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RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning

Author

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  • Jaswinder Singh

    (Griffith University)

  • Jack Hanson

    (Griffith University)

  • Kuldip Paliwal

    (Griffith University)

  • Yaoqi Zhou

    (Griffith University)

Abstract

The majority of our human genome transcribes into noncoding RNAs with unknown structures and functions. Obtaining functional clues for noncoding RNAs requires accurate base-pairing or secondary-structure prediction. However, the performance of such predictions by current folding-based algorithms has been stagnated for more than a decade. Here, we propose the use of deep contextual learning for base-pair prediction including those noncanonical and non-nested (pseudoknot) base pairs stabilized by tertiary interactions. Since only $$ $$>10,000 nonredundant RNAs made available through comparative analysis. The resulting method achieves large, statistically significant improvement in predicting all base pairs, noncanonical and non-nested base pairs in particular. The proposed method (SPOT-RNA), with a freely available server and standalone software, should be useful for improving RNA structure modeling, sequence alignment, and functional annotations.

Suggested Citation

  • Jaswinder Singh & Jack Hanson & Kuldip Paliwal & Yaoqi Zhou, 2019. "RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-13395-9
    DOI: 10.1038/s41467-019-13395-9
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    Cited by:

    1. Wenkai Wang & Chenjie Feng & Renmin Han & Ziyi Wang & Lisha Ye & Zongyang Du & Hong Wei & Fa Zhang & Zhenling Peng & Jianyi Yang, 2023. "trRosettaRNA: automated prediction of RNA 3D structure with transformer network," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    2. Menghan Liu & Erik Poppleton & Giulia Pedrielli & Petr Šulc & Dimitri P. Bertsekas, 2022. "ExpertRNA: A New Framework for RNA Secondary Structure Prediction," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2464-2484, September.
    3. Jiaxing Yang, 2024. "Predicting Distance matrix with large language models," Papers 2409.16333, arXiv.org.
    4. Yang Li & Chengxin Zhang & Chenjie Feng & Robin Pearce & P. Lydia Freddolino & Yang Zhang, 2023. "Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    5. Peicong Lin & Yumeng Yan & Huanyu Tao & Sheng-You Huang, 2023. "Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    6. Mark W. Lewis & Amit Verma & Todd T. Eckdahl, 2021. "Qfold: a new modeling paradigm for the RNA folding problem," Journal of Heuristics, Springer, vol. 27(4), pages 695-717, August.

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