Comparative study of tools for copy number variation detection using next-generation sequencing data

Copy number variation (CNV) plays an important role in disease susceptibility as a type of intermediate-scale structural variation (SV). Accurate CNV detection is crucial for understanding human genetic diversity, elucidating disease mechanisms, and advancing cancer genomics. A variety of CNV detection tools based on short sequencing reads from next-generation sequencing (NGS) have been developed. Although many researchers have conducted extensive comparisons of the detection performance of various tools, these studies have not fully considered the comprehensive impact of factors such as variant length, sequencing depth, tumor purity, and CNV types on tools performance. Therefore, we selected 12 widely used and representative detection tools to comprehensively compare their performance on both simulated and real data. For the simulated data, we compared their performance across six variant types under 36 configurations, including three variant lengths, four sequencing depths, and three tumor purities. For the real data, we used the overlapping density score (ODS) to evaluate the performance of the 12 detection tools. Additionally, we compared their time and space complexities. In this study, we analyzed the impact of each configuration on the tools and recommended the most suitable detection tools for each scenario. This study provides important guidance for researchers in selecting the appropriate variant detection tools for complex situations.