bioRxivBioinformatics
GAVIN - Gene-Aware Variant INterpretation for medical sequencing
K. Joeri van der VeldeiD, Eddy N de Boer, Cleo van Diemen, Birgit Sikkema-Raddatz, Kristin Abbott, Alain Knopperts, Lude FrankeiD, Rolf Sijmons, Tom J de Koning, Cisca WijmengaiD, Richard Sinke, Morris Swertz
Abstract
Here, we present GAVIN, a new method that delivers accurate classification of variants for next-generation sequencing molecular diagnostics. It is based on gene-specific calibrations of allele frequencies (from the ExAC database), effect impact (using SnpEff) and estimated deleteriousness (CADD scores) for >3,000 genes. In a benchmark on 18 clinical gene sets, we achieved a sensitivity of 91.6%, with a specificity of 78.2%. This accuracy was unmatched by 12 other tools we tested. We provide GAVIN as an online MOLGENIS service to annotate VCF files, and as open source executable for use in bioinformatic pipelines. It can be found at http://molgenis.org/gavin.
- organism: human
- assay: whole-exome sequencing
- research focus: variant effect prediction
- approach: statistical model
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