Modeling sample variables with an Experimental Factor Ontology

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.

Fig. 1.

EFO upper level structure used to organize the ontology with intermediate node examples.

Fig. 2.

Separating the ontology layer (EFO) from the data (ArrayExpress) and the presentation layers (Atlas).

Fig. 3.

Added value relations between classes in EFO. The figure illustrates the existential restrictions (i.e. one or more relationship) placed on some of the subclasses of the classes shown (classes shown in boxes).

Fig. 4.

Gene Expression Atlas Query for genes under- or overexpressed in mammalian ‘craniofacial tissues’.

Fig. 5.

Ontology-enabled search using EFO, showing query expansion for keyword ‘cancer’ with breast carcinoma selected. Subtypes (red), synonyms (green) and matches to the search term (yellow) shown in the ArrayExpress Archive.