van der Velde K Joeri, de Boer Eddy N, van Diemen Cleo C, Sikkema-Raddatz Birgit, Abbott Kristin M, Knopperts Alain, Franke Lude, Sijmons Rolf H, de Koning Tom J, Wijmenga Cisca, Sinke Richard J, Swertz Morris A
University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands.
Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Genome Biol. 2017 Jan 16;18(1):6. doi: 10.1186/s13059-016-1141-7.
We present Gene-Aware Variant INterpretation (GAVIN), a new method that accurately classifies variants for clinical diagnostic purposes. Classifications are based on gene-specific calibrations of allele frequencies from the ExAC database, likely variant impact using SnpEff, and estimated deleteriousness based on CADD scores for >3000 genes. In a benchmark on 18 clinical gene sets, we achieve a sensitivity of 91.4% and a specificity of 76.9%. This accuracy is unmatched by 12 other tools. We provide GAVIN as an online MOLGENIS service to annotate VCF files and as an open source executable for use in bioinformatic pipelines. It can be found at http://molgenis.org/gavin .
我们提出了基因感知变异解释（GAVIN），这是一种用于临床诊断目的的准确分类变异的新方法。分类基于来自ExAC数据库的等位基因频率的基因特异性校准、使用SnpEff的可能变异影响以及基于超过3000个基因的CADD评分的估计有害性。在18个临床基因集的基准测试中，我们实现了91.4%的灵敏度和76.9%的特异性。这种准确性是其他12种工具无法比拟的。我们将GAVIN作为在线MOLGENIS服务提供，用于注释VCF文件，并作为开源可执行文件用于生物信息管道。可在http://molgenis.org/gavin找到它。
