bio-workflows-chipseq-pipeline

---

name: bio-workflows-chipseq-pipeline description: End-to-end ChIP-seq workflow from FASTQ files to annotated peaks. Covers QC, alignment, peak calling with MACS3, and peak annotation with ChIPseeker. Use when processing ChIP-seq data from alignment through peak annotation. tool_type: mixed primary_tool: MACS3 workflow: true depends_on:

• read-qc/fastp-workflow
• read-alignment/bowtie2-alignment
• alignment-files/duplicate-handling
• chip-seq/peak-calling
• chip-seq/peak-annotation
• chip-seq/chipseq-qc qc_checkpoints:
• after_qc: "Q30 >85%, adapter content <5%"
• after_alignment: "Mapping rate >80%, unique mapping >70%"
• after_peaks: "FRiP >1% (ideally >5%), peak count reasonable" measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
• read_file
• run_shell_command

---

ChIP-seq Pipeline

Complete workflow from raw ChIP-seq FASTQ files to annotated peaks.

Workflow Overview

FASTQ files (IP + Input)
    |
    v
[1. QC & Trimming] -----> fastp
    |
    v
[2. Alignment] ---------> Bowtie2
    |
    v
[3. BAM Processing] ----> sort, markdup, filter
    |
    v
[4. Peak Calling] ------> MACS3
    |
    v
[5. QC] ----------------> FRiP, fingerprint plots
    |
    v
[6. Annotation] --------> ChIPseeker
    |
    v
Annotated peaks + QC report

Primary Path: Bowtie2 + MACS3 + ChIPseeker

Step 1: Quality Control with fastp

# Process both IP and Input samples
for sample in IP_rep1 IP_rep2 Input_rep1 Input_rep2; do
    fastp -i ${sample}_R1.fastq.gz -I ${sample}_R2.fastq.gz \
        -o trimmed/${sample}_R1.fq.gz -O trimmed/${sample}_R2.fq.gz \
        --detect_adapter_for_pe \
        --qualified_quality_phred 20 \
        --length_required 25 \
        --html qc/${sample}_fastp.html
done

Step 2: Alignment with Bowtie2

# Build index (once)
bowtie2-build genome.fa bt2_index/genome

# Align
for sample in IP_rep1 IP_rep2 Input_rep1 Input_rep2; do
    bowtie2 -p 8 -x bt2_index/genome \
        -1 trimmed/${sample}_R1.fq.gz \
        -2 trimmed/${sample}_R2.fq.gz \
        --no-mixed --no-discordant \
        --maxins 1000 \
        2> aligned/${sample}.log | \
    samtools view -@ 4 -bS -q 30 - | \
    samtools sort -@ 4 -o aligned/${sample}.bam
done

QC Checkpoint: Check alignment rate

• Overall alignment >80%
• Unique mapping >70%

Step 3: BAM Processing

for sample in IP_rep1 IP_rep2 Input_rep1 Input_rep2; do
    # Mark and remove duplicates
    samtools fixmate -m aligned/${sample}.bam - | \
    samtools sort - | \
    samtools markdup -r - aligned/${sample}.dedup.bam

    # Index
    samtools index aligned/${sample}.dedup.bam

    # Remove chrM reads (high mitochondrial is common)
    samtools view -h aligned/${sample}.dedup.bam | \
        grep -v chrM | \
        samtools view -b - > aligned/${sample}.final.bam
    samtools index aligned/${sample}.final.bam
done

Step 4: Peak Calling with MACS3

# Narrow peaks (TFs, sharp histone marks like H3K4me3)
macs3 callpeak \
    -t aligned/IP_rep1.final.bam aligned/IP_rep2.final.bam \
    -c aligned/Input_rep1.final.bam aligned/Input_rep2.final.bam \
    -f BAMPE \
    -g hs \
    -n experiment \
    --outdir peaks \
    -q 0.01

# Broad peaks (H3K27me3, H3K36me3)
macs3 callpeak \
    -t aligned/IP_rep1.final.bam aligned/IP_rep2.final.bam \
    -c aligned/Input_rep1.final.bam aligned/Input_rep2.final.bam \
    -f BAMPE \
    -g hs \
    -n experiment_broad \
    --outdir peaks \
    --broad \
    --broad-cutoff 0.1

Step 5: QC Metrics

# Calculate FRiP (Fraction of Reads in Peaks)
total_reads=$(samtools view -c aligned/IP_rep1.final.bam)
reads_in_peaks=$(bedtools intersect -a aligned/IP_rep1.final.bam -b peaks/experiment_peaks.narrowPeak -u | samtools view -c)
frip=$(echo "scale=4; $reads_in_peaks / $total_reads" | bc)
echo "FRiP: $frip"

