SCRY: Extending SPARQL with Custom Data Processing Methods for the Life Sciences

An ever-growing amount of life science databases are (partially) exposed as RDF graphs (e.g. UniProt, TCGA, DisGeNET, Human Protein Atlas), complementing traditional methods to disseminate biodata. The SPARQL query language provides a powerful tool to rapidly retrieve and integrate this data. However, the inability to incorporate custom data processing methods in SPARQL queries inhibits its application in many life science use cases. It should take far less effort to integrate data processing methods, such as BLAST, with SPARQL. We propose an effective framework for extending SPARQL with custom methods should fulfill four key requirements: generality, reusability, interoperability and scalability. We present SCRY, the SPARQL compatible service layer, which provides custom data processing within SPARQL queries. SCRY is a lightweight SPARQL endpoint that interprets parts of basic graph patterns as input for user defined procedures, generating an RDF graph against which the query is resolved on-demand. SCRY’s federationoriented design allows for easy integration with existing endpoints, extending SPARQL’s functionality to include custom data processing methods in a decoupled, standards-compliant, tool independent manner. We demonstrate the power of this approach by performing statistical analysis of a benchmark, and by incorporating BLAST in a query which simultaneously finds the tissues expressing Hemoglobin β and its homologs.