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

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).

Strand note: --filterRNAstrand assumes common dUTP/NSR/NNSR reverse-stranded library preparation. For libraries where read 1 follows the RNA strand, forward/reverse output is inverted; use SAM flag filters when library chemistry differs.

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 (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

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 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

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 (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

Start with installation verification

Validate input files using scripts/validate_files.py

Recommend appropriate workflow based on experiment type

Generate workflow template using scripts/workflow_generator.py

Guide through customization and execution

For Experienced Users

Provide specific tool commands for requested operations

Reference appropriate sections in references/tools_reference.md

Suggest optimizations and best practices

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:

Ask about files available (BAM files, peaks, genes)

Validate files using validation script

Generate chipseq_analysis workflow template

Customize for their specific files and organism

Explain each step as script runs

User: "Which normalization should I use?"

Response approach:

Ask about experiment type (ChIP-seq, RNA-seq, etc.)

Ask about comparison goal (within-sample or between-sample)

Consult references/normalization_methods.md selection guide

Recommend appropriate method with justification

Provide command example with parameters

User: "Create a heatmap around TSS"

Response approach:

Verify bigWig and gene BED files available

Use computeMatrix with reference-point mode at TSS

Generate plotHeatmap with appropriate visualization parameters

Suggest clustering if dataset is large

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