(A) Classification of set of ~70,000 open chromatin regions (DHS) identified in adult male mouse liver, based on relative intensities for a combination of H3K4me1 and H3K4me3 marks, and CTCF ChIP-seq data. Based on the combinatorial signal from these three datasets, five groups of DHS were identified: promoter-DHS, weak promoter-DHS, enhancer-DHS, weak enhancer-DHS, and insulator-DHS, as described in Materials and methods and in Figure 4—figure supplement 1A. (B) Shown is a heatmap representation of the simplified five-class DHS model shown in panel A, which captures features such as CNC enrichment at enhancers and K27ac enrichment at enhancers and promoters, with additional features described in Figure 4—figure supplement 1A. Color bar at the left matches colors used in panel A. (C) Scheme for using 19 published mouse liver H3K27ac ChIP-seq datasets to identify a core set of 503 liver super-enhancers using the ROSE software package (Supplementary file 2B). These 503 super-enhancers were identified in all 19 samples, indicating they are active in both male and female liver, and across multiple circadian time points. Enh, enhancer, WE, weak enhancer. (D) Genes associated with super-enhancers (SE) are more highly expressed (log2(FPKM +1) values) than genes associated with typical enhancers (TE), for both protein coding genes and liver-expressed multi-exonic lncRNA genes. The super-enhancer-adjacent genes are also more tissue specific (higher Tau score) than typical enhancer-adjacent genes. ****, KS t-test, p<0.0001 for pairwise comparisons of SE-adjacent genes vs. TE-adjacent genes. (E) Venn diagrams show substantial overlap between typical enhancer gene targets across tissues (liver, ESCs, ProB cells), but limited overlap between super-enhancer adjacent genes (within 10 kb of the super-enhancer) for the same tissues. The numbers represent the percent of genes targeted in a given tissue by the indicated class of enhancer (typical enhancers or super-enhancers) that are not targets of the corresponding class of enhancers in the other two tissues. For example, 93.2% of genes targeted by liver super-enhancers are not targeted by the set of super-enhancers identified in either Pro-B or mouse ESCs. Gene targets of each enhancer class were identified by GREAT using default parameters, then filtered to keep only those ≤10 kb from the enhancer. (F) ChIP and DNase-seq signal at typical enhancers and super-enhancers, scaled to their median length (1 kb and 44 kb respectively; indicated by distance between hash marks along the x-axis) flanked by 10 kb up- and down-stream. Super-enhancers show much greater accumulation of RNA polymerase 2, despite little or no apparent enrichment for the promoter mark H3K4me3. (G) Super-enhancers (SE) target distinct categories of genes than typical enhancers (TE) in mouse liver. Thus, while GO terms such as oxidoreductase activity are enriched in the set of gene targets for both classes of enhancers, only super-enhancers are enriched for transcription-regulated terms (e.g., Regulation of transcription, Steroid hormone receptor activity) (Supplementary file 2C,D). Numbers represent the overlap of GO terms (either Molecular Function or Biological Process) in any DAVID annotation cluster (with an enrichment score >1.3) enriched for genes regulated by either typical enhancers or super-enhancers.