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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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“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]
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QED Eukaryote Genetic Code and Principle of Information Flow and Biological Protein Synthesis
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Personalized Synthetic Electrocardiograms with OutcomesPreprint
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Theoretical and empirical study of the evolution of gene regulatory networks
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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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