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Combinatorial pattern discovery in biological sequences: The TEIRESIAS algorithm

I Rigoutsos1, A Floratos

  • 1Computational Biology Center, IBM Thomas J. Watson Research Center, York Town Heights, NY 10598, USA.

Bioinformatics (Oxford, England)
|April 1, 1998
PubMed
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This study introduces an efficient algorithm for discovering rigid patterns (motifs) in biological sequences. The method finds all maximal patterns appearing in a minimum number of sequences without exploring the entire pattern space.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Pattern Recognition

Background:

  • Discovering motifs in biological sequences is a critical challenge in bioinformatics.
  • Identifying recurring patterns aids in understanding biological functions and regulatory elements.

Purpose of the Study:

  • To present a novel, efficient algorithm for the discovery of rigid patterns (motifs) in biological sequences.
  • To ensure the algorithm identifies all maximal patterns meeting a user-defined frequency threshold.

Main Methods:

  • A combinatorial approach is employed, avoiding exhaustive enumeration of the pattern space.
  • The algorithm efficiently identifies maximal patterns present in a minimum specified number of sequences.
  • The method's running time is demonstrated to be quasi-linear to the output size.

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Main Results:

  • The algorithm successfully discovers previously reported patterns, validating its effectiveness.
  • It demonstrates the capability to automatically identify highly selective, sequence-specific patterns.
  • Experimental analysis confirms the algorithm's output-sensitive, quasi-linear time complexity.

Conclusions:

  • The developed algorithm offers an efficient and effective solution for motif discovery in biological sequences.
  • Its ability to find maximal, frequent patterns without full space exploration makes it a valuable tool.
  • The approach is suitable for identifying both known and novel biological patterns.