Modeling sample variables with an Experimental Factor Ontology

TLDR

Describing biological sample variables with ontologies is complex because cross‑domain experiments require multiple, rapidly changing, often non‑interoperable ontologies, creating barriers for resource users. The study aims to provide guidelines for applying community reference ontologies to data by describing the methodology and open‑source tools used to create an application ontology. The authors developed open‑source tools for creating ontology mappings, views, detecting changes, and integrating ontologies into interfaces to improve querying. They present the Experimental Factor Ontology, designed to support cross‑domain, application‑focused gene expression data use cases. The ontology and supplementary data are available at http://www.ebi.ac.uk/efo, contact [e-mail].

Abstract

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 Contact: [e-mail] Supplementary information: Supplementary data are available at Bioinformatics online.