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Copynumber: Efficient algorithms for single- and multi-track copy number segmentation

Gro Nilsen12, Knut Liestøl12, Peter Van Loo34, Hans Kristian Moen Vollan567, Marianne B Eide28, Oscar M Rueda9, Suet-Feung Chin9, Roslin Russell9, Lars O Baumbusch5, Carlos Caldas109, Anne-Lise Børresen-Dale56 and Ole Christian Lingjærde125*

  • * Corresponding author: Ole Christian Lingjærde

  • † Equal contributors

Author affiliations

1 Biomedical Informatics, Dept of Informatics, University of Oslo, Oslo, Norway

2 Centre for Cancer Biomedicine, University of Oslo, Oslo, Norway

3 Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK

4 Dept of Human Genetics, VIB and University of Leuven, Leuven, Belgium

5 Dept of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway

6 Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway

7 Dept of Oncology, Division of Cancer, Surgery and Transplantation, Oslo University Hospital Radiumhospitalet, Oslo, Norway

8 Dept of Immunology, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway

9 Breast Cancer Functional Genomics, Cancer Research UK Cambridge Research Institute and Dept of Oncology, University of Cambridge, Li Ka-Shing Centre, Cambridge, UK

10 Cambridge Breast Unit, Addenbrookes Hospital and Cambridge National Institute for Health Research Biomedical Research Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK

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Citation and License

BMC Genomics 2012, 13:591  doi:10.1186/1471-2164-13-591

Published: 4 November 2012



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.


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.


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.

Copy number; aCGH; Segmentation; Allele-specific segmentation; Penalized regression; Least squares; Bioconductor