Discovering copy number variation (CNV) in bacteria is not in the spotlight compared to the attention focused on CNV detection in eukaryotes. However, challenges arising from bacterial drug resistance bring further interest to the topic of CNV and its role in drug resistance. General CNV detection methods do not consider bacteria's features and there is space to improve detection accuracy. Here, we present a CNV detection method called CNproScan focused on bacterial genomes. CNproScan implements a hybrid approach and other bacteria-focused features and depends only on NGS data. We benchmarked our method and compared it to the previously published methods and we can resolve to achieve a higher detection rate together with providing other beneficial features, such as CNV classification. Compared with other methods, CNproScan can detect much shorter CNV events.

Department of Biomedical Engineering Brno University of Technology Brno Czech Republic

Department of Internal Medicine Hematology and Oncology University Hospital Brno Brno Czech Republic

Citace poskytuje Crossref.org

ProcaryaSV: structural variation detection pipeline for bacterial genomes using short-read sequencing