| URL: | http://www.ebi.ac.uk/efo |
| Full name: | Experimental Factor Ontology |
| Description: | Data-driven application ontology for annotation and data visualisation. EFO provides a systematic description of many experimental variables available in EBI databases, and for external projects such as the NHGRI GWAS catalog. It combines parts of several biological ontologies, such as UBERON anatomy, ChEBI chemical compounds, and Cell Ontology. The scope of EFO is to support the annotation, analysis and visualization of data handled by many groups at the EBI and as the core ontology for Open Targets. |
| Year founded: | 2010 |
| Last update: | 2019-3-19 |
| Version: | version3 |
| Accessibility: | |
| Country/Region: | United Kingdom |
| Data type: | |
| Data object: |
NA
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| Database category: | |
| Major species: |
NA
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| Keywords: |
| University/Institution: | European Bioinformatics Institute |
| Address: | EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK |
| City: | Cambridge |
| Province/State: | |
| Country/Region: | United Kingdom |
| Contact name (PI/Team): | James Malone |
| Contact email (PI/Helpdesk): | efo-users@ebi.ac.uk |
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Modeling sample variables with an Experimental Factor Ontology. [PMID: 20200009]
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. |