• DocumentCode
    3321332
  • Title

    A fast algorithm for RNA secondary structure prediction including pseudoknots

  • Author

    Tahi, Fariza ; Engelen, Stefan ; Regnier, Mireille

  • fYear
    2003
  • fDate
    10-12 March 2003
  • Firstpage
    11
  • Lastpage
    17
  • Abstract
    Many important RNA molecules contain pseudoknots, which are generally excluded by the definition of the secondary structure, mainly for computational reasons. Still, most existing algorithms for secondary structure prediction are not satisfactory in results and complexities, even when pseudoknots are not allowed. We present an algorithm, called P-DCFold, for the prediction of RNA secondary structures including all kinds of pseudoknots. It is based on the comparative approach. The helices are searched recursively, from more "likely" to less "likely", using the "Divide and Conquer" approach. This approach, which allows to limit the amount of searching, is possible when only non-interleaved helices are searched for. The pseudoknots are therefore searched in several steps, each helix of the pseudoknot being selected in a different step. P-DCFold has been applied to tmRNA and RnaseP sequences. In less than two seconds, their respective secondary structures, including their pseudoknots, have been recovered very efficiently.
  • Keywords
    macromolecules; molecular biophysics; molecular configurations; 2 s; RNA secondary structure prediction; RnaseP; computational methods; computational reasons; fast algorithm; noninterleaved helices; pseudoknots; recursive searches; secondary structures; spatial structures; tmRNA; Bioinformatics; Biological control systems; Context modeling; Databases; Evolution (biology); Predictive models; RNA; Robustness; Stochastic processes; Switches;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Bioinformatics and Bioengineering, 2003. Proceedings. Third IEEE Symposium on
  • Print_ISBN
    0-7695-1907-5
  • Type

    conf

  • DOI
    10.1109/BIBE.2003.1188924
  • Filename
    1188924