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Related Experiment Videos

Stochastic pairwise alignments.

U Mückstein1, I L Hofacker, P F Stadler

  • 1Institut für Theoretische Chemie und Molekulare Strukturbiologie Universität Wien, Vienna, Austria.

Bioinformatics (Oxford, England)
|October 19, 2002
PubMed
Summary
This summary is machine-generated.

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Sequence alignment is challenging due to variable conservation. New probabilistic methods generate ensembles of suboptimal alignments, improving reliability in bioinformatics by capturing more biologically relevant information than single optimal alignments.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Sequence Analysis

Background:

  • Sequence conservation varies significantly across nucleic acid and protein molecules.
  • High variability regions (mutational hotspots) and conserved regions coexist within the same molecular pairs.
  • The reliability of sequence alignment is heavily influenced by local sequence similarity, especially in variable regions where optimal alignment is arbitrary.

Purpose of the Study:

  • To address the ambiguity in sequence alignment, particularly in variable regions.
  • To introduce and discuss novel computational approaches for generating statistically weighted ensembles of alignments.
  • To enhance the reliability of bioinformatics methods by providing more comprehensive alignment information.

Main Methods:

Related Experiment Videos

  • Computation of the partition function over all canonical pairwise alignments.
  • Probabilistic backtracking to generate ensembles of suboptimal alignments with correct statistical weights.
  • Comparison of structure-based alignments with large samples of stochastic alignments.

    • Main Results:

    • The ensemble of possible alignments can be characterized by match probabilities P(ij) between sequence positions.
    • Probabilistic backtracking generates statistically weighted ensembles of suboptimal alignments.
    • Ensembles of suboptimal alignments contain correct alignments with significant probabilities, even when the optimal alignment differs from structural alignments.

    Conclusions:

    • Ensembles of suboptimal alignments offer greater reliability than single optimal alignments for bioinformatics applications.
    • These probabilistic approaches provide valuable reliability information not obtainable from traditional single optimal alignment methods.
    • The developed methods improve the handling of ambiguous alignments in regions of high sequence variability.