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Mining, compressing and classifying with extensible motifs.

Alberto Apostolico1, Matteo Comin, Laxmi Parida

  • 1Dipartimento di Ingegneria dell'lnformazione, Università di Padova, Padova, Italy. axa@dei.unipd.it

Algorithms for Molecular Biology : AMB
|May 26, 2006
PubMed
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Extensible motifs, a novel pattern class, enhance data compression and biological sequence classification. These flexible patterns improve codebook size and support unsupervised learning for structural inference.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Compression

Background:

  • Motif patterns of maximal saturation are crucial for biomolecular sequence discovery and data compression.
  • Traditional motif discovery can be computationally intensive.
  • Specialized
  • rigid
  • motifs offer efficient discovery in low polynomial time.

Purpose of the Study:

  • To introduce and explore the properties of "extensible" motifs.
  • To demonstrate the utility of extensible motifs in data compression and biological sequence analysis.

Main Methods:

  • Consideration of "extensible" motifs with elastic gap sequences.
  • Application of extensible motifs in data compression via textual substitution.

Related Experiment Videos

  • Utilizing extensible motifs for structural inference and classification.

    • Main Results:

    • Extensible motifs improve compression by reducing codebook size.
    • These motifs facilitate unsupervised classification and phylogeny reconstruction.
    • The elasticity of extensible motifs allows fitting variable-length sequence segments.

    Conclusions:

    • Off-line compression using extensible motifs offers advantages for biological sequence compression.
    • Extensible motifs provide a powerful tool for classifying biological sequences.
    • This approach supports structural inference and phylogenetic analysis.