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DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of enhancers
Open accessPreprint — published as 10.1038/s41588-022-01048-5 · Paper page
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 enhancer de novo design is considered impossible. 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 non-equivalent instances of the same TF motif that are determined by motif-flanking sequence and inter-motif distances. We validated these rules experimentally and demonstrated their conservation in human 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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“we and others have recently shown the power of convolutional neural networks (CNN) to model, predict, and interpret an enhancer accessibility or activity from its sequence12,19–21.”
From Enhancer grammar of liver cell types and hepatocyte zonation states (2022) · cites this paper as [19]
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“CNN-based enhancer modeling has recently gained traction, due to their capacity for the interpretation of enhancer grammar 12,19,20,74”
From Enhancer grammar of liver cell types and hepatocyte zonation states (2022) · cites this paper as [19]
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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
- Publisher
- openRxiv
- Published
- 2021-10-07
- Volume
- 54
- Issue
- 5
- Pages
- 613-624
- ISSN
- 1061-4036 (ISSN-L)
- OpenAlex
- W4280563114
- Cited by
- 284
- References
- 133
- Citations by year
- 2026: 64 · 2025: 76 · 2024: 64 · 2023: 55 · 2022: 23 · 2021: 1
- FWCI
- 17.87
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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