ArticleNature biotechnology2020
Deciphering eukaryotic gene-regulatory logic with 100 million random promoters.
Article in Nature biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 176 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
176 citing papers in PubMed.
- DESIGN: a framework for generative AI-enabled crop molecular design-driven breeding.Science China. Life sciences · 2026Review
- Synthetic Approaches as Empirical Tools for Testing Evolutionary Theory.Genome biology and evolution · 2026Review
- The role ofmBio · 2026Article
- The shape of fitness functions and the distribution of mutational effect sizes jointly limit adaptation by regulatory mutations.Nature ecology & evolution · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Opportunities for artificial intelligence and synthetic biology in designing living drug delivery systems.Advanced drug delivery reviews · 2026Review
- ExoShorkie: predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning.Bioinformatics (Oxford, England) · 2026Article
- Cis- and trans-regulatory factors contributing to divergent activity of the TDH3 promoter in Saccharomyces yeast.Molecular biology and evolution · 2026Article
- The adaptive landscapes of three globaleLife · 2026Article
- Association of APOC2 upstream variant rs10425530 with BMI predicted to overlap with NR2C2, GCM2 and NR2C1 binding sites.Human genomics · 2026Article
- Inference of fitness landscapes with heterogeneous patterns of epistasis across sites.bioRxiv : the preprint server for biology · 2026Article
- Probing the limits of genetic recoding using multi-omics-guided evolution.Nature communications · 2026Article
- Evaluating DNA Function Understanding in Genomic Language Models Using Evolutionarily Implausible Sequences.ACS synthetic biology · 2026Article
- Leveraging pre-trained AI models for robust promoter sequence design in synthetic biology.Biophysics reports · 2026Article
- Article
- Iterative design of a NAND hybrid riboswitch by deep batch Bayesian optimization.Nucleic acids research · 2026Article
- Design prokaryotic cis-regulatory elements using language model.Nucleic acids research · 2026Article
- Article
- Synthetic rewriting technologies in mammalian cells.Nature communications · 2026Review
- Inferring fungal cis-regulatory networks from genome sequences via unsupervised and interpretable representation learning.Genetics · 2026Article
116 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
How transcription factors (TFs) interpret cis-regulatory DNA sequence to control gene expression remains unclear, largely because past studies using native and engineered sequences had insufficient scale. Here, we measure the expression output of >100 million synthetic yeast promoter sequences that are fully random. These sequences yield diverse, reproducible expression levels that can be explained by their chance inclusion of functional TF binding sites. We use machine learning to build interpretable models of transcriptional regulation that predict ~94% of the expression driven from independent test promoters and ~89% of the expression driven from native yeast promoter fragments. These models allow us to characterize each TF's specificity, activity and interactions with chromatin. TF activity depends on binding-site strand, position, DNA helical face and chromatin context. Notably, expression level is influenced by weak regulatory interactions, which confound designed-sequence studies. Our analyses show that massive-throughput assays of fully random DNA can provide the big data necessary to develop complex, predictive models of gene regulation.
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.