A Local Structural Prediction Algorithm for RNA Triple Helix Structure

  • Bay-Yuan Hsu
  • Thomas K. F. Wong
  • Wing-Kai Hon
  • Xinyi Liu
  • Tak-Wah Lam
  • Siu-Ming Yiu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7986)


Secondary structure prediction (with or without pseudoknots) of an RNA molecule is a well-known problem in computational biology. Most of the existing algorithms have an assumption that each nucleotide can interact with at most one other nucleotide. This assumption is not valid for triple helix structure (a pseudoknotted structure with tertiary interactions). As these structures are found to be important in many biological processes, it is desirable to develop a prediction tool for these structures. We provide the first structural prediction algorithm to handle triple helix structures. Our algorithm runs in O(n 3) time where n is the length of input RNA sequence. The accuracy of the prediction is reasonably high, with average sensitivity and specificity over 80% for base pairs, and over 70% for tertiary interactions.


Single Base Active Node Adjunct Tree Triple Helix Nonterminal Symbol 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bay-Yuan Hsu
    • 1
  • Thomas K. F. Wong
    • 2
  • Wing-Kai Hon
    • 1
  • Xinyi Liu
    • 3
  • Tak-Wah Lam
    • 3
  • Siu-Ming Yiu
    • 3
  1. 1.Department of Computer ScienceNational Tsing Hua UniversityTaiwan
  2. 2.Ecosystem Sciences, CSIROAustralian Capital TerritoryCanberraAustralia
  3. 3.Department of Computer ScienceThe University of Hong KongHong Kong

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