ArticleNature methods2026
CREsted: modeling genomic and synthetic cell-type-specific enhancers across tissues and species.
Article in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Predictive design of tissue-specific mammalian enhancers that function in the mouse embryo.Nature genetics · 2026Article
- A distinct effector B cell population drives autoantibody production in SARS-CoV-2 infection.Immunity · 2026Article
- Evolutionary transfer learning enables organism-wide inference of mammalian enhancer landscapes.bioRxiv : the preprint server for biology · 2026Article
- ModelingbioRxiv : the preprint server for biology · 2026Article
- Multi-omic profiling of human and mouse dorsal root ganglia enables targeted gene delivery to nociceptors.bioRxiv : the preprint server for biology · 2026Article
- A consensus spinal cord cell type atlas across mouse, macaque, and human.bioRxiv : the preprint server for biology · 2026Article
- Evaluating single-cell ATAC-seq atlasing technologies using sequence-to-function modeling.Nature communications · 2026Article
- Identification of novel DNA sequence motifs that modulate transcription in T cells.BMC genomics · 2026Article
- Cross-species consensus atlas of the primate basal ganglia.bioRxiv : the preprint server for biology · 2025Article
- IceQream: Quantitative chromosome accessibility analysis using physical TF models.Nature communications · 2025Article
- Programming human cell type-specific gene expression via an atlas of AI-designed enhancers.bioRxiv : the preprint server for biology · 2025Article
- GAME: Genomic API for Model Evaluation.bioRxiv : the preprint server for biology · 2025Article
- Evaluating methods for the prediction of cell-type-specific enhancers in the mammalian cortex.Cell genomics · 2025Article
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Authors and funding
15 authors.
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Abstract
Sequence-based deep learning models have become the state of the art for analyzing the genomic regulatory code. Particularly for enhancers, these models excel at deciphering sequence grammar that underlies their activity. To enable end-to-end enhancer modeling and design, we developed a software package called CREsted (cis-regulatory element sequence training, explanation and design). It combines preprocessing and analysis of single-cell assay for transposase-accessible chromatin using sequencing data, modeling chromatin accessibility from sequence, sequence design and downstream analysis to decipher enhancer grammar. We demonstrate CREsted's functionality on a mouse cortex and a human peripheral blood mononuclear cell dataset. Additionally, we use CREsted to compare mesenchymal-like cancer cell states between tumor types, and we investigate different fine-tuning strategies of genomic foundation models within CREsted. Finally, we train a model on a zebrafish development atlas and use this to design and in vivo validate cell-type-specific enhancers. For varying datasets, we demonstrate that CREsted facilitates efficient training and analyses, enabling scrutinization of the enhancer logic and design of synthetic enhancers across tissues and species.
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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.