CNV-PCC: An efficient method for detecting copy number variations from next-generation sequencing data - Zhang, Tong; Dong, Jinxin; Jiang, Hua; Zhao, Zuyao; Zhou, Mengjiao; Yuan, Tianting (Frontiers in Bioengineering and Biotechnology, 2022)
Copy number variations (CNVs) have a major influence on the diversity of the human genome and the occurrence of many complex diseases. It is significant to detect and identify CNVs in the field of biology and biomedicine. The next-generation sequencing (NGS) technology provides rich data for the detection of CNVs, and numerous CNVs detection methods based on NGS data have been proposed. However, these methods are not reliable in detecting low-amplitude CNVs, especially when the length of CNVs is small. We propose a new method, CNV-PCC (Detection of Copy Number Variations based on Principal Component Classifier), to analyze and identify CNVs depending on the read depth and mapping quality of NGS data. CNV-PCC implements a two-stage segmentation strategy. It performs local segmentation after the global segmentation to enhance the identification capabilities of low-amplitude and small CNVs. Next, the outlier scores are calculated for each segment by PCC (Principal Component Classifier). Finally, the OTSU algorithm calculates the threshold to determine the CNVs regions. The analysis on the results of simulated data indicates that CNV-PCC outperforms the other methods for sensitivity and F1-score. Furthermore, on real sequencing samples, CNV-PCC shows high consistency with other methods. This study demonstrates that CNV-PCC is an effective method for detecting CNVs, even for low-amplitude and small CNVs.