deeptools

Trigger phrases

Phrases that activate this skill when typed to Claude Code:

• bamCoverage bigWig
• deepTools heatmap
• ChIP-seq visualization
• ATAC-seq profile
• computeMatrix plotHeatmap

What it does

deeptools is a Claude Code skill from K-Dense AI’s scientific-agent-skills repo. It turns Claude into a deepTools command-line expert for NGS data analysis — covering BAM to bigWig conversion (bamCoverage, bamCompare), cross-sample QC (multiBamSummary, plotCorrelation, plotPCA, plotFingerprint), and the signature heatmap/profile workflow (computeMatrix, plotHeatmap, plotProfile) for ChIP-seq peaks, ATAC-seq peaks, TSS regions, and gene bodies.

A session produces a series of shell commands or a Bash script that takes sorted, indexed BAM files as input and produces bigWig tracks and heatmap figures ready for manuscript inclusion.

When to use it

Reach for it when:

• You need to convert aligned BAM files to normalized bigWig tracks for browser visualization or downstream analysis
• You want to generate publication-style heatmaps showing signal enrichment over peaks, TSS, or gene bodies
• You’re assessing ChIP-seq quality — signal-to-noise via fingerprint plots, cross-sample correlation, and PCA

When not to reach for it:

• Peak calling — use MACS2/MACS3
• Differential binding analysis — use DiffBind or DESeq2 on count matrices

Install

Copy the SKILL.md from K-Dense AI’s deeptools folder into .claude/skills/deeptools/ in your project. Requires deepTools installed in your conda environment (conda install -c bioconda deeptools).

Trigger phrases: “bamCoverage bigWig”, “deepTools heatmap”, “ChIP-seq visualization”, “computeMatrix plotHeatmap”.

What a session looks like

A typical session has three phases:

1. Input and normalization specification. Describe the experiment type (ChIP-seq, ATAC-seq, RNA-seq), available BAM files (IP, input/control), and desired normalization (RPKM, CPM, BPM, or spike-in scaling). Claude generates the appropriate bamCoverage or bamCompare command with the correct flags.
2. Matrix computation. computeMatrix runs in reference-point or scale-regions mode, centered on the provided BED file of peaks or TSS annotations, with appropriate flanking window and bin size for the experiment type.
3. Visualization. plotHeatmap and plotProfile commands with appropriate color scales, sorted by signal intensity, produce publication-ready figures. Claude adds clustering options (k-means) for datasets with heterogeneous enrichment patterns.

Receipts

Where it works well:

• Standard ChIP-seq heatmaps for transcription factor peaks — the computeMatrix reference-point → plotHeatmap pipeline is reliable and the default parameters work well for sharp peaks
• Cross-sample QC with plotFingerprint — a key quality check that catches low-enrichment samples before they go into downstream analysis

Where it backfires:

• Very large BAM files (>50M reads) can make bamCoverage slow without the --numberOfProcessors flag; Claude doesn’t always add this automatically
• Spike-in normalization requires careful calculation of scaling factors from the spike-in BAM file — the command construction is correct but the scaling factor computation is your responsibility

Pattern that works: always run plotFingerprint first on your ChIP-seq BAMs before generating heatmaps — it quickly reveals whether enrichment is present, saving hours of downstream analysis on a failed experiment.

Source and attribution

Originally authored by K-Dense Inc.. The canonical SKILL.md lives in the deeptools folder of their public scientific-agent-skills repository.

License: MIT. Install, adapt, and redistribute with attribution preserved.

This page documents the skill from a practitioner’s perspective. For the formal spec and any updates, defer to the source repo.