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Learn MoreIncreased appreciation of 5-hydroxymethylation (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 DeepH&M model, we demonstrated that the 5hmC and 5mC levels predicted by DeepH&M were in high concordance with gold standard data predicted by TAB-seq and WGBS. The DeepH&M model can be applied to 7-week old frontal cortex and 79-week cerebellum revealing the robust generalizability of this method to other tissues from various biological time points. SOURCE: Daofeng Li (lidaof@gmail.com) - Ting Wang Lab Washington University in St. Louis
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