Skip to main content

Advertisement

SpringerLink
Log in

Diving deeper to predict noncoding sequence function

  • News & Views
  • Published:

From Nature Methods

View current issue Submit your manuscript

The deep learning–based method DeepSEA predicts the molecular function of noncoding DNA polymorphisms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1: Predicting local regulatory-element states from DNA sequence.

Kim Caesar/Nature Publishing Group

References

  1. Zhou, J. & Troyanskaya, O.G. Nat. Methods 12, 931–934 (2015).

    Article  CAS  Google Scholar 

  2. Pickrell, J.K. Am. J. Hum. Genet. 94, 559–573 (2014).

    Article  CAS  Google Scholar 

  3. Kimura, M. The Neutral Theory of Molecular Evolution (Cambridge University Press, 1983).

    Book  Google Scholar 

  4. Cooper, G.M. et al. Genome Res. 15, 901–913 (2005).

    Article  CAS  Google Scholar 

  5. Siepel, A. et al. Genome Res. 15, 1034–1050 (2005).

    Article  CAS  Google Scholar 

  6. Weedon, M.N. et al. Nat. Genet. 46, 61–64 (2014).

    Article  CAS  Google Scholar 

  7. ENCODE Project Consortium Nature 489, 57–74 (2012).

  8. Andersson, R. et al. Nature 507, 455–461 (2014).

    Article  CAS  Google Scholar 

  9. Roadmap Epigenomics Consortium et al. Nature 518, 317–330 (2015).

  10. Kircher, M. et al. Nat. Genet. 46, 310–315 (2014).

    Article  CAS  Google Scholar 

  11. Gulko, B., Hubisz, M.J., Gronau, I. & Siepel, A. Nat. Genet. 47, 276–283 (2015).

    Article  CAS  Google Scholar 

  12. Ritchie, G.R.S., Dunham, I., Zeggini, E. & Flicek, P. Nat. Methods 11, 294–296 (2014).

    Article  CAS  Google Scholar 

  13. Lee, D. et al. Nat. Genet. 47, 955–961 (2015).

    Article  CAS  Google Scholar 

  14. Mezlini, A.M., Fuligni, F., Shlien, A. & Goldenberg, A. Preprint at http://arxiv.org/abs/1508.07527 (2015).

  15. Patwardhan, R.P. et al. Nat. Biotechnol. 30, 265–270 (2012).

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Barbara E. Engelhardt is at the Department of Computer Science, Center for Statistics and Machine Learning, Princeton University, Princeton, New Jersey, USA.,

    Barbara E Engelhardt

  2. Christopher D. Brown is at the Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,

    Christopher D Brown

Authors
  1. Barbara E Engelhardt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christopher D Brown
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Barbara E Engelhardt or Christopher D Brown.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Engelhardt, B., Brown, C. Diving deeper to predict noncoding sequence function. Nat Methods 12, 925–926 (2015). https://doi.org/10.1038/nmeth.3604

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.3604

  • Springer Nature America, Inc.

Search

Navigation