title: RNAG: a new Gibbs sampler for predicting RNA secondary structure for unaligned sequences.
abstract: BACKGROUND
  RNA secondary structure plays an important role in the function of many RNAs, and structural features are often key to their interaction with other cellular components. Thus, there has been considerable interest in the prediction of secondary structures for RNA families. In this article, we present a new global structural alignment algorithm, RNAG, to predict consensus secondary structures for unaligned sequences. It uses a blocked Gibbs sampling algorithm, which has a theoretical advantage in convergence time. This algorithm iteratively samples from the conditional probability distributions P(Structure | Alignment) and P(Alignment | Structure). Not surprisingly, there is considerable uncertainly in the high-dimensional space of this difficult problem, which has so far received limited attention in this field. We show how the samples drawn from this algorithm can be used to more fully characterize the posterior space and to assess the uncertainty of predictions.
  
  
  RESULTS
  Our analysis of three publically available datasets showed a substantial improvement in RNA structure prediction by RNAG over extant prediction methods. Additionally, our analysis of 17 RNA families showed that the RNAG sampled structures were generally compact around their ensemble centroids, and at least 11 families had at least two well-separated clusters of predicted structures. In general, the distance between a reference structure and our predicted structure was large relative to the variation among structures within an ensemble.
  
  
  BACKGROUND
  The Perl implementation of the RNAG algorithm and the data necessary to reproduce the results described in Sections 3.1 and 3.2 are available at http://ccmbweb.ccv.brown.edu/rnag.html
  
  
  BACKGROUND
  charles_lawrence@brown.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Mathematics, Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA.
authors.forename: Donglai
authors.initials: D
authors.lastname: Wei
authors.forename: Lauren V
authors.initials: LV
authors.lastname: Alpert
authors.forename: Charles E
authors.initials: CE
authors.lastname: Lawrence
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D016000:Cluster Analysis; D008969:Molecular Sequence Data; D009690:Nucleic Acid Conformation; D012313:RNA / Q000737:chemistry*; D016415:Sequence Alignment; D017423:Sequence Analysis, RNA / Q000379:methods*
chemical_list: D012313:RNA