ArticleNature communications2022
Reference panel guided topological structure annotation of Hi-C data.
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 21 papers.
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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.
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Who cites it
21 citing papers in PubMed.
- Integrating heterogeneity into topologically associating domain boundary prediction in large genomic context in human.NAR genomics and bioinformatics · 2026Article
- BioMaster: Multi-agent system for automated bioinformatics analysis workflow.Patterns (New York, N.Y.) · 2026Article
- TM-Loop: Transformer multi-omics hierarchical detection of chromatin loop.Scientific reports · 2026Article
- Persistent homology-based multiscale Hi-C analysis uncovers TADs-mediated gene regulation.Scientific reports · 2026Article
- GraphLooper: predicting chromatin loops based on hierarchical multi-view graph pooling method.Briefings in bioinformatics · 2026Article
- HiCMamba: Enhancing Hi-C resolution and identifying 3D genome structures with state space modeling.PLoS computational biology · 2026Article
- iceDP: identifying inter-chromatin engagement via density peaks clustering algorithm.Briefings in bioinformatics · 2026Article
- RobusTAD: reference panel based annotation of nested topologically associating domains.Genome biology · 2025Article
- Review
- CGLoop: a neural network framework for chromatin loop prediction.BMC genomics · 2025Article
- DconnLoop: a deep learning model for predicting chromatin loops based on multi-source data integration.BMC bioinformatics · 2025Article
- COCOA: A Framework for Fine-scale Mapping of Cell-type-specific Chromatin Compartments Using Epigenomic Information.Genomics, proteomics & bioinformatics · 2025Article
- Read Mapping for Hi-C Analysis.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Computational methods for analysing multiscale 3D genome organization.Nature reviews. Genetics · 2024Review
- CD-Loop: a chromatin loop detection method based on the diffusion model.Frontiers in genetics · 2024Article
- The shape of chromatin: insights from computational recognition of geometric patterns in Hi-C data.Briefings in bioinformatics · 2023Review
- Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review.Biology · 2023Review
- Reference panel-guided super-resolution inference of Hi-C data.Bioinformatics (Oxford, England) · 2023Article
- The 3D genome and its impacts on human health and disease.Life medicine · 2023Review
- Cobind: quantitative analysis of the genomic overlaps.Bioinformatics advances · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately annotating topological structures (e.g., loops and topologically associating domains) from Hi-C data is critical for understanding the role of 3D genome organization in gene regulation. This is a challenging task, especially at high resolution, in part due to the limited sequencing coverage of Hi-C data. Current approaches focus on the analysis of individual Hi-C data sets of interest, without taking advantage of the facts that (i) several hundred Hi-C contact maps are publicly available, and (ii) the vast majority of topological structures are conserved across multiple cell types. Here, we present RefHiC, an attention-based deep learning framework that uses a reference panel of Hi-C datasets to facilitate topological structure annotation from a given study sample. We compare RefHiC against tools that do not use reference samples and find that RefHiC outperforms other programs at both topological associating domain and loop annotation across different cell types, species, and sequencing depths.
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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.