ARS | Publication request: Towards a reference plant trait ontology for modeling knowledge of plant traits and phenotypes

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: June 12, 2012
Publication Date: October 4, 2012
Citation: Arnaud, E., Cooper, L., Shrestha, R., Menda, N., Nelson, R., Ramil, M., Matteis, L., Skofic, M., Bastow, R., Jaiswal, P., Mueller, L., Mclaren, G. 2012. Towards a reference plant trait ontology for modeling knowledge of plant traits and phenotypes. Meeting Abstract. Paper No. 67.

Interpretive Summary: SoyBase is a genomics and genetics database for soybean. There are many other databases concerned with genetics and genomics of other crops species. Some of these species might benefit from the extensive data known about soybean. The ability to exchange or even point users to related data between databases has been hampered by the use of crop, clade or species specific terms. A controlled vocabulary applicable to all plant traits would allow the various databases to tag data with this vocabulary. These "tags" could be used by researchers to find all related data in any plant database regardless of the terms used by the individual crops or species. These "tags" can also be used to make automated querying of data possible across databases using web services.

Technical Abstract: Ontology engineering and knowledge modeling for the plant sciences is expected to contribute to the understanding of the basis of plant traits that determine phenotypic expression in a given environment. Several crop- or clade-specific plant trait ontologies have been developed to describe plant traits important for agriculture in order to address major scientific challenges such as food security. We present three successful species and/or clade-specific examples here: the Crop Ontology, the Solanaceae Genome Nework and SoyBase. These ontologies address the needs of crop scientists and plant breeders to quickly access a wide range of trait related data, but their scope limits their interoperability with one another. In this paper, we present our vision of disparate knowledge domains and that will support data integration and data mining across species.