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X-CAP improves pathogenicity prediction of stopgain variants

Rastogi, Ruchir, Stenson, Peter D., Cooper, David N. ORCID: https://orcid.org/0000-0002-8943-8484 and Bejerano, Gill 2022. X-CAP improves pathogenicity prediction of stopgain variants. Genome Medicine 14 (1) , 81. 10.1186/s13073-022-01078-y

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Abstract

Abstract: Stopgain substitutions are the third-largest class of monogenic human disease mutations and often examined first in patient exomes. Existing computational stopgain pathogenicity predictors, however, exhibit poor performance at the high sensitivity required for clinical use. Here, we introduce a new classifier, termed X-CAP, which uses a novel training methodology and unique feature set to improve the AUROC by 18% and decrease the false-positive rate 4-fold on large variant databases. In patient exomes, X-CAP prioritizes causal stopgains better than existing methods do, further illustrating its clinical utility. X-CAP is available at https://github.com/bejerano-lab/X-CAP.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Medicine
Additional Information: License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher: BioMed Central
Date of First Compliant Deposit: 1 August 2022
Date of Acceptance: 23 June 2022
Last Modified: 30 Nov 2022 08:26
URI: https://orca.cardiff.ac.uk/id/eprint/151595

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