AgroLD: A knowledge graph for the plant sciences
2021
Larmande, Pierre | Todorov, Konstantin | Diversité, adaptation, développement des plantes (UMR DIADE) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [Occitanie]) | Institut Français de Bioinformatique (IFB-core) ; Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | WEB Architecture x Semantic WEB x WEB of Data (WEB3) ; Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM) ; Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS) | ANR-19-DATA-0019,FooSIN,Participation française au GO FAIR Food Systems Implementation Network(2019) | ANR-18-CE23-0017,D2KAB,Des Données aux Connaissances en Agronomie et Biodiversité(2018)
International audience
Mostrar más [+] Menos [-]Inglés. Recent advances in sequencing technologies and high-throughput phenotyping have revolutionized the analysis in the field of the plant sciences. However, there is an urgent need to effectively integrate and assimilate complementary information to understand the biological system in its entirety. We have developed AgroLD, a knowledge graph that exploits Semantic Web technologies to integrate information on plant species and in this way facilitate the formulation and validation of new scientific hypotheses. AgroLD contains around 900M triples created by annotating and integrating more than 100 datasets coming from 15 data sources. Our objective is to offer a domain specific knowledge platform to answer complex biological and plant sciences questions related to the implication of genes in, for instance, plant disease resistance or adaptative responses to climate change. In this paper, we present results of the project, which focused on genomics, proteomics and phenomics. We present the AgroLD pipeline for lifting the data, the open source tools developed for these purposes, as well as the web application allowing to explore the data.
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