title: sTarPicker: a method for efficient prediction of bacterial sRNA targets based on a two-step model for hybridization.
abstract: BACKGROUND
  Bacterial sRNAs are a class of small regulatory RNAs involved in regulation of expression of a variety of genes. Most sRNAs act in trans via base-pairing with target mRNAs, leading to repression or activation of translation or mRNA degradation. To date, more than 1,000 sRNAs have been identified. However, direct targets have been identified for only approximately 50 of these sRNAs. Computational predictions can provide candidates for target validation, thereby increasing the speed of sRNA target identification. Although several methods have been developed, target prediction for bacterial sRNAs remains challenging.
  
  
  RESULTS
  Here, we propose a novel method for sRNA target prediction, termed sTarPicker, which was based on a two-step model for hybridization between an sRNA and an mRNA target. This method first selects stable duplexes after screening all possible duplexes between the sRNA and the potential mRNA target. Next, hybridization between the sRNA and the target is extended to span the entire binding site. Finally, quantitative predictions are produced with an ensemble classifier generated using machine-learning methods. In calculations to determine the hybridization energies of seed regions and binding regions, both thermodynamic stability and site accessibility of the sRNAs and targets were considered. Comparisons with the existing methods showed that sTarPicker performed best in both performance of target prediction and accuracy of the predicted binding sites.
  
  
  CONCLUSIONS
  sTarPicker can predict bacterial sRNA targets with higher efficiency and determine the exact locations of the interactions with a higher accuracy than competing programs. sTarPicker is available at http://ccb.bmi.ac.cn/starpicker/.
authors.affiliation: Center of Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing, China.
authors.forename: Xiaomin
authors.initials: X
authors.lastname: Ying
authors.forename: Yuan
authors.initials: Y
authors.lastname: Cao
authors.forename: Jiayao
authors.initials: J
authors.lastname: Wu
authors.forename: Qian
authors.initials: Q
authors.lastname: Liu
authors.forename: Lei
authors.initials: L
authors.lastname: Cha
authors.forename: Wuju
authors.initials: W
authors.lastname: Li
mesh_terms: D001185:Artificial Intelligence; D001426:Bacterial Proteins / Q000235:genetics; D020029:Base Pairing; D023281:Genomics; D008957:Models, Genetic; D009693:Nucleic Acid Hybridization; D048929:Prochlorococcus / Q000235:genetics; D020871:RNA Stability; D012329:RNA, Bacterial / Q000737:chemistry / Q000235:genetics* / Q000378:metabolism*; D012333:RNA, Messenger / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D038621:Regulatory Sequences, Ribonucleic Acid / Q000235:genetics*
chemical_list: D001426:Bacterial Proteins; D012329:RNA, Bacterial; D012333:RNA, Messenger; D038621:Regulatory Sequences, Ribonucleic Acid