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Proc Natl Acad Sci U S A. 1994 Feb 1;91(3):1059-63.

Hidden Markov models of biological primary sequence information.

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  • 1Division of Biology, California Institute of Technology, Pasadena 91125.

Abstract

Hidden Markov model (HMM) techniques are used to model families of biological sequences. A smooth and convergent algorithm is introduced to iteratively adapt the transition and emission parameters of the models from the examples in a given family. The HMM approach is applied to three protein families: globins, immunoglobulins, and kinases. In all cases, the models derived capture the important statistical characteristics of the family and can be used for a number of tasks, including multiple alignments, motif detection, and classification. For K sequences of average length N, this approach yields an effective multiple-alignment algorithm which requires O(KN2) operations, linear in the number of sequences.

PMID:
8302831
[PubMed - indexed for MEDLINE]
PMCID:
PMC521453
Free PMC Article
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