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COMPSTAT pp 89–100Cite as

Modelling Bacterial Genomes Using Hidden Markov Models

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Abstract

Long DNA sequences are often heterogeneous in composition. Hidden Markov models are then good statistical tools to identify homogeneous regions of the sequences. We compare different identification algorithms for hidden Markov chains and present some applications to bacterial genomes to illustrate the method.

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Author information

Authors and Affiliations

  1. Laboratoire de Statistique Médicale, UA CNRS 1323, Université Paris V, 45 rue des Saints-Pères, 75006, Paris, France

    Florence Muri

Authors
  1. Florence Muri
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Editor information

Editors and Affiliations

  1. Statistics Department, IACR-Rothamsted, AL5 2JQ, Harpenden, Herts, UK

    Roger Payne

  2. Department of Mathematics, University of Bristol, BS8 1TW, Bristol, UK

    Peter Green

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© 1998 Springer-Verlag Berlin Heidelberg

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Muri, F. (1998). Modelling Bacterial Genomes Using Hidden Markov Models. In: Payne, R., Green, P. (eds) COMPSTAT. Physica, Heidelberg. https://doi.org/10.1007/978-3-662-01131-7_8

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  • DOI: https://doi.org/10.1007/978-3-662-01131-7_8

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1131-5

  • Online ISBN: 978-3-662-01131-7

  • eBook Packages: Springer Book Archive

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