3.peak-calling/SKILL.md
Perform peak calling for ChIP-seq or ATAC-seq data using MACS3, with intelligent parameter detection from user feedback. Use it when you want to call peaks for ChIP-seq data or ATAC-seq data.
npx skillsauth add bisnake2001/chromskills peak-callingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill automatically performs core peak calling with MACS2 for ChIP-seq and ATAC-seq data, based on the BAM files in the current directory. It includes automatic experiment recognition and parameter selection.
Main steps include:
${proj_dir} in Step 0.genome_size to use (e.g. hs or mm). Never decide by yourself.filtered.bam) if available.${sample}_used_parameters.txt) with justification for each chosen option.${sample}.bam # filtered bam files
all_peak_calling/
peaks/
${sample}.narrowPeak # or ${sample}.broadPeak
temp/
logs/
${sample}_used_parameters.txt
Call:
mcp__project-init-tools__project_initwith:
sample: alltask: peak_callinggenome: provided by userThe tool will:
${sample}_peak_calling directory.${sample}_peak_calling directory, which will be used as ${proj_dir}.Command Example
find . -name "*.bam" | sort
CTCF, H3K27me3, ATAC).*.filtered.bam) if available.Command Example
samtools flagstat sample.bam | egrep "properly paired|singletons"
properly paired > 0 → Paired-end (-f BAMPE)singletons ≈ total → Single-end (-f BAM)| Detected Pattern | Experiment Type | Peak Type | Parameter Key Options |
|------------------|-----------------|------------|------------------------|
| TF name (CTCF, GATA1, MYC, TP53…) | TF ChIP-seq | Narrow | --call-summits -q 0.01 |
| Active histone marks (H3K4me3, H3K27ac, H3K9ac) | Histone (sharp) | Narrow | --call-summits -q 0.05 |
| Broad histone marks (H3K27me3, H3K9me3, H3K36me3) | Histone (broad) | Broad | --broad --broad-cutoff 0.1 -q 0.05 |
| H3K4me1 | Intermediate | Narrow | --call-summits -q 0.05 (optional --broad) |
| ATAC | ATAC-seq | Narrow | --nomodel --shift -100 --extsize 200 -q 0.05 |
Call:
with:
treatment_file: Path to treatment BAM file.control_file: Path to control/input BAM file. Required for ChIP-seq data. Prompt the user for the required file if not provided.genome_size: Always provided by user.name: Experiment name (prefix for output files).out_dir: ${proj_dir}/peaksbroad: If True, call broad peaks (for histone marks).broad_cutoff: Cutoff for broad region calling.qvalue: Q-value cutoff for peak detection. Prompt the user for the q value cutoff.format: use BAMPE for pair-end data, BAM for single-end data.nomodel: True for ATAC-seq, False for ChIP-seq.shift: Shift size in bp (e.g., -100 for ATAC-seq).extsize:"Extension size in bp (e.g., 200 for ATAC-seq).After auto-selection, the skill writes a log file:
Example content:
Genome detected:
Experiment type: H3K27me3 (broad histone)
Sequencing type: paired-end
Control used: input_control.bam
MACS3 mode: --broad --broad-cutoff 0.1 -q 0.05
Reasoning:
- Broad mark (H3K27me3) requires domain-level detection
- Control detected and applied
- Genome identified as <*>; using -g <*>
- Paired-end library; use -f BAMPE
development
Align ChIP-seq or ATAC-seq FASTQ files to a reference genome using Bowtie2, with strict input validation, library layout detection, output organization and logging. Use it when raw sequencing reads must be converted into sorted/indexed BAM files before downstream QC, peak calling, or footprinting.
development
Align bisulfite sequencing DNA methylation reads using Bismark only, with explicit validation of reference preparation, library layout detection, output organization, logging, and alignment QC. Use it for WGBS, RRBS, or other bisulfite-converted DNA methylation sequencing data when raw FASTQ files must be aligned before methylation extraction and downstream analysis.
devops
The TF-differential-binding pipeline performs differential transcription factor (TF) binding analysis from ChIP-seq datasets (TF peaks) using the DiffBind package in R. It identifies genomic regions where TF binding intensity significantly differs between experimental conditions (e.g., treatment vs. control, mutant vs. wild-type). Use the TF-differential-binding pipeline when you need to analyze the different function of the same TF across two or more biological conditions, cell types, or treatments using ChIP-seq data or TF binding peaks. This pipeline is ideal for studying regulatory mechanisms that underlie transcriptional differences or epigenetic responses to perturbations.
development
The differential-region-analysis pipeline identifies genomic regions exhibiting significant differences in signal intensity between experimental conditions using a count-based framework and DESeq2. It supports detection of both differentially accessible regions (DARs) from open-chromatin assays (e.g., ATAC-seq, DNase-seq) and differential transcription factor (TF) binding regions from TF-centric assays (e.g., ChIP-seq, CUT&RUN, CUT&Tag). The pipeline can start from aligned BAM files or a precomputed count matrix and is suitable whenever genomic signal can be summarized as read counts per region.