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Related Experiment Videos

Maximum discrimination hidden Markov models of sequence consensus

S R Eddy1, G Mitchison, R Durbin

  • 1Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|January 1, 1995
PubMed
Summary

We developed a new maximum discrimination method for hidden Markov models (HMMs) to analyze protein and nucleic acid sequences. This approach improves sensitivity in detecting distant sequence homologs compared to existing methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Hidden Markov models (HMMs) are widely used for sequence analysis.
  • Existing HMM methods can be limited by biased sequence data representation.
  • Sequence weighting is often employed to address data bias, but can be complex.

Purpose of the Study:

  • To introduce a novel maximum discrimination method for constructing HMMs.
  • To address limitations posed by biased sequence datasets in HMM construction.
  • To enhance the sensitivity of HMMs for detecting homologous sequences.

Main Methods:

  • Developed a maximum discrimination approach for HMMs.
  • Applied the method to protein and nucleic acid primary sequence consensus.

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  • Avoided the need for sequence weighting by compensating for data bias.

    • Main Results:

    • The maximum discrimination HMMs demonstrated superior sensitivity.
    • Outperformed other HMM methods and BLAST in detecting distant homologs.
    • Validation was performed on globin and protein kinase catalytic domain sequences.

    Conclusions:

    • Maximum discrimination HMMs offer a more sensitive tool for sequence homology detection.
    • The method effectively handles biased sequence data without weighting.
    • Represents a significant advancement in bioinformatics for sequence analysis.