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Bioinformatics 2006 22(14):e557-e565; doi:10.1093/bioinformatics/btl232
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

A sequence-based filtering method for ncRNA identification and its application to searching for riboswitch elements

Shaojie Zhang 1,*, Ilya Borovok 2, Yair Aharonowitz 2, Roded Sharan 3 and Vineet Bafna 1

1 Department of Computer Science and Engineering, University of California San Diego 9500 Gilman Drive, La Jolla, CA 92093, USA
2 Department of Molecular Microbiology and Biotechnology, Tel-Aviv University Tel-Aviv 69978, Israel
3 School of Computer Science, Tel-Aviv University Tel-Aviv 69978, Israel

*To whom correspondence should be adderessed.

Motivation: Recent studies have uncovered an "RNA world", in which non coding RNA (ncRNA) sequences play a central role in the regulation of gene expression. Computational studies on ncRNA have been directed toward developing detection methods for ncRNAs. State-of-the-art methods for the problem, like covariance models, suffer from high computational cost, underscoring the need for efficient filtering approaches that can identify promising sequence segments and speedup the detection process.

Results: In this paper we make several contributions toward this goal. First, we formalize the concept of a filter and provide figures of merit that allow comparison between filters. Second, we design efficient sequence based filters that dominate the current state-of-the-art HMM filters. Third, we provide a new formulation of the covariance model that allows speeding up RNA alignment. We demonstrate the power of our approach on both synthetic data and real bacterial genomes. We then apply our algorithm to the detection of novel riboswitch elements from the whole bacterial and archaeal genomes. Our results point to a number of novel riboswitch candidates, and include genomes that were not previously known to contain riboswitches.

Availability: The program is available upon request from the authors.

Contact: shzhang{at}cs.ucsd.edu


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