From default protocols to species-specific design: optimizing epi-ddRADseq for plant methylome profiling

DNA methylation adds an important layer of information to studies of plant diversity, adaptation, and crop improvement. However, genome-wide methylation profiling often relies on standardized commercial workflows optimized for a limited number of model or well-established species. This limitation reduces the effectiveness of reduced-representation methods, in which enzyme choice, fragment recovery, and size selection determine which methylome regions are sampled. Here, we present an epi-ddRADseq (epigenetic double-digest Restriction-site Associated DNA sequencing) design framework for plant methylome studies that combines in silico digestion, genome annotation, target-region recovery, narrow size selection, and bisulfite-compatible Y-shaped adapter design. We evaluated 14 carefully selected enzyme-pair combinations across 75 plant species with diverse genome sizes, genome structures, and taxonomic backgrounds. For each species, enzyme pairs and size-selection ranges were ranked according to total fragment recovery and representation of genes, upstream and downstream regions, transposable elements, intergenic regions, and NLR loci. Optimal designs varied among species and genomic targets, showing that enzyme selection is not determined by genome size alone. As a case study, publicly available soybean whole genome bisulfite sequencing (WGBS) data from control and inoculated samples were used to benchmark two epi-ddRADseq designs. The soybean-optimized design more closely reproduced WGBS methylation metaprofiles than the non-optimized alternative, showing higher correlation, lower error, and greater recovery of WGBS-supported differential methylation signals under the applied analysis framework. These results show that species-specific and objective-driven optimization can improve the informativeness of reduced-representation methylome data. More broadly, epi-ddRADseq provides a customizable and cost-effective strategy for large-scale plant epigenotyping, supporting greater methodological autonomy and the local implementation of omics approaches in plant research and breeding.