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 language | English |
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Article number | 75 |
Pages (from-to) | 75 |
Number of pages | 11 |
Journal | Genome medicine |
Volume | 12 |
Issue number | 1 |
DOIs | |
Publication status | Published - 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
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Evaluation datasets and pre-computed scores for: "CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations"
Li, S. (Contributor), ZENODO, 31-Oct-2019
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