MethylProphet: A Generalized Gene-Contextual Model for Inferring Whole-Genome DNA Methylation Landscape

DNA methylation (DNAm), an epigenetic modification, regulates gene expression, influences phenotypes, and encodes inheritable information, making it critical for disease diagnosis, treatment, and prevention. While human genome contains approximately 28 million CpG sites where DNAm can be measured, only 1–3% of these sites are typically available in most datasets due to complex experimental protocols and high costs, hindering insights from DNAm data. Leveraging the relationship between gene expression and DNAm offers promise for computational inference, but existing statistical, machine learning, and masking-based generative Transformers face critical limitations: they cannot infer DNAm at unmeasured CpGs or in new samples effectively. To overcome these challenges, we introduce MethylProphet, a gene-guided, context-aware Transformer model designed for DNAm inference. MethylProphet employs a Bottleneck MLP for efficient gene profile compression and a specialized DNA sequence tokenizer, integrating global gene expression patterns with local CpG context through a Transformer encoder architecture. Trained on whole-genome bisulfite sequencing data from ENCODE (1.6B training CpG-sample pairs; 322B tokens), MethylProphet demonstrates strong performance in hold-out evaluations, effectively inferring DNAm for unmeasured CpGs and new samples. In addition, its application to 10842 pairs of gene expression and DNAm samples at TCGA chromosome 1 (450M training CpGsample pairs; 91B tokens) highlights its potential to facilitate pan-cancer DNAm landscape inference, offering a powerful tool for advancing epigenetic research and precision medicine. All codes, data, protocols, and models are publicly available via https://github.com/xk-huang/methylprophet/ .