A Cluster-Based Approach for the Discovery of Copy Number Variations From Next-Generation Sequencing Data.

Guojun Liu,Junying Zhang

doi:10.3389/fgene.2021.699510

Abstract

The next-generation sequencing technology offers a wealth of data resources for the detection of copy number variations (CNVs) at a high resolution. However, it is still challenging to correctly detect CNVs of different lengths. It is necessary to develop new CNV detection tools to meet this demand. In this work, we propose a new CNV detection method, called CBCNV, for the detection of CNVs of different lengths from whole genome sequencing data. CBCNV uses a clustering algorithm to divide the read depth segment profile, and assigns an abnormal score to each read depth segment. Based on the abnormal score profile, Tukey’s fences method is adopted in CBCNV to forecast CNVs. The performance of the proposed method is evaluated on simulated data sets, and is compared with those of several existing methods. The experimental results prove that the performance of CBCNV is better than those of several existing methods. The proposed method is further tested and verified on real data sets, and the experimental results are found to be consistent with the simulation results. Therefore, the proposed method can be expected to become a routine tool in the analysis of CNVs from tumor-normal matched samples.

Highlights

The copy number variation (CNV) of DNA fragments has been widely recognized as a major type of structural variations, and can cause the amplification or deletion of DNA fragments, the lengths of which are greater than 1 kbp in the human genome (Freeman et al, 2006)
The sensitivity and false discovery rate (FDR) of the four methods are evaluated at six CNV length levels
The proposed CBCNV method was developed based on depth of coverage (DOC) profiles to detect CNVs using next-generation sequencing data, and is suitable for the detection of tumornormal matched samples

Summary

Introduction

The copy number variation (CNV) of DNA fragments has been widely recognized as a major type of structural variations, and can cause the amplification or deletion of DNA fragments, the lengths of which are greater than 1 kbp in the human genome (Freeman et al, 2006). Many experimental studies have proven that CNVs can change the doses of genes and lead to the reorganization of chromosome structure (Sharp et al, 2005; Magi et al, 2017; Pei et al, 2021b), and makes an important contribution to the occurrence and formation of tumors and various disorders (Pei et al, 2021a). It can cause schizophrenia and autism disorders in humans. It is still a difficult task to effectively detect CNVs of different lengths

Methods

Results

Conclusion