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Using the Sadakane compressed suffix tree to solve the all-pairs suffix-prefix problem.

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This study introduces a space-efficient solution for all-pairs suffix-prefix matching, crucial for genome assembly. The new method significantly reduces memory usage and offers a parallel algorithm for faster processing.

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Area of Science:

  • Bioinformatics
  • String Processing
  • Computational Biology

Background:

  • The all-pairs suffix-prefix matching problem is fundamental in string processing.
  • This problem is critical for de novo genome assembly, a major bioinformatics challenge.
  • Large datasets in bioinformatics necessitate fast and space-efficient algorithms.

Purpose of the Study:

  • To develop a space-economical solution for the all-pairs suffix-prefix matching problem.
  • To enhance the speed of this problem's solution using parallel computing.
  • To optimize algorithms by leveraging the Sadakane compressed index data structure.

Main Methods:

  • Implementation of a solution using the generalized Sadakane compressed suffix tree.
  • Development of a parallel algorithm for shared memory computers.
  • Optimization of both sequential and parallel algorithms using the Sadakane compressed index.

Main Results:

  • The Sadakane compressed index-based solution demonstrates significantly lower space consumption compared to non-compressed structures (suffix tree, enhanced suffix array).
  • Experimental results confirm the efficiency and scalability of the parallel algorithm with an increasing number of processors.

Conclusions:

  • The proposed method offers a superior space-efficient approach to all-pairs suffix-prefix matching.
  • The parallel algorithm provides substantial speed improvements for large-scale bioinformatics tasks like genome assembly.