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Detalle de: 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
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@gmail.com; rueda.om@gmail.com

Apa Reference Rueda, O. M., & Diaz-Uriarte, R. (2009). RJaCGH: Bayesian analysis of aCGH arrays for detecting copy number changes and recurrent regions. Bioinformatics, 25(15), 1959–1960. 10.1093/bioinformatics/btp307
Doi 10.1093/bioinformatics/btp307
Publication Date 6 May 2009
Publication Type Article
Researcher Ramon Díaz Uriarte
Ontology Bioinformatics Software and Tools, Analysis packages (R, Python, etc.), Variant calling, Bayesian statistics
Research Groups Bioinformatics and Computational Biology in Cancer Evolution
Tools

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