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Bioinformatics Advance Access originally published online on May 6, 2009
Bioinformatics 2009 25(15):1959-1960; doi:10.1093/bioinformatics/btp307
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© 2009 The Author(s)
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.

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

Oscar M. Rueda *,{dagger} and Ramon Diaz-Uriarte *

Structural Biology and Biocomputing Programme, Spanish National Cancer Center (CNIO), Melchor Fernández Almagro 3, Madrid, 28029, Spain

* To whom correspondence should be addressed.


  Abstract

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{at}gmail.com; rueda.om{at}gmail.com

{dagger} Present address: Breast Cancer Functional Genomics, Cancer Research UK, Cambridge, UK

Associate Editor: John Quackenbush

Received on March 4, 2009; revised on April 20, 2009; accepted on April 30, 2009

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