HIDDEN MARKOV-MODELS FOR SEQUENCE-ANALYSIS - EXTENSION AND ANALYSIS OF THE BASIC METHOD

Hidden Markov models (HMMs) ave a highly effective means of modeling a family of unaligned sequences or a common motif within a set of unali gned sequences. The trained HMM can then be used for discrimination or multiple alignment. The basic mathematical description of an HMM and its expectation-maximization training procedure is relatively straight forward In this paper, we review the mathematical extensions and heuri stics that move the method from the theoretical to the practical. We t hen experimentally analyze the effectiveness of model regularization, dynamic model modification and optimization strategies. Finally it is demonstrated on the SH2 domain how a domain can be found from unaligne d sequences using a special model type. The experimental work was comp leted with the aid of the Sequence Alignment and Modeling software sui te.