/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 >3.8 and deepTools 3.5.6-compatible dependencies. The upstream project recommends conda/bioconda for full dependency resolution; repo examples use uv with pinned PyPI installs for reproducible command-line workflows.
metadata:
  version: "1.3"
  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 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](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**: 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:

| Organism | Assembly | Size | Usage | |----------|----------|------|-------| | Human | GRCh38/hg38 | 2,913,022,398 | `--effectiveGenomeSize 2913022398` | | Human | T2T/CHM13CAT_v2 | 3,117,292,070 | `--effectiveGenomeSize 3117292070` | | Mouse | GRCm39/mm39 | 2,654,621,783 | `--effectiveGenomeSize 2654621783` | | Mouse | GRCm38/mm10 | 2,652,783,500 | `--effectiveGenomeSize 2652783500` | | Zebrafish | GRCz11 | 1,368,780,147 | `--effectiveGenomeSize 1368780147` | | *Drosophila* | dm6 | 142,573,017 | `--effectiveGenomeSize 142573017` | | *C. elegans* | ce10/ce11 | 100,286,401 | `--effectiveGenomeSize 100286401` |

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` / `--maxFrag