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R McEntire

Publications and source records attributed to R McEntire.

2 recordsLinked to original sources

Constructing ontology-driven protein family databases.

MOTIVATION: Protein family databases provide a central focus for scientific communities as well as providing useful resources to aide research. However, such resources require constant curation and often become outdated and discontinued. We have developed an ontology-driven system for capturing and managing protein family data that addresses the problems of maintenance and sustainability. RESULTS: Using protein phosphatases and ABC transporters as model protein families, we constructed two protein family database resources around a central DAML+OIL ontology. Each resource contains specialist information about each protein family, providing specialized domain-specific resources based on the same template structure. The formal structure, combined with the extraction of biological data using GO terms, allows for automated update strategies. Despite the functional differences between the two protein families, the ontology model was equally applicable to both, demonstrating the generic nature of the system. AVAILABILITY: The protein phosphatase resource, PhosphaBase, is freely available on the internet (http://www.bioinf.man.ac.uk/phosphabase). The DAML+OIL ontology for the protein phosphatases and the ABC transporters is available on request from the authors. CONTACT: kwolstencroft@cs.man.ac.uk.

ATP-Binding Cassette Transporters↗

An evaluation of ontology exchange languages for bioinformatics.

Ontologies are specifications of the concepts in a given field, and of the relationships among those concepts. The development of ontologies for molecular-biology information and the sharing of those ontologies within the bioinformatics community are central problems in bioinformatics. If the bioinformatics community is to share ontologies effectively, ontologies must be exchanged in a form that uses standardized syntax and semantics. This paper reports on an effort among the authors to evaluate alternative ontology-exchange languages, and to recommend one or more languages for use within the larger bioinformatics community. The study selected a set of candidate languages, and defined a set of capabilities that the ideal ontology-exchange language should satisfy. The study scored the languages according to the degree to which they satisfied each capability. In addition, the authors performed several ontology-exchange experiments with the two languages that received the highest scores: OML and Ontolingua. The result of those experiments, and the main conclusion of this study, was that the frame-based semantic model of Ontolingua is preferable to the conceptual graph model of OML, but that the XML-based syntax of OML is preferable to the Lisp-based syntax of Ontolingua.

Computational Biology↗