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A probabilistic approach to consensus multiple alignment.

B Lazareva-Ulitsky1, D Haussler

  • 1Department of Computer Science, University of California at Santa Cruz 95064, USA.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|June 25, 1999
PubMed
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The ASA method accurately estimates ancestral DNA sequences from noisy data. This tool refines DNA assemblies and aligns genomic sites, providing optimal and suboptimal consensus sequences.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Accurate reconstruction of ancestral DNA sequences is crucial for understanding evolutionary relationships and refining genome assemblies.
  • Existing methods may struggle with noisy data or lack the ability to provide probabilistic insights into sequence recovery limits.

Purpose of the Study:

  • To introduce ASA (dnA Sequence Alignment), a novel method for obtaining the maximum a posteriori probability (MAP) estimate of a consensus ancestral DNA sequence.
  • To evaluate ASA's performance in refining noisy DNA assembly regions, aligning genomic functional sites, and aligning star-like phylogenies.

Main Methods:

  • ASA employs a probabilistic maximization approach to identify optimal and suboptimal consensus ancestral sequences.
  • The method's performance is assessed through simulations with varying sequence lengths, coverage, and error rates (5-30%).

Related Experiment Videos

  • Comparisons are made against profile Hidden Markov Models (HMMs) and other DNA alignment techniques.

    • Main Results:

    • ASA effectively restores consensus sequences from noisy observations, performing near the theoretical best for given error rates.
    • The method demonstrates utility in aligning biological sequences, such as E.Coli promoters and human Alu-Sb repeats.
    • ASA provides probabilistic estimations, enabling the determination of limits for ancestral sequence recovery.

    Conclusions:

    • ASA is a robust and effective tool for ancestral DNA sequence reconstruction and alignment, particularly in the presence of sequencing errors.
    • The probabilistic framework of ASA offers valuable insights into the reliability of recovered ancestral sequences.
    • ASA represents a significant advancement over traditional alignment methods for specific phylogenetic and assembly refinement tasks.