NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.
npxskills add davila7/claude-code-templates--skill deeptoolsLoading…
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:
This skill should be used when:
For users new to deepTools, start with file validation and common workflows:
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.
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
See assets/quick_reference.md for frequently used commands and parameters.
uv pip install deeptools
deepTools workflows typically follow this pattern: QC → Normalization → Comparison/Visualization
When users request ChIP-seq QC or quality assessment:
scripts/workflow_generator.py chipseq_qcInterpreting results:
Full workflow details in references/workflows.md → "ChIP-seq Quality Control Workflow"
For full ChIP-seq analysis from BAM to visualizations:
Use scripts/workflow_generator.py chipseq_analysis to generate template.
Complete command sequences in references/workflows.md → "ChIP-seq Analysis 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).
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 requires Tn5 offset correction:
--ATACshiftTemplate: scripts/workflow_generator.py atacseq
Full workflow in references/workflows.md → "ATAC-seq Workflow"
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"
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"
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"
Choosing the correct normalization is critical for valid comparisons. Consult references/normalization_methods.md for comprehensive guidance.
Quick selection guide:
Normalization methods:
Full explanation: references/normalization_methods.md
RPGC normalization requires effective genome size. Common values:
| Organism | Assembly | Size | Usage |
|---|---|---|---|
| Human | GRCh38/hg38 | 2,913,022,398 | --effectiveGenomeSize 2913022398 |
| Mouse | GRCm38/mm10 | 2,652,783,500 | --effectiveGenomeSize 2652783500 |
| Zebrafish | GRCz11 | 1,368,780,147 | --effectiveGenomeSize 1368780147 |
| Drosophila | dm6 | 142,573,017 | --effectiveGenomeSize 142573017 |
Complete table with read-length-specific values: references/effective_genome_sizes.md
Many deepTools commands share these options:
Performance:
--numberOfProcessors, -p: Enable parallel processing (always use available cores)--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 filteringRead Processing:
--extendReads: Extend to fragment length (ChIP-seq: YES, RNA-seq: NO)--centerReads: Center at fragment midpoint for sharper signalsAlways validate files first using scripts/validate_files.py to check:
--region chr1:1-10000000 for parameter testing--extendReads 200--ignoreDuplicates in most cases--ignoreDuplicates after GC correction--filterRNAstrand forward/reverse for stranded libraries--ATACshift--numberOfProcessors 8 (or available cores)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
Run validation script to identify issues:
python scripts/validate_files.py --bam *.bam --bed regions.bed
Common errors and solutions explained in script output.
This skill includes comprehensive reference documentation:
Complete documentation of all deepTools commands organized by category:
Each tool includes:
Use this reference when: Users ask about specific tools, parameters, or detailed usage.
Complete workflow examples for common analyses:
Use this reference when: Users need complete analysis pipelines or workflow examples.
Comprehensive guide to normalization methods:
Use this reference when: Users ask about normalization, comparing samples, or which method to use.
Effective genome size values and usage:
Use this reference when: Users need genome size for RPGC normalization or GC bias correction.
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.
Generates customizable bash script templates for common deepTools workflows.
Available workflows:
chipseq_qc: ChIP-seq quality controlchipseq_analysis: Complete ChIP-seq analysisrnaseq_coverage: Strand-specific RNA-seq coverageatacseq: ATAC-seq with Tn5 correctionUsage:
# 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.
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.
scripts/validate_files.pyscripts/workflow_generator.pyreferences/tools_reference.md"Convert BAM to bigWig":
"Check ChIP quality":
"Create heatmap":
"Compare samples":
When users need detailed information:
references/tools_reference.mdreferences/workflows.md for complete analysis pipelinesreferences/normalization_methods.md for method selectionreferences/effective_genome_sizes.mdSearch references using grep patterns:
# Find tool documentation
grep -A 20 "^### toolname" references/tools_reference.md
# Find workflow
grep -A 50 "^## Workflow Name" references/workflows.md
# Find normalization method
grep -A 15 "^### Method Name" references/normalization_methods.md
User: "I need to analyze my ChIP-seq data"
Response approach:
User: "Which normalization should I use?"
Response approach:
references/normalization_methods.md selection guideUser: "Create a heatmap around TSS"
Response approach:
--numberOfProcessors to available cores--region for parameter testing| ce10/ce11 |
| 100,286,401 |
--effectiveGenomeSize 100286401 |
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