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Proceedings of the National Academy of Sciences of the United States of America logoLink to Proceedings of the National Academy of Sciences of the United States of America
. 1994 Feb 1;91(3):1059–1063. doi: 10.1073/pnas.91.3.1059

Hidden Markov models of biological primary sequence information.

P Baldi 1, Y Chauvin 1, T Hunkapiller 1, M A McClure 1
PMCID: PMC521453  PMID: 8302831

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.

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Selected References

These references are in PubMed. This may not be the complete list of references from this article.

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