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- Title
CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations.
- Authors
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.
- Abstract
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.
- Subjects
MICROBIAL virulence; FORECASTING; WEB services; MEDICAL genetics; COMPUTER-assisted drug design
- Publication
Genome Medicine, 2020, Vol 12, Issue 1, pN.PAG
- ISSN
1756-994X
- Publication type
Article
- DOI
10.1186/s13073-020-00775-w