Skip to content
Back to skills

Clip Peak Calling

ASecurity

Call protein-RNA binding site peaks from CLIP-seq data using CLIPper, PureCLIP, or Piranha. Use when identifying RBP binding sites from aligned CLIP reads.

  • 1,198 stars
  • 0 votes
  • 0 copies
  • 1 view
  • Added February 7, 2026
datapythongobash

Works with

  • cli

Security analysis

A100/100

Pro scans all 3 files and shows the line behind each finding

Scanned February 12, 2026

npx -y skills add GPTomics/bioSkills --skill clip-peak-calling --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Clip Peak Calling?

Add the live security badge to your README. It updates with every re-scan.

Security grade badge for Clip Peak Calling
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/gptomics-clip-peak-calling/badge)](https://www.skillsdirectory.com/skills/gptomics-clip-peak-calling)

More formats (shields.io, HTML) on the badges page. Keep it an A: scan every change in CI with Pro.

Download with Pro
SKILL.md
---
name: bio-clip-seq-clip-peak-calling
description: Call protein-RNA binding site peaks from CLIP-seq data using CLIPper, PureCLIP, or Piranha. Use when identifying RBP binding sites from aligned CLIP reads.
tool_type: cli
primary_tool: CLIPper
---

# CLIP-seq Peak Calling

## CLIPper (Recommended)

```bash
# Basic peak calling
clipper \
    -b deduped.bam \
    -s hg38 \
    -o peaks.bed \
    --save-pickle

# With FDR threshold
clipper \
    -b deduped.bam \
    -s hg38 \
    -o peaks.bed \
    --FDR 0.05 \
    --superlocal

# Specify gene annotations
clipper \
    -b deduped.bam \
    -s hg38 \
    --gene genes.bed \
    -o peaks.bed
```

## CLIPper Options

| Option | Description |
|--------|-------------|
| -b | Input BAM file |
| -s | Species (hg38, mm10) |
| -o | Output BED file |
| --FDR | FDR threshold (default 0.05) |
| --superlocal | Use superlocal background |
| --gene | Custom gene annotation BED |
| --save-pickle | Save intermediate data |

## PureCLIP (HMM-Based)

PureCLIP uses an HMM to model crosslink sites, incorporating enrichment and truncation signals.

```bash
# Installation
conda install -c bioconda pureclip

# Basic peak calling
pureclip \
    -i deduped.bam \
    -bai deduped.bam.bai \
    -g genome.fa \
    -o crosslink_sites.bed \
    -or binding_regions.bed \
    -nt 4

# -nt 4: Number of threads. Adjust based on CPU cores.
# -o: Single-nucleotide crosslink sites
# -or: Broader binding regions
```

### PureCLIP Options

| Option | Description |
|--------|-------------|
| -i | Input BAM file |
| -bai | BAM index file |
| -g | Reference genome FASTA |
| -o | Crosslink sites output |
| -or | Binding regions output |
| -nt | Number of threads |
| -iv | Interval file to restrict analysis |
| -dm | Min distance for merging |

### PureCLIP with Input Control

```bash
# With SMInput control BAM
pureclip \
    -i clip.bam \
    -bai clip.bam.bai \
    -g genome.fa \
    -ibam sminput.bam \
    -ibai sminput.bam.bai \
    -o crosslinks.bed \
    -or regions.bed \
    -nt 8

# -ibam/-ibai: Input control BAM for background modeling
```

### PureCLIP Output

```bash
# Crosslink sites BED contains:
# chr start end name score strand

# Score interpretation:
# Higher scores = more confident crosslink sites

# Filter by score
# score>=3: Medium confidence. Use 5+ for high confidence.
awk '$5 >= 3' crosslink_sites.bed > filtered_sites.bed
```

### PureCLIP for Different CLIP Types

```bash
# eCLIP (recommended settings)
pureclip -i eclip.bam -bai eclip.bam.bai -g genome.fa \
    -o sites.bed -or regions.bed -nt 4 -dm 8

# iCLIP (single-nucleotide resolution)
pureclip -i iclip.bam -bai iclip.bam.bai -g genome.fa \
    -o sites.bed -or regions.bed -nt 4

# PAR-CLIP (T-to-C transitions)
pureclip -i parclip.bam -bai parclip.bam.bai -g genome.fa \
    -o sites.bed -or regions.bed -nt 4
```

