This document provides complete workflow examples for common deepTools analyses.
Scanned 5/31/2026
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Skills Directoryopenskills install tools-only/X-Skills# deepTools Common Workflows
This document provides complete workflow examples for common deepTools analyses.
## ChIP-seq Quality Control Workflow
Complete quality control assessment for ChIP-seq experiments.
### Step 1: Initial Correlation Assessment
Compare replicates and samples to verify experimental quality:
```bash
# Generate coverage matrix across genome
multiBamSummary bins \
--bamfiles Input1.bam Input2.bam ChIP1.bam ChIP2.bam \
--labels Input_rep1 Input_rep2 ChIP_rep1 ChIP_rep2 \
-o readCounts.npz \
--numberOfProcessors 8
# Create correlation heatmap
plotCorrelation \
-in readCounts.npz \
--corMethod pearson \
--whatToShow heatmap \
--plotFile correlation_heatmap.png \
--plotNumbers
# Generate PCA plot
plotPCA \
-in readCounts.npz \
-o PCA_plot.png \
-T "PCA of ChIP-seq samples"
```
**Expected Results:**
- Replicates should cluster together
- Input samples should be distinct from ChIP samples
---
### Step 2: Coverage and Depth Assessment
```bash
# Check sequencing depth and coverage
plotCoverage \
--bamfiles Input1.bam ChIP1.bam ChIP2.bam \
--labels Input ChIP_rep1 ChIP_rep2 \
--plotFile coverage.png \
--ignoreDuplicates \
--numberOfProcessors 8
```
**Interpretation:** Assess whether sequencing depth is adequate for downstream analysis.
---
### Step 3: Fragment Size Validation (Paired-end)
```bash
# Verify expected fragment sizes
bamPEFragmentSize \
--bamfiles Input1.bam ChIP1.bam ChIP2.bam \
--histogram fragmentSizes.png \
--plotTitle "Fragment Size Distribution"
```
**Expected Results:** Fragment sizes should match library preparation protocols (typically 200-600bp for ChIP-seq).
---
### Step 4: GC Bias Detection and Correction
```bash
# Compute GC bias
computeGCBias \
--bamfile ChIP1.bam \
--effectiveGenomeSize 2913022398 \
--genome genome.2bit \
--fragmentLength 200 \
--biasPlot GCbias.png \
--frequenciesFile freq.txt
# If bias detected, correct it
correctGCBias \
--bamfile ChIP1.bam \
--effectiveGenomeSize 2913022398 \
--genome genome.2bit \
--GCbiasFrequenciesFile freq.txt \
--correctedFile ChIP1_GCcorrected.bam
```
**Note:** Only correct if significant bias is observed. Do NOT use `--ignoreDuplicates` with GC-corrected files.
---
### Step 5: ChIP Signal Strength Assessment
```bash
# Evaluate ChIP enrichment quality
plotFingerprint \
--bamfiles Input1.bam ChIP1.bam ChIP2.bam \
--labels Input ChIP_rep1 ChIP_rep2 \
--plotFile fingerprint.png \
--extendReads 200 \
--ignoreDuplicates \
--numberOfProcessors 8 \
--outQualityMetrics fingerprint_metrics.txt
```
**Interpretation:**
- Strong ChIP: Steep rise in cumulative curve
- Weak enrichment: Curve close to diagonal (input-like)
---
## ChIP-seq Analysis Workflow
Complete workflow from BAM files to publication-quality visualizations.
