cnvHiTSeq: integrative models for high-resolution copy number variation detection and genotyping using population sequencing data

Recent advances in sequencing technologies provide the means for identifying copy number variation (CNV) at an unprecedented resolution. A single next-generation sequencing experiment offers several features that can be used to detect CNV, yet current methods do not incorporate all available signatu...

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Detalles Bibliográficos
Autores principales: Bellos, Evangelos, Johnson, Michael R, M Coin, Lachlan J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4056371/
https://www.ncbi.nlm.nih.gov/pubmed/23259578
http://dx.doi.org/10.1186/gb-2012-13-12-r120
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author Bellos, Evangelos
Johnson, Michael R
M Coin, Lachlan J
author_facet Bellos, Evangelos
Johnson, Michael R
M Coin, Lachlan J
author_sort Bellos, Evangelos
collection PubMed
description Recent advances in sequencing technologies provide the means for identifying copy number variation (CNV) at an unprecedented resolution. A single next-generation sequencing experiment offers several features that can be used to detect CNV, yet current methods do not incorporate all available signatures into a unified model. cnvHiTSeq is an integrative probabilistic method for CNV discovery and genotyping that jointly analyzes multiple features at the population level. By combining evidence from complementary sources, cnvHiTSeq achieves high genotyping accuracy and a substantial improvement in CNV detection sensitivity over existing methods, while maintaining a low false discovery rate. cnvHiTSeq is available at http://sourceforge.net/projects/cnvhitseq
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spelling pubmed-40563712014-06-13 cnvHiTSeq: integrative models for high-resolution copy number variation detection and genotyping using population sequencing data Bellos, Evangelos Johnson, Michael R M Coin, Lachlan J Genome Biol Method Recent advances in sequencing technologies provide the means for identifying copy number variation (CNV) at an unprecedented resolution. A single next-generation sequencing experiment offers several features that can be used to detect CNV, yet current methods do not incorporate all available signatures into a unified model. cnvHiTSeq is an integrative probabilistic method for CNV discovery and genotyping that jointly analyzes multiple features at the population level. By combining evidence from complementary sources, cnvHiTSeq achieves high genotyping accuracy and a substantial improvement in CNV detection sensitivity over existing methods, while maintaining a low false discovery rate. cnvHiTSeq is available at http://sourceforge.net/projects/cnvhitseq BioMed Central 2012 2012-12-22 /pmc/articles/PMC4056371/ /pubmed/23259578 http://dx.doi.org/10.1186/gb-2012-13-12-r120 Text en Copyright © 2013 Bellos et al.; licensee BioMed Central Ltd.
spellingShingle Method
Bellos, Evangelos
Johnson, Michael R
M Coin, Lachlan J
cnvHiTSeq: integrative models for high-resolution copy number variation detection and genotyping using population sequencing data
title cnvHiTSeq: integrative models for high-resolution copy number variation detection and genotyping using population sequencing data
title_full cnvHiTSeq: integrative models for high-resolution copy number variation detection and genotyping using population sequencing data
title_fullStr cnvHiTSeq: integrative models for high-resolution copy number variation detection and genotyping using population sequencing data
title_full_unstemmed cnvHiTSeq: integrative models for high-resolution copy number variation detection and genotyping using population sequencing data
title_short cnvHiTSeq: integrative models for high-resolution copy number variation detection and genotyping using population sequencing data
title_sort cnvhitseq: integrative models for high-resolution copy number variation detection and genotyping using population sequencing data
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4056371/
https://www.ncbi.nlm.nih.gov/pubmed/23259578
http://dx.doi.org/10.1186/gb-2012-13-12-r120
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