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LeonChaoX
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deeptools

Convert BAM alignment files into normalized genome coverage tracks and perform QC.

783|65|Updated Feb 27, 2026
Search Tags:#heatmap#atac-seq#ngs#bam to bigwig#chip-seq qc#rna-seq coverage#genomics visualization
One-click install
npx skills add https://github.com/LeonChaoX/qinyan-academic-skills --skill deeptools-leonchaox
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deeptools
Source: https://github.com/LeonChaoX/qinyan-academic-skills/tree/main/skills/05-%E7%94%9F%E7%89%A9%E4%BF%A1%E6%81%AF%E4%B8%8E%E5%9F%BA%E5%9B%A0%E7%BB%84%E5%AD%A6/deeptools
Command: npx skills add https://github.com/LeonChaoX/qinyan-academic-skills --skill deeptools-leonchaox

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

deepTools automates the conversion, quality control, normalization, and visualization of high-throughput sequencing (NGS) alignment data so you can move from raw BAM files to interpretable genomic coverage tracks and publication-ready plots.

Core Features & Use Cases

  • BAM to coverage track conversion: Generate normalized bigWig/bedGraph signals for ChIP-seq, RNA-seq, ATAC-seq (via tools like bamCoverage).
  • Replicate/sample quality control: Assess enrichment and consistency using correlation, PCA, fingerprints, and coverage diagnostics (via multiBamSummary/plotCorrelation/plotPCA/plotFingerprint).
  • Heatmaps and meta-profiles: Produce TSS/peak-centered heatmaps and profile plots using computeMatrix, plotHeatmap, and plotProfile for common functional genomics summaries.
  • Experiment-type aware guidance: Includes workflow patterns and best-practice parameter reminders (e.g., extendReads usage differences between ChIP-seq and RNA-seq, ATAC Tn5 shifting).

Use Case: You have multiple ChIP-seq BAM replicates and want to validate data quality, generate normalized coverage tracks, and create a TSS heatmap plus enrichment at peak regions in a reproducible workflow.

Quick Start

Ask the AI to validate your BAM and BED inputs, then generate a ChIP-seq QC workflow script that computes correlation/PCA, coverage, fragment size QC, and ChIP enrichment fingerprint for your samples.

Frequently Asked Questions about deeptools

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I convert BAM files to normalized bigWig coverage tracks for ChIP-seq or RNA-seq?▼

To convert BAM files to normalized bigWig coverage tracks for ChIP-seq or RNA-seq, use bamCoverage with appropriate normalization methods like RPGC, CPM, or RPKM, applying experiment-specific read handling such as RNA-seq non-extension or ATAC Tn5 shifting.

What is the best way to generate TSS-centered heatmaps and meta-profiles from NGS data?▼

Generating TSS-centered heatmaps and meta-profiles from NGS data involves using computeMatrix to construct TSS or peak matrices, then visualizing the signal enrichment with plotHeatmap and plotProfile for publication-ready genomic coverage summaries.

How do I perform replicate quality control for ChIP-seq alignment data?▼

Replicate quality control for ChIP-seq alignment data is performed by computing multiBamSummary metrics to assess sample consistency, then generating correlation plots, PCA diagnostics, and enrichment fingerprints to validate data quality across replicates.

Does deepTools support different read extension parameters for ATAC-seq and ChIP-seq?▼

deepTools supports different read extension parameters for ATAC-seq and ChIP-seq workflows. It provides experiment-aware guidance, applying ATACshift for Tn5 shifting and extendReads for ChIP-seq, while explicitly omitting read extension for RNA-seq coverage.

How do I calculate effective genome size when normalizing genome coverage tracks?▼

Calculating effective genome size when normalizing genome coverage tracks requires specifying the correct parameter during bamCoverage normalization, particularly when using RPGC normalization, to ensure accurate scaling of the genome coverage signal.

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