Agent skill
bio-workflows-atacseq-pipeline
End-to-end ATAC-seq workflow from FASTQ files to differential accessibility and TF footprinting. Covers alignment, peak calling with MACS3, QC metrics, and optional TOBIAS footprinting.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/atacseq-pipeline
SKILL.md
ATAC-seq Pipeline
Complete workflow from raw ATAC-seq FASTQ files to accessibility peaks, differential analysis, and TF footprinting.
Workflow Overview
FASTQ files
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[1. QC & Trimming] -----> fastp (Nextera adapters)
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[2. Alignment] ---------> Bowtie2
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[3. BAM Processing] ----> filter, shift, dedup
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[4. Peak Calling] ------> MACS3
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[5. QC] ----------------> TSS enrichment, FRiP, fragment size
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[6. Differential] ------> DiffBind (optional)
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[7. Footprinting] ------> TOBIAS (optional)
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Accessibility peaks + TF activity
Primary Path: Bowtie2 + MACS3
Step 1: Quality Control with fastp
# ATAC-seq uses Nextera adapters
NEXTERA_R1="CTGTCTCTTATACACATCT"
NEXTERA_R2="CTGTCTCTTATACACATCT"
for sample in sample1 sample2 sample3; do
fastp -i ${sample}_R1.fastq.gz -I ${sample}_R2.fastq.gz \
-o trimmed/${sample}_R1.fq.gz -O trimmed/${sample}_R2.fq.gz \
--adapter_sequence ${NEXTERA_R1} \
--adapter_sequence_r2 ${NEXTERA_R2} \
--qualified_quality_phred 20 \
--length_required 25 \
--html qc/${sample}_fastp.html
done
Step 2: Alignment with Bowtie2
# Build index (once)
bowtie2-build genome.fa bt2_index/genome
# Align with ATAC-seq specific settings
for sample in sample1 sample2 sample3; do
bowtie2 -p 8 -x bt2_index/genome \
-1 trimmed/${sample}_R1.fq.gz \
-2 trimmed/${sample}_R2.fq.gz \
--very-sensitive \
--no-mixed --no-discordant \
-X 2000 \
2> aligned/${sample}.log | \
samtools view -@ 4 -bS -q 30 -f 2 - | \
samtools sort -@ 4 -o aligned/${sample}.bam
done
Step 3: BAM Processing
ATAC-seq requires special processing: removing mitochondrial reads, shifting reads for Tn5 insertion, and removing duplicates.
for sample in sample1 sample2 sample3; do
# Remove mitochondrial reads
samtools view -h aligned/${sample}.bam | \
grep -v chrM | \
samtools view -b - > aligned/${sample}.noMT.bam
# Mark and remove duplicates
samtools fixmate -m aligned/${sample}.noMT.bam - | \
samtools sort - | \
samtools markdup -r - aligned/${sample}.dedup.bam
samtools index aligned/${sample}.dedup.bam
# Shift reads for Tn5 (+ strand +4bp, - strand -5bp)
alignmentSieve -b aligned/${sample}.dedup.bam \
-o aligned/${sample}.shifted.bam \
--ATACshift \
-p 8
samtools index aligned/${sample}.shifted.bam
done
Alternative manual Tn5 shift with bedtools:
# Convert to BED and shift
bedtools bamtobed -i aligned/${sample}.dedup.bam | \
awk 'BEGIN{OFS="\t"} {if($6=="+"){$2=$2+4} else if($6=="-"){$3=$3-5} print}' | \
sort -k1,1 -k2,2n > aligned/${sample}.shifted.bed
Step 4: Peak Calling with MACS3
# Call peaks (use --shift and --extsize for shifted reads)
macs3 callpeak \
-t aligned/sample1.shifted.bam \
-f BAMPE \
-g hs \
-n sample1 \
--outdir peaks \
--nomodel \
--shift -75 \
--extsize 150 \
--keep-dup all \
-q 0.01
# For calling on all samples together
macs3 callpeak \
-t aligned/*.shifted.bam \
-f BAMPE \
-g hs \
-n consensus \
--outdir peaks \
--nomodel \
--shift -75 \
--extsize 150 \
-q 0.01
Step 5: ATAC-seq QC
# TSS enrichment (using deepTools)
computeMatrix reference-point \
-S bigwig/sample1.bw \
-R genes.bed \
--referencePoint TSS \
-a 2000 -b 2000 \
-o tss_matrix.gz
plotProfile -m tss_matrix.gz -o qc/tss_enrichment.pdf
# Fragment size distribution
samtools view aligned/sample1.dedup.bam | \
awk '{print sqrt($9^2)}' | \
sort | uniq -c | \
awk '{print $2"\t"$1}' > qc/fragment_sizes.txt
# FRiP calculation
total=$(samtools view -c aligned/sample1.shifted.bam)
in_peaks=$(bedtools intersect -a aligned/sample1.shifted.bam \
-b peaks/sample1_peaks.narrowPeak -u | samtools view -c)
