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The WM-q multiple exact string matching algorithm for DNA sequences.

Abdullah Ammar Karcioglu1, Hasan Bulut1

  • 1Department of Computer Engineering, Ege University, Izmir, Turkey.

Computers in Biology and Medicine
|August 1, 2021
PubMed
Summary

This study improves the Wu-Manber algorithm for multiple string matching in bioinformatics. The new WM-q algorithm uses a perfect hash function for DNA sequences, reducing runtime and comparisons.

Keywords:
DNA SequencesHash functionMultiple string matchingSequence analysisWu manber algorithm

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Area of Science:

  • Computer Science
  • Bioinformatics
  • Computational Biology

Background:

  • String matching algorithms are crucial for tasks like text search and bioinformatics sequence analysis.
  • Multiple string matching algorithms efficiently find patterns within large texts.
  • The Wu-Manber algorithm is a known multiple string matching method but suffers from issues like hash collisions.

Purpose of the Study:

  • To enhance the Wu-Manber algorithm for more efficient multiple exact string matching.
  • To address limitations of the traditional Wu-Manber algorithm, specifically hash collisions.
  • To propose a novel algorithm, WM-q, optimized for DNA sequence analysis.

Main Methods:

  • The study proposes the WM-q algorithm, an adaptation of the Wu-Manber algorithm.
  • WM-q utilizes a perfect hash function tailored for DNA sequences.
  • String matching is performed using variable block lengths determined by the perfect hash function, unlike the fixed block length in the original Wu-Manber.

Main Results:

  • The WM-q algorithm demonstrated improved performance metrics compared to traditional multiple exact string matching algorithms.
  • Evaluations on E. Coli and Human Chromosome1 datasets showed reduced average runtime.
  • The proposed algorithm also decreased the average number of character and hash comparisons.

Conclusions:

  • The WM-q algorithm offers a more efficient approach to multiple exact string matching for DNA sequences.
  • The use of perfect hash functions and variable block lengths effectively mitigates issues like hash collisions.
  • This improved algorithm contributes to faster and more accurate sequence analysis in bioinformatics.