Skyhawk: an artificial neural network-based discriminator for reviewing clinically significant genomic variants
by Ruibang Luo; Tak-Wah Lam; Michael C. Schatz
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 13, No. 5/6, 2020

Abstract: Genome sequencing has become an important tool in clinical practice. However, variant interpretation remains the bottleneck and may take a specialist several hours of work per patient. On average, one-fifth of this time is spent on visually confirming the authenticity of the candidate variants. We developed Skyhawk, an artificial neural network (ANN)-based discriminator that mimics the process of expert review on clinically significant genomics variants. Skyhawk runs in less than 1 min to review 10,000 variants, and about 30 min to review all variants in a typical whole-genome sequencing sample. Among the false positive singletons identified by GATK HaplotypeCaller, UnifiedGenotyper and 16GT in the HG005 GIAB sample, 79.7% were rejected by Skyhawk. Worked on the variants with unknown significance (VUS), Skyhawk marked most of the false positive variants for manual review and most of the true positive variants no need for review. Skyhawk is freely available at https://github.com/aquaskyline/Skyhawk.

Online publication date: Wed, 31-Mar-2021

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