PT  - JOURNAL ARTICLE
AU  - Luo, Ruibang
AU  - Lam, Tak-Wah
AU  - Schatz, Michael
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 HaplotypeCaller, 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/Skyhawk