Cross-species analysis of enhancer logic using deep learning
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Abstract
Deciphering the genomic regulatory code of enhancers is a key challenge in biology because this code underlies cellular identity. A better understanding of how enhancers work will improve the interpretation of noncoding genome variation and empower the generation of cell type–specific drivers for gene therapy. Here, we explore the combination of deep learning and cross-species chromatin accessibility profiling to build explainable enhancer models. We apply this strategy to decipher the enhancer code in melanoma, a relevant case study owing to the presence of distinct melanoma cell states. We trained and validated a deep learning model, called DeepMEL, using chromatin accessibility data of 26 melanoma samples across six different species. We show the accuracy of DeepMEL predictions on the CAGI5 challenge, where it significantly outperforms existing models on the melanoma enhancer of IRF4 . Next, we exploit DeepMEL to analyze enhancer architectures and identify accurate transcription factor binding sites for the core regulatory complexes in the two different melanoma states, with distinct roles for each transcription factor, in terms of nucleosome displacement or enhancer activation. Finally, DeepMEL identifies orthologous enhancers across distantly related species, where sequence alignment fails, and the model highlights specific nucleotide substitutions that underlie enhancer turnover. DeepMEL can be used from the Kipoi database to predict and optimize candidate enhancers and to prioritize enhancer mutations. In addition, our computational strategy can be applied to other cancer or normal cell types.
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Convolutional neural networks can model, predict, and interpret enhancer accessibility or activity from sequence.
“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 [20]
CNN-based enhancer modeling has recently gained traction due to its capacity for interpretation of enhancer grammar.
“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 [20]
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Self-citationCDK7 and MITF repress a transcription program involved in survival and drug tolerance in melanoma
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Perspective on recent developments and challenges in regulatory and systems genomics
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Deep learning applications advance plant genomics research
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Controllers of histone methylation-modifying enzymes in gastrointestinal cancers
Li, L., Song, Q., Zhou, J., & Ji, Q. (2024) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2024.116488 · Cited by 13 · see citing works
Zhang, Q., Wang, S., Li, Z., Pan, Y., & Huang, D. (2024) · Advanced Science · doi:10.1002/advs.202405685 · Cited by 13 · see citing works
Zhang, G., Fu, Y., Yang, L., Ye, F., Zhang, P., Zhang, S., Ma, L., Li, J., Wu, H., Han, X., et al. (2024) · Developmental Cell · doi:10.1016/j.devcel.2024.01.015 · Cited by 11 · see citing works
Polygraph: a software framework for the systematic assessment of synthetic regulatory DNA elements
Lal, A., Gunsalus, L. M., Gupta, A., Biancalani, T., & Eraslan, G. (2025) · Genome biology · doi:10.1186/s13059-025-03584-9 · Cited by 11 · see citing works
Oncogenic Enhancers in Leukemia
Mulet‐Lazaro, R., & Delwel, R. (2024) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-23-0211 · Cited by 10 · see citing works
The evolution of gene regulation in mammalian cerebellum development
Sarropoulos, I., Sepp, M., Yamada, T., Schäfer, P. S. L., Trost, N., Schmidt, J., Schneider, C., Drummer, C., Mißbach, S., Taskiran, I. I., et al. (2026) · Science · doi:10.1126/science.adw9154 · Cited by 10 · see citing works
Self-citationIdentification, Design, and Application of Noncoding Cis-Regulatory Elements
Xu, L., & Liu, Y. (2024) · Biomolecules · doi:10.3390/biom14080945 · Cited by 9 · see citing works
Motif-based models accurately predict cell type-specific distal regulatory elements
Cornejo‐Páramo, P., Zhang, X., Louis, L., Li, Z., Yang, Y., Wong, E. S. W., & Wong, E. S. W. (2025) · Nature Communications · doi:10.1038/s41467-025-65362-2 · Cited by 9 · see citing works
Decoding common and rare noncoding variant effects across cellular and developmental contexts
Marderstein, A. R., Kundu, S., Padhi, E. M., Deshpande, S., Wang, A., Robb, E., Sun, Y., Yun, C. M., Pomales-Matos, D. A., Xie, Y., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02619-6 · Cited by 9 · see citing works
Annotating the Insect Regulatory Genome
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Landscape of enhancer disruption and functional screen in melanoma cells
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INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants
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Large-scale genomic analysis of the domestic dog informs biological discoveryPreprint
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Cross-species prediction of histone modifications in plants via deep learning
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Evolution of homology: From archetype towards a holistic concept of cell type
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Deep learning for regulatory genomics: a survey of models, challenges, and applications
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Single-cell analysis of the epigenome and 3D chromatin architecture in the human retina
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Predicting RNA secondary structure by a neural network: what features may be learned?
