title: An assessment of bacterial small RNA target prediction programs.
abstract: Most bacterial regulatory RNAs exert their function through base-pairing with target RNAs. Computational prediction of targets is a busy research field that offers biologists a variety of web sites and software. However, it is difficult for a non-expert to evaluate how reliable those programs are. Here, we provide a simple benchmark for bacterial sRNA target prediction based on trusted E. coli sRNA/target pairs. We use this benchmark to assess the most recent RNA target predictors as well as earlier programs for RNA-RNA hybrid prediction. Moreover, we consider how the definition of mRNA boundaries can impact overall predictions. Recent algorithms that exploit both conservation of targets and accessibility information offer improved accuracy over previous software. However, even with the best predictors, the number of true biological targets with low scores and non-targets with high scores remains puzzling.
authors.affiliation: a Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS ; Université Paris-Sud ; Orsay Cedex , France.
authors.forename: Adrien
authors.initials: A
authors.lastname: Pain
authors.forename: Alban
authors.initials: A
authors.lastname: Ott
authors.forename: Hamza
authors.initials: H
authors.lastname: Amine
authors.forename: Tatiana
authors.initials: T
authors.lastname: Rochat
authors.forename: Philippe
authors.initials: P
authors.lastname: Bouloc
authors.forename: Daniel
authors.initials: D
authors.lastname: Gautheret
mesh_terms: D020029:Base Pairing / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D004926:Escherichia coli / Q000235:genetics*; D012329:RNA, Bacterial / Q000235:genetics*; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D020506:Untranslated Regions / Q000235:genetics
chemical_list: D012329:RNA, Bacterial; D012333:RNA, Messenger; D020506:Untranslated Regions
keywords: bacteria; sRNA; sRNA target prediction