# Promotion of Translational Research in Plants by the Development of a Knowledge Graph Framework: A case Study on Grain Legumes

Baptiste Imbert, Jonathan Kreplak, Raphaël Flores, Aubert Grégoire, Burstin Judith, Tayeh Nadim

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## To cite this version:

Baptiste Imbert, Jonathan Kreplak, Raphaël Flores, Aubert Grégoire, Burstin Judith, et al. Promotion of Translational Research in Plants by the Development of a Knowledge Graph Framework: A case Study on Grain Legumes. Plant & Animal Genome Conference (PAG), Jan 2024, San Diego (CA), United States. 2024. [hal-05249655](https://hal.inrae.fr/hal-05249655v1)

HAL Id: hal-05249655

https://hal.inrae.fr/hal-05249655v1

Submitted on 11 Sep 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Promotion of Translational Research in Plants by the Development of a Knowledge Graph Framework: A case Study on Grain Legumes

Baptiste Imbert¹, Jonathan Kreplak¹, Raphaël-Gauthier Flores²,³, Grégoire Aubert¹, Judith Burstin¹, Nadim Tayeh¹

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## ABSTRACT

Legume species are especially important to meet the challenges of agriculture but remain understudied. **Heterogeneous datasets** are available across species. Since legume species are phylogenetically close, their gene content and genome structure are to some extent conserved. With **comparative genomics**, available knowledge can be transfered amongst species. However, the **multiplicity of data sources** and the **non-harmonized description of the data** make this process complex. Here, we created two pipelines: the first infers orthology and identifies syntenic regions on chromosomes while the second integrates biological information such as an association between a genomic region and a phenotypic trait, gene expression or functional annotation, creating a knowledge graph framework called Ortho_KB. To show how the framework can be used, we here describe **OrthoLegKB**, an instance of Ortho_KB dedicated to legumes.

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In the example above, we see that homologs are found across 4 legume species and that the genetic context supports them as positiona orthologs.

**How to facilitate the comparison of several datasets dealing with the same subject?**

Precise vocabulary used to describe an experiment may be specific to a research community. Ontologies exist to solve this issue, formalizing shared concepts and making them explicit, also by organizing them in a taxonomic hierarchy.

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:param inferenceParams => ({ inCatRel: "HAS_SUBJECT", subCatRel: "SCO"});

MATCH (po:Resource:PO {label: "shoot system"})

YIELD node

WITH COLLECT(node condition_id) as shoot conditions

MATCH (g:Gene)-[expr]->(s:Sample)-->(c:Condition)-[:IS_TISSUE]->(r:Resource)

WHERE g.gene_id in $list genes of interest

AND c condition_id in shoot conditions

RETURN g.gene_id, s.sample_id, c condition_id, expr[normalized_count, r.label]

## Introduction

- **Legume** crops need to be improved to better resist biotic and abiotic stresses

- Close legume species face **similar stresses**

- **Genetic** and **-omic** data are available for different traits on different species and would be interesting to compare

- **Graph databases** are efficient to query highly connected data

## Methods

This work was based on the use of:

- **Nextflow** to create reproducible **pipelines**

- **Neo4j** to create a **graph database** to link heterogeneous data

- **Ontologies** to use a common vocabulary across datasets and species

## Results

- A structural pipeline to detect **orthology and synteny** was developed

- A functional pipeline to link **heterogeneous data** in a graph structure was generated

- **OrthoLegKB**, an instance of Ortho_KB, for grain legumes is now available

## PERSPECTIVES

- Adapt the graph model to host new data types

- Widen the use of

Ortho_KB and OrthoLegKB

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Access to OrthoLegKB

## CONCLUSIONS

- Translational research can be performed using graph databases to transfer heterogeneous knowledge across species in a single environment

- Ontologies and precise metadata are required for accurate comparisons