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Nucleic Acid Structure Prediction Including Pseudoknots Through Direct Enumeration of States: A User’s Guide to the LandscapeFold Algorithm

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RNA Structure Prediction

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

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Abstract

Here we detail the LandscapeFold secondary structure prediction algorithm and how it is used. The algorithm was previously described and tested in (Kimchi O et al., Biophys J 117(3):520–532, 2019), though it was not named there. The algorithm directly enumerates all possible secondary structures into which up to two RNA or single-stranded DNA sequences can fold. It uses a polymer physics model to estimate the configurational entropy of structures including complex pseudoknots. We detail each of these steps and ways in which the user can adjust the algorithm as desired. The code is available on the GitHub repository https://github.com/ofer-kimchi/LandscapeFold.

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Authors and Affiliations

  1. School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Ofer Kimchi

  2. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

    Ofer Kimchi

  3. School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Michael P. Brenner

  4. Department of Chemistry, University of Cambridge, Cambridge, UK

    Lucy J. Colwell

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  1. Ofer Kimchi
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  2. Michael P. Brenner
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  3. Lucy J. Colwell
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Correspondence to Ofer Kimchi .

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  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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Kimchi, O., Brenner, M.P., Colwell, L.J. (2023). Nucleic Acid Structure Prediction Including Pseudoknots Through Direct Enumeration of States: A User’s Guide to the LandscapeFold Algorithm. 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_4

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  • DOI: https://doi.org/10.1007/978-1-0716-2768-6_4

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