PreprintCited by 285Genomics and Chromatin DynamicsGene Regulatory Network AnalysisCRISPR and Genetic Engineering

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

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de Almeida, Bernardo P., Reiter, Franziska, Pagani, Michaela, Stark, Alexander·2021·Nature Genetics

DOI 10.1101/2021.10.05.463203

Preprint — published as 10.1038/s41588-022-01048-5 · Paper page

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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 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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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
ORCID: de Almeida, Bernardo P.
Stark, Alexander
Venue: Nature Genetics
Publisher: openRxiv
Published: 2021-10-07
Volume: 54
Issue: 5
Pages: 613-624
ISSN: 1061-4036 (ISSN-L)
DOI: 10.1101/2021.10.05.463203
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

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

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