Unified Gibbs Method for Biological Sequence Analysis

The biotechnology revolution stems from rapid advances in the biological sciences. One important product of these advances is a large and rapidly growing data base of biopolymer (DNA, RNA, and protein) sequences, which has attracted much attention from researchers in diierent elds. The great majority of the techniques generated for studying these data have been designed to analyze a single sequence or for the comparison of a pair of sequences. Multiple sequence analysis has remained a diicult challenge. In recent years, formal statistical models have shown potential in one such problem, multiple sequence alignment. In this article we describe a general statistical paradigm, the uniied Gibbs method, for the conversion of nearly any existing method for the analysis of a single sequence or for the comparison of a pair of sequences into a multiple sequence analysis method. Our previous successful experiences with the uniied Gibbs include the development of the site sampler, the motif sampler, and the PROBE. Here we demonstrate again the power of such a paradigm by describing a multiple sequence partitioning method for the delineation of subsequences indicative of underlying structural features. We also show that the simple Bayesian framework is useful for model selections even for pairwise sequence comparisons.