Development of a knowledge graph framework to ease and empower translational approaches in plant research: a use-case on grain legumes

Frontiers in Artificial Intelligence

Artificial Intelligence

Grégoire Aubert¹  Nadim Tayeh¹  Judith Burstin¹  Baptiste Imbert¹  Jonathan Kreplak¹  Raphaël-Gauthier Flores² 

[1] Agroécologie, INRAE, Institut Agro, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Dijon, France;Université Paris-Saclay, INRAE, URGI, Versailles, France;Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, Versailles, France;

关键词: graph database;    orthology;    ontology;    quantitative genetics;    gene expression;    comparative omics;    Ortho_KB;    OrthoLegKB;   

DOI  :  10.3389/frai.2023.1191122

 received in 2023-03-21, accepted in 2023-07-10,  发布年份 2023

来源: Frontiers

【 摘 要 】

While the continuing decline in genotyping and sequencing costs has largely benefited plant research, some key species for meeting the challenges of agriculture remain mostly understudied. As a result, heterogeneous datasets for different traits are available for a significant number of these species. As gene structures and functions are to some extent conserved through evolution, comparative genomics can be used to transfer available knowledge from one species to another. However, such a translational research approach is complex due to the multiplicity of data sources and the non-harmonized description of the data. Here, we provide two pipelines, referred to as structural and functional pipelines, to create a framework for a NoSQL graph-database (Neo4j) to integrate and query heterogeneous data from multiple species. We call this framework Orthology-driven knowledge base framework for translational research (Ortho_KB). The structural pipeline builds bridges across species based on orthology. The functional pipeline integrates biological information, including QTL, and RNA-sequencing datasets, and uses the backbone from the structural pipeline to connect orthologs in the database. Queries can be written using the Neo4j Cypher language and can, for instance, lead to identify genes controlling a common trait across species. To explore the possibilities offered by such a framework, we populated Ortho_KB to obtain OrthoLegKB, an instance dedicated to legumes. The proposed model was evaluated by studying the conservation of a flowering-promoting gene. Through a series of queries, we have demonstrated that our knowledge graph base provides an intuitive and powerful platform to support research and development programmes.

【 授权许可】

Unknown   
Copyright © 2023 Imbert, Kreplak, Flores, Aubert, Burstin and Tayeh.