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CMfinder--a covariance model based RNA motif finding algorithm.

Zizhen Yao1, Zasha Weinberg, Walter L Ruzzo

  • 1Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195-2350, USA. yzizhen@cs.washington.edu

Bioinformatics (Oxford, England)
|December 17, 2005
PubMed
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CMfinder accurately predicts conserved RNA motifs in unaligned sequences using a novel Bayesian approach. This tool enhances non-coding RNA discovery and characterization for database searches.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Recent discoveries of numerous non-coding RNAs (ncRNAs) necessitate advanced computational tools.
  • Existing RNA motif identification tools lack the desired quality and database search utility.
  • Advances in genome-scale RNA searching highlight the need for better motif characterization.

Purpose of the Study:

  • To develop an automated tool for high-quality identification and characterization of conserved RNA motifs.
  • To create a tool that can be readily used for efficient database searching of RNA motifs.
  • To address the shortcomings of previous RNA motif identification methods.

Main Methods:

  • Utilizes an expectation maximization algorithm with covariance models for motif description.

Related Experiment Videos

  • Integrates multiple techniques for comprehensive motif space searching.
  • Employs a Bayesian framework combining mutual information and folding energy for principled structure prediction.

    • Main Results:

    • CMfinder accurately predicts RNA motifs in unaligned sequences, performing well across varying sequence similarities.
    • Demonstrates robustness against extraneous and unrelated sequences, with reasonable speed and scalability.
    • Achieves 79% average per-base-pair accuracy on 19 known ncRNA families, outperforming alternative methods (max 60%).

    Conclusions:

    • CMfinder provides a powerful and accurate method for identifying conserved RNA motifs.
    • The resulting probabilistic models facilitate homology searches and iterative structural model refinement.
    • Enables accurate identification of RNA motifs and their homologs in deeply diverged species.