De la Fuente, I. M.; Carrasco-Pujante, J.; Fedetz, M.; Legarreta, L.; Malaina, I.; Camino-Pontes, B.; Perez-Yarza, G.; Martinez, L.; Cortes, J. M.; Lopez, J. I.

The information content of the genome has been extensively analyzed. However, a comparable quantitative framework for DNA methylation is still lacking. Without such quantification, the magnitude of this regulatory and dynamic epigenetic structure remains conceptually imprecise, even though methylation dysregulation is strongly linked to disease-related phenotypes and altered cellular identity. Here we address this gap by applying Shannon information theory to DNA methylation. We first consider methylation marks as binary or probabilistic regulatory states and estimate the theoretical upper-bound information capacity of the human methylome under simplifying assumptions. We then progressively refine this estimate by incorporating biologically relevant constraints, including methylation bias, bimodal methylation distributions, local CpG correlation, genomic regulatory class, and cell-type-discriminative methylation patterns. This approach allows us to distinguish between theoretical methylation capacity, statistical methylation entropy, and biologically interpretable regulatory information. Finally, we consider methylation information from a discriminative perspective, analyzing its contribution to distinguishing cell types and regulatory cellular states. Within this framework, mutual information between methylation patterns and cell identity provides a biologically constrained estimate of methylations role as an epigenetic identity code. Our layered analysis reconciles megabit-scale methylome capacity with compact, biologically interpretable identity signatures. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/735086v1_ufig1.gif" ALT="Figure 1"> View larger version (73K): [email protected]@19a9e62org.highwire.dtl.DTLVardef@14410forg.highwire.dtl.DTLVardef@1bb7c59_HPS_FORMAT_FIGEXP M_FIG C_FIG