Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Computational genomics

Crowdsourcing to predict RNA degradation and secondary structure

Nature Machine Intelligence volume 5, pages 101–103 (2023)Cite this article

Subjects

Predicting RNA degradation is a fundamental task in designing RNA-based therapeutic agents. Dual crowdsourcing efforts for dataset creation and machine learning were organized to learn biological rules and strategies for predicting RNA stability.

Prediction of RNA stability has many potential applications, including the design of RNA vaccines. However, finding predictive models for RNA degradation on a timescale that may facilitate clinical intervention is technically challenging. An innovative approach to this challenge is crowdsourcing, which can lead to flexible, diverse solutions to a task and reduce costs1. Previously, the promise of crowdsourcing has been demonstrated most prominently with the RNA folding game Eterna and associated projects2,3,4. Writing in Nature Machine Intelligence, Wayment-Steele et al.5 combine two separate crowdsourcing projects addressing different parts of the scientific challenge of predicting the chemical stability of RNA based on sequence and structure information.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: A dual crowdsourcing approach to tackle the challenge of predicting breakdown of RNA.

References

  1. Alonso, O. Info. Retrieval 16, 101–120 (2013).

    Article  Google Scholar 

  2. Andreasson, J. O. et al. Proc. Natl Acad. Sci. USA 119, e2112979119 (2022).

    Article  Google Scholar 

  3. Koodli, R. V. et al. PLoS Comput. Biol. 15, e1007059 (2019).

    Article  Google Scholar 

  4. Lee, J. et al. Proc. Natl Acad. Sci. USA 111, 2122–2127 (2014).

    Article  Google Scholar 

  5. Wayment-Steele, H. K. et al. Nat. Mach. Intell. 4, 1174–1184 (2022).

    Article  Google Scholar 

  6. Wayment-Steele, H. K. et al. Nucleic Acids Res. 49, 10604–10617 (2021).

    Article  Google Scholar 

  7. Danaee, P. et al. Nucleic Acids Res. 46, 5381–5394 (2018).

    Article  Google Scholar 

  8. Crichton, G., Pyysalo, S., Chiu, B. & Korhonen, A. BMC Bioinformatics 18, 368 (2017).

    Article  Google Scholar 

  9. Ding, D. Y. et al. in Biocomputing 2019: Proc. Pacific Symposium 18–29; https://doi.org/10.1142/9789813279827_0003 (2019).

  10. Capel, H., Feenstra, K. A. & Abeln, S. Sci. Rep. 12, 10487 (2022).

    Article  Google Scholar 

  11. Vaswani, A. et al. In Guyon, I. et al. (eds), Advances in Neural Information Processing Systems vol. 30, pp. 6000–6010 (NeurIPS, 2017).

  12. Du, B., Liu, Z. & Luo, F. Inf. Sci. 582, 287–301 (2022).

    Article  Google Scholar 

  13. Xu, Q. et al. Preprint at https://doi.org/10.48550/arXiv.2005.09267 (2020).

  14. Andriyanov, N. & Andriyanov, D. J. Phys. Conf. Ser. 1661, 012018 (2020).

    Article  Google Scholar 

  15. Agarwal, V. & Kelley, D. R. Genome Biol. 23, 245 (2022).

    Article  Google Scholar 

  16. Chen, C.-Y. et al. Proc. Natl Acad. Sci. USA 113, 206–211 (2016).

    Article  Google Scholar 

Download references

Acknowledgements

The author thanks L. Padgitt-Cobb and J. Valencia for reviewing this manuscript and providing valuable edits.

Author information

Authors and Affiliations

  1. Department of Biochemistry and Biophysics, School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA

    David A. Hendrix

Authors
  1. David A. Hendrix
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David A. Hendrix.

Ethics declarations

Competing interest

The author declares no competing interests.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hendrix, D.A. Crowdsourcing to predict RNA degradation and secondary structure. Nat Mach Intell 5, 101–103 (2023). https://doi.org/10.1038/s42256-023-00615-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42256-023-00615-7

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing