title: Robust prediction of consensus secondary structures using averaged base pairing probability matrices.
abstract: BACKGROUND
  Recent transcriptomic studies have revealed the existence of a considerable number of non-protein-coding RNA transcripts in higher eukaryotic cells. To investigate the functional roles of these transcripts, it is of great interest to find conserved secondary structures from multiple alignments on a genomic scale. Since multiple alignments are often created using alignment programs that neglect the special conservation patterns of RNA secondary structures for computational efficiency, alignment failures can cause potential risks of overlooking conserved stem structures.
  
  
  RESULTS
  We investigated the dependence of the accuracy of secondary structure prediction on the quality of alignments. We compared three algorithms that maximize the expected accuracy of secondary structures as well as other frequently used algorithms. We found that one of our algorithms, called McCaskill-MEA, was more robust against alignment failures than others. The McCaskill-MEA method first computes the base pairing probability matrices for all the sequences in the alignment and then obtains the base pairing probability matrix of the alignment by averaging over these matrices. The consensus secondary structure is predicted from this matrix such that the expected accuracy of the prediction is maximized. We show that the McCaskill-MEA method performs better than other methods, particularly when the alignment quality is low and when the alignment consists of many sequences. Our model has a parameter that controls the sensitivity and specificity of predictions. We discussed the uses of that parameter for multi-step screening procedures to search for conserved secondary structures and for assigning confidence values to the predicted base pairs.
  
  
  BACKGROUND
  The C++ source code that implements the McCaskill-MEA algorithm and the test dataset used in this paper are available at http://www.ncrna.org/papers/McCaskillMEA/.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, 2-42 Aomi, Koto-ku, Tokyo, 135-0064, Japan. kiryu-h@aist.go.jp
authors.forename: Hisanori
authors.initials: H
authors.lastname: Kiryu
authors.forename: Taishin
authors.initials: T
authors.lastname: Kin
authors.forename: Kiyoshi
authors.initials: K
authors.lastname: Asai
mesh_terms: D000465:Algorithms; D020137:Base Pair Mismatch / Q000235:genetics*; D020029:Base Pairing / Q000235:genetics*; D003198:Computer Simulation; D016384:Consensus Sequence / Q000235:genetics*; D008957:Models, Genetic; D015233:Models, Statistical; D012313:RNA / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods*; D017423:Sequence Analysis, RNA / Q000379:methods*
chemical_list: D012313:RNA