Hidden Markov models and optimized sequence alignments.
We present a formulation of the Needleman-Wunsch type algorithm for sequence alignment in which the mutation matrix is allowed to vary under the control of a hidden Markov process. The fully trainable model is applied to two problems in bioinformatics: the recognition of related gene/protein names and the alignment and scoring of homologous proteins.
Smith
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L
Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, Rm. 614D, Bldg. 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA. lsmith@ncbi.nlm.nih.gov
Yeganova
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Wilbur
W J
WJ
D000818:Animals; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D030541:Databases, Genetic; D005796:Genes / Q000235:genetics; D006801:Humans; D016239:MEDLINE / Q000706:statistics & numerical data; D008390:Markov Chains; D051379:Mice; D015233:Models, Statistical; D016415:Sequence Alignment / Q000379:methods* / Q000706:statistics & numerical data*; D012689:Sequence Homology, Nucleic Acid