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# Heterogeneous biological data integration with Semantic Web technologies using RDF or RDF-star formalisms generate topologically different graphs

Christophe Héligon, Olivier Dameron, Jacques Pécréaux

► To cite this version:

Christophe Héligon, Olivier Dameron, Jacques Pécréaux. Heterogeneous biological data integration with Semantic Web technologies using RDF or RDF-star formalisms generate topologically different graphs. JOBIM 2022, Jul 2022, Rennes, France. hal-05246893

HAL Id: hal-05246893

https://hal.science/hal-05246893v1

Submitted on 9 Sep 2025

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# Heterogeneous biological data integration with Semantic Web technologies using RDF or RDF-star formalisms generate topologically different graphs

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UMR

IRISA

CHRISTOPHE HÉLIGON<fnref n="1" />, OLIVIER DAMERON<fnref n="2" />, JACQUES PÉCRÉAUX<fnref n="1" />

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## PROBLEM

Investigating the mechanisms of cell division requires complex and time-consuming experimental approaches. It is, therefore, crucial to efficiently identify candidate genes involved in cell division. The roundworm *C. elegans* is a biological system, which detailed description and functional understanding require measuring multiple parameters at different scales over time. Such heterogeneous multi-scale data integration is challenging in many aspects due firstly to different data formats and information content to be represented and secondly to the lack of standard integration tools to date.

## OVERVIEW

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## CONTRIBUTIONS

We have identified the Semantic Web (SW) technologies as an appropriate general-purpose framework for data and metadata integration compatible with the FAIR principles.

SW mediated data integration generates relational graphs using the Resource Description Framework (RDF) as a standard model for data description. Integrating of our complementary heterogeneous datasets results in a complex data scheme (**3921820 nodes** and **10756313 relations**). A significant part of this complexity results from the extensive use of properties describing the relations between entities, which require to introducing supplementary nodes to instantiate the relations. Recently, RDF-star has been proposed as a candidate extension of RDF that addresses this topic[3]. Therefore, we set to compare the original RDF representation of our datasets with its RDF-star counterpart.

## METHOD

The work presented here is a preliminary study to evaluate the consequences of choosing RDF or RDF-star for *C. elegans* data integration and processing. We take advantage of Wormbase.org, which provides a curated biomedical data repository for *C. elegans*. We compare RDF and RDF-star grammars for expressivity, general performance for data storage, SPARQL queries and graph topology.

## RELATIONAL GRAPHS TOPOLOGY

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## PERFORMANCES

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**Average response time to the RDF-star formatted query (and its RDF equivalent):**

```sql
SELECT DISTINCT ?otherphenolabel ?genelabel ?refid
WHERE {
    VALUES ?phenolabel { "nematode phenotype" }
    ?phenoid rdfs:label ?phenolabel.
    ?otherphenoid rdfs:subClassOf* ?phenoid.
    ?otherphenoid rdfs:label ?otherphenolabel.
    ?geneid sio:001279 ?otherphenoid.
    <<?geneid sio:001279 ?otherphenoid>> dcterms:source ?refid.
    ?geneid rdfs:label ?genelabel.
}
```

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## DATA & KNOWLEDGE

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* **8 data types** : expression, interactions, identifiers, phenotypes, diseases, cross-references, omim, gene ontology.

* **5 nematodes specific ontologies.**

* **3 general purpose ontologies.**

## CONCLUSIONS

* RDF & RDF-star perform similarly using Fuseki SPARQL endpoint in our tests.

* Standard relations vocabulary is better adapted for use in RDF-star data representation

* Does different topology affect downstream processing?

## FUTURE DIRECTIONS

* Integrate additional data types in RDF & RDF-star

* Perform more systematic tests

* Compare performances of both graphs topology in downstream machine learning-based applications

* Process known biological data to prioritize sets of genes to be studied

<footnote n="1">¹ CNRS, Univ Rennes, IGDR - UMR 6290, 2 avenue du Professeur Léon Bernard, F-35000, Rennes, France</footnote>

<footnote n="2">² Univ Rennes, CNRS, Inria, IRISA - UMR 6074, 263 avenue du général Leclerc, F-35000, Rennes, France</footnote>