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Enhancer grammar of liver cell types and hepatocyte zonation states
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Smedile, F., Denaro, R., Crisafi, F., Giosa, D., D’Auria, G., Ferrer, M., Rosselli, R., Staege, M. S., Yakimov, M. M., Giuliano, L., et al. (2025) · Environmental Microbiology Reports · doi:10.1111/1758-2229.70106 · Cited by 5 · see citing works
The Regulatory Evolution of the Primate Fine-Motor SystemPreprint
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Self-citationModel Comparison of Heritability Enrichment Analysis in Livestock Population
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Identification of transcription factor co-binding patterns with non-negative matrix factorization
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Designing Interpretable Convolution-Based Hybrid Networks for GenomicsPreprint
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Evolutionary transfer learning enables organism-wide inference of mammalian enhancer landscapesPreprint
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Self-citationSOX10 Regulates Melanoma Metastasis Through the IRF1 ‐ ITGA3 / EphA2 ‐ FAK Pathway
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On the importance of evolutionary constraint for regulatory sequence identification
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Dissecting melanocytes to predict melanoma
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Self-citationSpecific SOX10 enhancer elements modulate phenotype plasticity and drug resistance in melanomaPreprint
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Self-citationData from AP-2α–Mediated Activation of E2F and EZH2 Drives Melanoma Metastasis
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Predictive models of the genetic bases underlying budding yeast fitness in multiple environments
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- Authors
- Minnoye, L., Taskiran, I. I., Mauduit, D., Fazio, M., Van Aerschot, L., Hulselmans, G., Christiaens, V., Makhzami, S., Seltenhammer, M., Karras, P., Primot, A., Cadieu, E., van Rooijen, E., Marine, J.-C., Egidy, G., Ghanem, G.-E., Zon, L., Wouters, J., & Aerts, S.
- Affiliations
- VIB-KU Leuven Center for Brain & Disease Research (BE)
- Boston Children's Hospital (US)
- Medical University of Vienna (AT)
- VIB-KU Leuven Center for Cancer Biology (BE)
- Centre National de la Recherche Scientifique (FR)
- AgroParisTech (FR)
- Université Libre de Bruxelles (BE)
- Corresponding author
- Aerts, Stein
- ORCID
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2020-07-30
- Volume
- 30
- Issue
- 12
- Pages
- 1815-1834
- ISSN
- 1088-9051 (ISSN-L)
- 1549-5469
- PMC
- PMC7706731
- OpenAlex
- W3046345618
- Cited by
- 116
- References
- 115
- Citations by year
- 2026: 19 · 2025: 21 · 2024: 21 · 2023: 17 · 2022: 18 · 2021: 19 · 2020: 1
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion)
- Lirias (submittedVersion, other-oa)
- HighWire Press Open Archive (submittedVersion)
- Funders
- European Research Council Consolidator (724226_cis-CONTROL)
- KU Leuven (C14/18/092)
- Foundation Against Cancer (2016-070)
- Fonds Wetenschappelijk Onderzoek (1S03317N)
- Kom op tegen Kanker
- Stand up to Cancer
- Flemish Cancer Society
- Stichting tegen Kanker
- Belgian Cancer Society
- CRB-Anim PIA1 (ANR-11-INBS-0003)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- chromatin accessibility, melanoma, transcription factor binding sites, enhancer architecture, nucleosome displacement
- MeSH terms
- Deep Learning, Animals, Dogs, Enhancer Elements, Genetic, Horses, Humans, Melanoma, Swine, Zebrafish, Gene Expression Regulation, Neoplastic, Computational Biology, Mice
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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.
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