Scalable Knowledge Graph Construction and Inference on Human Genome Variants

TL;DR

This paper presents a scalable method for constructing and querying large knowledge graphs from genomic variant data, enabling efficient analysis and inference in large-scale RNA-sequencing datasets.

Contribution

It introduces a novel approach to convert genomic variant data into a large, queryable knowledge graph using RDF and ontologies, supporting downstream analysis and machine learning.

Findings

Successfully built a large knowledge graph from COVID-19 patient data

Demonstrated effective classification using graph neural networks

Compared different GNN architectures for genomic data analysis

Abstract

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.