Sensitive health data should preferentially be analysed on site. In typical bioinformatics workows, public databases are duplicated and used by specialised tools to enrich the local datasets. In the case of genomic variation data, this process is called variant annotation. In this session we demonstrate variant annotation using federated SPARQL queries. We rst overview how clinico-genomic data can be modelled as a knowledge graph (KG), leveraging state-of-the-art biomedical ontologies. We then perform variant annotation by querying UniprotKB, a massive curated KG for gene and proteins. Our approach avoids public data duplication while maintaining genomic data on site and aligning it with FAIR principles. Our use-case is based on the ICAN project, a research program aimed at studying the physiopathology of cerebral berry aneurysms.
🌊
