Building knowledge graphs to speed up queries on genomic information
DOI:
https://doi.org/10.47187/perspectivas.8.1.250Keywords:
Genomic information, Knowledge graphs, SPARQL query language, BioinformaticsAbstract
This article provides an overview of the main formats used to store genomic information, as well as the process of building knowledge graphs from such data. The information extracted from data in the repository of the National Center for Biotechnology Information is processed and structured in the form of knowledge graphs, allowing complex queries to be performed using the SPARQL language. Representing genomic information in a format suitable for semantic querying and inference facilitates the identification of complex relationships between genes, proteins, and metabolites. This, in turn, supports the discovery of new bioactive compounds and promotes advances in fields such as medicine and biotechnology. Finally, it is demonstrated how both simple and compound queries in SPARQL can be applied to retrieve explicit and inferred relationships among the triples that make up the knowledge graphs, thus improving the depth and efficiency of genomic analysis.
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Copyright (c) 2026 Reynold Osuna González, Guillermo De Ita Luna
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