R a common predicted structure or alignment of initially identified SLSs.Web page of(page quantity not for citation purposes)BMC Genomics ,:biomedcentralAn instance of such households is Myt,reported in Figure .other redundancies decreased the amount of identified families to . Notwithstanding the starting population of SLS containing sequences,inside these families regions sharing principal structure similarity,but not a widespread SLS,may possibly,in principle,still be identified,and households with no recognizable shared secondary structure,have been indeed identified. Most of these Oxyresveratrol web sequences are,not surprisingly,discovered inside coding regions,where the formation of secondary structures is expected to be limited by the translation machinery. Even so,a few of these families coincide with intergenic sequence repeats,such as the S. pneumoniae BOX and P. putida REP sequences unable to form structures compatible together with the initially searched ones.Families sharing typical secondary structuresDiscussionIn a preceding study,a systematic analysis of putative SLSs discovered in bacterial genomes showed that they are inclined to be much more abundant and steady than those randomly formed in shuffled sequences of comparable size and base composition . This observation led to the hypothesis that,along with SLSs stochastically formed simply because of sequence composition,a sizeable quota is possibly the outcome of selective stress,as a result of want to preserve a biological function. SLScontaining secondary structures are identified to play a relevant part in several aspects of gene expression and its regulation. Structured RNAs are a functional component of enzymes like RNAse P ,or contribute for the formation of regulatory cisacting regions including riboswitches ,thermosensors ,transcriptional attenuators and terminators . Palindromic RNA sequence repeats may well also influence mRNA stability . Within this function,we describe a systematic process,schematically depicted in Figure ,to identify and classify families of repeated sequences,characterized by a shared secondary structure,inside the genomes of a representative set of bacteria,most of which of medical interest. To this aim,SLS containing sequences were initially clustered by sequence similarity and subsequently evaluated for their potential to type secondary structures. In most analyzed genomes,a fraction of SLSs may be grouped into clusters,containing at least nonoverlapping elements. No clusters had been found in on the analyzed genomes. Clustering by sequence similarity resulted in selection of clusters corresponding to just above ,SLSs,about on the complete SLS population: this figure could differ quite a lot in particular species,being PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/23204391 sensibly larger,up to ,in N. meningitidis,and substantially reduce in B. subtilis and P. multocida,where much less than . on the SLSs fall inside clusters. Clustering ended up by choosing a subset of SLSs distinct from the original population and characterized by a considerably higher probability of nonrandom folding (see Figure,indicating that choice depending on sequence similarity was quite efficient in enriching for structured regions. Numerous refinement measures developed the final set of clusters,reported in Table . Despite the fact that mature rRNA and tRNA genes were initially masked within the searched genomic sequences,some clusters were identified,which correspond to unmasked parts of ribosomal RNA precursor genes (Table. Similarly,some clusters correspond to SLSs contained within ISs,which escaped the initial filtering for many reasons. Removal of these two subsets andMost.
