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_name to verify parameters
- CLI:
<tool> --version then <tool> --help to 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
git 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.
# 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
# ROSE requires GFF format for peaks
awk 'BEGIN{OFS="\t"} {print $1,"peaks","enhancer",$2,$3,".",$6,".","ID="NR}' \
peaks.bed > peaks.gff
Filter Peaks for Enhancers
# Remove promoter peaks (within 2.5kb of TSS)
bedtools intersect -a peaks.bed -b promoters.bed -v > enhancer_peaks.bed
Alternative: HOMER Super-Enhancers
# Call super-enhancers with HOMER
findPeaks tag_dir/ -style super -o auto
# Or from existing peaks
findPeaks tag_dir/ -style super -i input_tag_dir/ \
-typical typical_enhancers.txt \
-superSlope -1000 \
> super_enhancers.txt
Alternative: SEanalysis
# R-based analysis
Rscript << 'EOF'
library(SEanalysis)
# Load H3K27ac signal at enhancers
signal <- read.table('enhancer_signal.txt', header=TRUE)
# Rank and identify super-enhancers
se_result <- identifySE(signal$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.
library(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
# 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 $0, rpkm
}' enhancer_counts.txt > enhancer_signal.txt
Downstream Analysis
Gene Assignment
# Assign super-enhancers to nearest genes
bedtools closest -a super_enhancers.bed -b genes.bed -d > se_gene_assignment.txt
Compare Conditions
Goal: Find super-enhancers gained or lost between two experimental conditions.
Approach: Convert super-enhancer tables to GRanges objects and use subsetByOverlaps with invert to identify condition-specific super-enhancers.
# Load SE from two conditions
se1 <- read.table('condition1_SE.txt', header=TRUE)
se2 <- read.table('condition2_SE.txt', header=TRUE)
# Find differential super-enhancers
library(GenomicRanges)
gr1 <- makeGRangesFromDataFrame(se1)
gr2 <- makeGRangesFromDataFrame(se2)
# Gained in condition 2
gained <- subsetByOverlaps(gr2, gr1, invert=TRUE)
# Lost in condition 2
lost <- subsetByOverlaps(gr1, gr2, invert=TRUE)
Enrichment of Disease Variants
# Check if GWAS SNPs enriched in super-enhancers
bedtools intersect -a gwas_snps.bed -b super_enhancers.bed -wa -wb > snps_in_SE.txt
# Calculate enrichment
total_snps=$(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
#!/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