Sparsification of RNA Structure Prediction Including Pseudoknots

  • Mathias Möhl
  • Raheleh Salari
  • Sebastian Will
  • Rolf Backofen
  • S. Cenk Sahinalp
Conference paper

DOI: 10.1007/978-3-642-15294-8_4

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6293)
Cite this paper as:
Möhl M., Salari R., Will S., Backofen R., Sahinalp S.C. (2010) Sparsification of RNA Structure Prediction Including Pseudoknots. In: Moulton V., Singh M. (eds) Algorithms in Bioinformatics. WABI 2010. Lecture Notes in Computer Science, vol 6293. Springer, Berlin, Heidelberg

Abstract

Although many RNA molecules contain pseudoknots, computational prediction of pseudoknotted RNA structure is still in its infancy due to high running time and space consumption implied by the dynamic programming formulations of the problem. In this paper, we introduce sparsification to significantly speedup the dynamic programming approaches for pseudoknotted RNA structure prediction, which also lower the space requirements. Although sparsification has been applied to a number of RNA-related structure prediction problems in the past few years, we provide the first application of sparsification to pseudoknotted RNA structure prediction specifically and to handling gapped fragments more generally - which has a much more complex recursive structure than other problems to which sparsification has been applied. We show that sparsification, when applied to the fastest, as well as the most general pseudoknotted structure prediction methods available, - respectively the Reeder-Giegerich algorithm and the Rivas-Eddy algorithm - reduces the number of ”candidate” substructures to be considered significantly. In fact, experimental results on the sparsified Reeder-Giegerich algorithm suggest a linear speedup over the unsparsified implementation.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Mathias Möhl
    • 1
  • Raheleh Salari
    • 2
  • Sebastian Will
    • 1
    • 3
  • Rolf Backofen
    • 1
  • S. Cenk Sahinalp
    • 2
  1. 1.Bioinformatics, Institute of Computer ScienceAlbert-Ludwigs-UniversitätFreiburgGermany
  2. 2.Lab for Computational Biology, School of Computing ScienceSimon Fraser UniversityBurnabyCanada
  3. 3.Computation and Biology LabCSAIL, MITCambridgeUSA

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