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

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 offeri...

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Published inBMC genomics Vol. 13; no. 1; p. 591
Main Authors Nilsen, Gro, Liestøl, Knut, Van Loo, Peter, Moen Vollan, Hans Kristian, Eide, Marianne B, Rueda, Oscar M, Chin, Suet-Feung, Russell, Roslin, Baumbusch, Lars O, Caldas, Carlos, Børresen-Dale, Anne-Lise, Lingjærde, Ole Christian
Format Journal Article
LanguageEnglish
Published London BioMed Central 04.11.2012
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects
aCGH
Algorithms
Allele-specific segmentation
Analysis
Animal Genetics and Genomics
Artificial chromosomes
Bioconductor
Biomedical and Life Sciences
Biomedical research
Breakpoints
Cancer
Case studies
Computer programs
Copy number
Copy number variations
Cytogenetics
Data processing
Development and progression
DNA
DNA - genetics
DNA Copy Number Variations
Dynamic programming
Gene Dosage
Genetic aspects
Genetics
Genome, Human
Genomes
Genomic Instability
Genomics
Human and rodent genomic
Humans
Least squares
Life Sciences
Lymphoma, Follicular - genetics
Medical research
Methods
Microarrays
Microbial Genetics and Genomics
Neoplasms - genetics
Oligonucleotide Array Sequence Analysis - methods
Penalized regression
Plant Genetics and Genomics
Polymorphism, Single Nucleotide
Programming languages
Proteomics
Segmentation
Single-nucleotide polymorphism
Software
Studies
Norway
Bioconductor
Segmentation
Copy number
Least squares
Penalized regression
aCGH
Allele-specific segmentation
Online AccessGet full text
ISSN1471-2164
1471-2164
DOI10.1186/1471-2164-13-591

