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Efficient edit distance with duplications and contractions.

Tamar Pinhas1, Shay Zakov2, Dekel Tsur1

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Summary
This summary is machine-generated.

We developed new algorithms for string edit distance, improving efficiency for duplications and contractions. These algorithms offer better time complexities for general and constrained problems, including RNA folding.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Theoretical Computer Science

Background:

  • String edit distance is fundamental in sequence analysis.
  • Existing algorithms struggle with duplications and contractions.
  • Efficient computation is crucial for large biological datasets.

Purpose of the Study:

  • To introduce novel algorithms for string edit distance incorporating duplications and contractions.
  • To achieve improved time complexities compared to existing methods.
  • To apply these algorithms to problems like RNA folding.

Main Methods:

  • Developed a general algorithm using min-plus matrix multiplication.
  • Introduced specialized algorithms for specific cost function constraints.
  • Utilized an efficient matrix-vector min-plus multiplication for acceleration.

Main Results:

  • Achieved O(|Σ|MP(n)) time and O(|Σ|n^2) space for the general algorithm.
  • Improved runtime to O|Σ|n^3log^2n for integer cost functions, enabling online processing.
  • Derived the asymptotically fastest O(n^3log^2n) algorithm for single-strand RNA folding.

Conclusions:

  • The proposed algorithms efficiently handle string edit distance with duplications and contractions.
  • The methods offer significant improvements in time complexity for various constraints.
  • The algorithms have direct applications in computational biology, particularly RNA folding.