CNValidatron: accurate and efficient validation of PennCNV calls using computer vision.

ABSTRACT:

Background

Large, rare copy number variants (CNVs) are a main source of genetic variation in the genome and are important in both evolution and disease risk. CNVs can be detected using different data sources, including genome sequencing, genotyping arrays and quantitative PCR experiments, but in most large cohorts, genotyping arrays remain the most prevalent source. Current methods to call CNVs from genotyping array data suffer from high false positive rates and while multiple approaches, including QC filtering, visual inspection of intensity tracks, and wet-lab validation are commonly applied to counter this problem, such methods are often non-specific (QC filtering) or inefficient (visual and wet-lab validations) at a genome-wide scale.

Results

We have assembled the larges

SUBMITTER: Montalbano S 

PROVIDER: S-EPMC12910869 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature