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Efficient sequential and parallel algorithms for finding edit distance based motifs.

Soumitra Pal1, Peng Xiao1, Sanguthevar Rajasekaran2

  • 1Department of Computer Science and Engineering, University of Connecticut, 371 Fairfield Road,, Storrs, 06269, CT, USA.

BMC Genomics
|August 25, 2016
PubMed
Summary
This summary is machine-generated.

Novel algorithms for the (l,d) Edit-distance-based Motif Search (EMS) problem significantly improve computational efficiency. These new sequential and parallel methods offer substantial speedups, advancing the state-of-the-art in biological pattern discovery.

Keywords:
Edit distanceMotifRadix sortTrie

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Algorithm Design

Background:

  • Motif search is crucial for identifying patterns in biological data.
  • The general motif search problem is computationally challenging, requiring efficient algorithms.
  • The (l,d) Edit-distance-based Motif Search (EMS) problem seeks motifs with a specified length and error tolerance.

Purpose of the Study:

  • To develop novel, exact, sequential, and parallel algorithms for the (l,d) Edit-distance-based Motif Search (EMS) problem.
  • To improve the efficiency and speed of solving the EMS problem compared to existing methods.
  • To introduce new techniques for neighborhood exploration and motif representation.

Main Methods:

  • Developed a novel neighborhood exploration technique focusing on candidates at distance 'd'.
  • Utilized a trie-based data structure for efficient storage and sorting in the sequential algorithm.
  • Employed arrays and a modified radix-sort for efficient sorting in the parallel algorithm for shared memory systems.

Main Results:

  • Sequential algorithms demonstrated over 20x speedup on instance (16,3) compared to the previous best algorithm.
  • Achieved significant speedups on other challenging instances like (9,2), (11,3), and (13,4).
  • The parallel algorithm exhibited over 600% scaling performance with 16 threads.

Conclusions:

  • The developed algorithms represent a significant advancement in the state-of-the-art for EMS solvers.
  • The introduced techniques are potentially applicable to related motif search problems like Planted Motif Search (PMS) and Simple Motif Search (SMS).
  • These efficient algorithms facilitate more effective pattern discovery in large biological datasets.