Abstract #302331

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JSM 2003 Abstract #302331
Activity Number: 311
Type: Invited
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #302331
Title: Protein Sequence Alignments and their Analysis
Author(s): Jun S. Liu*+
Companies: Harvard University
Address: 605 South Ave., Weston, MA, 02493,
Keywords: multiple sequence alignment ; Gibbs sampler ; hidden Markov model ; MCMC optimization
Abstract:

The problem of multiple sequence alignment (MSA) is central to bioinformatics research. Obtaining the correct alignment of distantly related proteins can provide us much insight in proteins' functions and structures. We describe a novel Bayesian MSA method based on a combination of the motif-based "propagation" model and the hidden Markov model (HMM). In this method, each sequence is regarded as an incidence of colinear motif blocks linked by "noisy" residues of flexible lengths. Each motif is modeled by a special HMM with its own insertion and deletion parameters. A Markov chain Monte Carlo algorithm is developed to simultaneously estimate the parameters and align the sequences. We show by examples how this newer approach improves upon the popular Psi-BLAST algorithm and our earlier method PROBE. In conjunction with the multiple sequence alignment, we also describe a method called Bayesian partitioning with pattern selection to discover from the alignment of multiple related proteins covarying positions of important functional or structural role.

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