deepTools: NGS Data Analysis Toolkit

Overview

deepTools is a comprehensive suite of Python command-line tools designed for processing and analyzing high-throughput sequencing data. Use deepTools to perform quality control, normalize data, compare samples, and generate publication-quality visualizations for ChIP-seq, RNA-seq, ATAC-seq, MNase-seq, and other NGS experiments.

Core capabilities:

• Convert BAM alignments to normalized coverage tracks (bigWig/bedGraph)
• Quality control assessment (fingerprint, correlation, coverage)
• Sample comparison and correlation analysis
• Heatmap and profile plot generation around genomic features
• Enrichment analysis and peak region visualization

When to Use This Skill

This skill should be used when:

• File conversion: "Convert BAM to bigWig", "generate coverage tracks", "normalize ChIP-seq data"
• Quality control: "check ChIP quality", "compare replicates", "assess sequencing depth", "QC analysis"
• Visualization: "create heatmap around TSS", "plot ChIP signal", "visualize enrichment", "generate profile plot"
• Sample comparison: "compare treatment vs control", "correlate samples", "PCA analysis"
• Analysis workflows: "analyze ChIP-seq data", "RNA-seq coverage", "ATAC-seq analysis", "complete workflow"
• Working with specific file types: BAM files, bigWig files, BED region files in genomics context

Quick Start

For users new to deepTools, start with file validation and common workflows:

1. Validate Input Files

Before running any analysis, validate BAM, bigWig, and BED files using the validation script:

python scripts/validate_files.py --bam sample1.bam sample2.bam --bed regions.bed

This checks file existence, BAM indices, and format correctness.

2. Generate Workflow Template

For standard analyses, use the workflow generator to create customized scripts:

# List available workflows
python scripts/workflow_generator.py --list

# Generate ChIP-seq QC workflow
python scripts/workflow_generator.py chipseq_qc -o qc_workflow.sh \
    --input-bam Input.bam --chip-bams "ChIP1.bam ChIP2.bam" \
    --genome-size 2913022398

# Make executable and run
chmod +x qc_workflow.sh
./qc_workflow.sh

3. Most Common Operations

See assets/quick_reference.md for frequently used commands and parameters.

Installation

uv pip install deeptools

Core Workflows

deepTools workflows typically follow this pattern: QC → Normalization → Comparison/Visualization

ChIP-seq Quality Control Workflow

When users request ChIP-seq QC or quality assessment:

• Generate workflow script using scripts/workflow_generator.py chipseq_qc
• Key QC steps:

- Sample correlation (multiBamSummary + plotCorrelation)

- PCA analysis (plotPCA)

- Coverage assessment (plotCoverage)

- Fragment size validation (bamPEFragmentSize)

- ChIP enrichment strength (plotFingerprint)

Interpreting results:

• Correlation: Replicates should cluster together with high correlation (>0.9)
• Fingerprint: Strong ChIP shows steep rise; flat diagonal indicates poor enrichment
• Coverage: Assess if sequencing depth is adequate for analysis

Full workflow details in references/workflows.md → "ChIP-seq Quality Control Workflow"

ChIP-seq Complete Analysis Workflow

For full ChIP-seq analysis from BAM to visualizations:

• Generate coverage tracks with normalization (bamCoverage)
• Create comparison tracks (bamCompare for log2 ratio)
• Compute signal matrices around features (computeMatrix)
• Generate visualizations (plotHeatmap, plotProfile)
• Enrichment analysis at peaks (plotEnrichment)

Use scripts/workflow_generator.py chipseq_analysis to generate template.

Complete command sequences in references/workflows.md → "ChIP-seq Analysis Workflow"

RNA-seq Coverage Workflow

For strand-specific RNA-seq coverage tracks:

Use bamCoverage with --filterRNAstrand to separate forward and reverse strands.

Important: NEVER use --extendReads for RNA-seq (would extend over splice junctions).

Use normalization: CPM for fixed bins, RPKM for gene-level analysis.

Template available: scripts/workflow_generator.py rnaseq_coverage

Details in references/workflows.md → "RNA-seq Coverage Workflow"

ATAC-seq Analysis Workflow

ATAC-seq requires Tn5 offset correction:

• Shift reads using alignmentSieve with --ATACshift
• Generate coverage with bamCoverage
• Analyze fragment sizes (expect nucleosome ladder pattern)
• Visualize at peaks if available

Template: scripts/workflow_generator.py atacseq

Full workflow in references/workflows.md → "ATAC-seq Workflow"

Tool Categories and Common Tasks

BAM/bigWig Processing

Convert BAM to normalized coverage:

bamCoverage --bam input.bam --outFileName output.bw \
    --normalizeUsing RPGC --effectiveGenomeSize 2913022398 \
    --binSize 10 --numberOfProcessors 8

Compare two samples (log2 ratio):

bamCompare -b1 treatment.bam -b2 control.bam -o ratio.bw \
    --operation log2 --scaleFactorsMethod readCount

Key tools: bamCoverage, bamCompare, multiBamSummary, multiBigwigSummary, correctGCBias, alignmentSieve

Complete reference: references/tools_reference.md → "BAM and bigWig File Processing Tools"

Quality Control

Check ChIP enrichment:

plotFingerprint -b input.bam chip.bam -o fingerprint.png \
    --extendReads 200 --ignoreDuplicates

Sample correlation:

multiBamSummary bins --bamfiles *.bam -o counts.npz
plotCorrelation -in counts.npz --corMethod pearson \
    --whatToShow heatmap -o correlation.png

Key tools: plotFingerprint, plotCoverage, plotCorrelation, plotPCA, bamPEFragmentSize

Complete reference: references/tools_reference.md → "Quality Control Tools"

Visualization

Create heatmap around TSS:

# Compute matrix
computeMatrix reference-point -S signal.bw -R genes.bed \
    -b 3000 -a 3000 --referencePoint TSS -o matrix.gz

# Generate heatmap
plotHeatmap -m matrix.gz -o heatmap.png \
    --colorMap RdBu --kmeans 3

Create profile plot:

plotProfile -m matrix.gz -o profile.png \
    --plotType lines --colors blue red

Key tools: computeMatrix, plotHeatmap, plotProfile, plotEnrichment

Complete reference: references/tools_reference.md → "Visualization Tools"

Normalization Methods

Choosing the correct normalization is critical for valid comparisons. Consult references/normalization_methods.md for comprehensive guidance.

