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

ABSTRACT:

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 transcript-controlled comparisons to a manually curated set of 126 test variants of various types drawn from data

SUBMITTER: Yen JL 

PROVIDER: S-EPMC5267466 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature