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.

BIOINFORMATICS

A wider field of view to predict expression

Nature Methods volume 18, pages 1155–1156 (2021)Cite this article

Subjects

A gene sequence-to-expression machine learning model achieves improved accuracy by incorporating information about potential long-range interactions.

Deep learning models have shown great promise in their ability to predict, solely on the basis of DNA sequence, the complex patterns of gene expression, chromatin accessibility, and histone modifications that arise in diverse cell types. However, current state-of-the-art predictive methods cannot model interactions with distal regulatory elements that are a few tens of kilobases away from the locus of interest. In this issue, Avsec et al.1 address this problem by using techniques developed in the field of natural language processing. They have trained a deep learning model, called Enformer, to predict thousands of epigenetic and transcriptional profiles from hundreds of human and mouse cell types using only the DNA sequence as input, incorporating information from up to 100 kb on either side of the target locus.

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

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

Buy

All prices are NET prices.

Fig. 1: Prediction of gene expression by Enformer.

References

  1. Avsec, Z. et al. Nat. Methods https://doi.org/10.1038/s41592-021-01252-x (2021).

    Article  Google Scholar 

  2. Kelley, D. R. et al. Genome Res. 28, 739–750 (2018).

    Article  CAS  Google Scholar 

  3. Ouyang, Z., Zhou, Q. & Wong, W. H. Proc. Natl Acad. Sci. USA 106, 21521–21526 (2009).

    Article  CAS  Google Scholar 

  4. Karlić, R. et al. Proc. Natl Acad. Sci. USA 107, 2926–2931 (2010).

    Article  Google Scholar 

  5. Kelley, D. R., Snoek, J. & Rinn, J. L. Genome Res. 26, 990–999 (2016).

    Article  CAS  Google Scholar 

  6. Zhou, J. et al. Nat. Genet. 50, 1171–1179 (2018).

    Article  CAS  Google Scholar 

  7. Waswani, A. et al. in Advances in Neural Information Processing Systems 30 (NIPS2017) 6000–6010 (2017).

  8. Gasperini, M., Tome, J. M. & Shendure, J. Nat. Rev. Genet. 21, 292–310 (2020).

    Article  CAS  Google Scholar 

  9. Fudenberg, G., Kelley, D. R. & Pollard, K. S. Nat. Methods 17, 1111–1117 (2020).

    Article  Google Scholar 

  10. Schreiber, J.M., Lu, Y.Y. & Noble, W.S. in ICML Workshop on Computational Biology (2020).

Download references

Acknowledgements

This work was supported by NIH award U01 HG009395.

Author information

Authors and Affiliations

  1. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Yang Young Lu & William Stafford Noble

  2. Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA

    William Stafford Noble

Authors
  1. Yang Young Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. William Stafford Noble
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to William Stafford Noble.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lu, Y.Y., Noble, W.S. A wider field of view to predict expression. Nat Methods 18, 1155–1156 (2021). https://doi.org/10.1038/s41592-021-01259-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-021-01259-4

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