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Hidden Markov models of biological primary sequence information

Baldi, Pierre and Chauvin, Yves and Hunkapiller, Tim and McClure, Marcella A. (1994) Hidden Markov models of biological primary sequence information. Proceedings of the National Academy of Sciences of the United States of America, 91 (3). pp. 1059-1063. ISSN 0027-8424. PMCID PMC521453. https://resolver.caltech.edu/CaltechAUTHORS:20141209-084332388

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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(KN^2) operations, linear in the number of sequences.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1073/pnas.91.3.1059DOIArticle
http://www.ncbi.nlm.nih.gov/pmc/articles/pmc521453/PubMed CentralArticle
Additional Information:© 1994 National Academy of Sciences. Communicated by Leroy Hood, October 12, 1993 (received for review January 14, 1993).
Issue or Number:3
PubMed Central ID:PMC521453
Record Number:CaltechAUTHORS:20141209-084332388
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20141209-084332388
Official Citation:Baldi, P., Chauvin, Y., Hunkapiller, T., & McClure, M. A. (1994). Hidden Markov models of biological primary sequence information. Proceedings of the National Academy of Sciences, 91(3), 1059-1063. doi: 10.1073/pnas.91.3.1059
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:52490
Collection:CaltechAUTHORS
Deposited By: Jason Perez
Deposited On:09 Dec 2014 21:23
Last Modified:04 Jun 2020 19:47

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