# Generate bigWig for visualization
bamCoverage -b aligned/IP_rep1.final.bam \
    -o bigwig/IP_rep1.bw \
    --normalizeUsing RPKM \
    -p 8

# Fingerprint plot (assess enrichment)
plotFingerprint \
    -b aligned/IP_rep1.final.bam aligned/Input_rep1.final.bam \
    --labels IP Input \
    -o qc/fingerprint.pdf

QC Checkpoint: Assess enrichment quality

• FRiP >1% (ideally >5% for good enrichment)
• Fingerprint shows clear separation between IP and Input

Step 6: Peak Annotation with ChIPseeker

library(ChIPseeker)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(org.Hs.eg.db)

txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene

# Read peaks
peaks <- readPeakFile('peaks/experiment_peaks.narrowPeak')

# Annotate
peak_anno <- annotatePeak(peaks, TxDb = txdb, annoDb = 'org.Hs.eg.db',
                          tssRegion = c(-3000, 3000))

# Visualize
plotAnnoPie(peak_anno)
plotDistToTSS(peak_anno)

# Export
write.csv(as.data.frame(peak_anno), 'peaks/annotated_peaks.csv')

# Get genes with peaks in promoter
promoter_peaks <- as.data.frame(peak_anno)
promoter_genes <- unique(promoter_peaks$SYMBOL[grepl('Promoter', promoter_peaks$annotation)])
write.table(promoter_genes, 'peaks/promoter_genes.txt', row.names = FALSE, col.names = FALSE, quote = FALSE)

Parameter Recommendations

Step	Parameter	Narrow Peaks	Broad Peaks
MACS3	--broad	No	Yes
MACS3	-q	0.01	-
MACS3	--broad-cutoff	-	0.1
MACS3	-g	hs/mm/ce/dm	Same
Bowtie2	-q (samtools)	30	30

Troubleshooting

Issue	Likely Cause	Solution
Few peaks	Low enrichment, wrong parameters	Check fingerprint, adjust -q threshold
Many peaks	High noise, PCR duplicates	Remove duplicates, use stricter -q
Low FRiP	Poor antibody, low enrichment	Check antibody, increase sequencing
Peaks in blacklist	Technical artifacts	Filter against ENCODE blacklist

Complete Pipeline Script

#!/bin/bash
set -e

THREADS=8
GENOME="genome.fa"
INDEX="bt2_index/genome"
IP_SAMPLES="IP_rep1 IP_rep2"
INPUT_SAMPLES="Input_rep1 Input_rep2"
OUTDIR="results"

mkdir -p ${OUTDIR}/{trimmed,aligned,peaks,qc,bigwig}

# Step 1: QC
for sample in $IP_SAMPLES $INPUT_SAMPLES; do
    fastp -i ${sample}_R1.fastq.gz -I ${sample}_R2.fastq.gz \
        -o ${OUTDIR}/trimmed/${sample}_R1.fq.gz \
        -O ${OUTDIR}/trimmed/${sample}_R2.fq.gz \
        --html ${OUTDIR}/qc/${sample}_fastp.html -w ${THREADS}
done

# Step 2-3: Align and process
for sample in $IP_SAMPLES $INPUT_SAMPLES; do
    bowtie2 -p ${THREADS} -x ${INDEX} \
        -1 ${OUTDIR}/trimmed/${sample}_R1.fq.gz \
        -2 ${OUTDIR}/trimmed/${sample}_R2.fq.gz \
        --no-mixed --no-discordant 2> ${OUTDIR}/qc/${sample}_align.log | \
    samtools view -@ ${THREADS} -bS -q 30 - | \
    samtools fixmate -m - - | \
    samtools sort -@ ${THREADS} - | \
    samtools markdup -r - ${OUTDIR}/aligned/${sample}.bam
    samtools index ${OUTDIR}/aligned/${sample}.bam
done

# Step 4: Peak calling
ip_bams=$(for s in $IP_SAMPLES; do echo "${OUTDIR}/aligned/${s}.bam"; done | tr '\n' ' ')
input_bams=$(for s in $INPUT_SAMPLES; do echo "${OUTDIR}/aligned/${s}.bam"; done | tr '\n' ' ')

macs3 callpeak -t ${ip_bams} -c ${input_bams} \
    -f BAMPE -g hs -n experiment \
    --outdir ${OUTDIR}/peaks -q 0.01

echo "Pipeline complete. Peaks: ${OUTDIR}/peaks/experiment_peaks.narrowPeak"

Related Skills

• chip-seq/peak-calling - MACS3 parameters and options
• chip-seq/peak-annotation - ChIPseeker annotation details
• chip-seq/differential-binding - Compare conditions with DiffBind
• chip-seq/chipseq-qc - Comprehensive QC metrics
• chip-seq/motif-analysis - Find enriched motifs in peaks