/deeptools

name: deeptools
description: NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.
license: BSD license
allowed-tools: Read Write Edit Bash
compatibility: Requires Python 3.12+ and deepTools 4.0.0 (including pysam and pyBigWig); samtools for sorting/indexing workflows. Network needed only for installation or retrieving public input data.
metadata:
  version: "2.0"
  last-reviewed: "2026-09-30"
  upstream-version: "4.0.0"
  skill-author: K-Dense Inc.

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](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](references/core_workflows.md) and [references/workflows.md](references/workflows.md). Per-tool options are in [references/tools_reference.md](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
• **ATAC-seq**: Use CPM for the shifted-alignment template; RPGC without extension failed on shifted BAMs in 4.0.0

**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**: The released 4.0.0 Rust implementation reduces to CPM; do not interpret it as gene TPM
• **None**: Raw counts (not recommended for comparisons)

Full explanation: `references/normalization_methods.md`

Effective Genome Sizes

RPGC normalization requires effective genome size. Examples from the **4.0.0 tagged source**, not universal assembly constants (the rolling documentation differs):

| Organism | Assembly | Size | Usage | |----------|----------|------|-------| | Human | GRCh38/hg38 | 2,913,022,398 | `--effectiveGenomeSize 2913022398` | | Human | T2T/CHM13CAT_v2 | 3,117,292,070 | `--eff