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Hidden Markov models in biology.

Claus Vogl1, Andreas Futschik

  • 1Institute of Animal Breeding and Genetics, University of Veterinary Medicine Vienna, Vienna, Austria.

Methods in Molecular Biology (Clifton, N.J.)
|March 12, 2010
PubMed
Summary
This summary is machine-generated.

Markov and Hidden Markov models (HMMs) are explained for genetic linkage mapping and sequence analysis. Algorithms like forward-backward, Viterbi, and Baum-Welch are presented for HMM applications.

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Area of Science:

  • Computational Biology
  • Genetics
  • Statistical Modeling

Background:

  • Markov models and Hidden Markov models (HMMs) are powerful statistical tools.
  • Their application in bioinformatics, particularly in linkage mapping and sequence analysis, is crucial for understanding genetic data.

Purpose of the Study:

  • To introduce Markov and Hidden Markov models (HMMs).
  • To demonstrate their application in genetic linkage mapping and sequence analysis.
  • To present key algorithms used in HMM analysis.

Main Methods:

  • Introduction to Markov and Hidden Markov models (HMMs).
  • Illustrative examples from genetic linkage mapping and sequence analysis.
  • Presentation of the forward-backward algorithm.
  • Explanation of the Viterbi algorithm.
  • Description of the Baum-Welch (Expectation-Maximization) algorithm.
  • Introduction to a Metropolis sampling scheme for HMMs.

Main Results:

  • The study provides a foundational understanding of Markov and Hidden Markov models.
  • It demonstrates the practical utility of these models in analyzing genetic data.
  • Key algorithms for parameter estimation and state prediction in HMMs are detailed.

Conclusions:

  • Markov and Hidden Markov models are versatile tools for bioinformatics.
  • The presented algorithms are essential for implementing HMMs in genetic analysis.
  • This work serves as an accessible introduction to HMMs for researchers in genetics and computational biology.