![]() With the advancement in high-throughput techniques, in addition to mutations and copy number variations, structural variations have gained importance for their role in genome instability leading to tumorigenesis. Therefore, this method can be regarded as a conventional method in the field of genomic mutation analysis of cancer samples.ĭiffuse large B-cell lymphoma (DLBCL) is the most common form of non-Hodgkin lymphoma and frequently develops through the accumulation of several genetic variations. In real data experiments, WAVECNV can detect more cancer genes than existing methods. It also has high precision in data with low purity and coverage. The results show that the sensitivity of WAVECNV is better than the existing methods. WAVECNV was tested and compared with peer methods in terms of simulated data and real cancer-sequencing data. Through this method, the information of the short variation region is retained. Finally, a statistical model is established, and the p-value test is used for calling CNVs. Then, according to the distance between genome bins and normal clusters, the outlier of each genome bin is evaluated. The algorithm uses wavelet clustering to process the read depth and determine the normal cluster and abnormal cluster according to the size of the cluster. This study proposes a new CNV-detection method named WAVECNV to solve this issue. Therefore, it is necessary to improve the sensitivity of algorithms to short-variation fragments. The existing methods have low sensitivity to variation regions with a short length and small variation range. This study demonstrates that CNV-PCC is an effective method for detecting CNVs, even for low CN duplications and small CNVs.Ĭopy number variation (CNV) detection based on second-generation sequencing technology is the basis of much gene research, but the read depth is affected by mapping errors, repeated reads, and GC bias. Furthermore, CNV-PCC shows high consistency on real sequencing samples with other methods. The analysis of simulated data results indicates that CNV-PCC outperforms the other methods for sensitivity and F1-score and improves breakpoint accuracy. Finally, the OTSU algorithm calculates the threshold to determine the CNVs regions. Next, the outlier scores are calculated for each segment by PCC (Principal Component Classifier). A two-stage segmentation strategy is then implemented to enhance the identification capabilities of low CN duplications and small CNVs. CNV-PPC first uses the split read signal to search for potential breakpoints. We propose a new method, CNV-PCC (detection of CNVs based on Principal Component Classifier), to identify CNVs in whole genome sequencing data. In addition, the RD-based approach can only obtain rough breakpoints. However, low CN (copy number of 3–4) duplication events are challenging to identify with existing methods, especially when the size of CNVs is small. ![]() ![]() The next-generation sequencing (NGS) technology provides rich data for detecting CNVs, and the read depth (RD)-based approach is widely used. Ĭopy number variations (CNVs) significantly influence the diversity of the human genome and the occurrence of many complex diseases. It is an open-source integrated pipeline available at vogetihrsh/icopydav and as Docker's image at. Performance of iCopyDAV is evaluated on both simulated data and real data for different sequencing depths. Here we show the effect of sequencing coverage, read length, bin size, data pre-treatment and seg-mentation approaches on accurate detection of the complete spectrum of CNVs. Parallelization of the seg-mentation algorithms makes the iCopyDAV platform even accessible on a desktop. An important feature of iCopyDAV is the functional annotation module that enables the user to identify and prioritize CNVs encompassing various functional elements, genomic features and disease-associations. It has a modular framework comprising five major modules: data pre-treatment, segmentation, variant calling, annotation and visualization. We have developed an integrated platform, iCopyDAV, to handle some of these issues in CNV detection in whole genome NGS data. However, accurate detection of CNVs from NGS data is not straightforward due to non-uniform coverage of reads resulting from various systemic biases. CNVs include both copy gain and copy loss events and their detection genome-wide is now possible using high-through-put, low-cost next generation sequencing (NGS) methods. Discovery of copy number variations (CNVs), a major category of structural variations, have dramatically changed our understanding of differences between individuals and provide an alternate paradigm for the genetic basis of human diseases.
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