ArticleNature communications2022
Connecting high-resolution 3D chromatin organization with epigenomics.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- GraphChIAr: genome-wide super-resolution reconstruction of protein-mediated remote chromatin interactions by augmenting hi-C interaction maps with multiple ChIP-seq profiles.Briefings in bioinformatics · 2026Article
- GraphLooper: predicting chromatin loops based on hierarchical multi-view graph pooling method.Briefings in bioinformatics · 2026Article
- ChromBERT: A foundation model for learning interpretable representations for context-specific transcriptional regulatory networks.Cell genomics · 2026Article
- An end-to-end generalizable deep learning framework to comprehensively analyze transcriptional regulation.Nature communications · 2026Article
- Modeling nascent transcription from chromatin landscape and structure with CLASTER.Genome biology · 2026Article
- Article
- Genome structure mapping with high-resolution 3D genomics and deep learning.bioRxiv : the preprint server for biology · 2025Article
- A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.Briefings in bioinformatics · 2024Review
- Mechanistic drivers of chromatin organization into compartments.Current opinion in genetics & development · 2024Review
- Learning Micro-C from Hi-C with diffusion models.PLoS computational biology · 2024Article
- A Lightweight Framework For Chromatin Loop Detection at the Single-Cell Level.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Spatial and temporal organization of the genome: Current state and future aims of the 4D nucleome project.Molecular cell · 2023Review
- Review
- A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome.Nucleic acids research · 2023Article
- Chromatin Organization and Transcriptional Programming of Breast Cancer Cell Identity.Endocrinology · 2023Review
- 3D genomic organization in cancers.Quantitative biology (Beijing, China) · 2023Review
- Review
- Chromosome-scale genome assembly ofFrontiers in plant science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The resolution of chromatin conformation capture technologies keeps increasing, and the recent nucleosome resolution chromatin contact maps allow us to explore how fine-scale 3D chromatin organization is related to epigenomic states in human cells. Using publicly available Micro-C datasets, we develop a deep learning model, CAESAR, to learn a mapping function from epigenomic features to 3D chromatin organization. The model accurately predicts fine-scale structures, such as short-range chromatin loops and stripes, that Hi-C fails to detect. With existing epigenomic datasets from ENCODE and Roadmap Epigenomics Project, we successfully impute high-resolution 3D chromatin contact maps for 91 human tissues and cell lines. In the imputed high-resolution contact maps, we identify the spatial interactions between genes and their experimentally validated regulatory elements, demonstrating CAESAR's potential in coupling transcriptional regulation with 3D chromatin organization at high resolution.
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.