Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Jan Zrimec, Christoph S. Börlin, Filip Buric, Azam Sheikh Muhammad, Rhongzen Chen, Verena Siewers, Vilhelm Verendel, Jens Nielsen, Mats Töpel and Aleksej Zelezniak ()
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Jan Zrimec: Chalmers University of Technology
Christoph S. Börlin: Chalmers University of Technology
Filip Buric: Chalmers University of Technology
Azam Sheikh Muhammad: Chalmers University of Technology
Rhongzen Chen: Chalmers University of Technology
Verena Siewers: Chalmers University of Technology
Vilhelm Verendel: Chalmers University of Technology
Jens Nielsen: Chalmers University of Technology
Mats Töpel: University of Gothenburg
Aleksej Zelezniak: Chalmers University of Technology

Nature Communications, 2020, vol. 11, issue 1, 1-16

Abstract: Abstract Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression levels remains unclear. Here we apply deep learning on over 20,000 mRNA datasets to examine the genetic regulatory code controlling mRNA abundance in 7 model organisms ranging from bacteria to Human. In all organisms, we can predict mRNA abundance directly from DNA sequence, with up to 82% of the variation of transcript levels encoded in the gene regulatory structure. By searching for DNA regulatory motifs across the gene regulatory structure, we discover that motif interactions could explain the whole dynamic range of mRNA levels. Co-evolution across coding and non-coding regions suggests that it is not single motifs or regions, but the entire gene regulatory structure and specific combination of regulatory elements that define gene expression levels.

Date: 2020
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DOI: 10.1038/s41467-020-19921-4

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