actions are based on the addition or removal of axioms of any complexity to/from entities retrieved by the query (OWL classes, properties, or instances). Once an OPPL script has been defined, the OPPL engine is passed this script and the ontology to be modified. The OPPL engine, in turn, modifies the ontology according to the changes defined in the OPPL script, generating a new ontology (Figures 1 and 2).

Bottom Line: Use cases are provided in order to demonstrate OPPL-Galaxy's capability for enriching, modifying and querying biomedical ontologies.Coupling OPPL-Galaxy with other bioinformatics tools of the Galaxy framework results in a system that is more than the sum of its parts.OPPL-Galaxy opens a new dimension of analyses and exploitation of biomedical ontologies, including automated reasoning, paving the way towards advanced biological data analyses.

View Article: PubMed Central - HTML - PubMed

Affiliation: Ontology Engineering Group, School of Computer Science, Technical University of Madrid (UPM), Boadilla del Monte, 28660, Spain. mikel.egana.aranguren@upm.es.

ABSTRACT

Background: Biomedical ontologies are key elements for building up the Life Sciences Semantic Web. Reusing and building biomedical ontologies requires flexible and versatile tools to manipulate them efficiently, in particular for enriching their axiomatic content. The Ontology Pre Processor Language (OPPL) is an OWL-based language for automating the changes to be performed in an ontology. OPPL augments the ontologists' toolbox by providing a more efficient, and less error-prone, mechanism for enriching a biomedical ontology than that obtained by a manual treatment.

Results: We present OPPL-Galaxy, a wrapper for using OPPL within Galaxy. The functionality delivered by OPPL (i.e. automated ontology manipulation) can be combined with the tools and workflows devised within the Galaxy framework, resulting in an enhancement of OPPL. Use cases are provided in order to demonstrate OPPL-Galaxy's capability for enriching, modifying and querying biomedical ontologies.

Conclusions: Coupling OPPL-Galaxy with other bioinformatics tools of the Galaxy framework results in a system that is more than the sum of its parts. OPPL-Galaxy opens a new dimension of analyses and exploitation of biomedical ontologies, including automated reasoning, paving the way towards advanced biological data analyses.

No MeSH data available.


Related in: MedlinePlus