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The "RNA world" represents a novel frontier for the study of fundamentalbiological processes and human diseases and is paving the way for thedevelopment of new drugs tailored to the patient's biomolecularcharacteristics. Although scientific data about coding and non-coding RNAmolecules are continuously produced and available from public repositories,they are scattered across different databases and a centralized, uniform, andsemantically consistent representation of the "RNA world" is still lacking. Wepropose RNA-KG, a knowledge graph encompassing biological knowledge about RNAsgathered from more than 50 public databases, integrating functionalrelationships with genes, proteins, and chemicals and ontologically groundedbiomedical concepts. To develop RNA-KG, we first identified, pre-processed, andcharacterized each data source; next, we built a meta-graph that provides anontological description of the KG by representing all the bio-molecularentities and medical concepts of interest in this domain, as well as the typesof interactions connecting them. Finally, we leveraged an instance-basedsemantically abstracted knowledge model to specify the ontological alignmentaccording to which RNA-KG was generated. RNA-KG can be downloaded in differentformats and also queried by a SPARQL endpoint. A thorough topological analysisof the resulting heterogeneous graph provides further insights into thecharacteristics of the "RNA world". RNA-KG can be both directly explored andvisualized, and/or analyzed by applying computational methods to inferbio-medical knowledge from its heterogeneous nodes and edges. The resource canbe easily updated with new experimental data, and specific views of the overallKG can be extracted according to the bio-medical problem to be studied.

RNA-KG: An ontology-based knowledge graph for representing interactions involving RNA molecules