@Article{Seemann:Gorodkin:Backofen:Unify_evolu_and:NAR2008,
  author =	 {Seemann, Stefan E. and Gorodkin, Jan and Backofen, Rolf},
  title =	 {Unifying evolutionary and thermodynamic information for 
                  {RNA} folding of multiple alignments},
  journal =	 NAR,
  issn =         {1362-4962},
  issn =         {0305-1048},
  year =	 {2008},
  volume =	 {36},
  number =	 {20},
  pages =	 {6355-62},
  user =	 {backofen},
  pmid =	 {18836192},
  doi = 	 {10.1093/nar/gkn544},
  abstract =	 {Computational methods for determining the secondary 
                  structure of RNA sequences from given alignments are 
                  currently either based on thermodynamic folding, 
                  compensatory base pair substitutions or both. However, there 
                  is currently no approach that combines both sources of 
                  information in a single optimization problem. Here, we 
                  present a model that formally integrates both the 
                  energy-based and evolution-based approaches to predict the 
                  folding of multiple aligned RNA sequences. We have 
                  implemented an extended version of Pfold that identifies 
                  base pairs that have high probabilities of being conserved 
                  and of being energetically favorable. The consensus 
                  structure is predicted using a maximum expected accuracy 
                  scoring scheme to smoothen the effect of incorrectly 
                  predicted base pairs. Parameter tuning revealed that the 
                  probability of base pairing has a higher impact on the RNA 
                  structure prediction than the corresponding probability of 
                  being single stranded. Furthermore, we found that 
                  structurally conserved RNA motifs are mostly supported by 
                  folding energies. Other problems (e.g. RNA-folding kinetics) 
                  may also benefit from employing the principles of the model 
                  we introduce. Our implementation, PETfold, was tested on a 
                  set of 46 well-curated Rfam families and its performance 
                  compared favorably to that of Pfold and RNAalifold.}
}