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DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers
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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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“The dataset is composed of 484,052 DNA sequences of size 249 nucleotides, each measured for their quantitative enhancer activity toward a developmental or a housekeeping promoter.”
From Nucleotide Transformer: building and evaluating robust foundation models for human genomics (2025) · cites this paper as de Almeida et al.12
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“in the case of housekeeping and developmental enhancer prediction, our model slightly surpassed (1%) and obtained lower (4%) correlation values, respectively (Fig. 2e), when compared to those of DeepSTARR12.”
From Nucleotide Transformer: building and evaluating robust foundation models for human genomics (2025) · cites this paper as [12]
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“The average proportion of tokens belonging to each element can be found in Supplementary Table 10.”
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Metadata
- Authors
- de Almeida, B. P., Reiter, F., Pagani, M., & Stark, A.
- Affiliations
- University of Vienna (AT)
- Research Institute of Molecular Pathology (AT)
- Corresponding author
- Stark, Alexander
- Venue
- Nature Genetics
- Published
- 2022-05
- Volume
- 54
- Issue
- 5
- Pages
- 613-624
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- OpenAlex
- W4280563114
- Cited by
- 283
- References
- 133
- Citations by year
- 2026: 63 · 2025: 76 · 2024: 64 · 2023: 55 · 2022: 23
- FWCI
- 17.89
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- European Commission
- Austrian Science Fund (F4303-B09, F 4303)
- KU Leuven
- Österreichische Forschungsförderungsgesellschaft
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Keywords
- enhancer activity prediction, transcription factor binding motifs, Drosophila melanogaster S2 cells, gene expression regulation
- MeSH terms
- Animals, Base Sequence, Binding Sites, Drosophila, Drosophila melanogaster, Enhancer Elements, Genetic, Transcription Factors, Gene Expression Regulation, Developmental
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