title: The super-n-motifs model: a novel alignment-free approach for representing and comparing RNA secondary structures.
abstract: BACKGROUND
  Comparing ribonucleic acid (RNA) secondary structures of arbitrary size uncovers structural patterns that can provide a better understanding of RNA functions. However, performing fast and accurate secondary structure comparisons is challenging when we take into account the RNA configuration (i.e. linear or circular), the presence of pseudoknot and G-quadruplex (G4) motifs and the increasing number of secondary structures generated by high-throughput probing techniques. To address this challenge, we propose the super-n-motifs model based on a latent analysis of enhanced motifs comprising not only basic motifs but also adjacency relations. The super-n-motifs model computes a vector representation of secondary structures as linear combinations of these motifs.
  
  
  RESULTS
  We demonstrate the accuracy of our model for comparison of secondary structures from linear and circular RNA while also considering pseudoknot and G4 motifs. We show that the super-n-motifs representation effectively captures the most important structural features of secondary structures, as compared to other representations such as ordered tree, arc-annotated and string representations. Finally, we demonstrate the time efficiency of our model, which is alignment free and capable of performing large-scale comparisons of 10 000 secondary structures with an efficiency up to 4 orders of magnitude faster than existing approaches.
  
  
  METHODS
  The super-n-motifs model was implemented in C ++. Source code and Linux binary are freely available at http://jpsglouzon.github.io/supernmotifs/ .
  
  
  BACKGROUND
  Shengrui.Wang@Usherbrooke.ca.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics o nline.
authors.affiliation: Department of Computer Science, Faculty of Science, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada.|RNA Group, Department of Biochemistry, Faculty of Medicine and Health Sciences, Applied Cancer Research Pavilion, Université de Sherbrooke, Sherbrooke, QC J1E 4K8, Canada.
authors.forename: Jean-Pierre Séhi
authors.initials: JS
authors.lastname: Glouzon
authors.affiliation: RNA Group, Department of Biochemistry, Faculty of Medicine and Health Sciences, Applied Cancer Research Pavilion, Université de Sherbrooke, Sherbrooke, QC J1E 4K8, Canada.
authors.forename: Jean-Pierre
authors.initials: JP
authors.lastname: Perreault
authors.affiliation: Department of Computer Science, Faculty of Science, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada.
authors.forename: Shengrui
authors.initials: S
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D017124:Conserved Sequence; D054856:G-Quadruplexes; D009690:Nucleic Acid Conformation; D059372:Nucleotide Motifs; D017433:Protein Structure, Secondary; D012313:RNA / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods*; D013997:Time Factors
chemical_list: D012313:RNA