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Bioinformatics Advance Access originally published online on December 15, 2005
Bioinformatics 2006 22(4):445-452; doi:10.1093/bioinformatics/btk008
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

CMfinder—a covariance model based RNA motif finding algorithm

Zizhen Yao 1,*, Zasha Weinberg 1 and Walter L. Ruzzo 1,2

1Department of Computer Science and Engineering, University of Washington Seattle WA 98195-2350, USA
2Department of Genome Sciences, University of Washington Seattle WA 98195-2350, USA

*To whom correspondence should be addressed.

Motivation: The recent discoveries of large numbers of non-coding RNAs and computational advances in genome-scale RNA search create a need for tools for automatic, high quality identification and characterization of conserved RNA motifs that can be readily used for database search. Previous tools fall short of this goal.

Results: CMfinder is a new tool to predict RNA motifs in unaligned sequences. It is an expectation maximization algorithm using covariance models for motif description, featuring novel integration of multiple techniques for effective search of motif space, and a Bayesian framework that blends mutual information-based and folding energy-based approaches to predict structure in a principled way.

Extensive tests show that our method works well on datasets with either low or high sequence similarity, is robust to inclusion of lengthy extraneous flanking sequence and/or completely unrelated sequences, and is reasonably fast and scalable. In testing on 19 known ncRNA families, including some difficult cases with poor sequence conservation and large indels, our method demonstrates excellent average per-base-pair accuracy—79% compared with at most 60% for alternative methods. More importantly, the resulting probabilistic model can be directly used for homology search, allowing iterative refinement of structural models based on additional homologs. We have used this approach to obtain highly accurate covariance models of known RNA motifs based on small numbers of related sequences, which identified homologs in deeply-diverged species.

Availability: Results and web server version are available at http://bio.cs.washington.edu/yzizhen/CMfinder/

Contact: yzizhen{at}cs.washington.edu

Supplementary information: Supplementary technical details are available at http://bio.cs.washington.edu/yzizhen/CMfinder/

Received on June 9, 2005; revised on December 12, 2005; accepted on December 13, 2005

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