OTSUCNV: an adaptive segmentation and OTSU-based anomaly classification method for CNV detection using NGS data

• Published in: BMC genomics Vol. 25; no. 1; pp. 126 - 11
• Main Authors: Xie, Kun, Ge, Xiaojun, Alvi, Haque A K, Liu, Kang, Song, Jianfeng, Yu, Qiang
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 30.01.2024; BioMed Central; BMC
• Subjects: Adaptive algorithms; Adaptive segmentation; Algorithms; Anomaly detection; Automatic classification; Complexity; Copy number; Copy number variation; Copy number variations; Data analysis; Data mining; Developmental disabilities; DNA sequencing; Gene expression; Gene sequencing; Genetic research; Genomes; Mental disorders; Methods; Next-generation sequencing; Nucleotide sequence; Nucleotide sequencing; OTSU; Outliers (statistics); Segmentation; Variation; Whole genome sequencing; China; Adaptive segmentation; OTSU; Copy number variation; Next-generation sequencing; Anomaly detection
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-024-10018-6

Copy-number variations (CNVs), which refer to deletions and duplications of chromosomal segments, represent a significant source of variation among individuals, contributing to human evolution and being implicated in various diseases ranging from mental illness and developmental disorders to cancer. Despite the development of several methods for detecting copy number variations based on next-generation sequencing (NGS) data, achieving robust detection performance for CNVs with arbitrary coverage and amplitude remains challenging due to the inherent complexity of sequencing samples. In this paper, we propose an alternative method called OTSUCNV for CNV detection on whole genome sequencing (WGS) data. This method utilizes a newly designed adaptive sequence segmentation algorithm and an OTSU-based CNV prediction algorithm, which does not rely on any distribution assumptions or involve complex outlier factor calculations. As a result, the effective detection of CNVs is achieved with lower computational complexity. The experimental results indicate that the proposed method demonstrates outstanding performance, and hence it may be used as an effective tool for CNV detection.