title: 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.
authors.affiliation: Faculty of Engineering, Department of Computer Sciences, University of Freiburg, Freiburg Initiative in Systems Biology, Freiburg, Germany. backofen@informatik.uni-freiburg.de
authors.forename: Rolf
authors.initials: R
authors.lastname: Backofen
authors.forename: Wolfgang R
authors.initials: WR
authors.lastname: Hess
mesh_terms: D001419:Bacteria / Q000235:genetics*; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D008969:Molecular Sequence Data; D009690:Nucleic Acid Conformation; D012329:RNA, Bacterial / Q000737:chemistry / Q000235:genetics*; D012333:RNA, Messenger / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D022661:RNA, Untranslated / Q000737:chemistry / Q000235:genetics*
chemical_list: D012329:RNA, Bacterial; D012333:RNA, Messenger; D022661:RNA, Untranslated