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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.
AbstractList 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.
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.
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.
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. Keywords: Copy number, aCGH, Segmentation, Allele-specific segmentation, Penalized regression, Least squares, Bioconductor
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.
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.
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.BACKGROUNDCancer 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.RESULTSA 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.CONCLUSIONSThe 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.
Doc number: 591 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.
ArticleNumber 591
Audience Academic
Author Baumbusch, Lars O
Lingjærde, Ole Christian
Rueda, Oscar M
Chin, Suet-Feung
Eide, Marianne B
Van Loo, Peter
Nilsen, Gro
Børresen-Dale, Anne-Lise
Liestøl, Knut
Caldas, Carlos
Moen Vollan, Hans Kristian
Russell, Roslin
AuthorAffiliation 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
1 Biomedical Informatics, Dept of Informatics, University of Oslo, 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
3 Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
4 Dept of Human Genetics, VIB and University of Leuven, Leuven, Belgium
10 Cambridge Breast Unit, Addenbrookes Hospital and Cambridge National Institute for Health Research Biomedical Research Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
2 Centre for Cancer Biomedicine, University of Oslo, Oslo, Norway
5 Dept of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway
6 Insti
AuthorAffiliation_xml – name: 4 Dept of Human Genetics, VIB and University of Leuven, Leuven, Belgium
– name: 7 Dept of Oncology, Division of Cancer, Surgery and Transplantation, Oslo University Hospital Radiumhospitalet, Oslo, Norway
– name: 6 Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
– name: 8 Dept of Immunology, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway
– name: 5 Dept of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway
– name: 9 Breast Cancer Functional Genomics, Cancer Research UK Cambridge Research Institute and Dept of Oncology, University of Cambridge, Li Ka-Shing Centre, Cambridge, UK
– name: 10 Cambridge Breast Unit, Addenbrookes Hospital and Cambridge National Institute for Health Research Biomedical Research Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
– name: 3 Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
– name: 2 Centre for Cancer Biomedicine, University of Oslo, Oslo, Norway
– name: 1 Biomedical Informatics, Dept of Informatics, University of Oslo, Oslo, Norway
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  surname: Nilsen
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  surname: Van Loo
  fullname: Van Loo, Peter
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  givenname: Hans Kristian
  surname: Moen Vollan
  fullname: Moen Vollan, Hans Kristian
  organization: Dept of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Dept of Oncology, Division of Cancer, Surgery and Transplantation, Oslo University Hospital Radiumhospitalet
– sequence: 5
  givenname: Marianne B
  surname: Eide
  fullname: Eide, Marianne B
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– sequence: 6
  givenname: Oscar M
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  surname: Chin
  fullname: Chin, Suet-Feung
  organization: Breast Cancer Functional Genomics, Cancer Research UK Cambridge Research Institute and Dept of Oncology, University of Cambridge, Li Ka-Shing Centre
– sequence: 8
  givenname: Roslin
  surname: Russell
  fullname: Russell, Roslin
  organization: Breast Cancer Functional Genomics, Cancer Research UK Cambridge Research Institute and Dept of Oncology, University of Cambridge, Li Ka-Shing Centre
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  givenname: Lars O
  surname: Baumbusch
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  givenname: Carlos
  surname: Caldas
  fullname: Caldas, Carlos
  organization: Breast Cancer Functional Genomics, Cancer Research UK Cambridge Research Institute and Dept of Oncology, University of Cambridge, Li Ka-Shing Centre, Cambridge Breast Unit, Addenbrookes Hospital and Cambridge National Institute for Health Research Biomedical Research Centre, Cambridge University Hospitals NHS Foundation Trust
– sequence: 11
  givenname: Anne-Lise
  surname: Børresen-Dale
  fullname: Børresen-Dale, Anne-Lise
  organization: Dept of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo
– sequence: 12
  givenname: Ole Christian
  surname: Lingjærde
  fullname: Lingjærde, Ole Christian
  email: ole@ifi.uio.no
  organization: Biomedical Informatics, Dept of Informatics, University of Oslo, Centre for Cancer Biomedicine, University of Oslo, Dept of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet
BackLink https://www.ncbi.nlm.nih.gov/pubmed/23442169$$D View this record in MEDLINE/PubMed
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COPYRIGHT 2012 BioMed Central Ltd.
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Keywords Bioconductor
Segmentation
Copy number
Least squares
Penalized regression
aCGH
Allele-specific segmentation
Language English
License This is an Open Access article distributed under the terms of the Creative Commons Attribution License(http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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SSID ssj0017825
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Snippet Background Cancer progression is associated with genomic instability and an accumulation of gains and losses of DNA. The growing variety of tools for measuring...
Cancer progression is associated with genomic instability and an accumulation of gains and losses of DNA. The growing variety of tools for measuring genomic...
Background Cancer progression is associated with genomic instability and an accumulation of gains and losses of DNA. The growing variety of tools for measuring...
Doc number: 591 Abstract Background: Cancer progression is associated with genomic instability and an accumulation of gains and losses of DNA. The growing...
Background: Cancer progression is associated with genomic instability and an accumulation of gains and losses of DNA. The growing variety of tools for...
Abstract Background Cancer progression is associated with genomic instability and an accumulation of gains and losses of DNA. The growing variety of tools for...
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StartPage 591
SubjectTerms aCGH
Algorithms
Allele-specific segmentation
Analysis
Animal Genetics and Genomics
Artificial chromosomes
Bioconductor
Biomedical and Life Sciences
Biomedical research
Breakpoints
Cancer
Case studies
Computer programs
Copy number
Copy number variations
Cytogenetics
Data processing
Development and progression
DNA
DNA - genetics
DNA Copy Number Variations
Dynamic programming
Gene Dosage
Genetic aspects
Genetics
Genome, Human
Genomes
Genomic Instability
Genomics
Human and rodent genomic
Humans
Least squares
Life Sciences
Lymphoma, Follicular - genetics
Medical research
Methods
Microarrays
Microbial Genetics and Genomics
Neoplasms - genetics
Oligonucleotide Array Sequence Analysis - methods
Penalized regression
Plant Genetics and Genomics
Polymorphism, Single Nucleotide
Programming languages
Proteomics
Segmentation
Single-nucleotide polymorphism
Software
Studies
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Title Copynumber: Efficient algorithms for single- and multi-track copy number segmentation
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