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Minimally overlapping words for sequence similarity search.

Martin C Frith1,2,3, Laurent Noé4, Gregory Kucherov5,6

  • 1Artificial Intelligence Research Center, AIST, Tokyo, Japan.

Bioinformatics (Oxford, England)
|December 21, 2020
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Summary

We introduce a novel sparse-seeding method for genetic sequence analysis using minimally overlapping words. This approach enhances sensitivity in sequence similarity searches compared to existing methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Genetic sequence analysis relies on identifying similarities within DNA reads and genomes.
  • Large-scale sequence data analysis often employs 'seeds' (exact matches) for rapid similarity detection.
  • Sparse seeding, selecting seeds from a subset of sequence positions, is crucial for handling massive datasets.

Purpose of the Study:

  • To investigate a simple sparse-seeding strategy using minimally overlapping words for enhanced sequence similarity search.
  • To compare the efficacy of this word-based sparse seeding against established 'minimizer' methods.
  • To explore the integration of this method with inexact seeding designs for further sensitivity improvements.

Main Methods:

  • Utilizing specific 'words' (e.g., 'ac', 'at', 'gc', 'gt') at sequence positions as seeds.
  • Maximizing sensitivity by selecting words with minimal overlaps, leveraging their anti-clumping property in random sequences.
  • Developing and testing software for designing and evaluating minimally overlapping words.

Main Results:

  • Demonstrated that minimally overlapping word-based seeding can outperform 'minimizer' sparse-seeding methods.
  • Provided evidence for the effectiveness of this sparse-seeding approach in sequence similarity detection.
  • Showcased the potential for unifying this method with inexact seed designs to boost sensitivity.

Conclusions:

  • The proposed minimally overlapping word-based sparse seeding offers a promising advancement for sequence similarity search.
  • This method provides a robust alternative to existing sparse-seeding techniques, particularly for large genetic datasets.
  • Further research is needed to fully optimize this approach for diverse sequence analysis applications.