Abstract
Motivation
Whole-genome sequencing (WGS) is widely used for copy number variation (CNV) detection. However, for most bacteria, their circular genome structure and high replication rate make reads more enriched near the replication origin. CNV detection based on read depth could be seriously influenced by such replication bias.
Results
We show that the replication bias is widespread using ∼200 bacterial WGS data. We develop CNV-BAC (CNV-Bacteria) that can properly normalize the replication bias and other known biases in bacterial WGS data and can accurately detect CNVs. Simulation and real data analysis show that CNV-BAC achieves the best performance in CNV detection compared with available algorithms.
Availability and implementation
CNV-BAC is available at https://github.com/XiDsLab/CNV-BAC.
Supplementary information
Supplementary data are available at Bioinformatics online.
The Impact of Errors in Copy Number Variation Detection Algorithms on Association Results
