In Science advances
Increased appreciation of 5-hydroxymethylcytosine (5hmC) as a stable epigenetic mark, which defines cell identity and disease progress, has engendered a need for cost-effective, but high-resolution, 5hmC mapping technology. Current enrichment-based technologies provide cheap but low-resolution and relative enrichment of 5hmC levels, while single-base resolution methods can be prohibitively expensive to scale up to large experiments. To address this problem, we developed a deep learning-based method, "DeepH&M," which integrates enrichment and restriction enzyme sequencing methods to simultaneously estimate absolute hydroxymethylation and methylation levels at single-CpG resolution. Using 7-week-old mouse cerebellum data for training the DeepH&M model, we demonstrated that the 5hmC and 5mC levels predicted by DeepH&M were in high concordance with whole-genome bisulfite-based approaches. The DeepH&M model can be applied to 7-week-old frontal cortex and 79-week-old cerebellum, revealing the robust generalizability of this method to other tissues from various biological time points.
He Yu, Jang Hyo Sik, Xing Xiaoyun, Li Daofeng, Vasek Michael J, Dougherty Joseph D, Wang Ting