Nature Communications (Aug 2022)

Controlling gene expression with deep generative design of regulatory DNA

  • Jan Zrimec,
  • Xiaozhi Fu,
  • Azam Sheikh Muhammad,
  • Christos Skrekas,
  • Vykintas Jauniskis,
  • Nora K. Speicher,
  • Christoph S. Börlin,
  • Vilhelm Verendel,
  • Morteza Haghir Chehreghani,
  • Devdatt Dubhashi,
  • Verena Siewers,
  • Florian David,
  • Jens Nielsen,
  • Aleksej Zelezniak

DOI
https://doi.org/10.1038/s41467-022-32818-8
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 17

Abstract

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Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Here the authors present EspressionGAN, a generative adversarial network that uses genomic and transcriptomic data to generate regulatory sequences.