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Bayesian adaptive alignment and inference

J Zhu1, J Liu, C Lawrence

  • 1Wadsworth Center for Laboratories and Research, Albany, NY 12201, USA. junzhu@wadsworth.org

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|January 1, 1997
PubMed
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This study introduces a novel Bayesian inference method for sequence alignment, bypassing traditional gap penalties and scoring matrices. The approach provides a more conservative significance measure than p-values, enhancing protein sequence analysis.

Area of Science:

  • Computational biology
  • Bioinformatics
  • Statistical modeling

Background:

  • Traditional sequence alignment methods rely on predefined gap penalties and scoring matrices.
  • These parameters can significantly influence alignment outcomes and biological interpretations.
  • A need exists for alignment methods that are less dependent on subjective parameter choices.

Purpose of the Study:

  • To develop a Bayesian inference framework for sequence alignment.
  • To perform alignment without specifying gap penalties or scoring matrices.
  • To assess alignment significance using Bayesian evidence.

Main Methods:

  • Utilizes a recursive algorithm incorporating Bayesian inference.
  • Forward step: Sums over all possible alignments for normalizing constants.

Related Experiment Videos

  • Backward step: Samples from the exact posterior distribution.
  • Significance assessment via Bayesian evidence, compared to classical p-values using shuffling simulations.

    • Main Results:

    • The method generates alignments comparable to local alignments by excluding unrelated subsequences.
    • Bayesian evidence serves as a conservative significance measure.
    • Analysis of GTPase superfamily proteins reveals flat posterior distributions for gap number and evolutionary distance, with some bimodal distributions.
    • Alignment of 1GIA with 1ETU demonstrates strong agreement with structural alignment.

    Conclusions:

    • The Bayesian approach offers a robust alternative for sequence alignment, reducing reliance on parameter selection.
    • Bayesian evidence provides a reliable significance metric, potentially more conservative than p-values.
    • The method is applicable to protein families, yielding insights into evolutionary distances and gap distributions.