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Real-world knowledge can be represented as a graph consisting of entities andrelationships between the entities. The need for efficient and scalablesolutions arises when dealing with vast genomic data, like RNA-sequencing.Knowledge graphs offer a powerful approach for various tasks in suchlarge-scale genomic data, such as analysis and inference. In this work,variant-level information extracted from the RNA-sequences of vaccine-na\"iveCOVID-19 patients have been represented as a unified, large knowledge graph.Variant call format (VCF) files containing the variant-level information wereannotated to include further information for each variant. The data records inthe annotated files were then converted to Resource Description Framework (RDF)triples. Each VCF file obtained had an associated CADD scores file thatcontained the raw and Phred-scaled scores for each variant. An ontology wasdefined for the VCF and CADD scores files. Using this ontology and theextracted information, a large, scalable knowledge graph was created. Availablegraph storage was then leveraged to query and create datasets for furtherdownstream tasks. We also present a case study using the knowledge graph andperform a classification task using graph machine learning. We also drawcomparisons between different Graph Neural Networks (GNNs) for the case study.

Scalable Knowledge Graph Construction and Inference on Human Genome Variants