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