RKDOSCNV: A Local Kernel Density-Based Approach to the Detection of Copy Number Variations by Using Next-Generation Sequencing Data

Liu, Guojun; Zhang, Junying; Yuan, Xiguo; Wei, Chao

doi:10.3389/fgene.2020.569227

Cited by 6 publications

(8 citation statements)

References 55 publications

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“…The one-dimensional RDS profile is converted into a two-dimensional

profile, which consists of RDS ratios and differences between adjacent RDS ratios and is described by Eq. 4 ( Liu et al, 2020 ).

where

represents the i-th RDS ratio,

denotes the i-th differences between adjacent RDS ratios.…”

Section: Methodsmentioning

confidence: 99%

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

Liu,

Yang,

2023

Front. Genet.

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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.

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“…The one-dimensional RDS profile is converted into a two-dimensional

profile, which consists of RDS ratios and differences between adjacent RDS ratios and is described by Eq. 4 ( Liu et al, 2020 ).

where

represents the i-th RDS ratio,

denotes the i-th differences between adjacent RDS ratios.…”

Section: Methodsmentioning

confidence: 99%

“…The one-dimensional RDS profile is converted into a two-dimensional RDS′ profile, which consists of RDS ratios and differences between adjacent RDS ratios and is described by Eq. 4 (Liu et al, 2020).…”

Section: Figurementioning

confidence: 99%

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

Liu,

Yang,

2023

Front. Genet.

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“…

where

represents the value of the n-th read depth segment, which is equal to the mean of all RDs contained in this segment. The RDS profile is converted to two-dimensional space to generate the

profile ( Liu et al, 2020 ), which is composed of the RDS ratio and differences between adjacent RDS ratios and is expressed by Eq. 3 .…”

Section: Methodsmentioning

confidence: 99%

A shortest path-based approach for copy number variation detection from next-generation sequencing data

2023

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Copy number variation (CNV) is one of the main structural variations in the human genome and accounts for a considerable proportion of variations. As CNVs can directly or indirectly cause cancer, mental illness, and genetic disease in humans, their effective detection in humans is of great interest in the fields of oncogene discovery, clinical decision-making, bioinformatics, and drug discovery. The advent of next-generation sequencing data makes CNV detection possible, and a large number of CNV detection tools are based on next-generation sequencing data. Due to the complexity (e.g., bias, noise, alignment errors) of next-generation sequencing data and CNV structures, the accuracy of existing methods in detecting CNVs remains low. In this work, we design a new CNV detection approach, called shortest path-based Copy number variation (SPCNV), to improve the detection accuracy of CNVs. SPCNV calculates the k nearest neighbors of each read depth and defines the shortest path, shortest path relation, and shortest path cost sets based on which further calculates the mean shortest path cost of each read depth and its k nearest neighbors. We utilize the ratio between the mean shortest path cost for each read depth and the mean of the mean shortest path cost of its k nearest neighbors to construct a relative shortest path score formula that is able to determine a score for each read depth. Based on the score profile, a boxplot is then applied to predict CNVs. The performance of the proposed method is verified by simulation data experiments and compared against several popular methods of the same type. Experimental results show that the proposed method achieves the best balance between recall and precision in each set of simulated samples. To further verify the performance of the proposed method in real application scenarios, we then select real sample data from the 1,000 Genomes Project to conduct experiments. The proposed method achieves the best F1-scores in almost all samples. Therefore, the proposed method can be used as a more reliable tool for the routine detection of CNVs.

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“…Based on the denoised read depth segment profile, Eqs. (2–5) ( Li Y. et al, 2019 ; Liu et al, 2020 ) are used to transform its dimensions.…”

Section: Methodsmentioning

confidence: 99%

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

Liu

Zhang

2021

Front. Genet.

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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.

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RKDOSCNV: A Local Kernel Density-Based Approach to the Detection of Copy Number Variations by Using Next-Generation Sequencing Data

Cited by 6 publications

References 55 publications

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

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

A shortest path-based approach for copy number variation detection from next-generation sequencing data

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

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