ArticleBriefings in bioinformatics2024
scENCORE: leveraging single-cell epigenetic data to predict chromatin conformation using graph embedding.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Origins and consequences of oncogenic 3D chromatin remodelling.Nature reviews. Cancer · 2026Review
- A graph neural network model for inferring interindividual variation from experimental biological data.Scientific reports · 2025Article
- MUSE: A Multi-slice Joint Analysis Method for Spatial Transcriptomics Experiments.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2025Article
- Navigating the 3D genome at single-cell resolution: techniques, computation, and mechanistic landscapes.Briefings in bioinformatics · 2025Review
- iMIRACLE: an Iterative Multi-View Graph Neural Network to Model Intercellular Gene Regulation from Spatial Transcriptomic Data.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2024Article
- Impeller: a path-based heterogeneous graph learning method for spatial transcriptomic data imputation.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
10 authors.
Funding
Abstract
Dynamic compartmentalization of eukaryotic DNA into active and repressed states enables diverse transcriptional programs to arise from a single genetic blueprint, whereas its dysregulation can be strongly linked to a broad spectrum of diseases. While single-cell Hi-C experiments allow for chromosome conformation profiling across many cells, they are still expensive and not widely available for most labs. Here, we propose an alternate approach, scENCORE, to computationally reconstruct chromatin compartments from the more affordable and widely accessible single-cell epigenetic data. First, scENCORE constructs a long-range epigenetic correlation graph to mimic chromatin interaction frequencies, where nodes and edges represent genome bins and their correlations. Then, it learns the node embeddings to cluster genome regions into A/B compartments and aligns different graphs to quantify chromatin conformation changes across conditions. Benchmarking using cell-type-matched Hi-C experiments demonstrates that scENCORE can robustly reconstruct A/B compartments in a cell-type-specific manner. Furthermore, our chromatin confirmation switching studies highlight substantial compartment-switching events that may introduce substantial regulatory and transcriptional changes in psychiatric disease. In summary, scENCORE allows accurate and cost-effective A/B compartment reconstruction to delineate higher-order chromatin structure heterogeneity in complex tissues.
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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.