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

Nature Genetics volume 54, pages 613–624 (2022)Cite this article

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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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Fig. 1: DeepSTARR quantitatively predicts enhancer activity genome wide from DNA sequence.
Fig. 2: DeepSTARR reveals important TF motif types that validate experimentally.
Fig. 3: Instances of the same TF motif have nonequivalent contributions to enhancer activity.
Fig. 4: Contribution of TF motifs depends on the flanking sequence.
Fig. 5: In silico analysis reveals distinct modes of motif cooperativity.
Fig. 6: Motif syntax rules dictate the contribution of TF motif instances in human enhancers.
Fig. 7: DeepSTARR designs synthetic enhancers using optimal sequence rules.

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Data availability

The raw sequencing data are available from GEO (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE183939. Data used to train and evaluate the DeepSTARR model as well as the final pretrained model are found on zenodo at https://doi.org/10.5281/zenodo.5502060. The pretrained DeepSTARR model is also available in the Kipoi model repository109 (http://kipoi.org/models/DeepSTARR/). Genome browser tracks showing genome-wide UMI-STARR-seq and DeepSTARR predictions in Drosophila S2 cells, including nucleotide contribution scores for all enhancer sequences, together with the enhancers used for mutagenesis, mutated motif instances and respective log2FC in enhancer activity, are available at https://genome.ucsc.edu/s/bernardo.almeida/DeepSTARR_manuscript. Dynamic sequence tracks (https://github.com/pkerpedjiev/higlass-dynseq) and contribution scores are also available as a Reservoir Genome Browser session at https://resgen.io/paper-data/Almeida...%202021%20-%20DeepSTARR/views. TF motif models were obtained from iRegulon (http://iregulon.aertslab.org/collections.html (ref. 101)). DNase-seq and ATAC-seq data in Drosophila S2 cells were obtained from refs. 63 and 110, respectively; nascent transcription from ref. 111 and H3K4me1 and H3K27ac chromatin marks from ref. 112. RepeatMasker dm3 annotations were obtained from http://www.repeatmasker.org/genomes/dm3/RepeatMasker-rm405-db20140131/dm3.fa.out.gz. Genomic DNase I footprinting data of RKO cells were downloaded from https://resources.altius.org/~jvierstra/projects/footprinting.2020/per.dataset/h.RKO-DS40362/. HCT116 DNase-seq, H3K27ac and H3K4me1 data were obtained from ENCODE97 (https://www.encodeproject.org/; ENCFF001SQU, ENCFF001WIJ, ENCFF001WIK, ENCFF175RBN, ENCFF228YKV, ENCFF851NWR, ENCFF927AHJ, ENCFF945KJN, ENCFF360XGA, ENCFF130JBP and ENCFF400KKD) and ATAC-seq data from ref. 96.

Code availability

Code used to process the genome-wide and oligonucleotide UMI-STARR-seq data, train DeepSTARR and predict the enhancer activity for new DNA sequences, as well as to reproduce the results, is available on GitHub (https://github.com/bernardo-de-almeida/DeepSTARR). The code and TF motif compendium are available from https://github.com/bernardo-de-almeida/motif-clustering.

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Acknowledgements

We thank A. Andersen (Life Science Editors), V. Loubiere and F. Lorbeer (IMP) for comments on the manuscript, G. Hulselmans and S. Aerts (KU Leuven) for sharing the TF motif PWM collection, and P. Kerpedjiev for generating the dynamic sequence tracks. Deep sequencing was performed at the Vienna Biocenter Core Facilities GmbH. Research in the Stark group is supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 647320) and by the Austrian Science Fund (FWF, F4303-B09). Basic research at the IMP is supported by Boehringer Ingelheim GmbH and the Austrian Research Promotion Agency (FFG).

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Authors and Affiliations

  1. Research Institute of Molecular Pathology, Vienna BioCenter, Campus-Vienna-BioCenter 1, Vienna, Austria

    Bernardo P. de Almeida, Franziska Reiter, Michaela Pagani & Alexander Stark

  2. Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna, Austria

    Bernardo P. de Almeida & Franziska Reiter

  3. Medical University of Vienna, Vienna BioCenter, Vienna, Austria

    Alexander Stark

Authors
  1. Bernardo P. de Almeida
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  2. Franziska Reiter
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  3. Michaela Pagani
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  4. Alexander Stark
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Contributions

B.P.d.A., F.R. and A.S. conceived the project. F.R. and M.P. performed all experiments. B.P.d.A. performed all computational analyses. B.P.d.A., F.R. and A.S. interpreted the data and wrote the manuscript. A.S. supervised the project.

Corresponding author

Correspondence to Alexander Stark.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Ziga Avsec and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–28, Tables 1–18, Methods and References.

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Supplementary Table 1

Supplementary Tables 1–18

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de Almeida, B.P., Reiter, F., Pagani, M. et al. DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers. Nat Genet 54, 613–624 (2022). https://doi.org/10.1038/s41588-022-01048-5

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  • DOI: https://doi.org/10.1038/s41588-022-01048-5

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