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RJaCGH: Bayesian analysis of aCGH arrays for detecting copy number changes and recurrent regions

Summary: Several methods have been proposed to detect copy number changes and recurrent regions of copy number variation from aCGH, but few methods return probabilities of alteration explicitly, which are the direct answer to the question ‘is this probe/region altered?’ RJaCGH fits a Non-Homogeneous...

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Detalles Bibliográficos
Autores principales: Rueda, Oscar M., Diaz-Uriarte, Ramon
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2712338/
https://www.ncbi.nlm.nih.gov/pubmed/19420051
http://dx.doi.org/10.1093/bioinformatics/btp307
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author Rueda, Oscar M.
Diaz-Uriarte, Ramon
author_facet Rueda, Oscar M.
Diaz-Uriarte, Ramon
author_sort Rueda, Oscar M.
collection PubMed
description Summary: Several methods have been proposed to detect copy number changes and recurrent regions of copy number variation from aCGH, but few methods return probabilities of alteration explicitly, which are the direct answer to the question ‘is this probe/region altered?’ RJaCGH fits a Non-Homogeneous Hidden Markov model to the aCGH data using Markov Chain Monte Carlo with Reversible Jump, and returns the probability that each probe is gained or lost. Using these probabilites, recurrent regions (over sets of individuals) of copy number alteration can be found. Availability: RJaCGH is available as an R package from CRAN repositories (e.g. http://cran.r-project.org/web/packages). Contact: rueda.om@gmail.com; rueda.om@gmail.com
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spelling pubmed-27123382009-07-21 RJaCGH: Bayesian analysis of aCGH arrays for detecting copy number changes and recurrent regions Rueda, Oscar M. Diaz-Uriarte, Ramon Bioinformatics Applications Note Summary: Several methods have been proposed to detect copy number changes and recurrent regions of copy number variation from aCGH, but few methods return probabilities of alteration explicitly, which are the direct answer to the question ‘is this probe/region altered?’ RJaCGH fits a Non-Homogeneous Hidden Markov model to the aCGH data using Markov Chain Monte Carlo with Reversible Jump, and returns the probability that each probe is gained or lost. Using these probabilites, recurrent regions (over sets of individuals) of copy number alteration can be found. Availability: RJaCGH is available as an R package from CRAN repositories (e.g. http://cran.r-project.org/web/packages). Contact: rueda.om@gmail.com; rueda.om@gmail.com Oxford University Press 2009-08-01 2009-05-06 /pmc/articles/PMC2712338/ /pubmed/19420051 http://dx.doi.org/10.1093/bioinformatics/btp307 Text en © 2009 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Rueda, Oscar M.
Diaz-Uriarte, Ramon
RJaCGH: Bayesian analysis of aCGH arrays for detecting copy number changes and recurrent regions
title RJaCGH: Bayesian analysis of aCGH arrays for detecting copy number changes and recurrent regions
title_full RJaCGH: Bayesian analysis of aCGH arrays for detecting copy number changes and recurrent regions
title_fullStr RJaCGH: Bayesian analysis of aCGH arrays for detecting copy number changes and recurrent regions
title_full_unstemmed RJaCGH: Bayesian analysis of aCGH arrays for detecting copy number changes and recurrent regions
title_short RJaCGH: Bayesian analysis of aCGH arrays for detecting copy number changes and recurrent regions
title_sort rjacgh: bayesian analysis of acgh arrays for detecting copy number changes and recurrent regions
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2712338/
https://www.ncbi.nlm.nih.gov/pubmed/19420051
http://dx.doi.org/10.1093/bioinformatics/btp307
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