Skip to main content


Fig. 2 | Arthritis Research & Therapy

Fig. 2

From: Thinking BIG rheumatology: how to make functional genomics data work for you

Fig. 2

Sample output from functional genomic assays. Data generated from lung alveolar macrophages (blue) and bone marrow monocytes (grey) isolated from mice [13]. a Genome browser view of raw data from ChIP-seq (H3K4me2, H3K4me1, H3K27ac), ATAC-seq, and 3′-biased RNA-seq in 50 kb locus around RAMP1 and CCR2/CCR5. Highlighted regions from left to right represent: promoter, active intragenic enhancer, and 3′ end of RAMP1 (blue); poised intergenic enhancer and promoter/3′ end of CCR2 (gray); and promoter/3′ end of CCR2 (yellow). Genomic coordinates given above and scale of each track indicated on the left. Genes represented by blue lines below: thin lines for introns, medium lines for untranslated regions (UTRs), thick lines for exons; arrows on the gene body specify gene direction. b Quantitative measures of functional elements in a. Promoter usage is given by H3K4me2, enhancer usage by H3K4me1, enhancer activity by H3K27ac, chromatin accessibility by ATAC-seq, and gene expression by RNA-seq. Values represent normalized density (read count per kb region length per million reads) for ATAC-seq and ChIP-seq, and normalized CPM (counts per million reads) for RNA-seq (note varying scale). RAMP1, example of lung-specific gene with constitutive promoter. CCR2, example of highly monocyte-specific gene with monocyte-specific promoter and enhancer. CCR5, example of nonexpressed gene with low promoter activity. c Heatmaps clustered into lung-specific and monocyte-specific functional elements indicating how data from individual genes are integrated into global analyses. Differential enhancer usage measured by absolute value of H3K4me1 in 6575 regions and differential gene expression measured by relative value of RNA-seq in 3348 genes. ATAC-seq Assay for Transposase Accessible Chromatin followed by high-throughput sequencing, RNA-seq RNA-sequencing

Back to article page