@article{Gelhausen-IntaRNAhelix-2019,
  author = 	 {Rick Gelhausen and Sebastian Will and Ivo L. Hofacker and Rolf Backofen and Martin Raden},
  title = 	 {{IntaRNAhelix} - Composing {RNA-RNA} interactions from stable inter-molecular helices boosts bacterial {sRNA} target prediction},
  journal={Journal of Bioinformatics and Computational Biology},
  year = 	 {2019},
  volume = 	 {17},
  number = {5},
  pages = {1940009},
  issn = {0219-7200},
  issn = {1757-6334},
  doi = {10.1142/S0219720019400092},
  user = 	 {mmann},
  abstract= 	 {Efficient computational tools for the identification of putative target RNAs regulated by prokaryotic sRNAs rely on thermodynamic models of RNA secondary structures.
While they typically predict RNA-RNA interaction complexes accurately, they yield many highly-ranked false positives in target screens.
One obvious source of this low specificity appears to be the disability of current secondary-structure-based models to reflect steric constraints, which nevertheless govern the kinetic formation of RNA-RNA interactions. 
For example, often- even thermodynamically favorable -extensions of short initial kissing hairpin interactions are kinetically prohibited, since this would require unwinding of intra-molecular helices as well as sterically impossible bending of the interaction helix.
Another source is the consideration of instable and thus unlikely subinteractions that enable better scoring of longer interactions.
In consequence, the efficient prediction methods that do not consider such effects show a high false positive rate.
To increase the prediction accuracy we devise IntaRNAhelix, a dynamic programming algorithm that length-restricts the runs of consecutive inter-molecular base pairs (perfect canonical stackings), which we hypothesize to implicitely model the steric and kinetic effects. The novel method is implemented by extending the state-of-the-art tool IntaRNA.
Our comprehensive bacterial sRNA target prediction benchmark demonstrates significant improvements of the prediction accuracy and enables more than 40-times faster computations. These results indicate- supporting our hypothesis -that stable helix composition increases the accuracy of interaction prediction models compared to the current state-of-the-art approach.}
}