Copy number variation estimation from multiple next-generation sequencing samples
Pages 555 - 557
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.
References
[1]
S. Yoon et al., "Sensitive and accurate detection of copy number variants using read depth of coverage", Genome Res., vol. 19, pp. 1586--1592, Sep 2009.
[2]
A. Abyzov et al., "CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing", Genome Res, vol. 21, no. 6, pp. 974--984, Jun 2011.
[3]
D. Chiang et al., "High-resolution mapping of copy-number alterations with massively parallel sequencing", Nat. Methods, vol. 6, pp. 99--103, Jan 2009.
[4]
C. Xie and M. T. Tammi, "CNV-seq, a new method to detect copy number variation using high-throughput sequencing", BMC Bioinformatics, vol. 10, pp. 80, 2009.
[5]
J. Duan et al., "Detection of copy number variation from next generation sequencing data with total variation penalized least square optimization", in IEEE international conference on bioinformatics and biomedicine workshops, Atlanta, GA, USA, Nov. 2011, pp. 3--12.
[6]
J. Duan et al., "Détection conjointe de discontinuités d'ordres différents dans un signal par minimisation de critère L2--L0", in Actes 22c coll. GRETSI, Dijon, France, Sep. 2009.
[7]
C. Soussen et al., "From Bernoulli-Gaussian deconvolution to sparse signal restoration", IEEE Trans. Signal Processing, vol. 59, no. 10, pp. 4572--4584, 2011.
[8]
J. Duan et al., "A continuation approach to estimate a solution path of mixed L2-L0 minimization problems", in Signal Processing with Adaptive Sparse Structured Representations (SPARS workshop), Saint-Malo, Apr. 2009, pp. 1--6.
[9]
D. M. Malioutov et al., "Homotopy continuation for sparse signal representation", in Proc. IEEE ICASSP, Philadephia, USA, Mar. 2005, vol. V, pp. 733--736.
[10]
H. Li et al., "The sequence alignment/map format and SAMtools", Bioinformatics, vol. 25, no. 16, pp. 2078--2079, Aug 2009.
[11]
D. R. Bentley et al., "Accurate whole human genome sequencing using reversible terminator chemistry", Nature, vol. 456, pp. 53--59, Nov 2008.
[12]
H. Akaike, "A new look at the statistical model identification", IEEE Trans. Automat. Contr., vol. AC-19, no. 6, pp. 716--723, Dec. 1974.
[13]
G. Schwarz, "Estimating the Dimension of a Model", Annals Statist., vol. 6, pp. 461--464, 1978.
[14]
K. E. Markon and R. F. Krueger, "An empirical comparison of information-theoretic selection criteria for multivariate behavior genetic models", Behavior Genetics, vol. 34, no. 6, pp. 593--610, 2004.
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- Copy number variation estimation from multiple next-generation sequencing samples
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Published In
October 2012
725 pages
ISBN:9781450316705
DOI:10.1145/2382936
- General Chair:
- Sanjay Ranka,
- Program Chairs:
- Tamer Kahveci,
- Mona Singh
Copyright © 2012 Authors.
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Association for Computing Machinery
New York, NY, United States
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Published: 07 October 2012
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BCB' 12
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BCB' 12: ACM International Conference on Bioinformatics, Computational Biology and Biomedicine
October 7 - 10, 2012
Florida, Orlando
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BCB '12 Paper Acceptance Rate 33 of 159 submissions, 21%;
Overall Acceptance Rate 254 of 885 submissions, 29%
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