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

Shuang Li, K.J. 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*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

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
JournalGenome Medicine
Volume12
Issue number1
DOIs
Publication statusPublished - 24 Aug 2020

Keywords

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

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