IhybCNV: An intra-hybrid approach for CNV detection from next-generation sequencing data

Copy number variation (CNV) is a common phenomenon in the human genome, accounting for a large proportion of structural variations. Accurate detection of CNVs has been regarded as a regular approach for studying cancer mechanisms and evolution and seeking targeted drugs for cancer diagnosis. Next-generation sequencing (NGS) data has provided an unprecedented opportunity for this task. Currently, a large number of methods have been developed for CNV detection by using NGS data. Each of them has its own viewpoint on the characteristics of CNVs associated with the features of NGS data. Due to the intrinsic complexity of CNVs, new methods with a comprehensive viewpoint on the characteristics of CNVs combined with NGS data are required for the detection of various forms of CNVs. In this paper, we propose an intra-hybrid approach, IhybCNV, for the detection of CNVs from NGS data. The central idea of the IhybCNV is that it regards CNVs as outlier events from various viewpoints on the read depth (RD) profile to be analyzed. More precisely, the IhybCNV integrates five individual detectors to calculate five types of outlier scores for each genome segment and then combines the five types of outlier scores into an anomaly score via locally selective combination in parallel outlier ensembles (LSCP). With the anomaly scores of the genome segments, the IhybCNV determines abnormal segments (CNVs) or normal segments by designing a binary clustering model (BCM). The performance of the IhybCNV is evaluated by carrying out simulation studies and real data applications and is compared with five peer methods. The experimental results demonstrate that the IhybCNV obtains a higher F1-score value than the other methods. Thus, the IhybCNV could be expected as a supplementary to current methods and might become a promising tool for the detection of genome mutations. (C) 2021 Elsevier Inc. All rights reserved.