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Linear-Time Algorithms for RNA Structure Prediction

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RNA Structure Prediction

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2586))

Abstract

RNA secondary structure prediction is widely used to understand RNA function. Existing dynamic programming-based algorithms, both the classical minimum free energy (MFE) methods and partition function methods, suffer from a major limitation: their runtimes scale cubically with the RNA length, and this slowness limits their use in genome-wide applications. Inspired by incremental parsing for context-free grammars in computational linguistics, we designed linear-time heuristic algorithms, LinearFold and LinearPartition, to approximate the MFE structure, partition function and base pairing probabilities. These programs are orders of magnitude faster than Vienna RNAfold and CONTRAfold on long sequences. More interestingly, LinearFold and LinearPartition lead to more accurate predictions on the longest sequence families for which the structures are well established (16S and 23S Ribosomal RNAs), as well as improved accuracies for long-range base pairs (500 +  nucleotides apart). This chapter provides protocols for using LinearFold and LinearPartition for secondary structure prediction.

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Acknowledgements

This work is supported in part by National Institutes of Health (R35GM145283 to D.H.M.)

Author information

Authors and Affiliations

  1. Baidu Research USA, Sunnyvale, CA, USA

    He Zhang & Liang Zhang

  2. School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR, USA

    He Zhang, Liang Zhang & Liang Huang

  3. Baidu Research USA, Sunnyvale, CA, USA

    Kaibo Liu

  4. School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR, USA

    Sizhen Li

  5. Dept. of Biochemistry & Biophysics, Center for RNA Biology, Rochester, NY, USA

    David H. Mathews

  6. Dept. of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, NY, USA

    David H. Mathews

Authors
  1. He Zhang
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  2. Liang Zhang
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  3. Kaibo Liu
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  4. Sizhen Li
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  5. David H. Mathews
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  6. Liang Huang
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Editor information

Editors and Affiliations

  1. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    Risa Karakida Kawaguchi

  2. Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan

    Junichi Iwakiri

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Zhang, H., Zhang, L., Liu, K., Li, S., Mathews, D.H., Huang, L. (2023). Linear-Time Algorithms for RNA Structure Prediction. In: Kawaguchi, R.K., Iwakiri, J. (eds) RNA Structure Prediction. Methods in Molecular Biology, vol 2586. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2768-6_2

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  • DOI: https://doi.org/10.1007/978-1-0716-2768-6_2

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2767-9

  • Online ISBN: 978-1-0716-2768-6

  • eBook Packages: Springer Protocols

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