### Step 1: Generate Normalized Coverage Tracks
```bash
# Input control
bamCoverage \
--bam Input.bam \
--outFileName Input_coverage.bw \
--normalizeUsing RPGC \
--effectiveGenomeSize 2913022398 \
--binSize 10 \
--extendReads 200 \
--ignoreDuplicates \
--numberOfProcessors 8
# ChIP sample
bamCoverage \
--bam ChIP.bam \
--outFileName ChIP_coverage.bw \
--normalizeUsing RPGC \
--effectiveGenomeSize 2913022398 \
--binSize 10 \
--extendReads 200 \
--ignoreDuplicates \
--numberOfProcessors 8
```
---
### Step 2: Create Log2 Ratio Track
```bash
# Compare ChIP to Input
bamCompare \
--bamfile1 ChIP.bam \
--bamfile2 Input.bam \
--outFileName ChIP_vs_Input_log2ratio.bw \
--operation log2 \
--scaleFactorsMethod readCount \
--binSize 10 \
--extendReads 200 \
--ignoreDuplicates \
--numberOfProcessors 8
```
**Result:** Log2 ratio track showing enrichment (positive values) and depletion (negative values).
---
### Step 3: Compute Matrix Around TSS
```bash
# Prepare data for heatmap/profile around transcription start sites
computeMatrix reference-point \
--referencePoint TSS \
--scoreFileName ChIP_coverage.bw \
--regionsFileName genes.bed \
--beforeRegionStartLength 3000 \
--afterRegionStartLength 3000 \
--binSize 10 \
--sortRegions descend \
--sortUsing mean \
--outFileName matrix_TSS.gz \
--outFileNameMatrix matrix_TSS.tab \
--numberOfProcessors 8
```
---
### Step 4: Generate Heatmap
```bash
# Create heatmap around TSS
plotHeatmap \
--matrixFile matrix_TSS.gz \
--outFileName heatmap_TSS.png \
--colorMap RdBu \
--whatToShow 'plot, heatmap and colorbar' \
--zMin -3 --zMax 3 \
--yAxisLabel "Genes" \
--xAxisLabel "Distance from TSS (bp)" \
--refPointLabel "TSS" \
--heatmapHeight 15 \
--kmeans 3
```
---
### Step 5: Generate Profile Plot
```bash
# Create meta-profile around TSS
plotProfile \
--matrixFile matrix_TSS.gz \
--outFileName profile_TSS.png \
--plotType lines \
--perGroup \
--colors blue \
--plotTitle "ChIP-seq signal around TSS" \
--yAxisLabel "Average signal" \
--xAxisLabel "Distance from TSS (bp)" \
--refPointLabel "TSS"
```
---
### Step 6: Enrichment at Peaks
```bash
# Calculate enrichment in peak regions
plotEnrichment \
--bamfiles Input.bam ChIP.bam \
--BED peaks.bed \
--labels Input ChIP \
--plotFile enrichment.png \
--outRawCounts enrichment_counts.tab \
--extendReads 200 \
--ignoreDuplicates
```
---
## RNA-seq Coverage Workflow
Generate strand-specific coverage tracks for RNA-seq data.
### Forward Strand
```bash
bamCoverage \
--bam rnaseq.bam \
--outFileName forward_coverage.bw \
--filterRNAstrand forward \
--normalizeUsing CPM \
--binSize 1 \
--numberOfProcessors 8
```
### Reverse Strand
```bash
bamCoverage \
--bam rnaseq.bam \
--outFileName reverse_coverage.bw \
--filterRNAstrand reverse \
--normalizeUsing CPM \
--binSize 1 \
--numberOfProcessors 8
```
**Important:** Do NOT use `--extendReads` for RNA-seq (would extend over splice junctions).
---
## Multi-Sample Comparison Workflow
Compare multiple ChIP-seq samples (e.g., different conditions or time points).
### Step 1: Generate Coverage Files
```bash
# For each sample
for sample in Control_ChIP Treated_ChIP; do
bamCoverage \
--bam ${sample}.bam \
--outFileName ${sample}.bw \
--normalizeUsing RPGC \
--effectiveGenomeSize 2913022398 \
--binSize 10 \
--extendReads 200 \
--ignoreDuplicates \
--numberOfProcessors 8
done
```
---
### Step 2: Compute Multi-Sample Matrix
```bash
computeMatrix scale-regions \
--scoreFileName Control_ChIP.bw Treated_ChIP.bw \
--regionsFileName genes.bed \
--beforeRegionStartLength 1000 \
--afterRegionStartLength 1000 \
--regionBodyLength 3000 \
--binSize 10 \
--sortRegions descend \
--sortUsing mean \
--outFileName matrix_multi.gz \
--numberOfProcessors 8
```
---
### Step 3: Multi-Sample Heatmap
```bash
plotHeatmap \
--matrixFile matrix_multi.gz \
--outFileName heatmap_comparison.png \
--colorMap Blues \
--whatToShow 'plot, heatmap and colorbar' \
--samplesLabel Control Treated \
--yAxisLabel "Genes" \
--heatmapHeight 15 \
--kmeans 4
```
---
### Step 4: Multi-Sample Profile
```bash
plotProfile \
--matrixFile matrix_multi.gz \
--outFileName profile_comparison.png \
--plotType lines \
--perGroup \
--colors blue red \
--samplesLabel Control Treated \
--plotTitle "ChIP-seq signal comparison" \
--startLabel "TSS" \
--endLabel "TES"
```
---
## ATAC-seq Workflow
Specialized workflow for ATAC-seq data with Tn5 offset correction.
