ArticleNature genetics2022
DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers.
Article in Nature genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 185 papers.
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Who cites it
185 citing papers in PubMed.
- Tangermeme: a toolkit for understanding cis-regulatory logic using deep learning models.Nature methods · 2026Article
- Genome-wide discovery ofeLife · 2026Article
- Mechanisms underlying disease-causing variants in promoters and enhancers.Nature genetics · 2026Review
- A practical guide to studying genome function using single-molecule genomics.Nature reviews. Molecular cell biology · 2026Review
- Article
- Predictive design of tissue-specific mammalian enhancers that function in the mouse embryo.Nature genetics · 2026Article
- Uniform processing and analysis of IGVF massively parallel reporter assay data with MPRAsnakeflow.Genome research · 2026Article
- Cross-Model Uncertainty-Aware Minimal Editing of Cis-Regulatory Elements for Cell-Type-Selective Design.Genes · 2026Article
- Balanced DNA interpolation improves learning of genetic distance-informed embeddings in plants.PLoS computational biology · 2026Article
- Emergence Biases in Molecular Evolution.Genome biology and evolution · 2026Review
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Systematic contextual biases in SegmentNT potentially relevant to other nucleotide transformer models.Nucleic acids research · 2026Article
- Positional interpretation of cis-regulatory code and nucleosome organization with deep learning models.Nature communications · 2026Article
- Unlocking the Regulatory Genome: Interpreting the Clinical Impact of Noncoding Variants in Genetic Cardiomyopathies.Circulation. Genomic and precision medicine · 2026Review
- Computational blueprints for cell fate programming.Stem cell reports · 2026Review
- Genome-wide modelling of plant transcription factor binding captures regulatory variants associated with phenotypic traits.Nature communications · 2026Article
- Genome evolution through polyploidy: Enhancing plant stress resilience in agriculture.Proceedings of the National Academy of Sciences of the United States of America · 2026Review
- PlantCTCIP: Chromatin Interaction Prediction Using Convolutional Neural Network and Transformer in Plants.Plant biotechnology journal · 2026Article
- Informational blueprints reveal condition-dependent gene regulatory architectures.bioRxiv : the preprint server for biology · 2026Article
- Large-scale data-driven pre-trained DNA models enhance performance across diverse genomics tasks.Nature communications · 2026Article
125 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Enhancer sequences control gene expression and comprise binding sites (motifs) for different transcription factors (TFs). Despite extensive genetic and computational studies, the relationship between DNA sequence and regulatory activity is poorly understood, and de novo enhancer design has been challenging. Here, we built a deep-learning model, DeepSTARR, to quantitatively predict the activities of thousands of developmental and housekeeping enhancers directly from DNA sequence in Drosophila melanogaster S2 cells. The model learned relevant TF motifs and higher-order syntax rules, including functionally nonequivalent instances of the same TF motif that are determined by motif-flanking sequence and intermotif distances. We validated these rules experimentally and demonstrated that they can be generalized to humans by testing more than 40,000 wildtype and mutant Drosophila and human enhancers. Finally, we designed and functionally validated synthetic enhancers with desired activities de novo.
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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 Socratic graph.