A ChromHMM model applicable to all 127 epigenomes was learned by virtually concatenating consolidated data corresponding to the core set of 5 chromatin marks assayed in all epigenomes (H3K4me3, H3K4me1, H3K36me3, H3K27me3, H3K9me3). The model was trained on 60 epigenomes with highest-quality data (Fig. 2k), which provided sufficient coverage of the different lineages and tissue types (Table S1 - Sheet QCSummary). The ChromHMM parameters used were as follows: Reads were shifted in the 5’ to 3’ direction by 100 bp. For each consolidated ChIP-seq dataset, read counts were computed in non-overlapping 200 bp bins across the entire genome. Each bin was discretized into two levels, 1 indicating enrichment and 0 indicating no enrichment. The binarization was performed by comparing ChIP-seq read counts to corresponding whole-cell extract control read counts within each bin and using a Poisson p-value threshold of 1e-4 (the default discretization threshold in ChromHMM). We trained several models with the number of states ranging from 10 states to 25 states. We decided to use a 15-state model (Fig. 4a-f, Extended Data 2b) for all further analyses since it captured all the key interactions between the chromatin marks, and because larger numbers of states did not capture sufficiently distinct interactions. The trained model was then used to compute the posterior probability of each state for each genomic bin in each reference epigenome. The regions were labeled using the state with the maximum posterior probability.