Computational prediction of sRNAs and their targets in bacteria
- PMID: 20061798
- DOI: 10.4161/rna.7.1.10655
Computational prediction of sRNAs and their targets in bacteria
Abstract
There is probably no major adaptive response in bacteria which does not have at least one small RNA (sRNA) as part of its regulatory network controlling gene expression. Thus, prokaryotic genomes encode dozens to hundreds of these riboregulators. Whereas the identification of putative sRNA genes during initial genome annotation is not yet common practice, their prediction can be done subsequently by various methods and with variable efficacy, frequently relying on comparative genome analysis. A large number of these sRNAs interact with their mRNA targets by antisense mechanisms. Yet, the computational identification of these targets appears to be challenging because frequently the partial and incomplete sequence complementarity is difficult to evaluate. Here we review the computational approaches for detecting bacterial sRNA genes and their targets, and discuss the current and future challenges that this exciting field of research is facing.
Similar articles
-
Small RNA gene identification and mRNA target predictions in bacteria.Bioinformatics. 2008 Dec 15;24(24):2807-13. doi: 10.1093/bioinformatics/btn560. Epub 2008 Oct 29. Bioinformatics. 2008. PMID: 18974076 Review.
-
sRNA Target Prediction Organizing Tool (SPOT) Integrates Computational and Experimental Data To Facilitate Functional Characterization of Bacterial Small RNAs.mSphere. 2019 Jan 30;4(1):e00561-18. doi: 10.1128/mSphere.00561-18. mSphere. 2019. PMID: 30700509 Free PMC article.
-
Predicting sRNAs and their targets in bacteria.Genomics Proteomics Bioinformatics. 2012 Oct;10(5):276-84. doi: 10.1016/j.gpb.2012.09.004. Epub 2012 Oct 23. Genomics Proteomics Bioinformatics. 2012. PMID: 23200137 Free PMC article. Review.
-
Structure and functional properties of prokaryotic small noncoding RNAs.Folia Microbiol (Praha). 2003;48(4):443-68. doi: 10.1007/BF02931326. Folia Microbiol (Praha). 2003. PMID: 14533476 Review.
-
High-throughput, kingdom-wide prediction and annotation of bacterial non-coding RNAs.PLoS One. 2008 Sep 12;3(9):e3197. doi: 10.1371/journal.pone.0003197. PLoS One. 2008. PMID: 18787707 Free PMC article.
Cited by
-
TargetRNA2: identifying targets of small regulatory RNAs in bacteria.Nucleic Acids Res. 2014 Jul;42(Web Server issue):W124-9. doi: 10.1093/nar/gku317. Epub 2014 Apr 21. Nucleic Acids Res. 2014. PMID: 24753424 Free PMC article.
-
Riboregulators and the role of Hfq in photosynthetic bacteria.RNA Biol. 2014;11(5):413-26. doi: 10.4161/rna.28035. Epub 2014 Feb 10. RNA Biol. 2014. PMID: 24651049 Free PMC article. Review.
-
PETcofold: predicting conserved interactions and structures of two multiple alignments of RNA sequences.Bioinformatics. 2011 Jan 15;27(2):211-9. doi: 10.1093/bioinformatics/btq634. Epub 2010 Nov 18. Bioinformatics. 2011. PMID: 21088024 Free PMC article.
-
cis-antisense RNA, another level of gene regulation in bacteria.Microbiol Mol Biol Rev. 2011 Jun;75(2):286-300. doi: 10.1128/MMBR.00032-10. Microbiol Mol Biol Rev. 2011. PMID: 21646430 Free PMC article. Review.
-
Identification of bacterial sRNA regulatory targets using ribosome profiling.Nucleic Acids Res. 2015 Dec 2;43(21):10308-20. doi: 10.1093/nar/gkv1158. Epub 2015 Nov 5. Nucleic Acids Res. 2015. PMID: 26546513 Free PMC article.
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources