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In Genome medicine ; h5-index 64.0

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 .

Li Shuang, van der Velde K Joeri, de Ridder Dick, van Dijk Aalt D J, Soudis Dimitrios, Zwerwer Leslie R, Deelen Patrick, Hendriksen Dennis, Charbon Bart, van Gijn Marielle E, Abbott Kristin, Sikkema-Raddatz Birgit, van Diemen Cleo C, Kerstjens-Frederikse Wilhelmina S, Sinke Richard J, Swertz Morris A

2020-Aug-24

Allele frequency, Clinical genetics, Exome sequencing, Genome diagnostics, Machine learning, Molecular consequence, Variant pathogenicity prediction