scWGBS-GPT: A Foundation Model for Capturing Long-Range CpG Dependencies in Single-Cell Whole-Genome Bisulfite Sequencing to Enhance Epigenetic Analysis
Liang, C.; Ye, P.; Yan, H.; Zheng, P.; Sun, J.; Wang, Y.; Li, Y.; Ren, Y.; Jiang, Y.; Xiang, J.; Zhang, S.; Jiang, L.; Bai, W.; Ma, X.; Chen, T.; Zuo, W.; Bai, L.; Ouyang, W.; Li, J.
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Single-cell DNA methylomes are challenging to interpret because of sparse CpG coverage and the complexity of genome-wide sequences. We present scDNAm-GPT, a universal foundation model that uses context-aware CpG tokenization, a Mamba backbone, and cross-attention to capture both local and global DNA methylation patterns. Trained on over one million single cells from 35 human and mouse tissues, scDNAm-GPT enables accurate cell clustering, zero-shot prediction of CpG effects on gene expression, improved trajectory inference, and reference-free deconvolution of cell types from cell-free DNA. The model hierarchically learns regulatory features, and its attention maps highlight functionally relevant regions, demonstrating high biological interpretability. These results establish scDNAm-GPT as a scalable and generalizable framework for single-cell epigenomic analysis, offering new opportunities to dissect epigenetic regulation in health and disease. Code is available at GitHub (https://github.com/ChaoqiLiang/scDNAm-GPT).
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