14. Probabilistic Models for the Study of Protein Evolution

  1. D. J. Balding3,
  2. M. Bishop4 and
  3. C. Cannings5
  1. J. L. Thorne1 and
  2. N. Goldman2

Published Online: 9 MAY 2008

DOI: 10.1002/9780470061619.ch14

Handbook of Statistical Genetics, Third Edition

Handbook of Statistical Genetics, Third Edition

How to Cite

Thorne, J. L. and Goldman, N. (2007) Probabilistic Models for the Study of Protein Evolution, in Handbook of Statistical Genetics, Third Edition (eds D. J. Balding, M. Bishop and C. Cannings), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470061619.ch14

Editor Information

  1. 3

    Imperial College of Science, Technology and Medicine, London, UK

  2. 4

    CNR-ITB, Milan, Italy

  3. 5

    Division of Genomic Medicine, University of Sheffield, Sheffield, UK

Author Information

  1. 1

    Departments of Genetics and Statistics, North Carolina State University, Raleigh, NC, USA

  2. 2

    EMBL-European Bioinformatics Institute, Hinxton, UK

Publication History

  1. Published Online: 9 MAY 2008
  2. Published Print: 24 AUG 2007

ISBN Information

Print ISBN: 9780470058305

Online ISBN: 9780470061619

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Keywords:

  • protein-coding sequences;
  • genotype–phenotype relationship;
  • continuous-time Markov process;
  • mitochondrially-encoded proteins;
  • amino acid composition;
  • transition–transversion bias;
  • proteincoding DNA sequences;
  • encoded amino acid sequence;
  • transition probability matrices;
  • Metropolis–Hastings algorithm

Summary

This chapter contains sections titled:

  • Introduction

  • Empirically Derived Models of Amino Acid Replacement

  • Amino Acid Composition

  • Heterogeneity of Replacement Rates Among Sites

  • Protein Structural Environments

  • Variation of Preferred Residues Among Sites

  • Models with a Physicochemical Basis

  • Codon-Based Models

  • Dependence Among Positions: Simulation

  • Dependence Among Positions: Inference

  • Conclusions

  • Acknowledgments

  • References