Using SPARQL to unify queries over data, ontologies, and machine learning models in the PhenomeBrowser knowledgebase

Abstract

We have developed the PhenomeBrowser knowledge base to integrate phenotype associations from a variety of sources into a single knowledge base. We use SPARQL as a unifying query language to access RDF data, perform Description Logic queries over ontologies, and compute the semantic similarity between entities in the knowledge base.

Keywords

• Phenotype
• Semantic Similarity
• SPARQL

Introduction

The similarity between phenotypes associated with entities studied in the life sciences can be used to reveal interactions between biomedical entities at a molecular level [ 12 ]. Entities that are similar phenotypically are often related to each other on a molecular level as well [ 15 ], and this principle can be used to suggest or discover novel relations between these entities. There are several databases that have been developed for integrating phenotypes and exploring relations between them such as Online Mendelian Inheritance in Men (OMIM) [ 5 ] and ClinVar [ 9 ], as well as integrated databases such as Monarch [ 10 ]. Key challenges in integrating and exploring phenotype data is the use of integrated phenotype vocabularies or ontologies that can systematically relate phenotype classes between di erent contexts such as the entity studied or the species in which phenotypes are observed (human, model organism, or non-model organism) [ 4 ]; the computation of semantic (phenotypic) similarity or relatedness [ 11 ]; and the ability to query phenotype-related information using a uniform and (ideally) standardized query language.

We developed the PhenomeBrowser knowledgebase as a semantic framework that combines an RDF-based knowledge base of phenotype associations collected from community resources and from in-house curation with the ability to perform Description Logic queries over phenotype (and other) ontologies as well as to perform some basic operations on a type of machine learning model. Copyright © 2022 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

The framework used to develop PhenomeBrowser is based on using SPARQL as query language for any structured data and Apache Lucene indices and queries (implemented in the form of ElasticSearch) for natural language information.

PhenomeBrowser currently contains over four million phenotype associations for genes, diseases, drugs, pathogens and chemical entities (metabolites). We incorporate the Vec2SPARQL method [ 8 ] over the Bio2Vec knowledge graph embedding repository (https://bio2vec.cbrc.kaust.edu.sa) to perform queries incorporating semantic similarity and machine learning, and we rely on the AberOWL services [ 7 ] to perform Description Logic Queries within SPARQL queries. The interface for PhenomeBrowser is based on these SPARQL queries and we provide access to PhenomeBrowser through SPARQL. The Phenomebrowser web portal further implements queries for speci c tasks such as nding gene{disease associaitons, host{pathogen or drug{target interactions, all based on phenotypic similarity. The PhenomeBrowser software and underlying components are available as Free Software (phenomebrowser.net) and can serve as an initial model on how to combine graph databases, Description Logic queries, and machine learning within a single framework uni ed through SPARQL as query language. 2

Design and Implementation

Core components of the PhenomeBrowser architecture are shown in Figure 1. Phenotype annotations data from community resources and in-house curation is transformed into RDF format. The transformed data is subsequently stored in an RDF store. We implemented a data intake work ow using snakemake [ 1 ] to achieve reproducible and robust automation. As data model for phenotype associations, we rely on community standards for phenotypes developed by the OBO Foundry initiative [ 14 ] and the Monarch project [ 10 ]. We use the Dublin Core vocabularies [ 2 ] to encode provenance information, and the OBO Relation Ontology [ 13 ] to encode relations. We use the integrated phenotype ontology PhenomeNet [ 6 ] to integrate phenotypes across di erent contexts. The data intake work ows also generate text indices for entity as well as classes and relations from the PhenomeNet ontology. Text indices are Apache Lucene indices implemented in ElasticSearch, and we make search of text indices available through a REST API that is complementary to the SPARQL endpoint for querying structured data.

Data that is ingested from public sources is passed to the ontology-based machine learning method DL2Vec [ 3 ] to generate embeddings for entities (and ontology classes) that can be used to compute similarity. The embeddings are added to the Bio2Vec repository which stores the embeddings and makes them available for similarity-based queries through a REST API and a special SPARQL endpoint implementing the Vec2SPARQL extensions [ 8 ].

