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Journal of Computational Biology
Applications of Hidden Markov Models for Characterization of Homologous DNA Sequences with a Common Gene

To cite this article:
Asger Hobolth, Jens Ledet Jensen. Journal of Computational Biology. March 2005, 12(2): 186-203. doi:10.1089/cmb.2005.12.186.

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Asger Hobolth
Bioinformatics Research Center, University of Aarhus, Aarhus, Denmark.
Jens Ledet Jensen
Department of Theoretical Statistics and MaPhySto, University of Aarhus, Aarhus, Denmark.

Identifying and characterizing the structure in genome sequences is one of the principal challenges in modern molecular biology, and comparative genomics offers a powerful tool. In this paper, we introduce a hidden Markov model that allows a comparative analysis of multiple sequences related by a phylogenetic tree, and we present an efficient method for estimating the parameters of the model. The model integrates structure prediction methods for one sequence, statistical multiple alignment methods, and phylogenetic information. This unified model is particularly useful for a detailed characterization of DNA sequences with a common gene. We illustrate the model on a variety of homologous sequences.

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