title: Efficient annotation of bacterial genomes for small, noncoding RNAs using the integrative computational tool sRNAPredict2.
abstract: 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.
authors.affiliation: Tufts University School of Medicine.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Livny
mesh_terms: D030541:Databases, Genetic; D016680:Genome, Bacterial; D016366:Open Reading Frames; D022661:RNA, Untranslated / Q000235:genetics*
chemical_list: D022661:RNA, Untranslated