echo "FRiP: $(echo "scale=4; $in_peaks/$total" | bc)"
QC Checkpoint: Assess ATAC quality
- TSS enrichment score >5 (ideally >10)
- FRiP >20%
- Nucleosome-free (<100bp) and mono/di-nucleosome peaks visible
Step 6: Differential Accessibility with DiffBind
library(DiffBind)
# Create sample sheet
samples <- data.frame(
SampleID = c('control_1', 'control_2', 'treated_1', 'treated_2'),
Condition = c('control', 'control', 'treated', 'treated'),
Replicate = c(1, 2, 1, 2),
bamReads = c('aligned/control_1.shifted.bam', 'aligned/control_2.shifted.bam',
'aligned/treated_1.shifted.bam', 'aligned/treated_2.shifted.bam'),
Peaks = c('peaks/control_1_peaks.narrowPeak', 'peaks/control_2_peaks.narrowPeak',
'peaks/treated_1_peaks.narrowPeak', 'peaks/treated_2_peaks.narrowPeak')
)
# Create DBA object
dba <- dba(sampleSheet = samples)
# Count reads in peaks
dba <- dba.count(dba)
# Normalize
dba <- dba.normalize(dba)
# Contrast
dba <- dba.contrast(dba, categories = DBA_CONDITION)
# Differential analysis
dba <- dba.analyze(dba)
# Report
report <- dba.report(dba)
write.csv(as.data.frame(report), 'differential_peaks.csv')
# Visualization
dba.plotMA(dba)
dba.plotVolcano(dba)
Step 7: TF Footprinting with TOBIAS
# Correct Tn5 bias
TOBIAS ATACorrect \
-b aligned/sample1.shifted.bam \
-g genome.fa \
-p peaks/consensus_peaks.narrowPeak \
--outdir footprinting \
--cores 8
# Score footprints
TOBIAS ScoreBigwig \
--signal footprinting/sample1_corrected.bw \
--regions peaks/consensus_peaks.narrowPeak \
--output footprinting/sample1_footprints.bw \
--cores 8
# Bind detection
TOBIAS BINDetect \
--motifs motifs.jaspar \
--signals footprinting/sample1_footprints.bw \
--genome genome.fa \
--peaks peaks/consensus_peaks.narrowPeak \
--outdir footprinting/bindetect \
--cores 8
# Differential footprinting (two conditions)
TOBIAS BINDetect \
--motifs motifs.jaspar \
--signals footprinting/control_footprints.bw footprinting/treated_footprints.bw \
--genome genome.fa \
--peaks peaks/consensus_peaks.narrowPeak \
--outdir footprinting/differential \
--cores 8
Parameter Recommendations
| Step | Parameter | Value |
|---|---|---|
| fastp | adapter | Nextera (CTGTCTCTTATACACATCT) |
| Bowtie2 | -X | 2000 (max insert size) |
| samtools | -q | 30 (MAPQ filter) |
| MACS3 | --shift | -75 (for Tn5 shift) |
| MACS3 | --extsize | 150 |
| MACS3 | -q | 0.01-0.05 |
Troubleshooting
| Issue | Likely Cause | Solution |
|---|---|---|
| High mitochondrial | Normal for ATAC | Filter chrM reads |
| Low TSS enrichment | Poor library, overdigestion | Check Tn5 concentration |
| Many small peaks | Tn5 insertion noise | Increase -q threshold |
| No nucleosome periodicity | Overdigestion | Adjust Tn5:DNA ratio |
Complete Pipeline Script
#!/bin/bash
set -e
THREADS=8
INDEX="bt2_index/genome"
GENOME="genome.fa"
SAMPLES="sample1 sample2 sample3"
OUTDIR="atac_results"
mkdir -p ${OUTDIR}/{trimmed,aligned,peaks,qc,bigwig}
# Step 1: QC
for sample in $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 \
--adapter_sequence CTGTCTCTTATACACATCT \
--html ${OUTDIR}/qc/${sample}_fastp.html -w ${THREADS}
done
# Step 2-3: Align and process
for sample in $SAMPLES; do
bowtie2 -p ${THREADS} -x ${INDEX} \
-1 ${OUTDIR}/trimmed/${sample}_R1.fq.gz \
-2 ${OUTDIR}/trimmed/${sample}_R2.fq.gz \
--very-sensitive --no-mixed --no-discordant -X 2000 \
2> ${OUTDIR}/qc/${sample}_bowtie2.log | \
samtools view -@ ${THREADS} -bS -q 30 -f 2 - | \
grep -v chrM | \
samtools fixmate -m - - | \
samtools sort -@ ${THREADS} - | \
samtools markdup -r - - | \
alignmentSieve --ATACshift -b /dev/stdin -o ${OUTDIR}/aligned/${sample}.bam
samtools index ${OUTDIR}/aligned/${sample}.bam
done
# Step 4: Peak calling
macs3 callpeak -t ${OUTDIR}/aligned/*.bam -f BAMPE -g hs \
-n consensus --outdir ${OUTDIR}/peaks \
--nomodel --shift -75 --extsize 150 -q 0.01
echo "Pipeline complete. Peaks: ${OUTDIR}/peaks/consensus_peaks.narrowPeak"
Related Skills
- atac-seq/atac-peak-calling - MACS3 ATAC parameters
- atac-seq/atac-qc - TSS enrichment, FRiP details
- atac-seq/differential-accessibility - DiffBind for ATAC
- atac-seq/footprinting - TOBIAS and HINT details
- chip-seq/peak-annotation - Annotate ATAC peaks to genes
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