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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2012
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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. |
format | Online Article Text |
id | pubmed-3268243 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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