title: Modeling sample variables with an Experimental Factor Ontology.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  http://www.ebi.ac.uk/efo.
authors.affiliation: Microarray Informatics Team, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. malone@ebi.ac.uk
authors.forename: James
authors.initials: J
authors.lastname: Malone
authors.forename: Ele
authors.initials: E
authors.lastname: Holloway
authors.forename: Tomasz
authors.initials: T
authors.lastname: Adamusiak
authors.forename: Misha
authors.initials: M
authors.lastname: Kapushesky
authors.forename: Jie
authors.initials: J
authors.lastname: Zheng
authors.forename: Nikolay
authors.initials: N
authors.lastname: Kolesnikov
authors.forename: Anna
authors.initials: A
authors.lastname: Zhukova
authors.forename: Alvis
authors.initials: A
authors.lastname: Brazma
authors.forename: Helen
authors.initials: H
authors.lastname: Parkinson
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D020869:Gene Expression Profiling / Q000379:methods