PT - JOURNAL ARTICLE AU - Luo, Ruibang AU - Lam, Tak-Wah AU - Schatz, Michael C. TI - Skyhawk: An Artificial Neural Network-based discriminator for reviewing clinically significant genomic variants AID - 10.1101/311985 DP - 2018 Jan 01 TA - bioRxiv PG - 311985 4099 - http://biorxiv.org/content/early/2018/05/01/311985.short 4100 - http://biorxiv.org/content/early/2018/05/01/311985.full AB - Motivation Many rare diseases and cancers are fundamentally diseases of the genome. In the past several years, genome sequencing has become one of the most important tools in clinical practice for rare disease diagnosis and targeted cancer therapy. However, variant interpretation remains the bottleneck as is not yet automated 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.Results We developed Skyhawk, an artificial neural network-based discriminator that mimics the process of expert review on clinically significant genomics variants. Skyhawk runs in less than one minute to review ten thousand variants, and among the false positive singletons identified by GATK Haplo-typeCaller, UnifiedGenotyper and 16GT in the HG005 GIAB sample, 79.7% were rejected by Skyhawk.Availability Skyhawk is easy to use and freely available at https://github.com/aquaskyline/SkyhawkContact rbluo{at}cs.hku.hkSupplementary information Supplementary data are available at Bioinformatics online.