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Prediction of Small, Noncoding RNAs in Bacteria Using Heterogeneous Data

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Journal J Math Biol
Date 2007 Mar 14
PMID 17354017
Citations 16
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Abstract

sRNAFinder is a new gene prediction system for systematic identification of noncoding genes in bacteria. Most noncoding RNAs in prokaryotes belong to a class of genes denoted as small RNAs (sRNAs). In the model organism Escherichia coli, over 70 sRNA genes have been identified, and the existence of many more has been hypothesized. While various sources of information have proven useful for prediction of novel sRNA genes, most computational approaches do not take advantage of the disparate sources of data available for identifying these noncoding RNA genes. We present a general probabilistic method for predicting sRNA genes in bacteria. The method, based on a general Markov model, is implemented in the computational tool sRNAFinder. sRNAFinder incorporates heterogeneous data sources for gene prediction, including primary sequence data, transcript expression data from microarray experiments, and conserved RNA structure information as determined from comparative genomics analysis. We demonstrate that sRNAFinder improves upon current tools for identifying small, noncoding genes in bacteria.

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References
1.
Carter R, Dubchak I, Holbrook S . A computational approach to identify genes for functional RNAs in genomic sequences. Nucleic Acids Res. 2001; 29(19):3928-38. PMC: 60242. DOI: 10.1093/nar/29.19.3928. View

2.
Selinger D, Cheung K, Mei R, Johansson E, Richmond C, Blattner F . RNA expression analysis using a 30 base pair resolution Escherichia coli genome array. Nat Biotechnol. 2000; 18(12):1262-8. DOI: 10.1038/82367. View

3.
Ermolaeva M, Khalak H, White O, Smith H, Salzberg S . Prediction of transcription terminators in bacterial genomes. J Mol Biol. 2000; 301(1):27-33. DOI: 10.1006/jmbi.2000.3836. View

4.
Hershberg R, Altuvia S, Margalit H . A survey of small RNA-encoding genes in Escherichia coli. Nucleic Acids Res. 2003; 31(7):1813-20. PMC: 152812. DOI: 10.1093/nar/gkg297. View

5.
Lenz D, Mok K, Lilley B, Kulkarni R, Wingreen N, Bassler B . The small RNA chaperone Hfq and multiple small RNAs control quorum sensing in Vibrio harveyi and Vibrio cholerae. Cell. 2004; 118(1):69-82. DOI: 10.1016/j.cell.2004.06.009. View