Authors | Daneels, D., Grau, I., Sengupta, D., Bonduelle, M.L., Farid, D., Croes, D., Nowé, A. and Van Dooren, S. |
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Abstract | High throughput screening (HTS) techniques, like mendeliome, whole exomeand genome screening, are becoming a routine in a clinical diagnosticsetting. However, classifying the identified genomic variants as benign or(likely) pathogenic, is still a tedious and time consuming process for the(clinical) geneticist. To facilitate this variant classification process, we havedeveloped GeVaCT, a standalone Java based tool that implements and automatizesa published variant classification scheme for autosomal dominantdisorders. GeVaCT currently supports annotated variant files from AlamutBatch (Interactive Biosoftware), with future plans to support input fromother variant annotation tools.The variant classification process currently implemented in GeVaCT is basedon a published scheme in the context of cardiac arrhythmias (Hofman et al.,2013). The implemented scheme consists of two phases: pre-processing andvariant classification. During pre-processing, the annotated variant file fromAlamut Batch is imported and filtered based on the presence of the variantin databases with described variants or a local database, the variant location,the coding effect and the variant allele frequency in an ethnically matchedpopulation. The variant classification workflow depends on the type ofvariant: either missense or nonsense/frame-shift. Each attribute used gets aweighted score that is summed up with the others to come to a first variantclassification. This first score is updated based on familial and functionalinformation obtained for the variant-of-interest. The final result is a classificationof the variant in one out of five classes ranging from non-pathogenicto pathogenic. |
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