Publication

CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

Li, S., van der Velde, K. J., de Ridder, D., van Dijk, A. D. J., Soudis, D., Zwerwer, L. R., Deelen, P., Hendriksen, D., Charbon, B., van Gijn, M. E., Abbott, K., Sikkema-Raddatz, B., van Diemen, C. C., Kerstjens-Frederikse, W. S., Sinke, R. J. & Swertz, M. A., 24-Aug-2020, In : Genome medicine. 12, 1, p. 75 11 p., 75.

Research output: Contribution to journalArticleAcademicpeer-review

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..

Original languageEnglish
Article number75
Pages (from-to)75
Number of pages11
JournalGenome medicine
Volume12
Issue number1
Publication statusPublished - 24-Aug-2020

    Keywords

  • Variant pathogenicity prediction, Machine learning, Exome sequencing, Molecular consequence, Allele frequency, Clinical genetics, Genome diagnostics, NONCODING VARIANTS, PREDICTION, IDENTIFICATION, ELEMENTS, SCORE

Download statistics

No data available

ID: 132511569