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Type: Artigo de periódico
Title: Comparing the performance of a reversible jump Markov chain Monte Carlo algorithm for DNA sequences alignment
Author: Alvarez, LJ
Garcia, NL
Rodrigues, ER
Abstract: Assume that K independent copies are made from a common prototype DNA sequence whose length is a random variable. In this paper, the problem of aligning those copies and therefore the problem of estimating the prototype sequence that produced the copies is addressed. A hidden Markov chain is used to model the copying procedure, and a reversible jump Markov chain Monte Carlo algorithm is used to sample the parameters of the model from their posterior distribution. Using the sample obtained, the Bayesian model and the prototype sequence may be selected using the maximum a posteriori estimate. A prior distribution for the prototype DNA sequence that incorporates a correlation among neighbouring bases is also considered. In addition, an analysis of the performance of the algorithm is presented when different scenarios are taken into account.
Subject: Bayesian inference
sequences alignment
reversible jump Markov chain Monte Carlo method
hidden Markov model
Potts model
Country: Inglaterra
Editor: Taylor & Francis Ltd
Rights: fechado
Identifier DOI: 10.1080/10629360500109226
Date Issue: 2006
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

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