Cited by 287Genomics and Chromatin DynamicsGene Regulatory Network AnalysisCRISPR and Genetic Engineering

DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers

Open access
de Almeida, Bernardo P., Reiter, Franziska, Pagani, Michaela, Stark, Alexander·2022·Nature Genetics

Published version of a preprint

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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Crossref · 10.1038/s41588-022-01048-5

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  • The DeepSTARR enhancer activity dataset is composed of 484,052 DNA sequences of size 249 nucleotides, each measured for quantitative enhancer activity toward a developmental or a housekeeping promoter.

    “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

    The Data preparation section states that the dataset contains 249-bp windows and 484,052 examples after augmentation. It also says reverse complements were added with the same output as their original sequences, so the paper does not support saying that each of the 484,052 sequences was separately measured. The DeepSTARR prediction section describes activities for both developmental and housekeeping promoters. (LLM, conf=0.88)

    Experimental — accuracy not yet measured
  • In housekeeping and developmental enhancer prediction, the NT model slightly surpassed (1%) and obtained lower (4%) correlation values, respectively, compared to DeepSTARR.

    “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.”

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Research works printed but not linked: 3

  1. Shrikumar, A., Greenside, P. & Kundaje, A. Learning important features through propagating activation differences. arXiv 1704.02685, (2017). · arXiv
  2. Abadi, M. et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv 1603.04467, (2016). · arXiv
  3. Kingma, D. P. & Ba, J. L. Adam: A method for stochastic optimization. arXiv 1412.6980, (2015). · arXiv

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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 · 2021: 1
FWCI
17.89
Type
journal article
Language
en
Open access
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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