Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure
Artikel i vetenskaplig tidskrift, 2020

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.

Författare

Jan Zrimec

Chalmers, Biologi och bioteknik, Systembiologi

Christoph Sebastian Börlin

Novo Nordisk Foundation Center for Biosustainability

Chalmers, Biologi och bioteknik, Systembiologi

Filip Buric

Chalmers, Biologi och bioteknik, Systembiologi

Muhammad Azam Sheikh

Chalmers, Data- och informationsteknik, CSE Verksamhetsstöd, Data Science Research Engineers

Rongzhen Chen

Chalmers, Data- och informationsteknik, CSE Verksamhetsstöd, Data Science Research Engineers

Verena Siewers

Chalmers, Biologi och bioteknik, Systembiologi

Vilhelm Verendel

Chalmers, Data- och informationsteknik, CSE Verksamhetsstöd, Data Science Research Engineers

Jens B Nielsen

Novo Nordisk Foundation Center for Biosustainability

Chalmers, Biologi och bioteknik, Systembiologi

Mats Töpel

Göteborgs universitet

Gothenburg Global Biodiversity Centre

Aleksej Zelezniak

Science for Life Laboratory (SciLifeLab)

Chalmers, Biologi och bioteknik, Systembiologi

Nature Communications

2041-1723 (ISSN)

Vol. 11 1 6141

Ämneskategorier

Evolutionsbiologi

Bioinformatik och systembiologi

Genetik

DOI

10.1038/s41467-020-19921-4

PubMed

33262328

Mer information

Senast uppdaterat

2020-12-10