title: sRNAscanner: a computational tool for intergenic small RNA detection in bacterial genomes.
abstract: BACKGROUND
  Bacterial non-coding small RNAs (sRNAs) have attracted considerable attention due to their ubiquitous nature and contribution to numerous cellular processes including survival, adaptation and pathogenesis. Existing computational approaches for identifying bacterial sRNAs demonstrate varying levels of success and there remains considerable room for improvement.
  
  
  RESULTS
  Here we have proposed a transcriptional signal-based computational method to identify intergenic sRNA transcriptional units (TUs) in completely sequenced bacterial genomes. Our sRNAscanner tool uses position weight matrices derived from experimentally defined E. coli K-12 MG1655 sRNA promoter and rho-independent terminator signals to identify intergenic sRNA TUs through sliding window based genome scans. Analysis of genomes representative of twelve species suggested that sRNAscanner demonstrated equivalent sensitivity to sRNAPredict2, the best performing bioinformatics tool available presently. However, each algorithm yielded substantial numbers of known and uncharacterized hits that were unique to one or the other tool only. sRNAscanner identified 118 novel putative intergenic sRNA genes in Salmonella enterica Typhimurium LT2, none of which were flagged by sRNAPredict2. Candidate sRNA locations were compared with available deep sequencing libraries derived from Hfq-co-immunoprecipitated RNA purified from a second Typhimurium strain (Sittka et al. (2008) PLoS Genetics 4: e1000163). Sixteen potential novel sRNAs computationally predicted and detected in deep sequencing libraries were selected for experimental validation by Northern analysis using total RNA isolated from bacteria grown under eleven different growth conditions. RNA bands of expected sizes were detected in Northern blots for six of the examined candidates. Furthermore, the 5'-ends of these six Northern-supported sRNA candidates were successfully mapped using 5'-RACE analysis.
  
  
  CONCLUSIONS
  We have developed, computationally examined and experimentally validated the sRNAscanner algorithm. Data derived from this study has successfully identified six novel S. Typhimurium sRNA genes. In addition, the computational specificity analysis we have undertaken suggests that approximately 40% of sRNAscanner hits with high cumulative sum of scores represent genuine, undiscovered sRNA genes. Collectively, these data strongly support the utility of sRNAscanner and offer a glimpse of its potential to reveal large numbers of sRNA genes that have to date defied identification. sRNAscanner is available from: http://bicmku.in:8081/sRNAscanner or http://cluster.physics.iisc.ernet.in/sRNAscanner/.
authors.affiliation: Centre of Excellence in Bioinformatics, School of Biotechnology, Madurai Kamaraj University, Madurai, Tamilnadu, India.
authors.forename: Jayavel
authors.initials: J
authors.lastname: Sridhar
authors.forename: Narmada
authors.initials: N
authors.lastname: Sambaturu
authors.forename: Suryanarayanan Ramkumar
authors.initials: SR
authors.lastname: Narmada
authors.forename: Radhakrishnan
authors.initials: R
authors.lastname: Sabarinathan
authors.forename: Hong-Yu
authors.initials: HY
authors.lastname: Ou
authors.forename: Zixin
authors.initials: Z
authors.lastname: Deng
authors.forename: Kanagaraj
authors.initials: K
authors.lastname: Sekar
authors.forename: Ziauddin Ahamed
authors.initials: ZA
authors.lastname: Rafi
authors.forename: Kumar
authors.initials: K
authors.lastname: Rajakumar
mesh_terms: D019295:Computational Biology / Q000379:methods*; D048168:Escherichia coli K12 / Q000235:genetics; D016680:Genome, Bacterial / Q000235:genetics*; D023281:Genomics; D021141:Nucleic Acid Amplification Techniques; D012329:RNA, Bacterial / Q000235:genetics*; D022661:RNA, Untranslated / Q000235:genetics*; D019779:Salmonella enterica / Q000235:genetics; D017422:Sequence Analysis, DNA; D014158:Transcription, Genetic
chemical_list: D012329:RNA, Bacterial; D022661:RNA, Untranslated