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Aligning genomes with inversions and swaps

J L Holloway1, P Cull

  • 1Oregon State University, USA.

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|January 1, 1994
PubMed
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This study introduces novel heuristics for biological sequence alignment, incorporating evolutionary events like swaps and inversions beyond traditional methods. These approximate algorithms aid in understanding complex genomic evolution and viral relationships.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Sequence alignment is crucial for understanding biological relationships.
  • Traditional alignment models often overlook complex evolutionary operations like segment swaps and inversions.
  • Existing models may not fully capture the evolutionary dynamics of certain biological sequences.

Purpose of the Study:

  • To develop and describe a family of heuristics for biological sequence alignment.
  • To incorporate evolutionary operations such as segment swaps and inversions into alignment models.
  • To provide a framework for analyzing genomic data with a broader evolutionary perspective.

Main Methods:

  • Development of approximate heuristics for sequence alignment.

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  • Inclusion of deletion, insertion, substitution, swap, and inversion operators in the evolutionary model.
  • Pairwise comparison of 20 Picornavirus genomes.
  • Detailed comparison of hepatitis delta virus with citrus exocortis viroid.

    • Main Results:

    • A novel set of heuristics for sequence alignment incorporating swaps and inversions was developed.
    • The heuristics provide an approximate method for alignment under a more comprehensive evolutionary model.
    • Comparative analyses of viral genomes were performed, highlighting potential evolutionary insights.

    Conclusions:

    • The proposed heuristics offer a more nuanced approach to biological sequence alignment.
    • Understanding evolutionary operations like swaps and inversions is key to accurate genomic analysis.
    • The study provides valuable comparative data for viral genomics and viroid research.