ArticleNature communications2019
In silico prediction of high-resolution Hi-C interaction matrices.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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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.
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Who cites it
47 citing papers in PubMed, 128 citations in OpenAlex.
- Physics-Based Modeling of Sparse Single-Cell Hi-C Uncovers Structural and Epigenetic Variability.International journal of molecular sciences · 2026Article
- UniversalEPI: robust prediction of cell type-specific and differential chromatin interactions from DNA sequence and chromatin accessibility.Nucleic acids research · 2026Article
- ChromNet: A Multi-Task Learning Framework for Cross-Cell Type Prediction of 3D Chromatin Interactions Using Epigenetic Signals.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Physical models reveal indirect reader protein interactions that facilitate epigenetic crosstalk.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Chrombus-XMBD: a graph convolution model predicting 3D-genome from chromatin features.Briefings in bioinformatics · 2025Article
- Unveiling chromatin dynamics with virtual epigenome.Nature communications · 2025Article
- Genetically regulated eRNA expression predicts chromatin contact frequency and reveals genetic mechanisms at GWAS loci.Nature communications · 2025Article
- Recipes and ingredients for deep learning models of 3D genome folding.Current opinion in genetics & development · 2025Review
- COCOA: A Framework for Fine-scale Mapping of Cell-type-specific Chromatin Compartments Using Epigenomic Information.Genomics, proteomics & bioinformatics · 2025Article
- Predicting chromatin conformation contact maps.PloS one · 2025Article
- Machine and Deep Learning Methods for Predicting 3D Genome Organization.Methods in molecular biology (Clifton, N.J.) · 2025Review
- ppHiC: Interactive exploration of Hi-C results on the ProteinPaint web portal.Computational and structural biotechnology journal · 2024Article
- A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.Briefings in bioinformatics · 2024Review
- Polymer Physics Models Reveal Structural Folding Features of Single-Molecule Gene Chromatin Conformations.International journal of molecular sciences · 2024Article
- Interpretable online network dictionary learning for inferring long-range chromatin interactions.PLoS computational biology · 2024Article
- Learning Micro-C from Hi-C with diffusion models.PLoS computational biology · 2024Article
- Article
- Computational methods for analysing multiscale 3D genome organization.Nature reviews. Genetics · 2024Review
- Predicting patient-specific enhancer-promoter interactions.Cell reports methods · 2023Article
- Cell-type-specific prediction of 3D chromatin organization enables high-throughput in silico genetic screening.Nature biotechnology · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
The three-dimensional (3D) organization of the genome plays an important role in gene regulation bringing distal sequence elements in 3D proximity to genes hundreds of kilobases away. Hi-C is a powerful genome-wide technique to study 3D genome organization. Owing to experimental costs, high resolution Hi-C datasets are limited to a few cell lines. Computational prediction of Hi-C counts can offer a scalable and inexpensive approach to examine 3D genome organization across multiple cellular contexts. Here we present HiC-Reg, an approach to predict contact counts from one-dimensional regulatory signals. HiC-Reg predictions identify topologically associating domains and significant interactions that are enriched for CCCTC-binding factor (CTCF) bidirectional motifs and interactions identified from complementary sources. CTCF and chromatin marks, especially repressive and elongation marks, are most important for HiC-Reg's predictive performance. Taken together, HiC-Reg provides a powerful framework to generate high-resolution profiles of contact counts that can be used to study individual locus level interactions and higher-order organizational units of the genome.
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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.