# Bio Chipseq Qc

ChIP-seq quality control metrics including FRiP (Fraction of Reads in Peaks)...

- Repository: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills (3090 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-07-21
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-qc -g -a claude-code -y`
Codex: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-qc -g -a codex -y`
Cursor: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-qc -g -a cursor -y`
Gemini CLI: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-qc -g -a gemini-cli -y`

## Description

ChIP-seq quality control metrics including FRiP (Fraction of Reads in Peaks), cross-correlation analysis (NSC/RSC), library complexity, and IDR (Irreproducibility Discovery Rate) for replicate concordance. Use to assess experiment quality before downstream analysis. Use when assessing ChIP-seq data quality metrics.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## Version Compatibility

Reference examples tested with: MACS3 3.0+, Subread 2.0+, bedtools 2.31+, deepTools 3.5+, pybedtools 0.9+, pysam 0.22+, samtools 1.19+

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- 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.

# ChIP-seq Quality Control

**"Assess the quality of my ChIP-seq experiment"** → Compute FRiP, cross-correlation (NSC/RSC), library complexity, and IDR replicate concordance to evaluate enrichment success.
- CLI: `deeptools plotFingerprint`, `phantompeakqualtools run_spp.R`
- Python: `pysam` + `pybedtools` for custom QC metrics

Quality metrics for assessing ChIP-seq experiment success and replicate reproducibility.

## FRiP (Fraction of Reads in Peaks)

**Goal:** Quantify enrichment strength by measuring the proportion of reads falling within called peaks.

**Approach:** Count reads overlapping peak regions and divide by total mapped reads.

### Calculate FRiP with bedtools

```bash
# Count reads in peaks
reads_in_peaks=$(bedtools intersect -a chip.bam -b peaks.narrowPeak -u | samtools view -c -)
total_reads=$(samtools view -c -F 260 chip.bam)

# Calculate FRiP
frip=$(echo "scale=4; $reads_in_peaks / $total_reads" | bc)
echo "FRiP: $frip"
```

### Calculate FRiP with featureCounts

```bash
# Convert peaks to SAF format
awk 'BEGIN{OFS="\t"} {print $4, $1, $2, $3, "."}' peaks.narrowPeak > peaks.saf

# Count reads in peaks
featureCounts -a peaks.saf -F SAF -o peak_counts.txt chip.bam

# FRiP from summary
grep -v "^#" peak_counts.txt.summary
```

### Calculate FRiP with pysam

```python
import pysam
import pybedtools

The rest: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/blob/HEAD/skills/bio-chipseq-qc/SKILL.md