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Stochastic Sampling of Structural Contexts Improves the Scalability and Accuracy of RNA 3D Module Identification

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Research in Computational Molecular Biology (RECOMB 2020)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 12074))

Abstract

RNA structures possess multiple levels of structural organization. Secondary structures are made of canonical (i.e. Watson-Crick and Wobble) helices, connected by loops whose local conformations are critical determinants of global 3D architectures. Such local 3D structures consist of conserved sets of non-canonical base pairs, called RNA modules. Their prediction from sequence data is thus a milestone toward 3D structure modelling. Unfortunately, the computational efficiency and scope of the current 3D module identification methods are too limited yet to benefit from all the knowledge accumulated in modules databases. Here, we introduce BayesPairing 2, a new sequence search algorithm leveraging secondary structure tree decomposition which allows to reduce the computational complexity and improve predictions on new sequences. We benchmarked our methods on 75 modules and 6380 RNA sequences, and report accuracies that are comparable to the state of the art, with considerable running time improvements. When identifying 200 modules on a single sequence, BayesPairing 2 is over 100 times faster than its previous version, opening new doors for genome-wide applications.

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Acknowledgements

The authors are greatly indebted to Anton Petrov for providing us with alignments between RNA PDB structures and Rfam families, which helped us match 3D modules to sequence alignments.

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Authors and Affiliations

  1. School of Computer Science, McGill University, Montreal, Canada

    Roman Sarrazin-Gendron, Hua-Ting Yao, Carlos G. Oliver & Jérôme Waldispühl

  2. LIX, CNRS UMR 7161, Ecole Polytechnique, Palaiseau, France

    Hua-Ting Yao & Yann Ponty

  3. Center for Soft and Living Matter, Institute for Basic Science, Ulsan, South Korea

    Vladimir Reinharz

  4. Département d’informatique, Université du Québec à Montréal, Montreal, Canada

    Vladimir Reinharz

Authors
  1. Roman Sarrazin-Gendron
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  2. Hua-Ting Yao
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  3. Vladimir Reinharz
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  4. Carlos G. Oliver
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  5. Yann Ponty
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  6. Jérôme Waldispühl
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Corresponding author

Correspondence to Jérôme Waldispühl .

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Editors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Russell Schwartz

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Sarrazin-Gendron, R., Yao, HT., Reinharz, V., Oliver, C.G., Ponty, Y., Waldispühl, J. (2020). Stochastic Sampling of Structural Contexts Improves the Scalability and Accuracy of RNA 3D Module Identification. In: Schwartz, R. (eds) Research in Computational Molecular Biology. RECOMB 2020. Lecture Notes in Computer Science(), vol 12074. Springer, Cham. https://doi.org/10.1007/978-3-030-45257-5_12

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  • DOI: https://doi.org/10.1007/978-3-030-45257-5_12

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