Deeptools

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==3.5.6

Upstream recommends conda/bioconda for full dependency resolution, especially on shared HPC systems:

conda install -c conda-forge -c bioconda deeptools

On Apple Silicon, upstream documents either the PyPI route above or an osx-64 conda environment when native conda packages are unavailable.

Core Workflows and Tool Categories

Complete command sequences for ChIP-seq QC, full ChIP-seq analysis, RNA-seq coverage, and ATAC-seq analysis — plus the BAM/bigWig processing, quality control, and visualization tool categories — are in references/core_workflows.md and references/workflows.md. Per-tool options are in references/tools_reference.md.

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 (per-bin length and library-size scaling)
• BPM: Bins per million, analogous to TPM-style scaling over binned signal
• 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)
• max / max/2: Supported values for --numberOfProcessors; useful under schedulers because recent deepTools releases detect CPU affinity more carefully
• --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 common dUTP-style stranded libraries; confirm library orientation before interpreting strand labels
• Normalization: CPM for bins, RPKM for genes

ATAC-seq Specific

• Apply Tn5 correction: Use alignmentSieve with --ATACshift
• Use only proper pairs for shifting: --ATACshift is equivalent to --shift 4 -5 5 -4 and filters to properly paired fragments
• 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:

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 (3 tools, including bigwigAverage)

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.

references/normalization_methods.md

Comprehensive guide to normalization methods:

• Detailed explanation of each method (RPGC, CPM, RPKM, BPM, etc.)
• When to use each method
• Formulas and interpretation
• Selection guide by experiment type
• Common pitfalls and solutions
• Quick reference table

Use this reference when: Users ask about normalization, comparing samples, or which method to use.

references/effective_genome_sizes.md

Effective genome size values and usage:

• Common organism values (human, mouse, fly, worm, zebrafish)
• Read-length-specific values
• Calculation methods
• When and how to use in commands
• Custom genome calculation instructions

Use this reference when: Users need genome size for RPGC normalization or GC bias correction.

Helper Scripts

scripts/validate_files.py

Validates BAM, bigWig, and BED files for deepTools analysis. Checks file existence, indices, and format.

Usage:

python scripts/validate_files.py --bam sample1.bam sample2.bam \
    --bed peaks.bed --bigwig signal.bw

When to use: Before starting any analysis, or when troubleshooting errors.

scripts/workflow_generator.py

Generates customizable bash script templates for common deepTools workflows.

Available workflows:

• chipseq_qc: ChIP-seq quality control
• chipseq_analysis: Complete ChIP-seq analysis
• rnaseq_coverage: Strand-specific RNA-seq coverage
• atacseq: ATAC-seq with Tn5 correction

Usage:

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

# Generate workflow
python scripts/workflow_generator.py chipseq_qc -o qc.sh \
    --input-bam Input.bam --chip-bams "ChIP1.bam ChIP2.bam" \
    --genome-size 2913022398 --threads 8

# Run generated workflow
chmod +x qc.sh
./qc.sh

When to use: Users request standard workflows or need template scripts to customize.

Assets

assets/quick_reference.md

Quick reference card with most common commands, effective genome sizes, and typical workflow pattern.

When to use: Users need quick command examples without detailed documentation.

Handling User Requests

For New Users

1. Start with installation verification
2. Validate input files using scripts/validate_files.py
3. Recommend appropriate workflow based on experiment type
4. Generate workflow template using scripts/workflow_generator.py
5. Guide through customization and execution

For Experienced Users

1. Provide specific tool commands for requested operations
2. Reference appropriate sections in references/tools_reference.md
3. Suggest optimizations and best practices
4. Offer troubleshooting for issues

For Specific Tasks

"Convert BAM to bigWig":

• Use bamCoverage with appropriate normalization
• Recommend RPGC or CPM based on use case
• Provide effective genome size for organism
• Suggest relevant parameters (extendReads, ignoreDuplicates, binSize)

"Check ChIP quality":

• Run full QC workflow or use plotFingerprint specifically
• Explain interpretation of results
• Suggest follow-up actions based on results

"Create heatmap":

• Guide through two-step process: computeMatrix → plotHeatmap
• Help choose appropriate matrix mode (reference-point vs scale-regions)
• Suggest visualization parameters and clustering options

"Compare samples":

• Recommend bamCompare for two-sample comparison
• Suggest multiBamSummary + plotCorrelation for multiple samples
• Guide normalization method selection

Referencing Documentation

When users need detailed information:

• Tool details: Direct to specific sections in references/tools_reference.md
• Workflows: Use references/workflows.md for complete analysis pipelines
• Normalization: Consult references/normalization_methods.md for method selection
• Genome sizes: Reference references/effective_genome_sizes.md

Example Interactions

User: "I need to analyze my ChIP-seq data"

Response approach:

1. Ask about files available (BAM files, peaks, genes)
2. Validate files using validation script
3. Generate chipseq_analysis workflow template
4. Customize for their specific files and organism
5. Explain each step as script runs

User: "Which normalization should I use?"

Response approach:

1. Ask about experiment type (ChIP-seq, RNA-seq, etc.)
2. Ask about comparison goal (within-sample or between-sample)
3. Consult references/normalization_methods.md selection guide
4. Recommend appropriate method with justification
5. Provide command example with parameters

User: "Create a heatmap around TSS"

Response approach:

1. Verify bigWig and gene BED files available
2. Use computeMatrix with reference-point mode at TSS
3. Generate plotHeatmap with appropriate visualization parameters
4. Suggest clustering if dataset is large
5. Offer profile plot as complement

Key Reminders

• File validation first: Always validate input files before analysis
• Normalization matters: Choose appropriate method for comparison type
• Extend reads carefully: YES for ChIP-seq, NO for RNA-seq
• Use all cores: Set --numberOfProcessors to available cores
• Test on regions: Use --region for parameter testing
• Check QC first: Run quality control before detailed analysis
• Document everything: Save commands for reproducibility
• Reference documentation: Use comprehensive references for detailed guidance

Source: K-Dense-AI/scientific-agent-skills → skills/deeptools/SKILL.md