5.ChIPseq-QC/SKILL.md
Performs ChIP-specific biological validation. It calculates metrics unique to protein-binding assays, such as Cross-correlation (NSC/RSC) and FRiP. Use this when you have filtered the BAM file and called peaks for ChIP-seq data. Do NOT use this skill for ATAC-seq data or general alignment statistics.
npx skillsauth add bisnake2001/chromskills ChIPseq-QCInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill performs a full ChIP-seq quality control analysis from aligned BAM files and peak files.
Main steps include:
${proj_dir} in Step 0.${sample}.bam # filtered bam files
${sample}.narrowPeak # or broadPeak
all_chip_qc/
${sample}_spp.txt
${sample}_crosscorr.pdf
${sample}_frip.txt
Call:
mcp__project-init-tools__project_initwith:
sample: alltask: atac_qcThe tool will:
all_chip_qc directory.all_chip_qc directory, which will be used as ${proj_dir}.Call:
bam_file: Path to BAM fileoutput_dir: ${proj_dir}/Output: ${sample}_spp.txt, ${sample}_crosscorr.pdf
Call:
Output: ${sample}_frip.txt
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.
data-ai
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.
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.