Assessing the pathogenicity of genetic variants is a critical aspect of genomic medicine and precision healthcare. Over the last decades, the identification of genetic variants and their characterization has become simpler (advent of high-throughput sequencing technologies, analysis, and visualization support tools, etc.). However, the quality of assessments to distinguish benign from pathogenic variants is critical to inform clinical decision-making and improve patient outcomes. In this article, we investigate the relationships using correlation tests between the characterization of genetic variants in the literature and their pathogenicity scores computed by two state-of-the-art assessment tools (SIFT and PolyPhen-2).
Keywords: PolyPhen-2; SIFT; Text-mining; Variant pathogenicity.