Simultaneous detection of small copy number variations (CNVs) (<0.5 kb) and 
single-nucleotide variants in clinically significant genes is of great interest 
for clinical laboratories. The analytical variability in next-generation 
sequencing (NGS) and artifacts in coverage data because of issues with 
mappability along with lack of robust bioinformatics tools for CNV detection 
have limited the utility of targeted NGS data to identify CNVs. We describe the 
development and implementation of a bioinformatics algorithm, copy number 
variation-random forest (CNV-RF), that incorporates a machine learning component 
to identify CNVs from targeted NGS data. Using CNV-RF, we identified 12 of 13 
deletions in samples with known CNVs, two cases with duplications, and 
identified novel deletions in 22 additional cases. Furthermore, no CNVs were 
identified among 60 genes in 14 cases with normal copy number and no CNVs were 
identified in another 104 patients with clinical suspicion of CNVs. All positive 
deletions and duplications were confirmed using a quantitative PCR method. 
CNV-RF also detected heterozygous deletions and duplications with a specificity 
of 50% across 4813 genes. The ability of CNV-RF to detect clinically relevant 
CNVs with a high degree of sensitivity along with confirmation using a low-cost 
quantitative PCR method provides a framework for providing comprehensive 
NGS-based CNV/single-nucleotide variant detection in a clinical molecular 
diagnostics laboratory.