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Sparse RNA Folding Revisited: Space-Efficient Minimum Free Energy Prediction

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Algorithms in Bioinformatics (WABI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9289))

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Abstract

RNA secondary structure prediction by energy minimization is the central computational tool for the analysis of structural non-coding RNAs and their interactions. Sparsification has been successfully applied to improve the time efficiency of various structure prediction algorithms while guaranteeing the same result; however, for many such folding problems, space efficiency is of even greater concern, in particular for long RNAs and complex folding algorithms. So far, space-efficient sparsified RNA folding with fold reconstruction was solved only for simple pseudo-energy models. Here, we revisit the problem of space-efficient free energy minimization. Whereas the space-efficient minimization of the free energy has been sketched before, the reconstruction of the optimum structure has not even been discussed. We show that this reconstruction is not possible in trivial extension of the method for simple energy models. Then, we present the time- and space-efficient sparsified free energy minimization algorithm SparseMFEFold, which guarantees optimal structure prediction. In particular, this novel algorithm provides efficient fold reconstruction based on dynamically garbage collected trace arrows. We provide theoretical and empirical results on the efficiency of the method. SparseMFEFold is free software, available at http://www.bioinf.uni-leipzig.de/~will/Software/SparseMFEFold.

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Notes

  1. 1.

    i.e., we replace the recursion \(L^p(i,j)\) by Eq. \((\hat{L}^p)\) and replace the symbol \(L^p(i,j)\) by \({\hat{L}}^p(i,j)\) in the recursion L.

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Author information

Authors and Affiliations

  1. Bioinformatics/IZBI, University Leipzig, Leipzig, Germany

    Sebastian Will

  2. Ingenuity Lab, National Institute of Nanotechnology, and Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Canada

    Hosna Jabbari

Authors
  1. Sebastian Will
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  2. Hosna Jabbari
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Correspondence to Hosna Jabbari .

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Editors and Affiliations

  1. University of Maryland, College Park, Maryland, USA

    Mihai Pop

  2. University of Lille, Lille, France

    Hélène Touzet

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Will, S., Jabbari, H. (2015). Sparse RNA Folding Revisited: Space-Efficient Minimum Free Energy Prediction. In: Pop, M., Touzet, H. (eds) Algorithms in Bioinformatics. WABI 2015. Lecture Notes in Computer Science(), vol 9289. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48221-6_19

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  • DOI: https://doi.org/10.1007/978-3-662-48221-6_19

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  • Online ISBN: 978-3-662-48221-6

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