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

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

Research output: Contribution to journalArticleAcademicpeer-review

73 Downloads (Pure)

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..

Original languageEnglish
Article number75
Pages (from-to)75
Number of pages11
JournalGenome medicine
Volume12
Issue number1
DOIs
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

Cite this