Exploring Disease Model Mouse Using Knowledge Graphs: Combining Gene Expression, Orthology, and Disease Datasets

Abstract

AbstractBackgroundThe RIKEN BRC develops and maintains the RIKEN BioResource MetaDatabase to help users explore appropriate target bioresources for their experiments and prepare precise and high-quality data infrastructures. The Swiss Institute of Bioinformatics develops two RDF datasets across multi species for the study of gene expression and orthology: Bgee and Orthologous MAtrix (OMA, an orthology database).MethodsThis study integrates the RIKEN BioResource knowledge graph with Resource Description Framework (RDF) datasets from Bgee, a gene expression database, the OMA, the DisGeNET, a human gene-disease association, Mouse Genome Informatics (MGI), UniProt, and four disease ontologies in the RIKEN BioResource MetaDatabase. Our aim is to explore which model organisms are most appropriate for specific medical science research applications, using SPARQL queries across the integrated datasets. More precisely, we attempt to explore disease-related genes, as well as anatomical parts where these genes are overexpressed and subsequently identify appropriate bioresource candidates available for specific disease research applications.ResultsWe illustrate the above through two use cases targeting either Alzheimer’s disease or melanoma. We identified two Alzheimer’s disease-related genes that were overexpressed in the prefrontal cortex (APP and APOE) and 21 RIKEN bioresources predicted to be relevant to Alzheimer’s disease research. Furthermore, executing a transitive search for the Uberon terms by using the Property Paths function, we identified two melanoma-related genes (HRAS and PTEN), and eight anatomical parts in which these genes were overexpressed, such as the “skin of limb” as an example. Finally, we compared the performance of the federated SPARQL query via the remote Bgee SPARQL endpoint with the performance of a centralized SPARQL query using the Bgee dataset as part of the RIKEN BioResource MetaDatabase.ConclusionsAs a result, we demonstrated that the performance of the federated approach degraded. We concluded that we improved the degradation of the query performance of the federated approach from the BioResource MetaDatabase to the SIB by refining the transferred data through the subquery and enhancing the server specifications, that is optimizing the triple store query evaluation.