Evidence
Brief Bioinform. 2024 Jan 22;25(2):bbae096. doi: 10.1093/bib/bbae096.
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.
PMID:38493342 | DOI:10.1093/bib/bbae096
Add to Google Keep
Estimated reading time: 4 minute(s)
Latest: Psychiatryai.com #RAISR4D
Cool Evidence: Engaging Young People and Students in Real-World Evidence ☀️
Real-Time Evidence Search [Psychiatry]
AI Research [Andisearch.com]
scENCORE: leveraging single-cell epigenetic data to predict chromatin conformation using graph embedding
🌐 90 Days
Evidence Blueprint
scENCORE: leveraging single-cell epigenetic data to predict chromatin conformation using graph embedding
☊ AI-Driven Related Evidence Nodes
(recent articles with at least 5 words in title)
More Evidence