## Piranha

```bash
# Basic usage
Piranha -s deduped.bam -o peaks.bed

# With p-value threshold
Piranha -s deduped.bam -o peaks.bed -p 0.01

# Stranded analysis
Piranha -s deduped.bam -o peaks.bed -p 0.01 -u

# Zero-truncated negative binomial
Piranha -s deduped.bam -o peaks.bed -d ZeroTruncatedNegativeBinomial
```

## PEAKachu (for PAR-CLIP)

```bash
# PAR-CLIP specific caller
peakachu adaptive \
    -c control.bam \
    -t treatment.bam \
    -r reference.fa \
    -o peakachu_peaks.gff
```

## MACS3 for CLIP (Alternative)

```bash
# Use narrow peak calling mode
macs3 callpeak \
    -t deduped.bam \
    -f BAM \
    -g hs \
    -n clip_peaks \
    --nomodel \
    --extsize 50 \
    -q 0.01
```

## Strand-Specific Peak Calling

```bash
# Split BAM by strand
samtools view -h -F 16 deduped.bam | samtools view -Sb - > plus_strand.bam
samtools view -h -f 16 deduped.bam | samtools view -Sb - > minus_strand.bam

# Call peaks on each strand
clipper -b plus_strand.bam -s hg38 -o peaks_plus.bed
clipper -b minus_strand.bam -s hg38 -o peaks_minus.bed

# Combine
cat peaks_plus.bed peaks_minus.bed | sort -k1,1 -k2,2n > peaks_all.bed
```

## Filter Peaks

```bash
# By score
awk '$5 >= 10' peaks.bed > peaks_filtered.bed

# By size
awk '($3 - $2) >= 20 && ($3 - $2) <= 200' peaks.bed > peaks_sized.bed

# By read count (if in name field)
awk '$5 >= 5' peaks.bed > peaks_min5reads.bed
```

## Merge Replicates

```bash
# Use bedtools to find consensus peaks
bedtools intersect -a rep1_peaks.bed -b rep2_peaks.bed -wa | \
    sort -u > consensus_peaks.bed

# Require overlap in N replicates
bedtools multiinter -i rep1.bed rep2.bed rep3.bed | \
    awk '$4 >= 2' | \
    bedtools merge > consensus_peaks.bed
```

## Peak Metrics

```python
import pandas as pd

def load_clip_peaks(bed_path):
    peaks = pd.read_csv(bed_path, sep='\t', header=None,
                        names=['chrom', 'start', 'end', 'name', 'score', 'strand'])
    return peaks

def peak_stats(peaks):
    stats = {
        'n_peaks': len(peaks),
        'mean_width': (peaks['end'] - peaks['start']).mean(),
        'median_score': peaks['score'].median(),
        'peaks_per_chrom': peaks.groupby('chrom').size().to_dict()
    }
    return stats

peaks = load_clip_peaks('peaks.bed')
print(peak_stats(peaks))
```

## Quality Metrics

| Metric | Good Value | Description |
|--------|------------|-------------|
| Peak count | 1,000-50,000 | Depends on RBP |
| Peak width | 20-100 nt | Typical for RBP footprint |
| FRiP | >0.1 | Fraction reads in peaks |

## Calculate FRiP

```bash
# Reads in peaks
reads_in_peaks=$(bedtools intersect -a deduped.bam -b peaks.bed -u | samtools view -c -)

# Total reads
total_reads=$(samtools view -c deduped.bam)

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

## Related Skills

- clip-alignment - Generate aligned BAM
- clip-preprocessing - UMI deduplication
- binding-site-annotation - Annotate peaks with gene features
- clip-motif-analysis - Find enriched motifs in peaks

Files in this skill

  • SKILL.md5.7 KB
  • examples/call_peaks.sh188 B
  • usage-guide.md1.3 KB

Attribution

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments

Loading comments…

Newsletter

Get the weekly list of safe new skills, and the ones we pulled.