deeptools
deepTools is a Python toolkit for processing and visualizing next-generation sequencing data that converts BAM alignment files to normalized coverage tracks, performs quality control assessments through fingerprints and correlation analysis, and generates publication-ready heatmaps and profile plots for ChIP-seq, RNA-seq, and ATAC-seq experiments. Use this skill when you need to normalize sequencing data, compare sample replicates, assess data quality, or create genomic visualizations around features like transcription start sites or peaks.
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills /tmp/deeptools && cp -r /tmp/deeptools/skills/deeptools ~/.claude/skills/deeptoolsSKILL.md
# 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:
```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 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` / `--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, coverHow to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
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