ArticleNature communications2024
DiffDomain enables identification of structurally reorganized topologically associating domains.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 19 citations in OpenAlex.
- Spatially resolved chromatin architectures in mammalian brain tissues.Nature methods · 2026Article
- Identification of differential topologically associating domains from low sequencing depth and pseudobulk chromatin contact maps.Genome research · 2026Article
- hicream: A flexible framework to identify significantly different regions in Hi-C data.Bioinformatics (Oxford, England) · 2026Article
- Origins and consequences of oncogenic 3D chromatin remodelling.Nature reviews. Cancer · 2026Review
- Bacterial 3D genome architecture: organization, regulation, and synthetic biology applications.Genome biology · 2026Review
- HyperDeepTAD: a topologically associated domains detection method based on multiway chromatin interaction data and deep learning.BMC genomics · 2026Article
- Repeat expansions inbioRxiv : the preprint server for biology · 2026Article
- Recent advances in single-cell bioinformatics for inferring higher-order chromatin contact maps.BMB reports · 2025Review
- Coordinated gene expression within sustained STAT3-associated chromatin conformations contributes to hepatocellular carcinoma progression.Cancer communications (London, England) · 2025Article
- Beckwith-Wiedemann syndrome and large offspring syndrome involve alterations in methylome, transcriptome, and chromatin configuration.NAR molecular medicine · 2025Article
- aplot: Simplifying the creation of complex graphs to visualize associations across diverse data types.Innovation (Cambridge (Mass.)) · 2025Article
- Integrated analysis of differential intra-chromosomal community interactions: A study of breast cancer.Artificial intelligence in medicine · 2025Article
- Navigating the 3D genome at single-cell resolution: techniques, computation, and mechanistic landscapes.Briefings in bioinformatics · 2025Review
- Microtubule mechanotransduction refines cytomegalovirus interactions with and remodeling of host chromatin.Nature communications · 2025Article
- Article
- 3D genome landscape of primary and metastatic colorectal carcinoma reveals the regulatory mechanism of tumorigenic and metastatic gene expression.Communications biology · 2025Article
- A comprehensive review and benchmark of differential analysis tools for Hi-C data.Briefings in bioinformatics · 2025Review
- Machine and Deep Learning Methods for Predicting 3D Genome Organization.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Topologically associating domains of chromatin on single-cell Hi-C data: a survey of bioinformatic tools and applications in the light of artificial intelligence.Frontiers in genetics · 2025Review
- Context-aware single-cell multiomics approach identifies cell-type-specific lung cancer susceptibility genes.Nature communications · 2024Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
Abstract
Topologically associating domains (TADs) are critical structural units in three-dimensional genome organization of mammalian genome. Dynamic reorganizations of TADs between health and disease states are associated with essential genome functions. However, computational methods for identifying reorganized TADs are still in the early stages of development. Here, we present DiffDomain, an algorithm leveraging high-dimensional random matrix theory to identify structurally reorganized TADs using high-throughput chromosome conformation capture (Hi-C) contact maps. Method comparison using multiple real Hi-C datasets reveals that DiffDomain outperforms alternative methods for false positive rates, true positive rates, and identifying a new subtype of reorganized TADs. Applying DiffDomain to Hi-C data from different cell types and disease states demonstrates its biological relevance. Identified reorganized TADs are associated with structural variations and epigenomic changes such as changes in CTCF binding sites. By applying to a single-cell Hi-C data from mouse neuronal development, DiffDomain can identify reorganized TADs between cell types with reasonable reproducibility using pseudo-bulk Hi-C data from as few as 100 cells per condition. Moreover, DiffDomain reveals differential cell-to-population variability and heterogeneous cell-to-cell variability in TADs. Therefore, DiffDomain is a statistically sound method for better comparative analysis of TADs using both Hi-C and single-cell Hi-C data.
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