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Related Experiment Videos

Calibrating E-values for hidden Markov models using reverse-sequence null models.

Kevin Karplus1, Rachel Karchin, George Shackelford

  • 1Department of Biomolecular Engineering, University of California, Santa Cruz, 95064, USA. karplus@soe.ucsc.edu

Bioinformatics (Oxford, England)
|August 27, 2005
PubMed
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Hidden Markov models (HMMs) use reverse-sequence null models to reduce false positives. A new theoretical distribution improves significance estimation for HMM scores, enhancing database search accuracy.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Modeling

Background:

  • Hidden Markov models (HMMs) are used to calculate sequence generation probabilities.
  • Log-odds scoring provides context by comparing probabilities against a null hypothesis.
  • Reverse-sequence null models reduce biases from sequence length and composition, decreasing false positives in database searches.

Purpose of the Study:

  • To address the challenge of accurately computing significance for reverse-sequence null model scores, which do not fit the standard Gumbel distribution.
  • To derive and evaluate new theoretical distributions for improved significance estimation of HMM scores.

Main Methods:

  • Derived a theoretical distribution for HMM scores based on the Gumbel distribution.
  • Developed parameter estimation methods using maximum likelihood and moment matching (least-squares fit for Student's t-distribution).

Related Experiment Videos

  • Evaluated distribution fits using hold-out data and assessed the impact on HMM-based fold-recognition methods.

    • Main Results:

    • The derived theoretical distribution showed improved tail fitting and reduced false positives compared to standard methods.
    • An ad hoc distribution with a stretched exponential tail performed even better.
    • Moment-matching methods provided better tail fits than maximum-likelihood methods for distribution parameter estimation.

    Conclusions:

    • A novel theoretical distribution improves the accuracy of significance estimates for HMM scores derived from reverse-sequence null models.
    • This improvement leads to more reliable results in HMM-based sequence analysis and fold-recognition tasks.
    • The study highlights the importance of appropriate statistical distributions for accurate interpretation of HMM search results.