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# Making Scientific Research More Efficient through the Use of Ontologies: An Illustration through the Knowledge Graph Ortho_KB for translational research

Baptiste Imbert, Jonathan Kreplak, Nadim Tayeh

► To cite this version:

Baptiste Imbert, Jonathan Kreplak, Nadim Tayeh. Making Scientific Research More Efficient through the Use of Ontologies: An Illustration through the Knowledge Graph Ortho_KB for translational research. PhenoHarmonIS, May 2024, Montpellier, France. hal-05249679

HAL Id: hal-05249679

https://hal.inrae.fr/hal-05249679v1

Submitted on 11 Sep 2025

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# Making Scientific Research More Efficient through the Use of Ontologies: An Illustration through the Knowledge Graph Ortho_KB for translational research

Baptiste Imbert

3rd year PhD student

<figure><img src="image_3.png" /></figure>

Meeting 4: Planteome and the Plant Stress Ontology
<figure><img src="image_4.png" /></figure>

CARBOHYDRATES

NITROGEN IN THE AIR

Microbes "fix" nitrogen in nodules on the roots of the pulse crops.

→ nitrogen source for next crops

→ diversification crops in rotations

## Agronomy

### Seed composition

<figure><img src="image_5.png" /></figure>

→ high protein content

→ high starch content

<figure><img src="image_6.png" /><figcaption>Food</figcaption></figure>

<figure><img src="image_7.png" /></figure>

Globally consumed

~ 11 main crop species used worldwide

Semba et al., Global Food Security, 2021

## Nutrition

<figure><img src="image_8.png" /><figcaption>Feed</figcaption></figure>

<figure><img src="image_9.png" /></figure>
<figure><img src="image_10.png" /><figcaption>Leaf weevils</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Bruchids</figcaption></figure>

## BIOTIC STRESSES

<figure><img src="image_12.png" /><figcaption>Root pathogens</figcaption></figure>

<figure><img src="image_13.png" /></figure>

## ABIOTIC STRESSES

<figure><img src="image_14.png" /><figcaption>Heat / drought</figcaption></figure>

and many others...
* Most stresses are common to several legume species

* We hypothesize that stress resistance/tolerance in close species emerges from a **conserved genetic determinism**

* A piece of information obtained on one species under particular experimental conditions can be:

- **compared** with information obtained from separate datasets

- **transferred** to other close species } **TRANSLATIONAL RESEARCH**

<figure><img src="image_15.png" /></figure>
* Most stresses are common to several legume species

* We hypothesize that stress resistance/tolerance in close species emerges from a **conserved genetic determinism**

* A piece of information obtained on one species under particular experimental conditions can be:

- **compared** with information obtained from separate datasets

- **transferred** to other close species ⌜**TRANSLATIONAL RESEARCH**

*How can we best take advantage of heterogeneous datasets and conduct successful translational research?*

<figure><img src="image_16.png" /></figure>
We developed **Ortho_KB**, a graph framework based on **neo4j**, to integrate **genetic** and **expression** data from multiple species and perform **translational research**.

<figure><img src="image_17.png" /></figure>
We developed **Ortho_KB**, a graph framework based on **neo4j**, to integrate **genetic** and **expression** data from multiple species and perform **translational research**.

<figure><img src="image_18.png" /></figure>
* From research papers and data from the lab, we observe:

- differences in the scientific vocabulary used

- mutiplicity of the methods used to measure a single trait

* **Issues of synonyms**
  * *bruchid beetles* (syn. *bruchid weevil*)
  * *bruchids* (syn. *pea weevil*)

<figure><img src="image_19.png" /><figcaption>Faba bean seeds infested by bruchids</figcaption></figure>

* **Inconsistencies in the spelling of the trait name**
  - resistance to seed weevil
  - seed weevil resistance
* From research papers and data from the lab, we observe:

- differences in the scientific vocabulary used

- mutiplicity of the methods used to measure a single trait

<figure><img src="image_20.png" /></figure>

Faba bean seeds infested
by bruchids

resistance to bruchids

percentage of seeds
showing infestation

can the larvae **get**
**inside** the seed?

percentage of
larval mortality

can the larvae
**develop** in the seed?
* From research papers and data from the lab, we observe:

- differences in the scientific vocabulary used

- mutiplicity of the methods used to measure a single trait

<figure><img src="image_21.png" /><figcaption>Faba bean seeds infested
by bruchids</figcaption></figure>

resistance to bruchids

Ontologies?

