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Nucleic Acids Research 2005 33(13):4096-4105; doi:10.1093/nar/gki715
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Published online 26 July 2005

© The Author 2005. Published by Oxford University Press. All rights reserved
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{at}oupjournals.org

Article

sRNAPredict: an integrative computational approach to identify sRNAs in bacterial genomes

Jonathan Livny*, Michael A. Fogel, Brigid M. Davis and Matthew K. Waldor

Department of Molecular Biology and Microbiology, Tufts University School of Medicine and Howard Hughes Medical Institute 136 Harrison Avenue, Boston, MA 02111, USA

*To whom correspondence should be addressed. Tel: +1 617 636 2778; Fax: +1 617 636 2723; Email: jonathan.livny{at}tufts.edu

Received May 18, 2005. Revised June 30, 2005. Accepted June 30, 2005.

Small non-coding bacterial RNAs (sRNAs) play important regulatory roles in a variety of cellular processes. Nearly all known sRNAs have been identified in Escherichia coli and most of these are not conserved in the majority of other bacterial species. Many of the E.coli sRNAs were initially predicted through bioinformatic approaches based on their common features, namely that they are encoded between annotated open reading frames and are flanked by predictable transcription signals. Because promoter consensus sequences are undetermined for most species, the successful use of bioinformatics to identify sRNAs in bacteria other than E.coli has been limited. We have created a program, sRNAPredict, which uses coordinate-based algorithms to integrate the respective positions of individual predictive features of sRNAs and rapidly identify putative intergenic sRNAs. Relying only on sequence conservation and predicted Rho-independent terminators, sRNAPredict was used to search for sRNAs in Vibrio cholerae. This search identified 9 of the 10 known or putative V.cholerae sRNAs and 32 candidates for novel sRNAs. Small transcripts for 6 out of 9 candidate sRNAs were observed by Northern analysis. Our findings suggest that sRNAPredict can be used to efficiently identify novel sRNAs even in bacteria for which promoter consensus sequences are not available.


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