A Deterministic Framework for Integrated Genome Variant Interpretation - The ‘GenomeVAP’

Abstract

High-throughput genomic sequencing generates vast amounts of data, yet the interpretation of individual-genetic variants remains hindered by the dispersion of relevant evidence across various databases. We present a modular, web-based framework (GenomeVAP) designed for deterministic evidence integration in genomic research. Unlike machine learning models that often introduce noise into genomic annotations or rely on opaque predictive thresholds, GenomeVAP utilizes a weighted, rule-based scoring methodology to synthesize evidence from primary repositories, including ClinVar,[1] dbSNP,[2] Ensembl,[3] and the GWAS Catalog.[4] We evaluate the framework's efficacy through representative batch entries of clinically significant variants, demonstrating that centralized, automated retrieval reduces manual querying time while maintaining high transparency and reproducibility. GenomeVAP is intended exclusively as a bioinformatics software framework to aid interpretation in healthcare and academic research fields