A variant by any name: quantifying annotation discordance across tools and clinical databases

Background: Clinical genomic testing is dependent on the robust identification and reporting of variant-level information in relation to disease. With the shift to high-throughput sequencing, a major challenge for clinical diagnostics is the cross-identification of variants called on their genomic position to resources that rely on transcript- or protein-based descriptions. Methods: We evaluated the accuracy of three tools (SnpEff, Variant Effect Predictor and Variation Reporter) that generate transcript and protein-based variant nomenclature from genomic coordinates according to guidelines by the Human Genome Variation Society (HGVS). Our evaluation was based on comparisons to a manually-curated list of 127 test variants of various types drawn from data sources, each with HGVS-compliant transcript and protein descriptors. We further evaluated the concordance between annotations generated by Snpeff and Variant Effect Predictor with those in major germline and cancer databases: ClinVar and COSMIC, respectively. Results: We find that there is substantial discordance between the annotation tools and databases in the description of insertion and/or deletions. Accuracy based on our ground truth set was between 80-90% for coding and ...Continue Reading