Copynumber: Efficient Algorithms for Single- and Multi-track Copy Number Segmentation

Overview

Journal BMC Genomics

Publisher Biomed Central

Specialty Genetics

Date 2013 Feb 28

PMID 23442169

Citations 162

Authors

Gro Nilsen

Knut Liestol

Peter Van Loo

Hans Kristian Moen Vollan

Marianne B Eide

Oscar M Rueda

Suet-Feung Chin

Roslin Russell

Lars O Baumbusch

Carlos Caldas

Anne-Lise Borresen-Dale

Ole Christian Lingjaerde

Affiliations

Soon will be listed here.

Abstract

Background: Cancer progression is associated with genomic instability and an accumulation of gains and losses of DNA. The growing variety of tools for measuring genomic copy numbers, including various types of array-CGH, SNP arrays and high-throughput sequencing, calls for a coherent framework offering unified and consistent handling of single- and multi-track segmentation problems. In addition, there is a demand for highly computationally efficient segmentation algorithms, due to the emergence of very high density scans of copy number.

Results: A comprehensive Bioconductor package for copy number analysis is presented. The package offers a unified framework for single sample, multi-sample and multi-track segmentation and is based on statistically sound penalized least squares principles. Conditional on the number of breakpoints, the estimates are optimal in the least squares sense. A novel and computationally highly efficient algorithm is proposed that utilizes vector-based operations in R. Three case studies are presented.

Conclusions: The R package copynumber is a software suite for segmentation of single- and multi-track copy number data using algorithms based on coherent least squares principles.

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References

Hupe P, Stransky N, Thiery J, Radvanyi F, Barillot E . Analysis of array CGH data: from signal ratio to gain and loss of DNA regions. Bioinformatics. 2004; 20(18):3413-22. DOI: 10.1093/bioinformatics/bth418. View

Aguirre A, Brennan C, Bailey G, Sinha R, Feng B, Leo C . High-resolution characterization of the pancreatic adenocarcinoma genome. Proc Natl Acad Sci U S A. 2004; 101(24):9067-72. PMC: 428474. DOI: 10.1073/pnas.0402932101. View

Lai W, Choudhary V, Park P . CGHweb: a tool for comparing DNA copy number segmentations from multiple algorithms. Bioinformatics. 2008; 24(7):1014-5. PMC: 2516369. DOI: 10.1093/bioinformatics/btn067. View

Marioni J, Thorne N, Valsesia A, Fitzgerald T, Redon R, Fiegler H . Breaking the waves: improved detection of copy number variation from microarray-based comparative genomic hybridization. Genome Biol. 2007; 8(10):R228. PMC: 2246302. DOI: 10.1186/gb-2007-8-10-r228. View

Shah S, Lam W, Ng R, Murphy K . Modeling recurrent DNA copy number alterations in array CGH data. Bioinformatics. 2007; 23(13):i450-8. DOI: 10.1093/bioinformatics/btm221. View

Yu T, Ye H, Sun W, Li K, Chen Z, Jacobs S . A forward-backward fragment assembling algorithm for the identification of genomic amplification and deletion breakpoints using high-density single nucleotide polymorphism (SNP) array. BMC Bioinformatics. 2007; 8:145. PMC: 1868765. DOI: 10.1186/1471-2105-8-145. View

Olshen A, Venkatraman E, Lucito R, Wigler M . Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics. 2004; 5(4):557-72. DOI: 10.1093/biostatistics/kxh008. View

Willenbrock H, Fridlyand J . A comparison study: applying segmentation to array CGH data for downstream analyses. Bioinformatics. 2005; 21(22):4084-91. DOI: 10.1093/bioinformatics/bti677. View

Tibshirani R, Wang P . Spatial smoothing and hot spot detection for CGH data using the fused lasso. Biostatistics. 2007; 9(1):18-29. DOI: 10.1093/biostatistics/kxm013. View

10.

Wiedswang G, Borgen E, Karesen R, Kvalheim G, Nesland J, Qvist H . Detection of isolated tumor cells in bone marrow is an independent prognostic factor in breast cancer. J Clin Oncol. 2003; 21(18):3469-78. DOI: 10.1200/JCO.2003.02.009. View

11.

Klijn C, Holstege H, de Ridder J, Liu X, Reinders M, Jonkers J . Identification of cancer genes using a statistical framework for multiexperiment analysis of nondiscretized array CGH data. Nucleic Acids Res. 2008; 36(2):e13. PMC: 2241875. DOI: 10.1093/nar/gkm1143. View

12.

Russnes H, Vollan H, Lingjaerde O, Krasnitz A, Lundin P, Naume B . Genomic architecture characterizes tumor progression paths and fate in breast cancer patients. Sci Transl Med. 2010; 2(38):38ra47. PMC: 3972440. DOI: 10.1126/scitranslmed.3000611. View

13.

Van Loo P, Nordgard S, Lingjaerde O, Russnes H, Rye I, Sun W . Allele-specific copy number analysis of tumors. Proc Natl Acad Sci U S A. 2010; 107(39):16910-5. PMC: 2947907. DOI: 10.1073/pnas.1009843107. View

14.

Picard F, Robin S, Lavielle M, Vaisse C, Daudin J . A statistical approach for array CGH data analysis. BMC Bioinformatics. 2005; 6:27. PMC: 549559. DOI: 10.1186/1471-2105-6-27. View

15.

Picard F, Lebarbier E, Hoebeke M, Rigaill G, Thiam B, Robin S . Joint segmentation, calling, and normalization of multiple CGH profiles. Biostatistics. 2011; 12(3):413-28. DOI: 10.1093/biostatistics/kxq076. View

16.

Zhang N, Siegmund D . A modified Bayes information criterion with applications to the analysis of comparative genomic hybridization data. Biometrics. 2007; 63(1):22-32. DOI: 10.1111/j.1541-0420.2006.00662.x. View

17.

Gerlinger M, Rowan A, Horswell S, Math M, Larkin J, Endesfelder D . Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012; 366(10):883-892. PMC: 4878653. DOI: 10.1056/NEJMoa1113205. View

18.

Coe B, Chari R, MacAulay C, Lam W . FACADE: a fast and sensitive algorithm for the segmentation and calling of high resolution array CGH data. Nucleic Acids Res. 2010; 38(15):e157. PMC: 2926625. DOI: 10.1093/nar/gkq548. View

19.

Scharpf R, Parmigiani G, Pevsner J, Ruczinski I . Hidden Markov models for the assessment of chromosomal alterations using high-throughput SNP arrays. Ann Appl Stat. 2009; 2(2):687-713. PMC: 2710854. DOI: 10.1214/07-AOAS155. View

20.

Wang L, Abyzov A, Korbel J, Snyder M, Gerstein M . MSB: a mean-shift-based approach for the analysis of structural variation in the genome. Genome Res. 2008; 19(1):106-17. PMC: 2612956. DOI: 10.1101/gr.080069.108. View