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Bioinformatics Advance Access published online on December 15, 2005
Bioinformatics, 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
Received June 9, 2005
Revised December 12, 2005
Accepted December 13, 2005

Article

CMfinder--a covariance model based RNA motif finding algorithm

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

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

* To whom correspondence should be addressed.
Zizhen Yao, E-mail: yzizhen{at}cs.washington.edu


  Abstract

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 data sets 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 to 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: Supplementary technical details, results and web server version available at http://bio.cs.washington.edu/yzizhen/CMfinder.


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