CAPICE: A computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations
2020
Li, Shuang | Velde, Van Der, K.J. | Ridder, De, Dick | Dijk, Van, Aalt D.J. | Soudis, Dimitrios | Zwerwer, Leslie R. | Deelen, Patrick | Hendriksen, Dennis | Charbon, Bart | Gijn, Van, Marielle E. | Abbott, Kristin | Sikkema-Raddatz, Birgit | Diemen, Van, Cleo C. | Kerstjens-Frederikse, Wilhelmina S. | Sinke, Richard J. | Swertz, Morris A.
Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) at https://github.com/molgenis/capice.
Mostrar más [+] Menos [-]Palabras clave de AGROVOC
Información bibliográfica
Este registro bibliográfico ha sido proporcionado por Wageningen University & Research
