G4SCOPE identifies sequence features and epigenetic contexts of chromatin G-quadruplexes

Cancer-associated epigenetic remodeling involves both DNA secondary structures and covalent cytosine modifications, yet predicting chromatin-constrained G-quadruplex (G4) folding remains challenging. Here, we develop G4SCOPE, a multi-task deep learning framework that predicts G4 occupancy and quantitative CUT&Tag signal directly from DNA sequence in isogenic human breast epithelial cells. Trained across wild-type, TP53-deficient, TP53/BRCA1-deficient and TP53/BRCA2-deficient backgrounds, the model integrates convolutional motif learning with transformer-based self-attention to capture local motifs and broader contextual sequence features without auxiliary epigenomic inputs. It distinguishes chromatin-associated G4s from GC-matched loci with in vitro G4-forming potential, captures quantitative variation in local CUT&Tag signal, and performs consistently across the four breast epithelial genotypes. Independent application in K562 cells further supports the portability of the framework to a distinct cellular context. Model-derived G4 representations also separate loci with distinct local 5-methylcytosine and 5-hydroxymethylcytosine profiles. Together, these results show that DNA sequence can predict chromatin G4 occupancy and quantitative CUT&Tag signal, and that the learned representations reflect differences in local cytosine-modification state.

• Type: Epigenetics
• Archive: European Genome-phenome Archive (EGA)