Version Compatibility
Reference examples tested with: GenomicRanges 1.54+, bedtools 2.31+, ggplot2 3.5+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>')then?function_nameto verify parameters - CLI:
<tool> --versionthen<tool> --helpto confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Super-Enhancer Calling
"Identify super-enhancers from H3K27ac ChIP-seq" → Stitch nearby enhancer peaks and rank by signal to find large regulatory domains controlling cell identity genes.
- CLI:
ROSE_main.py -g hg38 -i peaks.gff -r chip.bam -c input.bam
Identify super-enhancers (SEs) - large clusters of enhancers that control cell identity genes.
Background
Super-enhancers are:
- Large clusters of enhancer regions
- Marked by H3K27ac, Med1, BRD4
- Control cell identity genes
- Often altered in disease/cancer
ROSE (Rank Ordering of Super-Enhancers)
Installation
bashgit clone https://github.com/stjude/ROSE.git cd ROSE # Requires samtools, R, bedtools
Input Requirements
- BAM file - H3K27ac ChIP-seq aligned reads
- Peak file - Called peaks (BED or GFF)
- Genome annotation - TSS annotations
Run ROSE
Goal: Identify super-enhancers by stitching nearby enhancer peaks and ranking by H3K27ac signal.
Approach: Run ROSE_main.py with a GFF peak file, ChIP-seq BAM, and optional input control to stitch enhancers within 12.5 kb, rank by signal, and identify the inflection point separating super-enhancers from typical enhancers.
bash# Basic usage python ROSE_main.py \ -g HG38 \ -i peaks.gff \ -r h3k27ac.bam \ -o output_dir \ -s 12500 \ -t 2500 # With control/input python ROSE_main.py \ -g HG38 \ -i peaks.gff \ -r h3k27ac.bam \ -c input.bam \ -o output_dir
Key Parameters
| Parameter | Description | Default |
|---|---|---|
-s | Stitching distance | 12500 bp |
-t | TSS exclusion | 2500 bp |
-c | Control BAM | None |
Output Files
output_dir/ ├── *_AllEnhancers.table.txt # All enhancer regions ├── *_SuperEnhancers.table.txt # Super-enhancers only ├── *_Enhancers_withSuper.bed # BED with SE annotation └── *_Plot_points.png # Hockey stick plot
Prepare Input Files
Convert BED to GFF
bash# ROSE requires GFF format for peaks awk 'BEGIN{OFS="\t"} {print $1,"peaks","enhancer",$2,$3,".",$6,".","ID="NR}' \ peaks.bed > peaks.gff
Filter Peaks for Enhancers
bash# Remove promoter peaks (within 2.5kb of TSS) bedtools intersect -a peaks.bed -b promoters.bed -v > enhancer_peaks.bed
Alternative: HOMER Super-Enhancers
bash# Call super-enhancers with HOMER findPeaks tag_dir/ -style super -o auto # Or from existing peaks findPeaks tag_dir/ -style super -i input_tag_dir/ \ -typical typical_enhancers.txt \ -superSlope -1000 \ > super_enhancers.txt
Alternative: SEanalysis
bash# R-based analysis Rscript << 'EOF' library(SEanalysis) # Load H3K27ac signal at enhancers signal <- read.table('enhancer_signal.txt', header=TRUE) # Rank and identify super-enhancers se_result <- identifySE(signal$signal, method='ROSE') # Get super-enhancer IDs super_enhancers <- signal$id[se_result$is_super] write.table(super_enhancers, 'super_enhancers.txt', quote=FALSE, row.names=FALSE) EOF
Custom Hockey Stick Analysis (R)
Goal: Classify enhancers as super-enhancers vs typical using a custom hockey stick plot and inflection-point detection.
Approach: Rank enhancers by normalized signal, compute the slope at each point, find where the tangent exceeds 1 (inflection point), and classify all enhancers above the inflection as super-enhancers.
