Agronomic Linked Data (AgroLD): A knowledge-based system to enable integrative biology in agronomy

PLoS One. 2018 Nov 30;13(11):e0198270. doi: 10.1371/journal.pone.0198270. eCollection 2018.


Recent advances in high-throughput technologies have resulted in a tremendous increase in the amount of omics data produced in plant science. This increase, in conjunction with the heterogeneity and variability of the data, presents a major challenge to adopt an integrative research approach. We are facing an urgent need to effectively integrate and assimilate complementary datasets to understand the biological system as a whole. The Semantic Web offers technologies for the integration of heterogeneous data and their transformation into explicit knowledge thanks to ontologies. We have developed the Agronomic Linked Data (AgroLD-, a knowledge-based system relying on Semantic Web technologies and exploiting standard domain ontologies, to integrate data about plant species of high interest for the plant science community e.g., rice, wheat, arabidopsis. We present some integration results of the project, which initially focused on genomics, proteomics and phenomics. AgroLD is now an RDF (Resource Description Format) knowledge base of 100M triples created by annotating and integrating more than 50 datasets coming from 10 data sources-such as and TropGeneDB-with 10 ontologies-such as the Gene Ontology and Plant Trait Ontology. Our evaluation results show users appreciate the multiple query modes which support different use cases. AgroLD's objective is to offer a domain specific knowledge platform to solve complex biological and agronomical questions related to the implication of genes/proteins in, for instances, plant disease resistance or high yield traits. We expect the resolution of these questions to facilitate the formulation of new scientific hypotheses to be validated with a knowledge-oriented approach.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Agriculture*
  • Genome, Plant
  • Genomics*
  • Knowledge Bases*
  • Proteomics*

Grant support

This research was supported by the Computational Biology Institute of Montpellier (ANR-11-BINF-0002 - - project:, the Institut Francais de Bioinformatique (ANR-11-INBS-0013 - - project:, the Labex Agro (ANR-10-LABX-001-01 - - project: all bypass of the French ANR Investissements d’Avenir program ( The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.