CNV Deep Learning based Methodology for Recognition

Abstract

Background: Copy number variants (CNVs) account for a significant amount of genetic variation. Since many CNVs include genes that result in differential levels of gene expression, substantial normal phenotypic variation can be explained. Current efforts are directed toward a more comprehensive characterization of CNVs that will provide the basis for determining how genomic diversity impacts biological function, evolution and common diseases in human as well as plants. Methods: The analytical variability in next generation sequencing (NGS) and artifacts in coverage data along with lack of robust bioinformatics tools for CNV detection have limited the utility of targeted NGS data to identify CNVs. Literature has the evidence of development of deep learning-based pipeline that incorporates a machine learning component to identify CNVs from targeted NGS data. Result: It is believed that combining this with clinical “gold standard” (e.g. FISH) information, the CNV detection could be more accurate. This would lead to a new research direction, supplementing the existing NGS methods.