Motivation Knowledge graphs (KG) are being adopted in industry, commerce, and academia. Biomedical KG present a challenge due to the complexity, size, and heterogeneity of the underlying information. Results In this work we present the Scalable Precision Medicine Open Knowledge Engine (SPOKE), a biomedical KG connecting millions of concepts via semantically meaningful relationships. SPOKE contains 27 million nodes of 21 different types and 53 million edges of 55 types downloaded from 41 databases. The graph is built on the framework of 11 ontologies that maintain its structure, enable mappings, and facilitate navigation. SPOKE is built weekly by python scripts which download each resource, check for integrity and completeness, and then create a “parent table” of nodes and edges. Graph queries are translated by a REST API and users can submit searches directly via an API or a graphical user interface. Conclusions/Significance: SPOKE enables the integration of seemingly disparate information to support precision medicine efforts. Availability The SPOKE neighborhood explorer is available at https://spoke.rbvi.ucsf.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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