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

Genome Medicine
Jennifer L YenDeanna M Church

Abstract

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. 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 transcript-controlled comparisons to a manually curated set of 126 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 and those in major germline and cancer databases: ClinVar and COSMIC, respectively. We find that there is substantial discordance between the annotation tools and databases in the description of insertions and/or deletions. Using our ground truth set of variants, constructed specifically to identify chal...Continue Reading

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Related Concepts

Data Accuracy
Computer Programs and Programming
Variation (Genetics)
Genome, Human
Computational Molecular Biology
Online Mendelian Inheritance In Man
INDEL Mutation
Molecular Sequence Annotation
Malignant Neoplasms
Classification

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