Signal Processing Based CNV Detection in Bacterial Genomes

Abstract

Copy number variation (CNV) plays important role in drug resistance in bacterial genomes. It is one of the prevalent forms of structural variations which leads to duplications or deletions of regions with varying size across the genome. So far, most studies were concerned with CNV in eukaryotic, mainly human, genomes. The traditional laboratory methods as microarray genome hybridization or genotyping methods are losing its effectiveness with the omnipotent increase of fully sequenced genomes. Methods for CNV detection are predominantly targeted at eukaryotic sequencing data and only a few of tools is available for CNV detection in prokaryotic genomes. In this paper, we propose the CNV detection algorithm derived from state-of-the-art methods for peaks detection in the signal processing domain. The modified method of GC normalization with higher resolution is also presented for the needs of the CNV detection. The performance of the algorithms are discussed and analyzed.