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Optimization of ribosomal RNA profile alignments

E A O'Brien1, C Notredame, D G Higgins

  • 1Department of Biochemistry, University College, Cork, Ireland and EMBL-European Bioinformatics Institute, Hinxton, Cambridge CB10 1RQ, UK. emmet@chah.ucc.ie

Bioinformatics (Oxford, England)
|June 20, 1998
PubMed
Summary

Profile alignment methods improve ribosomal RNA (rRNA) sequence alignment accuracy. A novel weighting scheme, prioritizing similar sequences, enhances alignment precision when used with traditional methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Evolution

Background:

  • Maintaining large alignments of ribosomal RNA (rRNA) sequences is crucial for biological research.
  • Current methods for updating rRNA alignments involve manual and automatic approaches.
  • Profile alignment methods offer potential for optimizing rRNA sequence alignment.

Purpose of the Study:

  • To evaluate profile alignment methods for ribosomal RNA (rRNA) sequence alignment.
  • To optimize parameter choices and sequence weighting schemes for improved alignment accuracy.
  • To develop and test a new sequence weighting strategy for rRNA alignments.

Main Methods:

  • Empirical comparison of various sequence weighting schemes on a large eukaryotic SSU rRNA alignment.

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  • Systematic evaluation across a range of gap penalties.
  • Development of a novel weighting scheme prioritizing sequence similarity within profiles.

    • Main Results:

    • The new weighting scheme, which emphasizes sequences most similar to the new sequence, demonstrated superior performance.
    • This novel scheme, when integrated with traditional weighting methods, yielded the most accurate alignments.
    • Performance was assessed using a comprehensive dataset of eukaryotic small subunit rRNA sequences.

    Conclusions:

    • Profile alignment methods, particularly with optimized weighting, significantly enhance rRNA sequence alignment.
    • The developed weighting scheme offers a more accurate approach for incorporating new sequences into existing alignments.
    • This work provides a refined methodology for large-scale rRNA sequence analysis.