Abstract #302312

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JSM 2003 Abstract #302312
Activity Number: 125
Type: Contributed
Date/Time: Monday, August 4, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #302312
Title: Sequential Monte Carlo and Dirichlet Mixtures for Extracting Protein Alignment Models.
Author(s): Tanya Logvinenko*+ and Jun S. Liu
Companies: Stanford University and Harvard University
Address: 2 Carver St. #2, Somerville, MA, 02143,
Keywords: multiple sequence alignment ; Bayesian statistics ; sequential Monte Carlo ; hidden Markov model ; Gibbs Sampler
Abstract:

Multiple sequence alignment (MSA) can be viewed as computation of a posterior mean of a position specific profile matrix (PSPM), given a set of protein sequences to be aligned. Conditional distribution of a sequence given a PSPM, can be described by a Hidden Markov Model, with a "hidden" state being an unknown alignment path A which generates sequence S from (unknown) PSPM. We propose a novel querycentric Bayesian algorithm which applies Sequential Monte Carlo (SMC) framework to align the sequences and create the position specific profile matrix, regarding unknown missing data and imputing them sequentially. After information from all sequences is incorporated into the final profile, the sequences are realigned to the profile, and Gibbs Sampler is introduced to improve the multiple alignment. The use of SMC and Gibbs Sampler allows the procedure to avoid getting stuck in a local mode. To capture information contained in a column of aligned amino acids our method uses a Drichlet Mixture prior. Examples and comparisons of this MSA method show performance at least as good as the performance of the best existing methods available for MSA.

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