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Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach pp 241–248Cite as

Inference of Evolutionary Trees from DNA and Protein Sequence Data

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Abstract

The evolutionary history of life is inscribed in the DNA and protein sequences of living organisms, and we can infer evolutionary trees from these sequences. DNAs and proteins are sequences of four types of bases and of twenty types of amino acids, respectively. At each site of a sequence, one type of base or amino acid may change to another one during evolution, and the substitutions occur independently between lineages after they separate in an evolutionary tree. These substitutions can be best regarded as stochastic, and therefore, it is appropriate to infer evolutionary trees in the framework of maximum likelihood (ML) based on a statistical model for base or amino acid substitutions.

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Author information

Authors and Affiliations

  1. Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106, Japan

    Masami Hasegawa

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  1. Masami Hasegawa
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Editor information

Editors and Affiliations

  1. Department of Statistics, The University of Tennessee, Knoxville, TN, USA

    H. Bozdogan

  2. Information & Decision Sciences, University of Illinois at Chicago, Chicago, IL, USA

    S. L. Sclove

  3. Department of Mathematics & Statistics, Bowling Green State University, Bowling Green, OH, USA

    A. K. Gupta

  4. Department of Mathematical Sciences, Bentley College, Waltham, MA, USA

    D. Haughton

  5. The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-Ku, Tokyo, Japan

    G. Kitagawa , T. Ozaki  & K. Tanabe ,  & 

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© 1994 Springer Science+Business Media Dordrecht

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Hasegawa, M. (1994). Inference of Evolutionary Trees from DNA and Protein Sequence Data. In: Bozdogan, H., et al. Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-0854-6_11

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  • DOI: https://doi.org/10.1007/978-94-011-0854-6_11

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-4368-7

  • Online ISBN: 978-94-011-0854-6

  • eBook Packages: Springer Book Archive

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