ExCNVSS: A Noise-Robust Method for Copy Number Variation Detection in Whole Exome Sequencing Data

Kong, Jinhwa; Shin, Jae-Moon; Won, Jung-Im; Lee, Keonbae; Lee, Unjoo; Yoon, Jeehee

doi:10.1155/2017/9631282

Cited by 4 publications

(5 citation statements)

References 23 publications

(38 reference statements)

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“…Because MLPA is the most widely used method for detecting CNVs in humans, we validated our method using the largest reported MLPA-confirmed data set. [14][15][16][17][18][19][20][21][22][23][24][25][26] We evaluated the performance of CCR-CNV and compared it with DECoN and Atlas-CNV. CCR-CNV was superior in terms of sensitivity.…”

Section: Discussionmentioning

confidence: 99%

“…However, proper diagnoses of exonic deletions or duplications using targeted NGS data have proved to be challenging. [14][15][16][17][18][19][20][21][22][23][24][25][26] In addition, MLPA cannot be performed individually on all genes in an NGS panel because of the associated expenses.…”

Section: Introductionmentioning

confidence: 99%

“…27 Various methods have been developed to identify exonic CNVs in gene panel sequencing data. [14][15][16][17][18][19][20][21][22][23][24][25][26] These methods often use complex data normalization algorithms that reduce sensitivity for exonic CNVs and provide highly segmented copy number regions, resulting in high false positive rates. The use of sophisticated algorithms such as machine learning in clinical settings is difficult.…”

Section: Introductionmentioning

confidence: 99%

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Consistent count region–copy number variation (CCR-CNV): an expandable and robust tool for clinical diagnosis of copy number variation at the exon level using next-generation sequencing data

Kim¹,

Lee²,

Yun³

et al. 2022

Genetics in Medicine

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Despite the importance of exonic copy number variations (CNVs) in human genetic diseases, reliable next-generation sequencing-based methods for detecting them are unavailable. We developed an expandable and robust exonic CNV detection tool called consistent count region (CCR)-CNV. Methods: In total, about 1000 samples of the truth set were used for validating CCR-CNV. We compared CCR-CNV performance with 2 well-known CNV tools. Finally, to overcome the limitations of CCR-CNV, we devised a combined approach. Results: The mean sensitivity and specificity of CCR-CNV alone were above 95%, which was superior to that of other CNV tools, such as DECoN and Atlas-CNV. However, low covered region and positive predictive value and high false discovery rate act as obstacles to its use in clinical settings. The combined approach showed much improved performance than CCR-CNV alone. Conclusion:In this study, we present a novel diagnostic tool that allows the identification of exonic CNVs with high confidence using various reagents and clinical next-generation sequencing platforms. We validated this method using the largest multiple ligation-dependent probe amplification-confirmed data set, including sufficient copy normal control data. The approach, combined with existing CNV tools, allows the implementation of CCR-CNV in clinical settings.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Consistent count region–copy number variation (CCR-CNV): an expandable and robust tool for clinical diagnosis of copy number variation at the exon level using next-generation sequencing data

Kim¹,

Lee²,

Yun³

et al. 2022

Genetics in Medicine

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“…To correct the imbalance library size effect, we multiply d s,c by the ratio of the sums of read-count of the sample and control in all exonic regions [11,15]…”

Section: Correcting the Imbalance Library Size Effectmentioning

confidence: 99%

Copy Number Variation Detection Using Total Variation

Zare

Nabavi

2019

Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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Next-generation sequencing (NGS) technologies offer new opportunities for precise and accurate identification of genomic aberrations, including copy number variations (CNVs). For highthroughput NGS data, using depth of coverage has become a major approach to identify CNVs, especially for whole exome sequencing (WES) data. Due to the high level of noise and biases of read-count data and complexity of the WES data, existing CNV detection tools identify many false CNV segments. Besides, NGS generates a huge amount of data, requiring to use effective and efficient methods. In this work, we propose a novel segmentation algorithm based on the total variation approach to detect CNVs more precisely and efficiently using WES data. The proposed method also filters out outlier read-counts and identifies significant change points to reduce false positives. We used real and simulated data to evaluate the performance of the proposed method and compare its performance with those of other commonly used CNV detection methods. Using simulated and real data, we show that the proposed method outperforms the existing CNV detection methods in terms of accuracy and false discovery rate and has a faster runtime compared to the circular binary segmentation method.

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“…Exomes of the patient’s first-degree unaffected relatives (both parents and siblings) were also sequenced to allow the study of ‘de novo’ variations which, so far, have been under investigated in RRMS. The exome variations observed here may help the study of relevant single nucleotide polymorphisms, de novo- and structural genomic variations—such as copy-number alterations that may be associated with the disease [ 4 – 8 ]. In total, 28 individuals have been sequenced including eight RRMS patients as well as a set of another 20 unaffected first-degree relatives.…”

Section: Objectivementioning

confidence: 99%

Exome sequencing of multiple-sclerosis patients and their unaffected first-degree relatives

et al. 2017

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ObjectivesThe understanding of complex multifactorial diseases requires the availability of a variety of data for a large-number of affected individuals. In this data note here we provide whole exome sequencing data from a set of non-familiar multiple-sclerosis (MS) patients as well as their unaffected first-degree relatives. This data might help the identification of genomic alterations, including single nucleotide polymorphisms, de novo variations and structural genomic variations, such as copy-number alterations that may impact this disease.Data descriptionThis dataset comprises the full exome of 28 Brazilian subjects grouped in eight distinct families, consisting of four complete trios (mother–patient–father) plus another four complete trios with one added unaffected sibling. In total, we present the full exome data of eight patients diagnosed with recurrent remittent multiple sclerosis. Diagnoses were made by experienced neurologists and all enrolled patients had at least 5 years of follow up and specific MS treatment. Exomes were sequenced from leukocyte-derived DNA, after the capture of exons using biotinylated probes, in the Ion Proton platform. For each exome we generated an average of 66.1 million good quality mapped reads with an average length of ~ 160nt. On average, for 90% of the exome a vertical coverage above 20× was reached.

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ExCNVSS: A Noise-Robust Method for Copy Number Variation Detection in Whole Exome Sequencing Data

Cited by 4 publications

References 23 publications

Consistent count region–copy number variation (CCR-CNV): an expandable and robust tool for clinical diagnosis of copy number variation at the exon level using next-generation sequencing data

Consistent count region–copy number variation (CCR-CNV): an expandable and robust tool for clinical diagnosis of copy number variation at the exon level using next-generation sequencing data

Copy Number Variation Detection Using Total Variation

Exome sequencing of multiple-sclerosis patients and their unaffected first-degree relatives

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