IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v10y2019i1d10.1038_s41467-019-13395-9.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-019-13395-9
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-019-13395-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    4. 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.
    5. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-13395-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.