Detection of copy number variations based on a local distance using next-generation sequencing data

Abstract

As one of the main types of structural variation in the human genome, copy number variation (CNV) plays an important role in the occurrence and development of human cancers. Next-generation sequencing (NGS) technology can provide base-level resolution, which provides favorable conditions for the accurate detection of CNVs. However, it is still a very challenging task to accurately detect CNVs from cancer samples with different purity and low sequencing coverage. Local distance-based CNV detection (LDCNV), an innovative computational approach to predict CNVs using NGS data, is proposed in this work. LDCNV calculates the average distance between each read depth (RD) and its k nearest neighbors (KNNs) to define the distance of KNNs of each RD, and the average distance between the KNNs for each RD to define their internal distance. Based on the above definitions, a local distance score is constructed using the ratio between the distance of KNNs and the internal distance of KNNs for each RD. The local distance scores are used to fit a normal distribution to evaluate the significance level of each RDS, and then use the hypothesis test method to predict the CNVs. The performance of the proposed method is verified with simulated and real data and compared with several popular methods. The experimental results show that the proposed method is superior to various other techniques. Therefore, the proposed method can be helpful for cancer diagnosis and targeted drug development.

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

“…Using the simulation data generated above, we compared its performance with four different peer methods, which are FREEC [ 11 ], CNV-LOF [ 15 ], KNNCNV [ 31 ], and LDCNV [ 20 ]. Figure 4 shows the experimental results of these methods on simulation data, where the experimental results for each different coverage are averaged over a total of 90 samples for 3 different variant configurations and 30 sequencing repetitions of the simulation.…”

“…IhybCNV [ 19 ] improves detection performance by integrating results from different detectors. LDCNV [ 20 ] blends global and local and presents a better anomaly score computation algorithm based on KNN that more accurately captures the degree of abnormality. Restricted by the intrinsic complexity of NGS data, how to efficiently retrieve valuable information from the heavy data and how to set thresholds with more confidence still has to be researched further to further evaluate the data features in order to forecast CNV more consistently through simple and interpretable computational algorithms.…”