3. Bayesian Methods in Biological Sequence Analysis

  1. D. J. Balding2,
  2. M. Bishop3 and
  3. C. Cannings4
  1. Jun S. Liu and
  2. T. Logvinenko

Published Online: 9 MAY 2008

DOI: 10.1002/9780470061619.ch3

Handbook of Statistical Genetics, Third Edition

Handbook of Statistical Genetics, Third Edition

How to Cite

Liu, J. S. and Logvinenko, T. (2007) Bayesian Methods in Biological Sequence Analysis, 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.ch3

Editor Information

  1. 2

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

  2. 3

    CNR-ITB, Milan, Italy

  3. 4

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

Author Information

  1. Department of Statistics, Harvard University, Cambridge, MA, USA

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:

  • expectation-maximization algorithm;
  • transcription factor binding sites and modules;
  • Bayesian analysis;
  • maximum likelihood estimation;
  • Markov chain;
  • position-specific profile;
  • forward–backward method;
  • sequence-weighing strategy;
  • motif elements;
  • biological sequence analysis

Summary

This chapter contains sections titled:

  • Introduction

  • Overview of the Bayesian Methodology

  • Hidden Markov Model: A General Introduction

  • Pairwise Alignment of Biological Sequences

  • Multiple Sequence Alignment

  • Finding Recurring Patterns in Biological Sequences

  • Joint Analysis of Sequence Motifs and Expression Microarrays

  • Summary

  • Acknowledgments

  • Appendix A: Markov Chain Monte Carlo Methods

  • References