The DOI matches its Crossref record; 176 of the 176 references it cites were found in a registry or an index.
Hold out the genome: a roadmap to solving the cis-regulatory code
Open accessPublished version of a preprint
Abstract
Gene expression is regulated by transcription factors that work together to read cis-regulatory DNA sequences. The 'cis-regulatory code' - how cells interpret DNA sequences to determine when, where and how much genes should be expressed - has proven to be exceedingly complex. Recently, advances in the scale and resolution of functional genomics assays and machine learning have enabled substantial progress towards deciphering this code. However, the cis-regulatory code will probably never be solved if models are trained only on genomic sequences; regions of homology can easily lead to overestimation of predictive performance, and our genome is too short and has insufficient sequence diversity to learn all relevant parameters. Fortunately, randomly synthesized DNA sequences enable testing a far larger sequence space than exists in our genomes, and designed DNA sequences enable targeted queries to maximally improve the models. As the same biochemical principles are used to interpret DNA regardless of its source, models trained on these synthetic data can predict genomic activity, often better than genome-trained models. Here we provide an outlook on the field, and propose a roadmap towards solving the cis-regulatory code by a combination of machine learning and massively parallel assays using synthetic DNA.
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In the citing paper
“it has been argued that the natural genome contains only few instances of many TF combinations and binding site arrangements, and it is in a statistical sense underpowered to “learn the rules by which it is decoded.”28,29”
From Design principles of cell-state-specific enhancers in hematopoiesis (2025) · cites this paper as [29]
In the citing paper
“The performance of our models on sequence design and genome interpretation tasks highlights the importance of pairwise TF interactions in achieving regulatory specificity and illustrates the benefits of working with non-natural sequences for learning the rules of gene regulation.29”
From Design principles of cell-state-specific enhancers in hematopoiesis (2025) · cites this paper as [29]
Citations
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Initial sequencing and analysis of the human genome
International Human Genome Sequencing Consortium, Whitehead Institute for Biomedical Research, C. F. G. R., Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., Devon, K., Dewar, K., et al. (2001) · Nature · doi:10.1038/35057062 · Cited by 24,216
The sequence of the human genome
Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J., Sutton, G. G., Smith, H. O., Yandell, M., Evans, C. A., Holt, R. A., et al. (2001) · Science · doi:10.1126/science.1058040 · Cited by 13,909
Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome
Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., et al. (2009) · Science · doi:10.1126/science.1181369 · Cited by 8,898
Protein complex prediction with AlphaFold-Multimer
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al. (2021) · doi:10.1101/2021.10.04.463034 · Cited by 3,808
Evolutionary Rate at the Molecular Level
KIMURA, M. (1968) · Nature · doi:10.1038/217624a0 · Cited by 3,747
Systematic Localization of Common Disease-Associated Variation in Regulatory DNA
Maurano, M. T., Humbert, R., Rynes, E., Thurman, R. E., Haugen, E., Wang, H., Reynolds, A. P., Sandstrom, R., Qu, H., Brody, J., et al. (2012) · Science · doi:10.1126/science.1222794 · Cited by 3,716
The Human Transcription Factors
Lambert, S. A., Jolma, A., Campitelli, L. F., Das, P. K., Yin, Y., Albu, M., Chen, X., Taipale, J., Hughes, T. R., & Weirauch, M. T. (2018) · Cell · doi:10.1016/j.cell.2018.01.029 · Cited by 3,399
Self-citationThe complete sequence of a human genome
Nurk, S., Koren, S., Rhie, A., Rautiainen, M., Bzikadze, A. V., Mikheenko, A., Vollger, M. R., Altemose, N., Uralsky, L., Gershman, A., et al. (2022) · Science · doi:10.1126/science.abj6987 · Cited by 3,136
Predicting effects of noncoding variants with deep learning–based sequence model
Zhou, J., & Troyanskaya, O. G. (2015) · Nature Methods · doi:10.1038/nmeth.3547 · Cited by 2,338
Research works printed but not linked: 3
- Yuh, C. H. & Davidson, E. H. Modular cis-regulatory organization of Endo16, a gut-specific gene of the sea urchin embryo. Dev. Camb. Engl. 122, 1069–1082 (1996).
- Friedman, R. Z. et al. Active learning of enhancer and silencer regulatory grammar in photoreceptors. Preprint at bioRxiv https://doi.org/10.1101/2023.08.21.554146 (2023). · DOI
- Rafi, A. M. et al. Evaluation and optimization of sequence-based gene regulatory deep learning models. Preprint at bioRxiv https://doi.org/10.1101/2023.04.26.538471 (2023). · DOI
Cited by
Sorted by citations
Machine-guided design of cell-type-targeting cis-regulatory elements
Gosai, S. J., Castro, R. I., Fuentes, N., Butts, J. C., Mouri, K., Alasoadura, M., Kales, S., Nguyen, T. T. L., Noche, R. R., Rao, A. S., et al. (2024) · Nature · doi:10.1038/s41586-024-08070-z · Cited by 142
Rood, J. E., Hupalowska, A., & Regev, A. (2024) · Cell · doi:10.1016/j.cell.2024.07.035 · Cited by 139
Deciphering the impact of genomic variation on function
Writing group (ordered by contribution), Engreitz, J., Lawson, H. A., Singh, H., Starita, L. M., Hon, G. C., Carter, H. K., Sahni, N., Reddy, T. E., Lin, X., et al. (2024) · Nature · doi:10.1038/s41586-024-07510-0 · Cited by 105
Predicting gene expression from DNA sequence using deep learning models
Barbadilla-Martínez, L., Klaassen, N. H. M., van Steensel, B., & de Ridder, J. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00841-2 · Cited by 89
DNA-guided transcription factor interactions extend human gene regulatory code
Xie, Z., Sokolov, I., Osmala, M., Xue, Y., Bower, G., Pett, J. P., Chen, Y., Wang, K., Cavga, A. D., Popov, A. N., et al. (2025) · Nature · doi:10.1038/s41586-025-08844-z · Cited by 64
Self-citation
Metadata
- Authors
- de Boer, C. G., & Taipale, J.
- Affiliations
- University of British Columbia (CA)
- University of Helsinki (FI)
- Corresponding author
- de Boer, Carl G.
- Taipale, Jussi
- Venue
- Nature
- Published
- 2023-12-13
- Volume
- 625
- Issue
- 7993
- Pages
- 41-50
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4389670750
- Cited by
- 107
- References
- 175
- Citations by year
- 2026: 34 · 2025: 40 · 2024: 29 · 2023: 4
- FWCI
- 2.85
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- Stem Cell Network (ECR-C4R1-7)
- Michael Smith Health Research BC
- Medical Research Council (MR/V000500/1)
- Biotechnology and Biological Sciences Research Council (BB/V00736X/1)
- 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 › Genomics and Phylogenetic Studies
- Keywords
- transcription factor binding, gene expression regulation, synthetic DNA sequences
- MeSH terms
- Machine Learning, DNA, Models, Genetic, Regulatory Sequences, Nucleic Acid, Transcription Factors, Sequence Analysis, DNA, Genomics
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