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

Autor:	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
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	Variant pathogenicity prediction Machine learning Exome sequencing Molecular consequence Allele frequency Clinical genetics Medicine Genetics QH426-470
Zdroj:	Genome Medicine, Vol 12, Iss 1, Pp 1-11 (2020)
Druh dokumentu:	article
ISSN:	1756-994X
DOI:	10.1186/s13073-020-00775-w
Popis:	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 .
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/aa39ee356dcc409a882f0c4904055b68 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.