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

Abstract

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– www.agrold.org), 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 Gramene.org 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.

GreenPhylDB v5: a comparative pangenomic database for plant genomes

“…It now takes advantage of the bootstrap and D3.js frameworks to improve the user experience and to be more responsive. Alternatively, it can also be accessed programmatically using Resource Description Framework (RDF) as implemented in AgroLD, a knowledge-based system relying on semantic web technologies ( 32 ).…”

AgroLD: A Knowledge Graph for the Plant Sciences

“…OntoForce 7 developed a new tool named DISQOVER for data discovery in life sciences. However, to the best of our knowledge, there was no equivalent in the plant sciences field before the AgroLD platform [4] was launched in 2015. In a related topic, KNETMINER [38] is a graph database for plant molecular network that has been developed with Neo4J and provides also a subset of its datasets through a SPARQL endpoint.…”

“…Our hypothesis is that weaving these data and disparate information into a knowledge graph (KG) would enable the formulation and validation of research hypotheses that would link genotype to phenotype, hence unlocking the potential of the currently available decentralized scientific data. We have developed AgroLD (for Agronomy Linked Data) [4], 4 a FAIR knowledge graph powered by Semantic Web technologies as a structure to integrate data, to enable knowledge sharing and to allow information retrieval at scale. It is designed to integrate available information on various plant model species in the agronomic domain such as rice, arabidopsis and wheat, to name a few.…”

Bluster or Lustre: Can AI Improve Crops and Plant Health?

“…Dai et al proposed HRGRN [69], which is an example of Graph powered database for Arabidopsis signaling transduction, metabolism and gene regulatory networks. While Venkatesan et al [70] developed Agronomic Linked Data (AgroLD), a knowledge-based system that uses Semantic Web technologies and standard ontologies to integrate and query data for various plant species, including corn, rice and wheat. KnetMiner [71] represents another plant specific and extensive knowledge base that was developed with the aim to accelerate the gene-trait discovery process.…”