ReviewNature2024
Hold out the genome: a roadmap to solving the cis-regulatory code.
Review in Nature, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
67 citing papers in PubMed.
- Decoding RNA-protein interactions using high-throughput methods.RNA biology · 2026Review
- Non-coding DNA dynamics and its roles as a physical barrier in genome safeguarding across early development and evolution.Heredity · 2026Review
- GHT-SELEX demonstrates unexpectedly high intrinsic sequence specificity and complex DNA binding of many human transcription factors.Nature methods · 2026Article
- Cross-Model Uncertainty-Aware Minimal Editing of Cis-Regulatory Elements for Cell-Type-Selective Design.Genes · 2026Article
- A token-pruning framework enables efficient representation of the human genome for RNA modification analysis.Bioinformatics (Oxford, England) · 2026Article
- Interpretable distillation reveals that deep learning splicing models suffer from pervasive confounders and blind spots.Genome biology · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- ExoShorkie: predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning.Bioinformatics (Oxford, England) · 2026Article
- Lentiviral single-cell MPRA of synthetic enhancers reveals motif affinity-based encoding of cell state specificity.Genome biology · 2026Article
- A scalable approach to investigating sequence-to-function predictions from personal genomes.Nature methods · 2026Article
- Genome-wide modelling of plant transcription factor binding captures regulatory variants associated with phenotypic traits.Nature communications · 2026Article
- Gene regulatory networks: from correlative models to causal explanations.Nature reviews. Genetics · 2026Review
- Sequence design for three-dimensional genome folding using Akita Semifreddo.bioRxiv : the preprint server for biology · 2026Article
- Multi-scale dissection, compaction and derivatization of mammalian developmental enhancers.bioRxiv : the preprint server for biology · 2026Article
- Retracing and rewriting the evolutionary trajectories of mammalian developmental enhancers.bioRxiv : the preprint server for biology · 2026Article
- A Cytosine Deaminase-Based Genomic Footprinting Assay (cFOOT-seq) for Detecting Transcription Factor Occupancy.Bio-protocol · 2026Article
- EPInformer: scalable and integrative prediction of gene expression from promoter-enhancer sequences with multimodal epigenomic profiles.Nature communications · 2026Article
- Engineering genetic elements for microbial protein expression systems: Advances, challenges, applications, and prospects.Synthetic and systems biotechnology · 2026Review
- Review
- Inferring fungal cis-regulatory networks from genome sequences via unsupervised and interpretable representation learning.Genetics · 2026Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
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.
Indexed as
Identifiers
38093018What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.