Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states
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Abstract
To understand the complex relationship between histone mark activity and gene expression, recent advances have used in silico predictions based on large-scale machine learning models. However, these approaches have omitted key contributing factors like cell state, histone mark function or distal effects, which impact the relationship, limiting their findings. Moreover, downstream use of these models for new biological insight is lacking. Here, we present the most comprehensive study of this relationship to date – investigating seven histone marks in eleven cell types across a diverse range of cell states. We used convolutional and attention-based models to predict transcription from histone mark activity at promoters and distal regulatory elements. Our work shows that histone mark function, genomic distance and cellular states collectively influence a histone mark’s relationship with transcription. We found that no individual histone mark is consistently the strongest predictor of gene expression across all genomic and cellular contexts. This highlights the need to consider all three factors when determining the effect of histone mark activity on transcriptional state. Furthermore, we conducted in silico histone mark perturbation assays, uncovering functional and disease related loci and highlighting frameworks for the use of chromatin deep learning models to uncover new biological insight.
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Murphy, A. (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.10940542 · Cited by 1 · see citing works
Self-citation
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1 of the 91 references couldn’t be matched to a known paper.
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Predicting cell type-specific epigenomic profiles accounting for distal genetic effects
Murphy, A. E., Beardall, W., Rei, M., Phuycharoen, M., & Skene, N. (2024) · Nature Communications · doi:10.1038/s41467-024-54441-5 · Cited by 12 · see citing works
Self-citationComplex-specific inhibitors for interrogating ATAC histone acetyltransferase complex
Liu, S., Liu, J., Wu, Y., Yao, X., Li, X., Dong, X., Li, Q., Cheung, H. J. H., Wong, K. Y., Li, Y., et al. (2026) · Nature Chemical Biology · doi:10.1038/s41589-025-02132-7 · Cited by 4 · see citing works
Aggarwal, N., Schmitz, J. E., Laufer, L., Rahmann, S., Walter, J., & Salhab, A. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag591 · Cited by 2 · see citing works
Raby, J., Frosi, G., White, F., Laperle, J., & Jacques, P.-É. (2025) · bioRxiv · doi:10.1101/2025.09.04.670545 · Cited by 3 · see citing works
The exit from naive pluripotency: a platform for the study of enhancer mechanistics
Jonasson, M. E., & Buecker, C. (2025) · Biochemical Society Transactions · doi:10.1042/bst20253037 · Cited by 1 · see citing works
Temporal Expression Prediction by Integrating Genome Dynamics via Spatio-temporal GNNsPreprint
Kaya, B., & Sefer, E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.07.678022
Wei, J., Xue, Y., Chai, H., & Gao, Y. Q. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag902
TPCAV: Interpreting deep learning genomics models via concept attributionPreprint
Yang, J., & Mahony, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.20.700723 · Cited by 1 · see citing works
Altered histone modifications in Aedes aegypti midguts following Rift Valley fever virus exposure
Ogg, H. A., Mikol, Z. M., King, D. C., Mire, C. E., Arhouma, Z., Nishimura, E. O., Kading, R. C., & Campbell, C. L. (2026) · Scientific Reports · doi:10.1038/s41598-026-37729-y
Korbolina, E. E., Gubina, M., Bryzgalov, L. O., Degtyareva, A. O., Evseenko, A., Antonseva, E. V., Korbut, A. I., Rykova, E. Y., Klimontov, V. V., Kzhyshkowska, J. G., et al. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27167202
- Authors
- Murphy, A. E., Askarova, A., Lenhard, B., Skene, N. G., & Marzi, S. J.
- Affiliations
- UK Dementia Research Institute (GB)
- MRC London Institute of Medical Sciences (GB)
- King's College London (GB)
- Corresponding author
- Murphy, Alan E
- Marzi, Sarah J
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-12-11
- Volume
- 53
- Issue
- 4
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC11879020
- OpenAlex
- W4405296620
- Cited by
- 10
- References
- 91
- Citations by year
- 2026: 6 · 2025: 3 · 2024: 1
- FWCI
- 1.36
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Portal (King's College London) (submittedVersion, cc-by)
- Funders
- UK Dementia Research Institute (UKDRI-5008, UKDRI-6009)
- Medical Research Council
- Edmond and Lily Safra Early Career Fellowship Program
- UKRI Future Leaders Fellowship (MR/T04327X/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Keywords
- histone modifications, gene expression prediction, regulatory distance, cell states, transcription, distal regulatory elements, promoter, attention-based models, convolutional neural networks
- MeSH terms
- Deep Learning, Chromatin, Gene Expression Regulation, Histones, Humans, Promoter Regions, Genetic, Transcription, Genetic, Histone Code
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.
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