Hidden Markov models for sequence analysis: extension and analysis of the basic method.
Hidden Markov models (HMMs) are a highly effective means of modeling a family of unaligned sequences or a common motif within a set of unaligned 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 straightforward. In this paper, we review the mathematical extensions and heuristics that move the method from the theoretical to the practical. We then 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 unaligned sequences using a special model type. The experimental work was completed with the aid of the Sequence Alignment and Modeling software suite.
Hughey
R
R
University of California, Santa Cruz 95064, USA.
Krogh
A
A
D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D001483:Base Sequence; D016208:Databases, Factual; D005069:Evaluation Studies as Topic; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D016415:Sequence Alignment / Q000379:methods / Q000706:statistics & numerical data; D017421:Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data; D012984:Software; D018909:src Homology Domains / Q000235:genetics