Journal Article

Modeling sample variables with an Experimental Factor Ontology

James Malone, Ele Holloway, Tomasz Adamusiak, Misha Kapushesky, Jie Zheng, Nikolay Kolesnikov, Anna Zhukova, Alvis Brazma and Helen Parkinson

in Bioinformatics

Volume 26, issue 8, pages 1112-1118
Published in print April 2010 | ISSN: 1367-4803
Published online March 2010 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btq099

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Motivation: Describing biological sample variables with ontologies is complex due to the cross-domain nature of experiments. Ontologies provide annotation solutions; however, for cross-domain investigations, multiple ontologies are needed to represent the data. These are subject to rapid change, are often not interoperable and present complexities that are a barrier to biological resource users.

Results: We present the Experimental Factor Ontology, designed to meet cross-domain, application focused use cases for gene expression data. We describe our methodology and open source tools used to create the ontology. These include tools for creating ontology mappings, ontology views, detecting ontology changes and using ontologies in interfaces to enhance querying. The application of reference ontologies to data is a key problem, and this work presents guidelines on how community ontologies can be presented in an application ontology in a data-driven way.

Availability: http://www.ebi.ac.uk/efo

Contact: malone@ebi.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6281 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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