Advertisement

Efficient Annotation of Bacterial Genomes for Small, Noncoding RNAs Using the Integrative Computational Tool sRNAPredict2

  • Jonathan Livny
Part of the Methods in Molecular Biology™ book series (MIMB, volume 395)

Summary

sRNAs are small noncoding RNAs that have been shown to perform diverse regulatory roles in a number of prokaryotes. Although several bioinformatic approaches have proven effective in identifying bacterial sRNAs, implementing these approaches presents significant computational challenges that have limited their use. To address these computational challenges, the author has developed and made publicly available sRNAPredict2, a program that facilitates the efficient prediction of putative sRNA-encoding genes in the intergenic regions of bacterial genomes. sRNAPredict2 identifies putative sRNAs by integrating genome-wide predictions of several different genetic features that are commonly associated with sRNA-encoding genes and identifying instances in which these features are colocalized in intergenic regions of the genome.

Keywords

sRNAs sRNAPredict2 bioinformatics annotation 

References

  1. 1.
    Dennis, P. P. and Omer, A. (2005) Small non-coding RNAs in Archaea. Curr. Opin. Microbiol. 8, 685–694.CrossRefPubMedGoogle Scholar
  2. 2.
    Gottesman, S. (2005) Micros for microbes: non-coding regulatory RNAs in bacteria. Trends Genet. 21, 399–404.CrossRefPubMedGoogle Scholar
  3. 3.
    Gottesman, S. (2004) The small RNA regulators of Escherichia coli: roles and mechanisms. Annu Rev Microbiol. 58, 303–328.CrossRefPubMedGoogle Scholar
  4. 4.
    Hershberg, R., Altuvia, S., and Margalit, H. (2003) A survey of small RNA-encoding genes in Escherichia coli. Nucleic Acids Res. 31, 1813–1820.CrossRefPubMedGoogle Scholar
  5. 5.
    Livny, J., Fogel, M. A., Davis, B. M., and Waldor, M. K. (2005) sRNAPredict: an integrative computational approach to identify sRNAs in bacterial genomes. Nucleic Acids Res. 33, 4096–4105.CrossRefPubMedGoogle Scholar
  6. 6.
    Alifano, P., Rivellini, F., Limauro, D., Bruni, C. B., and Carlomagno, M. S. (1991) A consensus motif common to all Rho-dependent prokaryotic transcription terminators. Cell 64, 553–563.CrossRefPubMedGoogle Scholar
  7. 7.
    Livny, J., Brencic, A., Lory, S., and Waldor, M. K. (2006) Identification of 17 Pseudomonas aeruginosa sRNAs and prediction of sRNA-encoding genes in 10 diverse pathogens using the bioinformatic tool sRNAPredict2. Nucleic Acids Res. 34, 3484–3493.CrossRefPubMedGoogle Scholar
  8. 8.
    Griffiths-Jones, S., Moxon, S., Marshall, M., Khanna, A., Eddy, S. R. and Bateman, A. (2005) Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res. 33, D121–D124.CrossRefPubMedGoogle Scholar
  9. 9.
    Altschul, S. F., Madden, T. L., Schaffer, A. A., et al. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402.CrossRefPubMedGoogle Scholar
  10. 10.
    Macke, T. J., Ecker, D. J., Gutell, R. R., Gautheret, D., Case, D. A., and Sampath, R. (2001) RNAMotif, an RNA secondary structure definition and search algorithm. Nucleic Acids Res. 29, 4724–4735.CrossRefPubMedGoogle Scholar
  11. 11.
    Ermolaeva, M. D., Khalak, H. G., White, O., Smith, H. O., and Salzberg, S. L. (2000) Prediction of transcription terminators in bacterial genomes. J. Mol. Biol. 301, 27–33.CrossRefPubMedGoogle Scholar
  12. 12.
    Rivas, E. and Eddy, S. R. (2001) Noncoding RNA gene detection using comparative sequence analysis. BMC Bioinformatics. 2, 8.CrossRefPubMedGoogle Scholar

Copyright information

© Humana Press Inc. 2007

Authors and Affiliations

  • Jonathan Livny
    • 1
  1. 1.Tufts University School of MedicineBoston

Personalised recommendations