Petagraph: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data - Benjamin J. Stear; Taha Mohseni Ahooyi; J. Alan Simmons; Charles Kollar; Lance Hartman; Katherine Beigel; Aditya Lahiri; Shubha Vasisht; Tiffany J. Callahan; Christopher M. Nemarich; Jonathan C. Silverstein; Deanne M. Taylor (Scientific Data)
Abstract Over the past decade, there has been substantial growth in both the quantity and complexity of available biomedical data. In order to more efficiently harness this extensive data and alleviate challenges associated with integration of multi-omics data, we developed Petagraph, a biomedical knowledge graph that encompasses over 32 million nodes and 118 million relationships. Petagraph leverages more than 180 ontologies and standards in the Unified Biomedical Knowledge Graph (UBKG) to embed millions of quantitative genomics data points. Petagraph provides a cohesive data environment that enables users to efficiently analyze, annotate, and discern relationships within and across complex multi-omics datasets supported by UBKG’s annotation scaffold. We demonstrate how queries on Petagraph can generate meaningful results across various research contexts and use cases.