rlibrary(ggplot2) # Load enhancer signal data enhancers <- read.table('enhancer_signal.txt', header=TRUE) # Rank by signal enhancers <- enhancers[order(enhancers$signal), ] enhancers$rank <- 1:nrow(enhancers) # Find inflection point (tangent = 1) # Normalize ranks and signal to 0-1 enhancers$rank_norm <- enhancers$rank / max(enhancers$rank) enhancers$signal_norm <- enhancers$signal / max(enhancers$signal) # Calculate slope at each point n <- nrow(enhancers) slopes <- diff(enhancers$signal_norm) / diff(enhancers$rank_norm) inflection <- which(slopes > 1)[1] # Classify enhancers$type <- ifelse(enhancers$rank >= inflection, 'Super-Enhancer', 'Typical') # Plot ggplot(enhancers, aes(rank, signal, color = type)) + geom_point(size = 0.5) + scale_color_manual(values = c('Super-Enhancer' = 'red', 'Typical' = 'grey60')) + geom_vline(xintercept = inflection, linetype = 'dashed') + labs(x = 'Enhancer Rank', y = 'H3K27ac Signal', title = 'Super-Enhancer Identification') + theme_bw() ggsave('hockey_stick_plot.pdf', width = 8, height = 6) # Output super-enhancers super_enhancers <- enhancers[enhancers$type == 'Super-Enhancer', ] write.table(super_enhancers, 'super_enhancers.txt', sep = '\t', quote = FALSE, row.names = FALSE)
Calculate Enhancer Signal
bash# Get H3K27ac signal at peak regions bedtools multicov -bams h3k27ac.bam -bed enhancer_peaks.bed > enhancer_counts.txt # Normalize by peak size awk 'BEGIN{OFS="\t"} { size = $3 - $2 rpm = ($NF / TOTAL_READS) * 1e6 rpkm = rpm / (size / 1000) print $0, rpkm }' enhancer_counts.txt > enhancer_signal.txt
Downstream Analysis
Gene Assignment
bash# Assign super-enhancers to nearest genes bedtools closest -a super_enhancers.bed -b genes.bed -d > se_gene_assignment.txt
Compare Conditions
Goal: Find super-enhancers gained or lost between two experimental conditions.
Approach: Convert super-enhancer tables to GRanges objects and use subsetByOverlaps with invert to identify condition-specific super-enhancers.
r# Load SE from two conditions se1 <- read.table('condition1_SE.txt', header=TRUE) se2 <- read.table('condition2_SE.txt', header=TRUE) # Find differential super-enhancers library(GenomicRanges) gr1 <- makeGRangesFromDataFrame(se1) gr2 <- makeGRangesFromDataFrame(se2) # Gained in condition 2 gained <- subsetByOverlaps(gr2, gr1, invert=TRUE) # Lost in condition 2 lost <- subsetByOverlaps(gr1, gr2, invert=TRUE)
Enrichment of Disease Variants
bash# Check if GWAS SNPs enriched in super-enhancers bedtools intersect -a gwas_snps.bed -b super_enhancers.bed -wa -wb > snps_in_SE.txt # Calculate enrichment total_snps=$(wc -l < gwas_snps.bed) snps_in_se=$(wc -l < snps_in_SE.txt) se_coverage=$(awk '{sum += $3-$2} END {print sum}' super_enhancers.bed) genome_size=3000000000 expected=$(echo "$total_snps * $se_coverage / $genome_size" | bc -l) enrichment=$(echo "$snps_in_se / $expected" | bc -l) echo "Enrichment: $enrichment"
Complete Workflow
bash#!/bin/bash set -euo pipefail H3K27AC_BAM=$1 PEAKS_BED=$2 OUTPUT_DIR=$3 mkdir -p $OUTPUT_DIR echo "=== Convert peaks to GFF ===" awk 'BEGIN{OFS="\t"} {print $1,"peaks","enhancer",$2,$3,".",$6,".","ID="NR}' \ $PEAKS_BED > $OUTPUT_DIR/peaks.gff echo "=== Run ROSE ===" python ROSE_main.py \ -g HG38 \ -i $OUTPUT_DIR/peaks.gff \ -r $H3K27AC_BAM \ -o $OUTPUT_DIR \ -s 12500 \ -t 2500 echo "=== Summary ===" n_typical=$(grep -c "Typical" $OUTPUT_DIR/*_AllEnhancers.table.txt || echo 0) n_super=$(wc -l < $OUTPUT_DIR/*_SuperEnhancers.table.txt) echo "Typical enhancers: $n_typical" echo "Super-enhancers: $n_super"
Related Skills
- chip-seq/peak-calling - Call H3K27ac peaks first
- chip-seq/peak-annotation - Annotate SE to genes
- chip-seq/differential-binding - Compare SE between conditions
- data-visualization/genome-tracks - Visualize SE regions
