Accurate detection of CNV based on single-nucleotide variants recalibration and image classification from whole genome sequencing

Abstract

Copy number variations (CNVs) play an important role in the genome aberrations and human diseases. Comprehensive discovery of CNVs from whole genome sequencing data remains difficult because of low sensitivity and high false detective rate (FDR). We presented a novel framework which integrated SNV-based recalibration probabilistic model and image classification architecture (ImageCNV) for CNVs discovery. A Naive Bayesian model and a deep neural network InceptionV3 were adopted to infer candidate CNVs, and we utilize the benchmark datasets to evaluate the performance of our framework. ImageCNV yielded comparable sensitivity and lower FDR, complementing other methods based on different signals and providing a new perspective for the detection of CNVs. ImageCNV is freely available at https://github.com/minqing1/ImageCNV.