bio-atac-seq-atac-peak-calling
Call accessible chromatin regions from ATAC-seq BAM files using MACS3, MACS2, Genrich, or HMMRATAC. Use when identifying open chromatin from aligned ATAC-seq, choosing between point-source vs HMM peak callers, applying ENCODE-style pseudoreplicate IDR, removing blacklist regions, or fixing 501bp consensus peaks for downstream differential analysis.
npx skills add BioTender-max/awesome-bio-agent-skills --skill atac-peak-calling --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.
What it does
Call accessible chromatin regions from ATAC-seq BAM files using MACS3, MACS2, Genrich, or HMMRATAC. It guides choosing between point-source vs HMM peak callers, applying ENCODE-style pseudoreplicate IDR, removing blacklist regions, and fixing 501bp consensus peaks for downstream differential analysis.
How it works
Describes canonical command patterns for each tool and scenario:
- macs2 callpeak with BAM/BAMPE inputs and ENCODE-like options (e.g., -p 0.01, -B, --SPMR, --nomodel, --shift -75, --extsize 150, -f BAM or BAMPE).
- macs3 hmmratac invocation for single-sample HMM-based calling.
- Genrich in joint-replicate mode with -j and -e/ -E options; guidance on down-sampling and PCR-dup removal.
- ENCODE 4 recommendations: per-replicate calls with -p 0.01 and pooled/pseudoreplicates using samtools pseudoreplication; use IDR to derive reproducible peaks.
- Shift-extend vs BAMPE explanations for Pattern A vs Pattern B input methods, including -f BAM --shift --extsize versus -f BAMPE.
- Effective genome size considerations and how to parameterize -g hs/mm vs the deeper deepTools-derived values.
- Handling of broad vs narrow peak calls, and when to use each for differential analysis or SE annotation.
When to use it
Triggered by: identifying open chromatin from ATAC-seq data, choosing between point-source vs HMM peak callers, applying ENCODE pseudoreplicate IDR, or removing blacklist regions, or adjusting peak consensus sizing for differential analysis.
What it can touch
Uses tools listed in the skill: CLI tools including macs2, macs3, Genrich, HMMRATAC; supports -t inputs, -f input format specifications, -g genome size specifiers, -e/ -E blacklist files, and IDR-related steps. The skill folder ships executable scripts that implement these patterns.
Caveats
Cites version compatibility requirements (MACS3 3.0.2+, MACS2 2.2.9+, Genrich 0.6.1+, HMMRATAC 1.2+; samtools 1.19+, bedtools 2.31+, IDR 2.0.4+), with instructions to verify versions via <tool> --version and <tool> --help. Notes potential issues with broad vs narrow modes, depth sensitivity for HMMRATAC, and ENCODE 4 vs ENCODE 3 differences. Includes handling for chromap aligners and warnings about shift behavior when using BAMPE inputs. No claims about outcomes beyond the explicit patterns and guidance provided.
## Version Compatibility Reference examples tested with: MACS3 3.0.2+, MACS2 2.2.9+, Genrich 0.6.1+, HMMRATAC 1.2+ (now bundled in MACS3 as `macs3 hmmratac`), samtools 1.19+, bedtools 2.31+, IDR 2.0.4+. Before using code patterns, verify installed versions match. If versions differ: - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws unexpected errors, introspect the installed binary (`<tool> -h`) and adapt the example to match the actual CLI rather than retrying. # ATAC-seq Peak Calling **"Call accessible regions from my ATAC-seq BAM"** -> Identify Tn5-hypersensitive open chromatin, treating fragments as point insertion events (not protein-bound regions as in ChIP-seq) and accounting for the lack of input control. - CLI (canonical, ENCODE 4): `macs2 callpeak -t atac.bam -f BAMPE -g hs -n sample --nomodel --shift -75 --extsize 150 --keep-dup all -B --SPMR -p 0.01` - CLI (HMM-based, single sample): `macs3 hmmratac -i atac.bam -n sample --outdir hmm_out` - CLI (joint replicates): `Genrich -j -t rep1.bam,rep2.bam -o peaks.narrowPeak -e chrM -E blacklist.bed` The `-p 0.01` (loose) plus IDR is the ENCODE pattern: low stringency increases peak overlap between r
- Version Compatibility
- Algorithmic Taxonomy
- Shift-Extend vs BAMPE: The Critical Choice
- Effective Genome Size
- Effective Genome Size: When It Matters
- Per-Tool Failure Modes
- MACS2/MACS3 -- Confounded NFR + broad accessibility
- Genrich -- Replicate weighting and chrM exclusion
- MACS3 hmmratac (HMMRATAC) -- Depth and fragment-size dependence
- HOMER findPeaks -- Window-size sensitivity
- Aligner choice -- chromap vs bwa-mem2 vs bowtie2 affects peak shape
- Single-sample (no replicate) -- Rotation / circular-shift permutation
- ENCODE 3 vs ENCODE 4 Differences
- Super-Enhancer Detection
ROSE expects H3K27ac BAM but works on ATAC narrowPeak with care ROSE_main.py -g hg38 -i atac_peaks.gff -r atac.bam -o rose_out/ -t 2500 Per-replicate peak calling (loose threshold) macs2 callpeak \ Pooled (all replicates) Pseudoreplicates (split each rep BAM in half) samtools view -b -h -s 1.5 rep1.filt.dedup.bam > rep1.psr1.bam # seed.fraction samtools view -b -h -s 2.5 rep1.filt.dedup.bam > rep1.psr2.bam # different seed Sort peaks by p-value (column 8) so IDR scores by significance sort -k8,8nr rep1_peaks.narrowPeak > rep1.sorted.narrowPeak
What does the bio-atac-seq-atac-peak-calling skill do?
Call accessible chromatin regions from ATAC-seq BAM files using MACS3, MACS2, Genrich, or HMMRATAC. Use when identifying open chromatin from aligned ATAC-seq, choosing between point-source vs HMM peak callers, applying ENCODE-style pseudoreplicate IDR, removing blacklist regions, or fixing 501bp consensus peaks for downstream differential analysis.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill atac-peak-calling --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 BioTender-max/awesome-bio-agent-skills, a repository with 135 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.