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Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data

Summary: More and more cancer studies use next-generation sequencing (NGS) data to detect various types of genomic variation. However, even when researchers have such data at hand, single-nucleotide polymorphism arrays have been considered necessary to assess copy number alterations and especially l...

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Autores principales: Boeva, Valentina, Popova, Tatiana, Bleakley, Kevin, Chiche, Pierre, Cappo, Julie, Schleiermacher, Gudrun, Janoueix-Lerosey, Isabelle, Delattre, Olivier, Barillot, Emmanuel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3268243/
https://www.ncbi.nlm.nih.gov/pubmed/22155870
http://dx.doi.org/10.1093/bioinformatics/btr670
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author Boeva, Valentina
Popova, Tatiana
Bleakley, Kevin
Chiche, Pierre
Cappo, Julie
Schleiermacher, Gudrun
Janoueix-Lerosey, Isabelle
Delattre, Olivier
Barillot, Emmanuel
author_facet Boeva, Valentina
Popova, Tatiana
Bleakley, Kevin
Chiche, Pierre
Cappo, Julie
Schleiermacher, Gudrun
Janoueix-Lerosey, Isabelle
Delattre, Olivier
Barillot, Emmanuel
author_sort Boeva, Valentina
collection PubMed
description Summary: More and more cancer studies use next-generation sequencing (NGS) data to detect various types of genomic variation. However, even when researchers have such data at hand, single-nucleotide polymorphism arrays have been considered necessary to assess copy number alterations and especially loss of heterozygosity (LOH). Here, we present the tool Control-FREEC that enables automatic calculation of copy number and allelic content profiles from NGS data, and consequently predicts regions of genomic alteration such as gains, losses and LOH. Taking as input aligned reads, Control-FREEC constructs copy number and B-allele frequency profiles. The profiles are then normalized, segmented and analyzed in order to assign genotype status (copy number and allelic content) to each genomic region. When a matched normal sample is provided, Control-FREEC discriminates somatic from germline events. Control-FREEC is able to analyze overdiploid tumor samples and samples contaminated by normal cells. Low mappability regions can be excluded from the analysis using provided mappability tracks. Availability: C++ source code is available at: http://bioinfo.curie.fr/projects/freec/ Contact: freec@curie.fr Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-32682432012-01-30 Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data Boeva, Valentina Popova, Tatiana Bleakley, Kevin Chiche, Pierre Cappo, Julie Schleiermacher, Gudrun Janoueix-Lerosey, Isabelle Delattre, Olivier Barillot, Emmanuel Bioinformatics Applications Note Summary: More and more cancer studies use next-generation sequencing (NGS) data to detect various types of genomic variation. However, even when researchers have such data at hand, single-nucleotide polymorphism arrays have been considered necessary to assess copy number alterations and especially loss of heterozygosity (LOH). Here, we present the tool Control-FREEC that enables automatic calculation of copy number and allelic content profiles from NGS data, and consequently predicts regions of genomic alteration such as gains, losses and LOH. Taking as input aligned reads, Control-FREEC constructs copy number and B-allele frequency profiles. The profiles are then normalized, segmented and analyzed in order to assign genotype status (copy number and allelic content) to each genomic region. When a matched normal sample is provided, Control-FREEC discriminates somatic from germline events. Control-FREEC is able to analyze overdiploid tumor samples and samples contaminated by normal cells. Low mappability regions can be excluded from the analysis using provided mappability tracks. Availability: C++ source code is available at: http://bioinfo.curie.fr/projects/freec/ Contact: freec@curie.fr Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2012-02-01 2011-12-06 /pmc/articles/PMC3268243/ /pubmed/22155870 http://dx.doi.org/10.1093/bioinformatics/btr670 Text en © The Author(s) 2011. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Boeva, Valentina
Popova, Tatiana
Bleakley, Kevin
Chiche, Pierre
Cappo, Julie
Schleiermacher, Gudrun
Janoueix-Lerosey, Isabelle
Delattre, Olivier
Barillot, Emmanuel
Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data
title Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data
title_full Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data
title_fullStr Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data
title_full_unstemmed Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data
title_short Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data
title_sort control-freec: a tool for assessing copy number and allelic content using next-generation sequencing data
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3268243/
https://www.ncbi.nlm.nih.gov/pubmed/22155870
http://dx.doi.org/10.1093/bioinformatics/btr670
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