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.
Scanned 9/5/2026
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---
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
---
<!--
# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# All Rights Reserved.
#
# This code is proprietary and confidential.
# Unauthorized copying of this file, via any medium is strictly prohibited.
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# 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
```bash
# 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
```bash
# 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
```bash
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
```bash
# 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
```bash
# 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: $frip"
# Generate bigWig for visualization
bamCoverage -b aligned/IP_rep1.final.bam \
-o bigwig/IP_rep1.bw \
--normalizeUsing RPKM \
-p 8
# Fingerprint plot (assess enrichment)
plotFingerprint \
-b aligned/IP_rep1.final.bam aligned/Input_rep1.final.bam \
--labels IP Input \
-o qc/fingerprint.pdf
```
**QC Checkpoint:** Assess enrichment quality
- FRiP >1% (ideally >5% for good enrichment)
- Fingerprint shows clear separation between IP and Input
### Step 6: Peak Annotation with ChIPseeker
```r
library(ChIPseeker)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(org.Hs.eg.db)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
# Read peaks
peaks <- readPeakFile('peaks/experiment_peaks.narrowPeak')
# Annotate
peak_anno <- annotatePeak(peaks, TxDb = txdb, annoDb = 'org.Hs.eg.db',
tssRegion = c(-3000, 3000))
# Visualize
plotAnnoPie(peak_anno)
plotDistToTSS(peak_anno)
# Export
write.csv(as.data.frame(peak_anno), 'peaks/annotated_peaks.csv')
# Get genes with peaks in promoter
promoter_peaks <- as.data.frame(peak_anno)
promoter_genes <- unique(promoter_peaks$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
```bash
#!/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
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