deepTools NGS Toolkit

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:

1. Generate workflow script using scripts/workflow_generator.py chipseq_qc
2. 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:

1. Generate coverage tracks with normalization (bamCoverage)
2. Create comparison tracks (bamCompare for log2 ratio)
3. Compute signal matrices around features (computeMatrix)
4. Generate visualizations (plotHeatmap, plotProfile)
5. 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:

1. Shift reads using alignmentSieve with --ATACshift
2. Generate coverage with bamCoverage
3. Analyze fragment sizes (expect nucleosome ladder pattern)
4. 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:

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

1. Start with QC: Run correlation, coverage, and fingerprint analysis before proceeding
2. Test on small regions: Use --region chr1:1-10000000 for parameter testing
3. Document commands: Save full command lines for reproducibility
4. Use consistent normalization: Apply same method across samples in comparisons
5. 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

1. Use multiple processors: --numberOfProcessors 8 (or available cores)
2. Increase bin size for faster processing and smaller files
3. Process chromosomes separately for memory-limited systems
4. Pre-filter BAM files using alignmentSieve to create reusable filtered files
5. 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: