Non Animal Testing Database

Deep learning for regulatory DNA design

Chalmers University of Technology, Gothenburg, Sweden
The design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Using mutagenesis typically requires screening sizable random DNA libraries, which limits the designs to span merely a short section of the promoter and restricts their control of gene expression. Here, the researchers prototype a deep learning strategy based on generative adversarial networks (GAN) by learning directly from genomic and transcriptomic data. The ExpressionGAN tool can traverse the entire regulatory sequence-expression landscape in a gene-specific manner, generating regulatory DNA with prespecified target mRNA levels spanning the whole gene regulatory structure including coding and adjacent non-coding regions.
Controlling gene expression with deep generative design of regulatory DNA
Jan Zrimec, Aleksej Zelezniak
Added on: 12-16-2022
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