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Bioinformatics Advance Access originally published online on September 10, 2009
Bioinformatics 2009 25(22):2897-2905; doi:10.1093/bioinformatics/btp537
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© The Author(s) 2009. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

De novo computational prediction of non-coding RNA genes in prokaryotic genomes

Thao T. Tran 1,2, Fengfeng Zhou 2, Sarah Marshburn 3, Mark Stead 3, Sidney R. Kushner 3 and Ying Xu 2,4,*

1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 2Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics and BioEnergy Science Center (BESC), 3Department of Genetics, University of Georgia, Athens, GA, USA and 4College of Computer Science and Technology, Jilin University, Changchun, China

*To whom correspondence should be addressed.


  Abstract

Motivation: The computational identification of non-coding RNA (ncRNA) genes represents one of the most important and challenging problems in computational biology. Existing methods for ncRNA gene prediction rely mostly on homology information, thus limiting their applications to ncRNA genes with known homologues.

Results: We present a novel de novo prediction algorithm for ncRNA genes using features derived from the sequences and structures of known ncRNA genes in comparison to decoys. Using these features, we have trained a neural network-based classifier and have applied it to Escherichia coli and Sulfolobus solfataricus for genome-wide prediction of ncRNAs. Our method has an average prediction sensitivity and specificity of 68% and 70%, respectively, for identifying windows with potential for ncRNA genes in E.coli. By combining windows of different sizes and using positional filtering strategies, we predicted 601 candidate ncRNAs and recovered 41% of known ncRNAs in E.coli. We experimentally investigated six novel candidates using Northern blot analysis and found expression of three candidates: one represents a potential new ncRNA, one is associated with stable mRNA decay intermediates and one is a case of either a potential riboswitch or transcription attenuator involved in the regulation of cell division. In general, our approach enables the identification of both cis- and trans-acting ncRNAs in partially or completely sequenced microbial genomes without requiring homology or structural conservation.

Availability: The source code and results are available at http://csbl.bmb.uga.edu/publications/materials/tran/.

Contact: xyn{at}bmb.uga.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Ivo Hofacker

Received on April 14, 2009; revised on August 22, 2009; accepted on September 7, 2009

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