Copy number variation estimation from multiple next-generation sequencing samples

Junbo Duan; Ji Gang Zhang; Hongbao Cao; Hong Wen Deng; Yu Ping Wang

doi:10.1145/2382936.2383019

Copy number variation estimation from multiple next-generation sequencing samples

Junbo Duan, Ji Gang Zhang, Hongbao Cao, Hong Wen Deng, Yu Ping Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Robust and accurate detection of copy number variations (CNVs) from next-generation sequencing (NGS) data is challenging. Because of the high fluctuation of read depth signal, most existing methods, which use only one data sample, yield high false positive rate and low power. By integrating information from multiple samples, the detection could be improved. In this paper, a method to detect CNVs from multiple samples is proposed. The proposed method explores the concurrency of read depth signals across multiple samples, promising to increase the detection power. Our experiments on real data sets show that the proposed method can improve the CNV detection over several existing ones.

Original language	English
Title of host publication	2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012
Pages	555-557
Number of pages	3
DOIs	https://doi.org/10.1145/2382936.2383019
State	Published - 2012
Externally published	Yes
Event	2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012 - Orlando, FL, United States Duration: Oct 7 2012 → Oct 10 2012

Publication series

Name	2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012

Conference

Conference	2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012
Country/Territory	United States
City	Orlando, FL
Period	10/7/12 → 10/10/12

Keywords

ℓ-0 norm penalty
Copy number variation
Next generation sequencing
Set of solutions
Sparse modeling
The 1000 Genome Project

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10.1145/2382936.2383019

Cite this

Duan, J., Zhang, J. G., Cao, H., Deng, H. W., & Wang, Y. P. (2012). Copy number variation estimation from multiple next-generation sequencing samples. In 2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012 (pp. 555-557). (2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012). https://doi.org/10.1145/2382936.2383019

@inproceedings{ac11c33fa36f458fbdd5431e0fea4cfb,

title = "Copy number variation estimation from multiple next-generation sequencing samples",

abstract = "Robust and accurate detection of copy number variations (CNVs) from next-generation sequencing (NGS) data is challenging. Because of the high fluctuation of read depth signal, most existing methods, which use only one data sample, yield high false positive rate and low power. By integrating information from multiple samples, the detection could be improved. In this paper, a method to detect CNVs from multiple samples is proposed. The proposed method explores the concurrency of read depth signals across multiple samples, promising to increase the detection power. Our experiments on real data sets show that the proposed method can improve the CNV detection over several existing ones.",

keywords = "ℓ-0 norm penalty, Copy number variation, Next generation sequencing, Set of solutions, Sparse modeling, The 1000 Genome Project",

author = "Junbo Duan and Zhang, {Ji Gang} and Hongbao Cao and Deng, {Hong Wen} and Wang, {Yu Ping}",

year = "2012",

doi = "10.1145/2382936.2383019",

language = "Ingl{\'e}s",

isbn = "9781450316705",

series = "2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012",

pages = "555--557",

booktitle = "2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012",

note = "2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012 ; Conference date: 07-10-2012 Through 10-10-2012",

}

Duan, J, Zhang, JG, Cao, H, Deng, HW & Wang, YP 2012, Copy number variation estimation from multiple next-generation sequencing samples. in 2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012. 2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012, pp. 555-557, 2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012, Orlando, FL, United States, 10/7/12. https://doi.org/10.1145/2382936.2383019

Copy number variation estimation from multiple next-generation sequencing samples. / Duan, Junbo; Zhang, Ji Gang; Cao, Hongbao et al.
2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012. 2012. p. 555-557 (2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Copy number variation estimation from multiple next-generation sequencing samples

AU - Duan, Junbo

AU - Zhang, Ji Gang

AU - Cao, Hongbao

AU - Deng, Hong Wen

AU - Wang, Yu Ping

PY - 2012

Y1 - 2012

N2 - Robust and accurate detection of copy number variations (CNVs) from next-generation sequencing (NGS) data is challenging. Because of the high fluctuation of read depth signal, most existing methods, which use only one data sample, yield high false positive rate and low power. By integrating information from multiple samples, the detection could be improved. In this paper, a method to detect CNVs from multiple samples is proposed. The proposed method explores the concurrency of read depth signals across multiple samples, promising to increase the detection power. Our experiments on real data sets show that the proposed method can improve the CNV detection over several existing ones.

AB - Robust and accurate detection of copy number variations (CNVs) from next-generation sequencing (NGS) data is challenging. Because of the high fluctuation of read depth signal, most existing methods, which use only one data sample, yield high false positive rate and low power. By integrating information from multiple samples, the detection could be improved. In this paper, a method to detect CNVs from multiple samples is proposed. The proposed method explores the concurrency of read depth signals across multiple samples, promising to increase the detection power. Our experiments on real data sets show that the proposed method can improve the CNV detection over several existing ones.

KW - ℓ-0 norm penalty

KW - Copy number variation

KW - Next generation sequencing

KW - Set of solutions

KW - Sparse modeling

KW - The 1000 Genome Project

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DO - 10.1145/2382936.2383019

M3 - Contribución a la conferencia

AN - SCOPUS:84869419476

SN - 9781450316705

T3 - 2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012

SP - 555

EP - 557

BT - 2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012

T2 - 2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012

Y2 - 7 October 2012 through 10 October 2012

ER -

Copy number variation estimation from multiple next-generation sequencing samples

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Keywords

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