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Hidden Markov models for sequence analysis: extension and analysis of the basic method

R Hughey1, A Krogh

  • 1University of California, Santa Cruz 95064, USA.

Computer Applications in the Biosciences : CABIOS
|April 1, 1996
PubMed
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Hidden Markov models (HMMs) provide a powerful method for analyzing unaligned biological sequences. This study details practical extensions and experimental analysis of HMMs for sequence modeling and domain identification.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Sequence Analysis

Background:

  • Hidden Markov models (HMMs) are effective for modeling unaligned sequences and identifying common motifs.
  • The basic mathematical framework and expectation-maximization training are well-established.
  • Practical application requires extensions beyond theoretical descriptions.

Purpose of the Study:

  • To review mathematical extensions and heuristics for practical HMM application.
  • To experimentally analyze model regularization, dynamic modification, and optimization strategies.
  • To demonstrate HMM utility in identifying specific domains, like the SH2 domain, from unaligned sequences.

Main Methods:

  • Review of mathematical extensions and heuristics for HMMs.

Related Experiment Videos

  • Experimental analysis of model regularization and optimization techniques.
  • Application of specialized HMMs for domain identification on unaligned sequences.

    • Main Results:

    • Mathematical extensions and heuristics significantly enhance the practical utility of HMMs.
    • Model regularization and dynamic modification strategies improve HMM performance.
    • Successful identification of the SH2 domain from unaligned sequences using specialized HMMs.

    Conclusions:

    • HMMs, with practical extensions, are a robust tool for sequence analysis and motif discovery.
    • Experimental validation confirms the effectiveness of advanced HMM strategies.
    • The methodology is applicable to identifying biological domains within large sequence datasets.