histone-aggregation
Build comprehensive histone mark maps by aggregating narrowPeak data across multiple ENCODE experiments, donors, and labs. Use when the user wants to answer "where is this histone mark present in my tissue?" by combining peak calls from multiple studies into a union peak set with confidence annotations. Handles cross-lab batch effects, broad vs narrow marks, and ENCODE blocklist filtering.
npx skills add majiayu000/claude-skill-registry --skill histone-aggregation-ammawla-encode-toolkit --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Aggregate Histone ChIP-seq Peaks Across Studies ## When to Use - User wants to combine histone ChIP-seq peaks across multiple ENCODE experiments for a tissue or cell type - User asks "where is H3K27ac in pancreas?" or "build a histone mark map for liver" - User needs a union peak set from multiple donors, labs, or replicates - User wants to create a consensus binding map from multiple ChIP-seq datasets - Example queries: "aggregate all H3K4me3 peaks in brain", "combine histone marks across donors", "build enhancer map from H3K27ac data" Build a comprehensive map of histone mark binding for a tissue/cell type by merging narrowPeak files from multiple ENCODE experiments into a union peak set. ## Scientific Rationale **The question**: "Does my tissue have this histone mark, and at what genomic locations?" This is a **detection/cataloging** question, not a differential one. Once a histone mark passes noise thresholds (ENCODE IDR, quality metrics), detection is binary — the mark is either bound or not. If detected in one donor but not another, that region is still a real binding site. Individual variation and technical differences (lab, depth, antibody lot) explain *absence*, not that
- When to Use
- Scientific Rationale
- Literature Support
- Step 1: Find All Available Experiments
- Step 2: Quality-Gate Each Experiment
- Include if:
- Exclude if:
- Step 3: Download IDR Thresholded NarrowPeak Files
- Step 4: Per-Sample Noise Filtering
- 4a. ENCODE Blocklist Filtering (Amemiya et al. 2019)
- 4b. SignalValue Filtering (Perna et al. 2024)
- Step 5: Union Merge Across Samples
- 5a. Handling Broad vs Narrow Marks
- 5b. Label Peaks by Sample Before Merge
Download ENCODE blocklist for GRCh38 from:
For mm10: https://github.com/Boyle-Lab/Blacklist/blob/master/lists/mm10-blacklist.v2.bed.gz
bedtools intersect -a sample.narrowPeak -b hg38-blacklist.v2.bed -v > sample.filtered.narrowPeak
Calculate the 25th percentile of the signalValue DISTRIBUTION for this sample
awk -v t="$THRESHOLD" '$7 >= t' sample.filtered.narrowPeak > sample.qfiltered.narrowPeak
Tag each sample's peaks with a unique sample ID (column 4 = name field)
awk -v sid="sample1" 'BEGIN{OFS="\t"} {$4=sid; print}' sample1.qfiltered.narrowPeak > sample1.tagged.bed
awk -v sid="sample2" 'BEGIN{OFS="\t"} {$4=sid; print}' sample2.qfiltered.narrowPeak > sample2.tagged.bed
Concatenate all tagged peaks
cat sample*.tagged.bed > all_peaks.bedWhat does the histone-aggregation skill do?
Build comprehensive histone mark maps by aggregating narrowPeak data across multiple ENCODE experiments, donors, and labs. Use when the user wants to answer "where is this histone mark present in my tissue?" by combining peak calls from multiple studies into a union peak set with confidence annotations. Handles cross-lab batch effects, broad vs narrow marks, and ENCODE blocklist filtering.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill histone-aggregation-ammawla-encode-toolkit --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.