Post-composing ontology terms for efficient phenotyping in plant breeding.

Area of Science:

• Agricultural Science
• Bioinformatics
• Data Science

Background:

• Ontologies standardize data for improved quality, integration, and comparison in databases.
• Plant breeding requires both standardized data and flexibility for specific experimental needs.

Purpose of the Study:

• To implement a post-composing tool within Breedbase for creating tailored phenotypic data terms.
• To reconcile the need for standardized crop ontologies with the flexibility required by plant breeders.

Main Methods:

• Developed a post-composing tool using orthogonal ontologies within the Breedbase digital ecosystem.
• Enabled users to create granular, reusable terms across categories like plant anatomy, treatments, and breeding cycles.

Main Results:

• Users can generate highly specific terms for accurate phenotyping, accommodating unique experimental designs.
• Post-composed terms are reusable within Breedbase instances but not exported to reference ontologies.

Conclusions:

• The post-composing tool enhances Breedbase's utility by allowing customized phenotyping data capture.
• This approach balances data standardization with the practical flexibility needed for efficient plant breeding.