Uncovering dormancy stage predictors in sweet cherry through DNA methylation and machine learning integration

This study highlights the usefulness of combining high-resolution methylation data with interpretable ML techniques to identify robust dormancy biomarkers. The enrichment of the features associated with dormancy within the transposable elements and the proximal regions of genes suggests an epigenetic regulation through the remodeling of chromatin mediated by TE. These findings contribute to a deeper understanding of dormancy mechanisms and offer a basis for the development of non-destructive tools based on methylation to improve phenological management in perennial fruit crops.