Post-composing ontology terms for efficient phenotyping in plant breeding

Ontologies are widely used in databases to standardize data, improving data quality, integration, and ease of comparison. Within ontologies tailored to diverse use cases, post-composing user-defined terms reconciles the demands for standardization on the one hand and flexibility on the other. In many instances of Breedbase, a digital ecosystem for plant breeding designed for genomic selection, the goal is to capture phenotypic data using highly curated and rigorous crop ontologies, while adapting to the specific requirements of plant breeders to record data quickly and efficiently. For example, post-composing enables users to tailor ontology terms to suit specific and granular use cases such as repeated measurements on different plant parts and special sample preparation techniques. To achieve this, we have implemented a post-composing tool based on orthogonal ontologies providing users with the ability to introduce additional levels of phenotyping granularity tailored to unique experimental designs. Post-composed terms are designed to be reused by all breeding programs within a Breedbase instance but are not exported to the crop reference ontologies. Breedbase users can post-compose terms across various categories, such as plant anatomy, treatments, temporal events, and breeding cycles, and, as a result, generate highly specific terms for more accurate phenotyping.

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Publication Details

Journal
Database
Published
2025-01-01
DOI
https://doi.org/10.1093/database/baaf020
Citations
1
Primary Topic
Plant Pathogens and Fungal Diseases
Type
article
Field-Weighted Citation Impact
1.15

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article

Post-composing ontology terms for efficient phenotyping in plant breeding

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1 citations
Database
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1.15
article

Post-composing ontology terms for efficient phenotyping in plant breeding

Violet Akech, Bryan J. Ellerbrock, Elizabeth Arnaud, Lukas A. Mueller, David Lyon, Églantine Fauvelle, Naama Menda, Dominique Dufour, Paterne A. Agre, Marie‐Angélique Laporte, Laurel Cooper, Karima Meghar, Thierry Tran, Afolabi Agbona, Amos Asiimwe, Christiano C. Simoes, Isaak Tecle, Prasad Peteti, Mirella Flores-Gonzalez, Christine Nyaga, Srikanth Kumar Karaikal
article en
1 citations

Abstract

Ontologies are widely used in databases to standardize data, improving data quality, integration, and ease of comparison. Within ontologies tailored to diverse use cases, post-composing user-defined terms reconciles the demands for standardization on the one hand and flexibility on the other. In many instances of Breedbase, a digital ecosystem for plant breeding designed for genomic selection, the goal is to capture phenotypic data using highly curated and rigorous crop ontologies, while adapting to the specific requirements of plant breeders to record data quickly and efficiently. For example, post-composing enables users to tailor ontology terms to suit specific and granular use cases such as repeated measurements on different plant parts and special sample preparation techniques. To achieve this, we have implemented a post-composing tool based on orthogonal ontologies providing users with the ability to introduce additional levels of phenotyping granularity tailored to unique experimental designs. Post-composed terms are designed to be reused by all breeding programs within a Breedbase instance but are not exported to the crop reference ontologies. Breedbase users can post-compose terms across various categories, such as plant anatomy, treatments, temporal events, and breeding cycles, and, as a result, generate highly specific terms for more accurate phenotyping.

DatabaseVol. 2025
Centre de Coopération Internationale en Recherche Agronomique pour le Développement (FR), Oregon State University (US), Alliance Bioversity International - CIAT (IT), Lawrence Berkeley National Laboratory (US), Cornell University (US), International Institute of Tropical Agriculture (UG), Institut Agro Montpelier (FR), Institut de Recherche pour le Développement (FR), National Agricultural Research Organisation (UG), UMR QualiSud, Clemson University (US), Ithaca College (US)
National Science Foundation
Openalex Percentile: Top 26%
Plant Pathogens and Fungal Diseases
1.15
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