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Updated: Jun 6, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Compositional adjustment of Dirichlet mixture priors.

Xugang Ye1, Yi-Kuo Yu, Stephen F Altschul

  • 1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|December 7, 2010
PubMed
Summary

This study introduces a method to adjust Dirichlet mixture priors for protein sequence alignment, improving accuracy for proteins with non-standard amino acid compositions. The approach efficiently modifies priors to match desired compositions, enhancing alignment scoring. Keywords: Dirichlet mixture priors, protein sequence alignment, amino acid composition.

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Last Updated: Jun 6, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Statistical Modeling

Background:

  • Dirichlet mixture priors offer a Bayesian framework for scoring protein sequence alignments.
  • These priors can model different types of protein positions and generalize to multiple sequence alignments.
  • Existing Dirichlet mixtures imply background amino acid frequencies from training data, potentially limiting their use for proteins with non-standard compositions.

Purpose of the Study:

  • To develop a method for adjusting Dirichlet mixture priors to match specific, desired amino acid compositions.
  • To ensure the adjusted priors minimize relative-entropy-based distance while accommodating new compositional profiles.
  • To create a computationally efficient algorithm for compositional adjustment of protein profile HMMs.

Main Methods:

  • Formulated the problem as finding an adjusted Dirichlet mixture (Θ") that implies a target composition while minimizing relative entropy to the original mixture (Θ).
  • Simplified the problem by fixing mixture parameters and Dirichlet parameter sums, allowing only the center of mass to vary.
  • Employed a Lagrange-Newton method to minimize a quadratic cost function derived from the relative entropy of Dirichlet distributions for efficient parameter adjustment.

Main Results:

  • Demonstrated that Dirichlet mixtures and symmetric substitution matrices are related but not equivalent formalisms for sequence alignment.
  • Developed and implemented an efficient algorithm for compositionally adjusting Dirichlet mixture priors.
  • Achieved precise compositional adjustment of a 20-component Dirichlet mixture prior for proteins in under half a second on a standard workstation.

Conclusions:

  • The developed method enables accurate compositional adjustment of Dirichlet mixture priors, enhancing their applicability to diverse protein families.
  • This compositional adaptation is crucial for improving the performance of Bayesian sequence alignment scoring methods.
  • The computational efficiency of the method makes it practical for routine use in bioinformatics pipelines.