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Related Experiment Videos

The performance of several multiple-sequence alignment programs in relation to secondary-structure features for an

R E Hickson1, C Simon, S W Perrey

  • 1Department of Genetics, University of Hawaii at Manoa, USA.

Molecular Biology and Evolution
|March 31, 2000
PubMed
Summary

Evaluating multiple sequence alignment programs for animal mitochondrial 12S rRNA revealed that CLUSTAL W, Divide and Conquer, and TreeAlign offer the most accurate and robust alignments. Optimal parameter selection is crucial for reliable results in evolutionary analyses.

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Area of Science:

  • Bioinformatics
  • Molecular Evolution
  • Computational Biology

Background:

  • Accurate multiple sequence alignment is fundamental for phylogenetic analysis and understanding evolutionary relationships.
  • Evaluating the performance of different alignment algorithms is essential for selecting appropriate tools for specific biological datasets, such as ribosomal RNA (rRNA) sequences.

Purpose of the Study:

  • To assess the performance of five global multiple sequence alignment programs: CLUSTAL W, Divide and Conquer, Malign, PileUp, and TreeAlign.
  • To determine the accuracy and robustness of these programs when aligning animal mitochondrial 12S ribosomal RNA (rRNA) sequences, using conserved motifs derived from secondary structure information as a benchmark.
  • To investigate the impact of varying parameter values, particularly gap costs, on alignment quality.

Main Methods:

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  • Utilized a dataset comprising partial animal mitochondrial 12S rRNA sequences from five vertebrate and five invertebrate taxa.
  • Employed conserved sequence motifs, identified through secondary structure-based alignment, to score the quality of alignments generated by each program.
  • Systematically varied program parameters, focusing on gap costs, to evaluate alignment consistency and accuracy across different settings.

Main Results:

  • All tested programs achieved reasonable motif alignment accuracy under specific parameter conditions.
  • Overall sequence similarity to the structure-based alignment ranged from 25%-34% when considering the entire sequence.
  • CLUSTAL W, Divide and Conquer, and TreeAlign demonstrated the highest accuracy and robustness across varied gap costs.
  • PileUp showed poor performance with increasing gap costs, and Malign's accuracy fluctuated.
  • Default program settings did not yield optimal results, and direct parameter value comparisons between programs were not always effective.

Conclusions:

  • Alignment of rRNA sequences, especially less conserved regions, requires careful consideration and should be supplemented with secondary structure information and conserved motifs.
  • The choice of gap costs significantly impacts alignment accuracy and consistency, with no universal optimal value.
  • Even conserved motifs can be poorly aligned if located near insertions or deletions.
  • The tested portion of the 12S rRNA gene was insufficient for resolving distant evolutionary relationships within the dataset.
  • CLUSTAL W, Divide and Conquer, and TreeAlign are recommended for aligning animal mitochondrial 12S rRNA, but careful parameter optimization is necessary.