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A sequential method for discovering probabilistic motifs in proteins.

K Blekas1, D I Fotiadis, A Likas

  • 1Department of Computer Science, University of Ioannina, P.O. Box 1186, GR-45110 Ioannina, Greece. kblekas@cs.uoi.gr

Methods of Information in Medicine
|March 18, 2004
PubMed
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This study introduces a greedy algorithm for discovering common motifs in biosequences using a mixture model. It outperforms existing methods by identifying conserved motifs and larger protein fingerprints.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Discovering recurring patterns (motifs) in biological sequences is crucial for understanding gene function and regulation.
  • Existing motif discovery algorithms like MEME have limitations in handling multiple motifs and motif occurrences.

Purpose of the Study:

  • To propose a novel greedy algorithm for learning mixture of motifs models.
  • To enhance motif discovery in biosequences through likelihood maximization.

Main Methods:

  • A greedy algorithm sequentially adds motif components to a mixture model.
  • Employs a combined global and local search for parameter initialization.
  • Utilizes hierarchical clustering for initial candidate motif identification and accelerated searching.

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Main Results:

  • The algorithm was validated on both artificial and real biological datasets.
  • Demonstrated superior performance compared to the MEME algorithm.
  • Identified motifs with significant conservation and produced larger protein fingerprints.

Conclusions:

  • The proposed greedy algorithm is a promising approach for probabilistic motif discovery in biological sequences.
  • Effectively overcomes MEME's limitation of erasing motif occurrences during incremental learning.
  • Offers an effective incremental mixture modeling strategy for enhanced motif identification.