CLIP-seq Pipeline

Pipeline Overview

FASTQ → QC → UMI extract → Trim adapters → Align → Filter → Dedup → Peak call → Annotate → Motifs

CLIP Method Variants

Method	UMI	Crosslink Site	Adapter
HITS-CLIP	Optional	Deletions	3' adapter
PAR-CLIP	Optional	T→C mutations	3' adapter
iCLIP	Required	5' of read	3' adapter
eCLIP	Required	5' of read	3' adapter

Step 1: Quality Control

# Initial QC
fastqc reads.fastq.gz -o qc_pre/

# Check for adapter contamination and UMI structure
# For eCLIP: expect 10nt UMI at read start
zcat reads.fastq.gz | head -n 100 | cut -c1-15

Step 2: UMI Extraction

# eCLIP (10nt UMI at 5' end)
umi_tools extract \
    --stdin=reads.fastq.gz \
    --bc-pattern=NNNNNNNNNN \
    --stdout=extracted.fastq.gz \
    --log=umi_extract.log

# iCLIP (5nt experimental barcode + 5nt UMI)
umi_tools extract \
    --stdin=reads.fastq.gz \
    --bc-pattern=NNNNNXXXXX \
    --stdout=extracted.fastq.gz

Step 3: Adapter Trimming

# Trim 3' adapter (common eCLIP adapter)
cutadapt -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCA \
    --minimum-length 20 \
    --quality-cutoff 20 \
    -o trimmed.fastq.gz \
    extracted.fastq.gz

# For paired UMI adapters
cutadapt -a AGATCGGAAGAGCACACGTCT \
    -A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT \
    --minimum-length 20 \
    -o trimmed_R1.fq.gz -p trimmed_R2.fq.gz \
    extracted_R1.fq.gz extracted_R2.fq.gz

Step 4: Alignment

# Build STAR index (once)
STAR --runMode genomeGenerate \
    --genomeDir star_index \
    --genomeFastaFiles genome.fa \
    --sjdbGTFfile genes.gtf \
    --sjdbOverhang 100

# Align with STAR (optimized for short CLIP reads)
STAR --genomeDir star_index \
    --readFilesIn trimmed.fastq.gz \
    --readFilesCommand zcat \
    --outFilterMismatchNmax 2 \
    --outFilterMultimapNmax 1 \
    --outSAMtype BAM SortedByCoordinate \
    --outSAMattributes All \
    --alignEndsType EndToEnd \
    --outFileNamePrefix clip_

Step 5: Alignment Filtering

# Remove unmapped and low-quality reads
samtools view -b -F 4 -q 10 clip_Aligned.sortedByCoord.out.bam > filtered.bam
samtools index filtered.bam

# Optional: remove reads mapping to rRNA/tRNA
bedtools intersect -v -abam filtered.bam -b rrna_trna.bed > filtered_norRNA.bam

Step 6: PCR Deduplication

# UMI-aware deduplication
umi_tools dedup \
    -I filtered.bam \
    -S dedup.bam \
    --output-stats=dedup_stats

samtools index dedup.bam

# Check deduplication rate
echo "Duplication rate:" $(grep "Input Reads" dedup_stats.log | awk '{print $3}')

Step 7: Peak Calling

# CLIPper (recommended)
clipper -b dedup.bam -s hg38 -o peaks.bed --FDR 0.05 --superlocal

# Alternative: Piranha
Piranha -s dedup.bam -o piranha_peaks.bed -p 0.01

# For PAR-CLIP with T→C mutations
PARalyzer settings.ini

# Strand-specific calling
samtools view -h -F 16 dedup.bam | samtools view -Sb - > plus.bam
samtools view -h -f 16 dedup.bam | samtools view -Sb - > minus.bam
clipper -b plus.bam -s hg38 -o peaks_plus.bed
clipper -b minus.bam -s hg38 -o peaks_minus.bed
cat peaks_plus.bed peaks_minus.bed | sort -k1,1 -k2,2n > peaks_stranded.bed

