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Characterization of multiple sequence alignment errors using complete-likelihood score and position-shift map.

BMC bioinformatics·2016
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General continuous-time Markov model of sequence evolution via insertions/deletions: local alignment probability

Kiyoshi Ezawa1,2

  • 1Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, 820-8502, Japan. kezawa.ezawa3@gmail.com.

BMC Bioinformatics
|September 29, 2016
PubMed
Summary

Calculating DNA sequence alignment probabilities is crucial for understanding evolution. This study develops approximate methods to accurately compute these probabilities, especially for insertions and deletions (indels), providing a valuable reference for future models.

Keywords:
Evolutionary simulationIndel likelihoodInsertion/deletion (indel)Perturbation theoryPower-law length distributionPractically exact solutionSequence alignment probabilityStochastic evolutionary model

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Evolutionary Genetics

Background:

  • Insertions and deletions (indels) are major drivers of DNA sequence divergence.
  • Accurate calculation of sequence alignment probabilities is essential for evolutionary studies.
  • Previous work introduced a perturbative formulation for ab initio alignment probability calculation.

Purpose of the Study:

  • To approximately calculate local alignment probabilities using a developed formulation.
  • To assess the accuracy of approximate methods for pairwise and multiple sequence alignments.
  • To provide a reference point for other indel probabilistic models.

Main Methods:

  • Numerical computation of indel history contributions for pairwise and multiple sequence alignments.
  • Derivation and numerical solution of integral equations for practically exact local PWA probabilities.
  • Development of an algorithm for first-approximate MSA probability calculation.

Main Results:

  • Total parsimonious contributions approximated multiplication factors well for moderate gap sizes and branch lengths.
  • Approximate methods demonstrated good accuracy in calculating ab initio alignment probabilities.
  • Comparison with a sequence evolution simulator (Dawg) validated the first-approximate MSA probability calculations.

Conclusions:

  • Approximate methods offer accurate ab initio alignment probability calculations under biologically realistic models.
  • The developed formulation serves as a sound reference for other indel probabilistic models.
  • The study advances the computational analysis of evolutionary sequence changes.