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 local files, readable BAM/BAI/CSI structure and coordinate order, bigWig headers, and every BED row. It does not establish assembly identity or biological suitability.

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"

# 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 venv --python 3.13 .venv-deeptools
uv pip install --python .venv-deeptools/bin/python deepTools==4.0.0
source .venv-deeptools/bin/activate
bamCoverage --version

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

conda create -n deeptools -c conda-forge -c bioconda python=3.13 deeptools=4.0.0 samtools

The 4.0.0 PyPI release includes native macOS Intel/Apple Silicon and Linux wheels. Conda availability varies by platform; the PyPI workflow above was exercised on macOS.

4.0 migration: The five rewritten commands use Rust. bamCoverage, bamCompare, and multiBamSummary no longer accept --ignoreDuplicates; use --samFlagExclude 1024 only after duplicate marking. bamCompare no longer accepts SES. Its released Rust backend accepts RPGC despite a contradictory rolling-doc note; several advertised operations are incorrect (see review). --exactScaling is removed from the rewritten coverage commands because scaling now uses all reads. See references/review.md for verified contracts and remaining limits.

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: Quantify with an annotation-aware gene/transcript workflow; bamCoverage RPKM scales genomic bins, not genes

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