@Article{Will_Joshi_Hofacker-LocAR_Accur_bound-2012,
  author =	 {Will, Sebastian and Joshi, Tejal and Hofacker, Ivo L. and 
                  Stadler, Peter F. and Backofen, Rolf},
  title =	 {{LocARNA}-{P}: {Accurate} boundary prediction and improved 
                  detection of structural {RNAs}},
  journal =	 {RNA},
  year =	 {2012},
  volume =	 {18},
  number =	 {5},
  pages =	 {900-14},
  user =	 {will},
  pmid =	 {22450757},
  doi = 	 {10.1261/rna.029041.111},
  issn = 	 {1469-9001},
  issn = 	 {1355-8382},
  abstract =	 {Current genomic screens for noncoding RNAs (ncRNAs) predict 
                  a large number of genomic regions containing potential 
                  structural ncRNAs. The analysis of these data requires 
                  highly accurate prediction of ncRNA boundaries and 
                  discrimination of promising candidate ncRNAs from weak 
                  predictions. Existing methods struggle with these goals 
                  because they rely on sequence-based multiple sequence 
                  alignments, which regularly misalign RNA structure and 
                  therefore do not support identification of structural 
                  similarities. To overcome this limitation, we compute 
                  columnwise and global reliabilities of alignments based on 
                  sequence and structure similarity; we refer to these 
                  structure-based alignment reliabilities as STARs. The 
                  columnwise STARs of alignments, or STAR profiles, provide a 
                  versatile tool for the manual and automatic analysis of 
                  ncRNAs. In particular, we improve the boundary prediction of 
                  the widely used ncRNA gene finder RNAz by a factor of 3 from 
                  a median deviation of 47 to 13 nt. Post-processing RNAz 
                  predictions, LocARNA-P's STAR score allows much stronger 
                  discrimination between true- and false-positive predictions 
                  than RNAz's own evaluation. The improved accuracy, in this 
                  scenario increased from AUC 0.71 to AUC 0.87, significantly 
                  reduces the cost of successive analysis steps. The 
                  ready-to-use software tool LocARNA-P produces 
                  structure-based multiple RNA alignments with associated 
                  columnwise STARs and predicts ncRNA boundaries. We provide 
                  additional results, a web server for LocARNA/LocARNA-P, and 
                  the software package, including documentation and a pipeline 
                  for refining screens for structural ncRNA, at 
                  http://www.bioinf.uni-freiburg.de/Supplements/LocARNA-P/.}
}