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Algorithms for identifying local molecular sequence features.

S Karlin1, M Morris, G Ghandour

  • 1Department of Mathematics, Stanford University, CA 94305.

Computer Applications in the Biosciences : CABIOS
|March 1, 1988
PubMed
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This study introduces efficient algorithms for detecting molecular sequence patterns like repeats and matches, even with errors. These methods are applied to analyze viral and bacteriophage genomic data.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Identifying local molecular sequence features is crucial for understanding genome function.
  • Existing methods may struggle with sequence variations and errors.
  • Accurate analysis of viral and bacteriophage genomes is essential for molecular biology research.

Purpose of the Study:

  • To develop and present efficient algorithms for identifying molecular sequence features.
  • To enable the detection of repeats, dyad symmetry, and aligned matches with error tolerance.
  • To demonstrate the applicability of these algorithms to specific viral and bacteriophage genomic sequences.

Main Methods:

  • Algorithm development for sequence analysis.
  • Implementation of methods to identify repeats and dyad symmetry.

Related Experiment Videos

  • Application of algorithms to Epstein-Barr virus, Varicella-Zoster virus, and bacteriophage lambda and T7 genomes.

    • Main Results:

    • Efficient identification of local molecular sequence features.
    • Successful detection of repeats, dyad symmetry pairings, and aligned matches.
    • Demonstrated utility in analyzing complex viral and bacteriophage genomic data.

    Conclusions:

    • The developed algorithms provide an efficient approach for molecular sequence analysis.
    • These methods are effective in identifying key sequence features in viral genomes.
    • The findings contribute to advancements in bioinformatics and genomic sequence analysis.