RNA-KG: An ontology-based knowledge graph for representing interactions involving RNA molecules
CoRR(2023)
摘要
The "RNA world" represents a novel frontier for the study of fundamental
biological processes and human diseases and is paving the way for the
development of new drugs tailored to the patient's biomolecular
characteristics. Although scientific data about coding and non-coding RNA
molecules are continuously produced and available from public repositories,
they are scattered across different databases and a centralized, uniform, and
semantically consistent representation of the "RNA world" is still lacking. We
propose RNA-KG, a knowledge graph encompassing biological knowledge about RNAs
gathered from more than 50 public databases, integrating functional
relationships with genes, proteins, and chemicals and ontologically grounded
biomedical concepts. To develop RNA-KG, we first identified, pre-processed, and
characterized each data source; next, we built a meta-graph that provides an
ontological description of the KG by representing all the bio-molecular
entities and medical concepts of interest in this domain, as well as the types
of interactions connecting them. Finally, we leveraged an instance-based
semantically abstracted knowledge model to specify the ontological alignment
according to which RNA-KG was generated. RNA-KG can be downloaded in different
formats and also queried by a SPARQL endpoint. A thorough topological analysis
of the resulting heterogeneous graph provides further insights into the
characteristics of the "RNA world". RNA-KG can be both directly explored and
visualized, and/or analyzed by applying computational methods to infer
bio-medical knowledge from its heterogeneous nodes and edges. The resource can
be easily updated with new experimental data, and specific views of the overall
KG can be extracted according to the bio-medical problem to be studied.
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