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A hidden Markov model for progressive multiple alignment.

Ari Löytynoja1, Michel C Milinkovitch

  • 1Unit of Evolutionary Genetics, Free University of Brussels (ULB), cp 300, Institute of Molecular Biology and Medicine, rue Jeener & Brachet 12, B-6041 Gosselies, Belgium. aloytyno@ulb.ac.be

Bioinformatics (Oxford, England)
|August 13, 2003
PubMed
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This study introduces a novel multiple sequence alignment method using hidden Markov models (HMMs) and probabilistic evolutionary models. It effectively filters unreliable alignments by calculating column posterior probabilities, improving accuracy.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics and Proteomics

Background:

  • Progressive algorithms are standard heuristics for multiple sequence alignment.
  • There is a need for probabilistic methods to assess the reliability of sequence alignments.

Purpose of the Study:

  • To develop a novel multiple sequence alignment method.
  • To incorporate probabilistic reliability measures into sequence alignment.
  • To improve the accuracy and filtering of multiple sequence alignments.

Main Methods:

  • Combines a hidden Markov model (HMM) approach with a progressive alignment algorithm.
  • Utilizes a probabilistic evolution model for character substitution.
  • Iteratively performs pairwise alignments guided by a guide tree to construct the multiple alignment.

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Main Results:

  • The method computes the minimum posterior probability for each column in the alignment.
  • This posterior probability strongly correlates with the alignment's correctness.
  • Enables efficient filtering of unreliably aligned columns.

Conclusions:

  • The proposed method offers a probabilistic framework for multiple sequence alignment.
  • It provides a reliable metric for assessing alignment quality.
  • Facilitates the identification and removal of inaccurate alignment regions.