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NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.
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NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization. 📦 Ships with scientific-agent-skills
</> SKILL.md
deeptools.SKILL.md
---
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": "1.1", "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
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## 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:
```bash
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:
```bash
# 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
```bash
uv pip install deepTools==3.5.6
```
Upstream recommends conda/bioconda for full dependency resolution, especially on shared HPC systems:
```bash
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:
1. **Generate workflow script** using `scripts/workflow_generator.py chipseq_qc`
2. **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:
1. **Generate coverage tracks** with normalization (bamCoverage)
2. **Create comparison tracks** (bamCompare for log2 ratio)
3. **Compute signal matrices** around features (computeMatrix)
4. **Generate visualizations** (plotHeatmap, plotProfile)
5. **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:
1. **Shift reads** using alignmentSieve with `--ATACshift`
2. **Generate coverage** with bamCoverage
3. **Analyze fragment sizes** (expect nucleosome ladder pattern)
4. **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:**
```bash
bamCoverage --bam input.bam --outFileName output.bw \
--normalizeUsing RPGC --effectiveGenomeSize 2913022398 \
--binSize 10 --numberOfProcessors 8
```
**Compare two samples (log2 ratio):**
```bash
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:**
```bash
plotFingerprint -b input.bam chip.bam -o fingerprint.png \
--extendReads 200 --ignoreDuplicates
```
**Sample correlation:**
```bash
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:**
```bash
# 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:**
```bash
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:
| 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` / `--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:**
```bash
samtools index input.bam
```
**Out of memory:**
Process chromosomes individually using `--region`:
```bash
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:
```bash
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:**
```bash
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:**
```bash
# 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
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