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A comprehensive comparison of multiple sequence alignment programs.

J D Thompson1, F Plewniak, O Poch

  • 1Laboratoire de Biologie Structurale, Institut de Génétique et de Biologie Moléculaire et Cellulaire, (CNRS/INSERM/ULP), BP 163, 67404 Illkirch Cedex, France.

Nucleic Acids Research
|June 22, 1999
PubMed
Summary
This summary is machine-generated.

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New protein sequence alignment programs and iterative algorithms improve accuracy, even at low sequence identities. However, iterative methods can diverge with varied sequences, and local alignment excels with insertions.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Protein sequence alignment is crucial for understanding protein function and evolution.
  • Recent advancements include improved alignment programs and novel iterative algorithms.
  • The 'twilight zone' (10-20% residue identity) presents a significant challenge for accurate alignment.

Purpose of the Study:

  • To systematically evaluate commonly used protein sequence alignment programs.
  • To assess the performance of iterative algorithms compared to traditional methods.
  • To provide guidelines for selecting optimal alignment strategies based on sequence characteristics.

Main Methods:

  • Utilized the BAliBASE benchmark dataset for comprehensive testing.
  • Compared the accuracy of multiple protein sequence alignment programs.

Related Experiment Videos

  • Analyzed the impact of iterative algorithms and sequence divergence on alignment quality.

    • Main Results:

    • The best alignment programs achieved 47% average residue accuracy below 20% identity.
    • Iterative algorithms generally improved accuracy but increased computation time.
    • Local alignment methods were superior for sequences with large insertions or extensions.

    Conclusions:

    • Iterative algorithms show promise but require careful application, especially with divergent sequences.
    • Combining multiple alignment programs with different techniques enhances overall accuracy.
    • Findings offer insights for developing improved protein sequence alignment algorithms.