Step 8: Peak Annotation

# Annotate with gene features
bedtools intersect -a peaks.bed -b genes.gtf -wo > peaks_annotated.txt

# Or use HOMER
annotatePeaks.pl peaks.bed hg38 > peaks_homer_annotated.txt

# Feature distribution
awk -F'\t' '{print $8}' peaks_homer_annotated.txt | sort | uniq -c | sort -rn

Step 9: Motif Analysis

# Extract peak sequences
bedtools getfasta -fi genome.fa -bed peaks.bed -s -fo peaks.fa

# HOMER motif finding (RNA mode)
findMotifs.pl peaks.fa fasta motif_output -rna -len 5,6,7,8 -p 8

# MEME-ChIP
meme-chip -oc meme_output -dna peaks.fa -meme-mod zoops -meme-nmotifs 10

Step 10: Cross-link Site Analysis

# For iCLIP/eCLIP: identify crosslink sites (read 5' ends)
bedtools genomecov -ibam dedup.bam -bg -5 -strand + > crosslinks_plus.bg
bedtools genomecov -ibam dedup.bam -bg -5 -strand - > crosslinks_minus.bg

# For PAR-CLIP: identify T→C conversion sites
# Requires specialized tools like PARpipe

Quality Checkpoints

Step	Metric	Expected
Raw	Read count	>10M
Trimmed	Reads >20bp	>80%
Aligned	Mapping rate	>50%
Dedup	Unique rate	>20%
Peaks	Peak count	1,000-50,000
Peaks	Median width	20-100 nt
FRiP	Reads in peaks	>10%

# Calculate FRiP
reads_in_peaks=$(bedtools intersect -a dedup.bam -b peaks.bed -u | samtools view -c -)
total_reads=$(samtools view -c dedup.bam)
frip=$(echo "scale=4; $reads_in_peaks / $total_reads" | bc)
echo "FRiP: $frip"

Complete Pipeline Script

#!/bin/bash
set -euo pipefail

SAMPLE=$1
READS=$2
GENOME_DIR=$3
GENOME_FA=$4

mkdir -p qc trimmed aligned peaks motifs

# QC
fastqc $READS -o qc/

# UMI extract
umi_tools extract --stdin=$READS --bc-pattern=NNNNNNNNNN \
    --stdout=trimmed/${SAMPLE}_extracted.fq.gz

# Trim
cutadapt -a AGATCGGAAGAGCACACGTCT --minimum-length 20 \
    -o trimmed/${SAMPLE}_trimmed.fq.gz trimmed/${SAMPLE}_extracted.fq.gz

# Align
STAR --genomeDir $GENOME_DIR --readFilesIn trimmed/${SAMPLE}_trimmed.fq.gz \
    --readFilesCommand zcat --outFilterMismatchNmax 2 --outFilterMultimapNmax 1 \
    --outSAMtype BAM SortedByCoordinate --outFileNamePrefix aligned/${SAMPLE}_

# Filter and dedup
samtools view -b -F 4 -q 10 aligned/${SAMPLE}_Aligned.sortedByCoord.out.bam | \
    samtools sort -o aligned/${SAMPLE}_filtered.bam
samtools index aligned/${SAMPLE}_filtered.bam
umi_tools dedup -I aligned/${SAMPLE}_filtered.bam -S aligned/${SAMPLE}_dedup.bam
samtools index aligned/${SAMPLE}_dedup.bam

# Peaks
clipper -b aligned/${SAMPLE}_dedup.bam -s hg38 -o peaks/${SAMPLE}_peaks.bed

# Motifs
bedtools getfasta -fi $GENOME_FA -bed peaks/${SAMPLE}_peaks.bed -s -fo peaks/${SAMPLE}.fa
findMotifs.pl peaks/${SAMPLE}.fa fasta motifs/${SAMPLE} -rna -len 5,6,7 -p 4

echo "Pipeline complete for $SAMPLE"

Related Skills

• clip-seq/clip-preprocessing - Detailed preprocessing
• clip-seq/clip-alignment - Alignment optimization
• clip-seq/clip-peak-calling - Peak caller comparison
• clip-seq/binding-site-annotation - Feature annotation
• clip-seq/clip-motif-analysis - Motif discovery