### Step 1: Shift Reads for Tn5 Correction
```bash
alignmentSieve \
--bam atacseq.bam \
--outFile atacseq_shifted.bam \
--ATACshift \
--minFragmentLength 38 \
--maxFragmentLength 2000 \
--ignoreDuplicates
```
---
### Step 2: Generate Coverage Track
```bash
bamCoverage \
--bam atacseq_shifted.bam \
--outFileName atacseq_coverage.bw \
--normalizeUsing RPGC \
--effectiveGenomeSize 2913022398 \
--binSize 1 \
--numberOfProcessors 8
```
---
### Step 3: Fragment Size Analysis
```bash
bamPEFragmentSize \
--bamfiles atacseq.bam \
--histogram fragmentSizes_atac.png \
--maxFragmentLength 1000
```
**Expected Pattern:** Nucleosome ladder with peaks at ~50bp (nucleosome-free), ~200bp (mono-nucleosome), ~400bp (di-nucleosome).
---
## Peak Region Analysis Workflow
Analyze ChIP-seq signal specifically at peak regions.
### Step 1: Matrix at Peaks
```bash
computeMatrix reference-point \
--referencePoint center \
--scoreFileName ChIP_coverage.bw \
--regionsFileName peaks.bed \
--beforeRegionStartLength 2000 \
--afterRegionStartLength 2000 \
--binSize 10 \
--outFileName matrix_peaks.gz \
--numberOfProcessors 8
```
---
### Step 2: Heatmap at Peaks
```bash
plotHeatmap \
--matrixFile matrix_peaks.gz \
--outFileName heatmap_peaks.png \
--colorMap YlOrRd \
--refPointLabel "Peak Center" \
--heatmapHeight 15 \
--sortUsing max
```
---
## Troubleshooting Common Issues
### Issue: Out of Memory
**Solution:** Use `--region` parameter to process chromosomes individually:
```bash
bamCoverage --bam input.bam -o chr1.bw --region chr1
```
### Issue: BAM Index Missing
**Solution:** Index BAM files before running deepTools:
```bash
samtools index input.bam
```
### Issue: Slow Processing
**Solution:** Increase `--numberOfProcessors`:
```bash
# Use 8 cores instead of default
--numberOfProcessors 8
```
### Issue: bigWig Files Too Large
**Solution:** Increase bin size:
```bash
--binSize 50 # or larger (default is 10-50)
```
---
## Performance Tips
1. **Use multiple processors:** Always set `--numberOfProcessors` to available cores
2. **Process regions:** Use `--region` for testing or memory-limited environments
3. **Adjust bin size:** Larger bins = faster processing and smaller files
4. **Pre-filter BAM files:** Use `alignmentSieve` to create filtered BAM files once, then reuse
5. **Use bigWig over bedGraph:** bigWig format is compressed and faster to process
---
## Best Practices
1. **Always check QC first:** Run correlation, coverage, and fingerprint analysis before proceeding
2. **Document parameters:** Save command lines for reproducibility
3. **Use consistent normalization:** Apply same normalization method across samples in a comparison
4. **Verify reference genome match:** Ensure BAM files and region files use same genome build
5. **Check strand orientation:** For RNA-seq, verify correct strand orientation
6. **Test on small regions first:** Use `--region chr1:1-1000000` for testing parameters
7. **Keep intermediate files:** Save matrices for regenerating plots with different settings
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