ArticleNature communications2020
Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
91 citing papers in PubMed, 180 citations in OpenAlex.
- Review
- A deep learning model captures position-specific effects of plant regulatory sequences and suggests genes under complex regulation.Plant physiology · 2026Article
- ExoShorkie: predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning.Bioinformatics (Oxford, England) · 2026Article
- Finding the perfect promoter for Cas9 in homing gene drives using single cell transcriptome data.Nature communications · 2026Article
- Sustainable ectoine production: microbial fermentation, recombinant biosynthesis, downstream processing, and AI-driven process optimization.Bioprocess and biosystems engineering · 2026Review
- Predicting and interpreting protein and phosphoprotein abundance from pan-cancer and single-cell transcriptomes.iScience · 2026Article
- EPInformer: scalable and integrative prediction of gene expression from promoter-enhancer sequences with multimodal epigenomic profiles.Nature communications · 2026Article
- Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications.Chemical reviews · 2026Review
- Article
- Biophysical constraints on mRNA decay rates shape macroevolutionary divergence in steady-state abundances.bioRxiv : the preprint server for biology · 2025Article
- Deep generative optimization of mRNA codon sequences for enhanced mRNA translation and therapeutic efficacy.Nature communications · 2025Article
- Predicting gene expression from DNA sequence using deep learning models.Nature reviews. Genetics · 2025Review
- In vivo pathway optimization in yeast via loxpsym-mediated shuffling of upstream activating sequences.Nucleic acids research · 2025Article
- De novo promoter design method based on deep generative and dynamic evolution algorithm.Nucleic acids research · 2025Article
- In silico prediction of variant effects: promises and limitations for precision plant breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Learning the sequence code of protein expression in human immune cells.Science advances · 2025Article
- Chemical imaging for biological systems: techniques, AI-driven processing, and applications.Journal of materials chemistry. B · 2025Review
- Cutting-edge deep-learning based tools for metagenomic research.National science review · 2025Review
- Precise engineering of gene expression by editing plasticity.Genome biology · 2025Article
- Computational scoring and experimental evaluation of enzymes generated by neural networks.Nature biotechnology · 2025Article
31 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.