Quick selection guide:

• ChIP-seq coverage: Use RPGC or CPM
• ChIP-seq comparison: Use bamCompare with log2 and readCount
• RNA-seq bins: Use CPM
• RNA-seq genes: Use RPKM (accounts for gene length)
• ATAC-seq: Use RPGC or CPM

Normalization methods:

• RPGC: 1× genome coverage (requires --effectiveGenomeSize)
• CPM: Counts per million mapped reads
• RPKM: Reads per kb per million (accounts for region length)
• BPM: Bins per million
• None: Raw counts (not recommended for comparisons)

Full explanation: references/normalization_methods.md

Effective Genome Sizes

RPGC normalization requires effective genome size. Common values:

Organism	Assembly	Size	Usage
Human	GRCh38/hg38	2,913,022,398	--effectiveGenomeSize 2913022398
Mouse	GRCm38/mm10	2,652,783,500	--effectiveGenomeSize 2652783500
Zebrafish	GRCz11	1,368,780,147	--effectiveGenomeSize 1368780147
Drosophila	dm6	142,573,017	--effectiveGenomeSize 142573017
C. elegans	ce10/ce11	100,286,401	--effectiveGenomeSize 100286401

Complete table with read-length-specific values: references/effective_genome_sizes.md

Common Parameters Across Tools

Many deepTools commands share these options:

Performance:

• --numberOfProcessors, -p: Enable parallel processing (always use available cores)
• --region: Process specific regions for testing (e.g., chr1:1-1000000)

Read Filtering:

• --ignoreDuplicates: Remove PCR duplicates (recommended for most analyses)
• --minMappingQuality: Filter by alignment quality (e.g., --minMappingQuality 10)
• --minFragmentLength / --maxFragmentLength: Fragment length bounds
• --samFlagInclude / --samFlagExclude: SAM flag filtering

Read Processing:

• --extendReads: Extend to fragment length (ChIP-seq: YES, RNA-seq: NO)
• --centerReads: Center at fragment midpoint for sharper signals

Best Practices

File Validation

Always validate files first using scripts/validate_files.py to check:

• File existence and readability
• BAM indices present (.bai files)
• BED format correctness
• File sizes reasonable

Analysis Strategy

• Start with QC: Run correlation, coverage, and fingerprint analysis before proceeding
• Test on small regions: Use --region chr1:1-10000000 for parameter testing
• Document commands: Save full command lines for reproducibility
• Use consistent normalization: Apply same method across samples in comparisons
• Verify genome assembly: Ensure BAM and BED files use matching genome builds

ChIP-seq Specific

• Always extend reads for ChIP-seq: --extendReads 200
• Remove duplicates: Use --ignoreDuplicates in most cases
• Check enrichment first: Run plotFingerprint before detailed analysis
• GC correction: Only apply if significant bias detected; never use --ignoreDuplicates after GC correction

RNA-seq Specific

• Never extend reads for RNA-seq (would span splice junctions)
• Strand-specific: Use --filterRNAstrand forward/reverse for stranded libraries
• Normalization: CPM for bins, RPKM for genes

ATAC-seq Specific

• Apply Tn5 correction: Use alignmentSieve with --ATACshift
• Fragment filtering: Set appropriate min/max fragment lengths
• Check nucleosome pattern: Fragment size plot should show ladder pattern

Performance Optimization

• Use multiple processors: --numberOfProcessors 8 (or available cores)
• Increase bin size for faster processing and smaller files
• Process chromosomes separately for memory-limited systems
• Pre-filter BAM files using alignmentSieve to create reusable filtered files
• Use bigWig over bedGraph: Compressed and faster to process

Troubleshooting

Common Issues

BAM index missing:

samtools index input.bam

Out of memory:

Process chromosomes individually using --region:

bamCoverage --bam input.bam -o chr1.bw --region chr1

Slow processing:

Increase --numberOfProcessors and/or increase --binSize

bigWig files too large:

Increase bin size: --binSize 50 or larger

Validation Errors

Run validation script to identify issues:

python scripts/validate_files.py --bam *.bam --bed regions.bed

Common errors and solutions explained in script output.

Reference Documentation

This skill includes comprehensive reference documentation:

references/tools_reference.md

Complete documentation of all deepTools commands organized by category:

• BAM and bigWig processing tools (9 tools)
• Quality control tools (6 tools)
• Visualization tools (3 tools)
• Miscellaneous tools (2 tools)

Each tool includes:

• Purpose and overview
• Key parameters with explanations
• Usage examples
• Important notes and best practices

Use this reference when: Users ask about specific tools, parameters, or detailed usage.

references/workflows.md

Complete workflow examples for common analyses:

• ChIP-seq quality control workflow
• ChIP-seq complete analysis workflow
• RNA-seq coverage workflow
• ATAC-seq analysis workflow
• Multi-sample comparison workflow
• Peak region analysis workflow
• Troubleshooting and performance tips

Use this reference when: Users need complete analysis pipelines or workflow examples.

ref