Work

X-CAP improves pathogenicity prediction of stopgain variants

Year: 2022

Type: article

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 .

Source: Genome Medicine

DOI: 10.1186/s13073-022-01078-y

Authors: Ruchir Rastogi, Peter D. Stenson, David N. Cooper, Gill Bejerano

Institutions: Stanford University, Cardiff University, Stanford Medicine

Language: English

Topic: Genomics and Rare Diseases

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $4,480