<figure><img src="image_22.png" /></figure>

percentage of seeds
showing infestation

<figure><img src="image_23.png" /></figure>

percentage of
larval mortality

can the larvae **get**
**inside** the seed?

can the larvae
**develop** in the seed?
# Neo4j can handle the integration of ontologies

* Genes are functionally annotated by transferring annotation using sequence similarity, with the **Gene Ontology** (The Gene Ontology Consortium, *Genetics*, 2023) and **MapMan** (Schwacke et al., *Molecular Plant*, 2019)

* These ontologies are included in the graph of the database using the **neosemantics** plugin for Neo4j:

<figure><img src="image_24.png" /></figure>

- each ontology term is a node with properties (name, label, uri)

- terms are linked to their parent node(s) by a **subClassOf** (SCO) edge
# Neo4j can handle the integration of ontologies

* Genes are functionally annotated by transferring annotation using sequence similarity, with the **Gene Ontology** (The Gene Ontology Consortium, *Genetics*, 2023) and **MapMan** (Schwacke et al., *Molecular Plant*, 2019)

* These ontologies are included in the graph of the database using the **neosemantics** plugin for Neo4j:

<figure><img src="image_25.png" /></figure>

- each ontology term is a node with properties (name, label, uri)

- terms are linked to their parent node(s) by a **subClassOf** (SCO) edge

*How to precisely annotate complex entities like **RNA-seq conditions** and **genetic loci** (QTL mapping, GWAS) with ontologies?*
# We use existing ontologies for metadata annotation

* Ontologies that allow to describe metadata for a variety of species are needed and used

* RNA-seq conditions are annotated using ontologies from Planteome (Cooper et al., *Nucleic Acids Res*, 2018):

- **anatomy**: PO

- **growth stage**: PO

- **stress**: PECO

- **growth structure**: PECO

**PO**: Plant Ontology

**PECO**: Plant Experimental Condition Ontology

<figure><img src="image_26.png" /></figure>
# We enrich legume datasets with ontologies

* Support for both annotated RNA-seq and QTL metadata with ontologies is already built into the Ortho_KB framework

* Will soon be available in the public version

<figure><img src="image_27.png" /></figure>

https://ortholegkb.versailles.inrae.fr/browse/
* QTL are being manually annotated with the same descriptors as for RNA-seq, with the addition of trait descriptors using the **Plant Trait Ontology (TO)**

* Some terms are missing in Planteome ontologies

- Temporary terms connected to main ontologies are created

- Working alongside Planteome to add them

<table><thead><tr><th></th><th>Number of annotated QTL</th></tr></thead><tbody><tr><td>January</td><td>1,109</td></tr><tr><td>May</td><td>2,206</td></tr></tbody></table>

<figure><img src="image_28.png" /></figure>
* Ortho_KB can be queried in Cypher for QTL connected to TO terms

<figure><img src="image_29.png" /></figure>

MATCH (q:QTL)-[rel:HAS_TRAIT]->(trait:TO)

RETURN rel.original_desc, trait.label

<figure><img src="image_30.png" /></figure>

<table><thead><tr><td>Original trait name</td><td>TO trait name</td></tr></thead><tbody><tr><td>TGW</td><td>1000-grain weight</td></tr><tr><td>Thousand seed weight</td><td>1000-grain weight</td></tr><tr><td>1000 seed weight (g)</td><td>1000-grain weight</td></tr></tbody></table>

<figure><img src="image_31.png" /></figure>

It is now possible to easily retrieve all the genetic loci associated
Transitivity (logical inference) allows to collect QTL annotated
with the term "seed weevil resistance" or any more specific term

<figure><img src="image_32.png" /><figcaption>Faba bean seeds
infested
by bruchids</figcaption></figure>

<figure><img src="image_33.png" /></figure>

MATCH (trait:TO { label: "seed weevil resistance" })

CALL n10s.inference.nodesInCategory(trait, {
inCatRel: "HAS_TRAIT", subCatRel: "SCO" }) yield node as **qtl**

RETURN **qtl**, **trait**

<figure><img src="image_34.png" /></figure>

seed weevil larval
development trait

seed weevil
resistance

insect
damage
response
development

seed
weevil
infestation
<figure><img src="image_35.png" /></figure>

1) **QTL** are annotated by ontologies for their trait
<figure><img src="image_36.png" /></figure>

1) **QTL** are annotated by ontologies for their trait

2) **QTL** are located on chromosomes and encompass **Genes**
<figure><img src="image_37.png" /></figure>

1) **QTL** are annotated by ontologies for their trait

2) **QTL** are located on chromosomes and encompass **Genes**

3) **Genes** belong to groups of homologs called **Orthogroups**
<figure><img src="image_38.png" /></figure>

