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This study introduces fast algorithms for approximate circular string matching, significantly improving computational speed for biological sequence analysis. The developed library functions offer substantial performance gains over traditional methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Stringology

Background:

  • Circular string matching is crucial in biological contexts.
  • Existing algorithms for exact matching are optimal, but approximate matching is underdeveloped.
  • This research addresses the need for efficient approximate circular string matching algorithms.

Purpose of the Study:

  • To develop fast average-case algorithms for approximate circular string matching.
  • To adapt these algorithms for both Hamming and edit distance models.
  • To provide practical, implemented library functions for biological applications.

Main Methods:

  • Developed a suboptimal average-case algorithm for exact circular string matching (O(n) time).
  • Extended the exact matching solution to create two fast average-case algorithms for approximate matching with k-mismatches.
  • Algorithms achieve O(n) time complexity for k=O(m/logm) under Hamming and edit distance models.

Main Results:

  • Presented two efficient average-case algorithms for approximate circular string matching.
  • Demonstrated O(n) time complexity for moderate k values.
  • Implemented algorithms as library functions, showing >1000x speedup compared to naïve approaches in experiments.

Conclusions:

  • The developed algorithms provide fast average-case performance for approximate circular string matching.
  • The library functions are practical and can be integrated into biological pipelines.
  • Source code is freely available, promoting wider adoption and research.