ArticleNature communications2025
Modeling and designing enhancers by introducing and harnessing transcription factor binding units.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- Expression-linked promoter selection (ELiPS) engineers short, strong ubiquitous promoters for gene therapy applications.bioRxiv : the preprint server for biology · 2026Article
- AI-empowered human microbiome research.Gut · 2026Review
- Elastic enhancer network tunes equilibrium thermodynamics of liquid liquid phase separation in super enhancers.iScience · 2026Article
- Engineering genetic elements for microbial protein expression systems: Advances, challenges, applications, and prospects.Synthetic and systems biotechnology · 2026Review
- From Natural Discovery to AI-Guided Design: A Curated Collection of Compact Enhancers for Crop Engineering.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Generating functional plasmid origins with OriGen.Nucleic acids research · 2025Article
- The specificity landscape of WRKY transcription factors reveals the bidirectional influence of non-CG methylation.Nucleic acids research · 2025Article
- esMPRA: an easy-to-use systematic pipeline for MPRA experiment quality control and data analysis.Bioinformatics (Oxford, England) · 2025Article
- Modeling and designing enhancers by introducing and harnessing transcription factor binding units.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Enhancers serve as pivotal regulators of gene expression throughout various biological processes by interacting with transcription factors (TFs). While transcription factor binding sites (TFBSs) are widely acknowledged as key determinants of TF binding and enhancer activity, the significant role of their surrounding context sequences remains to be quantitatively characterized. Here we propose the concept of transcription factor binding unit (TFBU) to modularly model enhancers by quantifying the impact of context sequences surrounding TFBSs using deep learning models. Based on this concept, we develop DeepTFBU, a comprehensive toolkit for enhancer design. We demonstrate that designing TFBS context sequences can significantly modulate enhancer activities and produce cell type-specific responses. DeepTFBU is also highly efficient in the de novo design of enhancers containing multiple TFBSs. Furthermore, DeepTFBU enables flexible decoupling and optimization of generalized enhancers. We prove that TFBU is a crucial concept, and DeepTFBU is highly effective for rational enhancer design.
Indexed as
Identifiers
What 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.