scholarly journals Validation of an ultra-fast CNV calling tool for Next Generation Sequencing data using MLPA-verified copy number alterations

2018 ◽  
Author(s):  
Bas Tolhuis ◽  
Hans Karten
Keyword(s):  
Next Generation Sequencing ◽  
Copy Number ◽  
False Positive Rate ◽  
Copy Number Variations ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Single Node ◽  
Generation Sequencing ◽  
Cnv Detection

AbstractDNA Copy Number Variations (CNVs) are an important source for genetic diversity and pathogenic variants. Next Generation Sequencing (NGS) methods have become increasingly more popular for CNV detection, but its data analysis is a growing bottleneck. Genalice CNV is a novel tool for detection of CNVs. It takes care of turnaround time, scalability and cost issues associated with NGS computational analysis. Here, we validate Genalice CNV with MLPA-verified exon CNVs and genes with normal copy numbers. Genalice CNV detects 61 out of 62 exon CNVs and its false positive rate is less than 1%. It analyzes 96 samples from a targeted NGS assay in less than 45 minutes, including read alignment and CNV detection, using a single node. Furthermore, we describe data quality measures to minimize false discoveries. In conclusion, Genalice CNV is highly sensitive and specific, as well as extremely fast, which will be beneficial for clinical detection of CNVs.

scholarly journals cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate

2012 ◽  
Vol 40 (9) ◽  
pp. e69-e69 ◽  
Author(s):  
Günter Klambauer ◽  
Karin Schwarzbauer ◽  
Andreas Mayr ◽  
Djork-Arné Clevert ◽  
Andreas Mitterecker ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
False Discovery Rate ◽  
Copy Number ◽  
Copy Number Variations ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
False Discovery ◽  
Generation Sequencing

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

2021 ◽  
Vol 12 ◽  
Author(s):  
Guojun Liu ◽  
Junying Zhang
Keyword(s):  
Next Generation Sequencing ◽  
Copy Number ◽  
Read Depth ◽  
Copy Number Variations ◽  
Experimental Results ◽  
Data Sets ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing ◽  
Cnv Detection

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.

scholarly journals CNV-MEANN: A Neural Network and Mind Evolutionary Algorithm-Based Detection of Copy Number Variations From Next-Generation Sequencing Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Tihao Huang ◽  
Junqing Li ◽  
Baoxian Jia ◽  
Hongyan Sang
Keyword(s):  
Neural Network ◽  
Next Generation Sequencing ◽  
Evolutionary Algorithm ◽  
Copy Number ◽  
Copy Number Variations ◽  
Detection Methods ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing

Copy number variation (CNV), is defined as repetitions or deletions of genomic segments of 1 Kb to 5 Mb, and is a major trigger for human disease. The high-throughput and low-cost characteristics of next-generation sequencing technology provide the possibility of the detection of CNVs in the whole genome, and also greatly improve the clinical practicability of next-generation sequencing (NGS) testing. However, current methods for the detection of CNVs are easily affected by sequencing and mapping errors, and uneven distribution of reads. In this paper, we propose an improved approach, CNV-MEANN, for the detection of CNVs, involving changing the structure of the neural network used in the MFCNV method. This method has three differences relative to the MFCNV method: (1) it utilizes a new feature, mapping quality, to replace two features in MFCNV, (2) it considers the influence of the loss categories of CNV on disease prediction, and refines the output structure, and (3) it uses a mind evolutionary algorithm to optimize the backpropagation (neural network) neural network model, and calculates individual scores for each genome bin to predict CNVs. Using both simulated and real datasets, we tested the performance of CNV-MEANN and compared its performance with those of seven widely used CNV detection methods. Experimental results demonstrated that the CNV-MEANN approach outperformed other methods with respect to sensitivity, precision, and F1-score. The proposed method was able to detect many CNVs that other approaches could not, and it reduced the boundary bias. CNV-MEANN is expected to be an effective method for the analysis of changes in CNVs in the genome.

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