When querying data, we use the AberOWL [ 7 ] SPARQL endpoint to execute queries that incorporate deductive inference over Semantic Web ontologies. AberOWL is an ontology repository that provides reasoning over ontologies as a service. The queries further federate to the Vec2SPARQL endpoints provided by Bio2Vec, and therefore combine querying RDF phenotype data, phenotype ontologies (through AberOWL), and semantic similarity (through Bio2Vec). 3

Querying using SPARQL

One application of computing phenotype similarity is ranking candidate genes for a disease [ 15 ]. Using PhenomeBrowser's integrated SPARQL endpoint, we can perform this operation through SPARQL and therefore suggest gene{disease associations. Figure 2 shows a query for nding genes that are phenotypically similar to ventricular septal defect (HP:0011623). In the rst section of the query, the content of the FILTER block performs a Description Logic Query to retrieve all classes that are equivalent to or subclasses of the ventricular septal defect phenotype from the HPO; the query is performed using the AberOWL ontology repository and reasoning service which expands the query and replaces it with the URIs of the classes resulting from the query. Subclasses of ventricular septal defect in the HPO include Tetralogy of Fallot as well as several more speci c forms of Tetralogy of Fallot, and also includes ventricular septal defect itself (as the query is re exive).

The second section of the query contains a federated query to the Bio2Vec SPARQL endpoint and uses the mostSimilar function; the mostSimilar function is implemented by the Vec2SPARQL method and executes the phenotypic similarity search for the diseases selected in the rst section of the query on the Bio2Vec SPARQL endpoint. The mostSimilar function is a custom SPARQL function that takes as arguments the dataset identify in Bio2Vec, the identi er for the entity within the dataset, the number of entities to retrieve (in our case, we retrieve the three most similar entities to our query), and the type (using rdf:type) of the entity (in our case, the entity type is gene). In the third sec

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX pb: <http://phenomebrowser.net/> PREFIX b2v: <http://bio2vec.net/function#> PREFIX b2vd: <http://bio2vec.net/dataset#>> SELECT ?simGene ?simGeneLabel ?genePhenotype ?genePhenotypeLabel WHERE { {

SELECT ?disease FROM <http://phenomebrowser.net> WHERE { ?association rdf:type rdf:Statement . ?association rdf:object ?phenotype .

FILTER ( ?phenotype in ( OWL subeq <http://phenomebrowser.net/sparql> <HP> {

'ventricular septal defect' }

) ) . ?association rdf:subject ?disease .

?disease rdf:type pb:Disease . } LIMIT 20 } .

SERVICE <https://bio2vec.cbrc.kaust.edu.sa/ds/query> {

(?simGene ?val ?x ?y) b2v:mostSimilar(b2vd:dataset_4 ?disease 3 pb:Gene) . } GRAPH <http://phenomebrowser.net> { ?simGene rdfs:label ?simGeneLabel . ?geneAssociation rdf:subject ?simGene . ?geneAssociation rdf:object ?genePhenotype .

?genePhenotype rdfs:label ?genePhenotypeLabel .

} } ORDER BY asc(?simGeneLabel) Fig. 2: SPARQL query nding genes that are phenotypically similar to ventricular septal defect. tion of the query, we add labels to genes found in the second section of the query and their associated phenotypes. 4

Conclusion

We developed the PhenomeBrowser knowledgebase as a semantic framework that integrates querying over graph databases, ontologies, and knowledge graph embeddings, using SPARQL as a unifying and standardized query language. PhenomeBrowser is accessible at http://phenomebrowser.net.

Acknowledgements

We acknowledge use of the resources of the KAUST Supercomputing Core Laboratories.

Funding References

This work was supported by funding from King Abdullah University of Science and Technology (KAUST) O ce of Sponsored Research (OSR) under Award No. URF/1/3790-01-01, URF/1/4355-01-01, FCC/1/1976-08-01, and FCC/1/197608-08.

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