Improved gibbs sampling for detecting mosaic structures in DNA sequence alignments

Werhli, A., Grzegorczyk, M., Chiang, M.-T. and Husmeier, D. (2006) Improved gibbs sampling for detecting mosaic structures in DNA sequence alignments. In: Urfer, W. and Turkman, M.A. (eds.) Mosaic Structures in DNA Sequence Alignments. Centro Internacional de Matematica: Coimbra, Portugal, pp. 23-34. ISBN 9899501107

Abstract

A recently proposed method for detecting mosaic structures in DNA sequence alignments is based on the combination of hidden Markov models (HMMs) with phylo- genetic trees. Inference is done in a Bayesian way by sampling the model parameters and hidden state sequences from the posterior distribution with Markov chain Monte Carlo (MCMC). In an earlier method, proposed in [1], this was effected with a nested Gibbs-within-Gibbs scheme. The present article discusses a modification of the MCMC sampling method, based on a modification of the standard forward-backward algorithm and an unnested Gibbs sampling procedure. We have tested the modified algorithm on various synthetic and real-world DNA sequence alignments, where we have observed a significant improvement in the mixing and convergence of the Markov chain. As a practical consequence, the computational costs are substantially reduced, and the predictions become more reliable.

• Item Type: Book Sections
• Status: Published
• Glasgow Author(s) Enlighten ID: Husmeier, Professor Dirk
• Authors: Werhli, A., Grzegorczyk, M., Chiang, M.-T., and Husmeier, D.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Publisher: Centro Internacional de Matematica
• ISBN: 9899501107