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Modeling Sample Variables with an Experimental Factor Ontology

Overview
Journal Bioinformatics
Publisher Oxford University Press
Specialty Biology
Date 2010 Mar 5
PMID 20200009
Citations 282
Authors
James Malone
Ele Holloway
Tomasz Adamusiak
Misha Kapushesky
Jie Zheng
Nikolay Kolesnikov
Anna Zhukova
Alvis Brazma
Helen Parkinson
Affiliations
Soon will be listed here.
Abstract

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.

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