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Related Experiment Videos

A flexible motif search technique based on generalized profiles

P Bucher1, K Karplus, N Moeri

  • 1Swiss Institute for Experimental Cancer Research, Epalinges, Switzerland.

Computers & Chemistry
|March 1, 1996
PubMed
Summary

A new flexible motif search technique uses a generalized profile syntax and a specialized search method to find multiple motif instances within sequences. This approach integrates various motif descriptors and offers clear significance tests for local alignments.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Motif discovery is crucial for understanding biological sequences.
  • Existing methods use diverse descriptors like patterns, weight matrices, and Hidden Markov Models (HMMs).
  • A unified approach is needed to handle multiple motif instances efficiently.

Purpose of the Study:

  • To introduce a flexible motif search technique with a novel profile syntax.
  • To develop a motif search method optimized for finding multiple occurrences in a single sequence.
  • To analyze the relationship between the new generalized profiles and existing biomolecular motif descriptors, particularly HMMs.

Main Methods:

  • Development of a generalized profile syntax as a motif definition language.

Related Experiment Videos

  • Implementation of a motif search method tailored for multiple instance discovery.
  • Detailed analysis of generalized profiles and their equivalence to a specific class of HMMs.
  • Provision of conversion procedures between generalized profiles and HMMs.
  • Mathematical formulation of the motif search problem and a new definition for alignment disjointness.

    • Main Results:

    • The generalized profile syntax unifies various motif descriptors (patterns, weight matrices, HMMs).
    • Generalized profiles are mathematically equivalent to a specific class of HMMs.
    • Conversion procedures between generalized profiles and HMMs are established.
    • The method allows for clear and simple significance tests for local alignments.
    • A precise mathematical definition of the motif search problem is provided.

    Conclusions:

    • The presented technique offers a flexible and unified framework for motif discovery.
    • The equivalence to HMMs provides a stochastic model interpretation for local alignments.
    • The method facilitates robust significance testing and precise problem definition for motif searching.