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Characterization of pairwise and multiple sequence alignment errors.

Giddy Landan1, Dan Graur

  • 1Department of Biology & Biochemistry, University of Houston, Houston, TX 77204-5001, USA. giddy.landan@gmail.com

Gene
|July 11, 2008
PubMed
Summary
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Multiple sequence alignment (MSA) reconstruction contains numerous errors, especially with increasing sequence divergence. These alignment errors, including gap underestimation, are often unavoidable even with known evolutionary parameters.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Accurate sequence alignment is crucial for understanding evolutionary relationships and functional genomics.
  • Simulations of sequence evolution provide a benchmark for evaluating the accuracy of alignment algorithms.

Purpose of the Study:

  • To quantify errors in pairwise and multiple sequence alignments (MSAs) by comparing simulated true alignments with reconstructed ones.
  • To investigate how sequence divergence and alignment reconstruction methods influence error rates and types.

Main Methods:

  • Comparison of true alignments from simulated sequence evolution with reconstructed alignments.
  • Analysis of error types (e.g., indel positioning, gap number) and their correlation with sequence divergence.

Related Experiment Videos

  • Evaluation of the impact of guide-tree quality in progressive MSA.

    • Main Results:

    • Reconstructed alignments contain substantial errors, increasing rapidly with sequence divergence.
    • Errors shift from local indel misplacements in closely related sequences to complex, widespread mis-reconstructions in divergent sequences.
    • A systematic bias towards underestimating gaps results in shorter reconstructed MSAs.
    • Alignment errors are unavoidable, even when evolutionary parameters are known, due to sub-optimal or co-optimal true alignment features.

    Conclusions:

    • Current MSA reconstruction methods are prone to significant errors, particularly for divergent sequences.
    • Alignment errors are inherent to the process and not solely dependent on input data quality or known parameters.
    • Guide-tree quality has a minimal impact on overall MSA error levels in progressive alignment.