1) **QTL** are annotated by ontologies for their trait

2) **QTL** are located on chromosomes and encompass **Genes**

3) **Genes** belong to groups of homologs called **Orthogroups**

4) **Syntenic blocks** link genes from homologous (colinear) chromosome regions
<figure><img src="image_39.png" /></figure>

seed weevil
resistance

<figure><img src="image_40.png" /></figure>

FABA BEAN chr06

PEA chr07

1) **QTL** are annotated by ontologies for their trait

2) **QTL** are located on chromosomes and encompass **Genes**

3) **Genes** belong to groups of homologs called **Orthogroups**

4) **Syntenic blocks** link genes from homologous (colinear) chromosome regions
We can search for syntenic QTL across species

<figure><img src="image_41.png" /></figure>

1) **QTL** are annotated by ontologies for their trait

2) **QTL** are located on chromosomes and encompass **Genes**

3) **Genes** belong to groups of homologs called **Orthogroups**

4) **Syntenic blocks** link genes from homologous (colinear) chromosome regions
We can search for syntenic QTL across species

<figure><img src="image_42.png" /></figure>

We identified syntenic QTL for seed weevil resistance, in pea (Aznar-Fernandez et al. *Sci Rep*, 2020) and faba bean (Ohm et al., *Front. Plant Sci.*, 2024).

Genes ortholog to the *Arabidopsis thaliana* "subunit of the MEDIATOR 16 complex", involved in cell wall composition (Sorek et al., *PNAS*, 2015), in plant immunity against cold (Hemsley et al., *Plant Cell*, 2014) and pathogens

(Wang et al., *Plant Physiology*, 2015).
We can search for syntenic QTL across species

<figure><img src="image_43.png" /></figure>

The 2 genes are expressed in most tissues

We identified syntenic QTL for seed weevil resistance, in pea (Aznar-Fernandez et al. *Sci Rep*, 2020) and faba bean (Ohm et al., *Front. Plant Sci.*, 2024).

Genes ortholog to the *Arabidopsis thaliana* "subunit of the MEDIATOR 16 complex", involved in cell wall composition (Sorek et al., *PNAS*, 2015), in plant immunity against cold (Hemsley et al., *Plant Cell*, 2014) and pathogens

(Wang et al., *Plant Physiology*, 2015).
We can search for colocalizing QTL

<figure><img src="image_44.png" /></figure>

Do we have genetic evidence
that the 2 identified MED16
subunit genes may be
associated with other traits?
We can search for colocalizing QTL

<figure><img src="image_45.png" /></figure>

Do we have genetic evidence
that the 2 identified MED16
subunit genes may be
associated with other traits?

QTL confidence interval size < 50 Mb
<figure><img src="image_46.png" /></figure>

seed weevil
resistance

Do we have genetic evidence
that the 2 identified MED16
subunit genes may be
associated with other traits?

QTL confidence interval size < 50 Mb

The pea gene is also under TSW06_8 → **1000-grain weight** (Klein et al., *Sci Rep*, 2020) and EL_DI21_Flo1_4 → **days to flowering** when subjected to root rot exposure (Leprévost, *Front. Plant Sci.*, 2023)

A syntenic ortholog exists on Medicago truncatula chr04 under EL-4 → **cell membrane stability** in freezing conditions (Avia et al., *Theor Appl Genet*, 2013).
Conclusion

* We created a database to query genetic and -omic data from legumes: **OrthoLegKB**, based on the Ortho_KB framework

* To ensure the consistency of metadata, we used Planteome ontologies to annotate entities

* We can therefore compare datasets and transfer information across species

<figure><img src="image_47.png" /></figure>
* Enrich the Planteome database with our legume QTL annotation

* Use DRAGON-AI (Toro et al. *arXiv*, 2023) or similar tools to ease the annotation thanks to autocompletion

* Add more information from the litterature

- link to The Arabidopsis Information Resource (TAIR)

* Update the public database with:

- new RNA-seq and genetic data

- 1 new species: chickpea
Thanks to all the ECP team and
especially to:

• Nadim Tayeh (supervisor)

• Judith Burstin (co-supervisor)

• Jonathan Kreplak (bioinformatics engineer)

• Grégoire Aubert (engineer)

<figure><img src="image_48.png" /></figure>

<figure><img src="image_49.png" /></figure>
SUPPLEMENTARY
* QTL: 87 different traits integrated with ontologies (including « new » terms)

~ 13 conditions

~ 12 organs

~ From 1,109 annotated QTL when PAG to 2,206 in the actual dev version (public datasets) (29 datasets)

* RNA-seq: 26 tissues ; 14 exp conditions; 17 dev stages (30 datasets)

Update planned in the coming months