Transcription of a gene produces mRNA consisting of three distinct domains viz. 5’ UTR (UnTranslated regions of RNAs), ORF (Open Reading Frame) and 3’ UTR of which both the UTRs do not get translated. In bacteria, some UTRs have structured RNA motifs that directly bind cellular metabolites or recruit specific RNA binding proteins. These interactions enable post-transcriptional control of gene expression. We are interesting in identifying structured RNAs in bacterial genomes. To predict RNA secondary structures in bacteria, I implemented a covariance-based method (using Infernal v1.0.2). In this method, base-pairing information of nucleotides (covariance) is converted into a tree, which is then used to search against bacterial genomes using HMM (Hidden Markov Models). My work highlights how covariance analysis could help in 1) identifying a conserved class of structured RNA motifs in GC rich bacteria, where most sequence-based small RNA predictions have failed due to challenges in promoter/terminator identification; and 2) identifying a novel classes of metabolite binding RNAs.