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ChromHMM outputs both the learned chromatin-state model parameters and the chromatin-state assignments for each genomic position. The learned emission and transition parameters are returned in both text and image format (Fig. 1), automatically grouping chromatin states with similar emission parameters or proximal genomic locations, although a user-specified reordering can also be used (Supplementary Figs. 12 and Supplementary Note). ChromHMM enables the study of the likely biological roles of each chromatin state based on enrichment in diverse external annotations and experimental data, shown as heat maps and tables (Fig. 1), both for direct genomic overlap and at various distances from a chromatin state (Supplementary Fig. 3). ChromHMM also generates custom genome browser tracks6 that show the resulting chromatin-state segmentation in dense view (single color-coded track) or expanded view (each state shown separately) (Fig. 1). All the files ChromHMM produces by default are summarized on a webpage (Supplementary Data).
ChromHMM also enables the analysis of chromatin states across multiple cell types. When the chromatin marks are common across the cell types, a common model can be learned by a virtual concatenation of the chromosomes of all cell types. Alternatively a model can be learned by a virtual stacking of all marks across cell types, or independent models can be learned in each cell type. Lastly, ChromHMM supports the comparison of models with different number of chromatin states based on correlations...