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A Practical Guide to Phylogenetics for Nonexperts
12:00

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Published on: February 5, 2014

Sequence alignment, mutual information, and dissimilarity measures for constructing phylogenies.

Orion Penner1, Peter Grassberger, Maya Paczuski

  • 1Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada. openner@phas.ucalgary.ca

Plos One
|January 20, 2011
PubMed
Summary
This summary is machine-generated.

Information theory offers a new way to measure sequence similarity using mutual information (MI). This approach provides more accurate phylogenetic distance measures than traditional methods, outperforming them in various analyses.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Information Theory

Background:

  • Existing sequence alignment algorithms rely on heuristic scoring, not objective distance metrics.
  • Traditional distance measures (e.g., p-, log-det) often oversimplify evolutionary assumptions.
  • Information theory, specifically mutual information (MI), offers a model-independent similarity measure.

Purpose of the Study:

  • To develop a robust method for estimating mutual information (MI) from sequence alignments.
  • To introduce novel, objective distance metrics for phylogenetic analysis based on MI.
  • To compare the performance of MI-based distances against traditional phylogenetic measures.

Main Methods:

  • Utilized algorithmic (Kolmogorov) and Shannon information theory for MI estimation.
  • Developed a method to extract MI from global pairwise sequence alignments.
  • Proposed a modification to normalized compression distance to ensure additivity for phylogenetics.

Main Results:

  • Achieved robust MI estimates from global pairwise alignments, comparable to alignment-free methods.
  • MI-based distance measures, including a simplified Shannon entropy version, outperformed traditional metrics (Kimura, log-det) in phylogenetic analyses.
  • Demonstrated superior performance across various animal mitochondrial DNA datasets.

Conclusions:

  • MI-based distance measures offer superior performance in distance-based phylogeny compared to conventional methods.
  • Simplified MI measures can be readily integrated into existing bioinformatics software.
  • Information theory provides a powerful framework for advancing sequence analysis and evaluating alignment algorithms.