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deeptools

Convert and normalize NGS BAM files into bigWig coverage tracks and QC outputs.

Updated May 24, 2026
Search Tags:#pca#heatmap#ngs#bam to bigwig#chip-seq qc#rna-seq coverage#compute matrix
One-click install
npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill deeptools-estrella-231
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deeptools
Source: https://github.com/Estrella-231/Mathematical_modeling_tongmeng/tree/main/.agents/skills/deeptools
Command: npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill deeptools-estrella-231

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

deepTools helps you process and analyze NGS alignment data so you can quickly validate experiments, normalize coverage, compare conditions, and generate clear genomic visualizations without manual, error-prone steps.

Core Features & Use Cases

  • Convert and normalize coverage tracks: Transform BAM files into bigWig/bedGraph with appropriate normalization (e.g., RPGC/CPM/RPKM) for downstream genome browser viewing and comparisons.
  • Run experiment QC and replicate diagnostics: Produce fingerprint, correlation, PCA, coverage, and fragment-size checks to assess data quality and consistency.
  • Generate publication-style heatmaps and profiles: Build matrices around genomic features (TSS, genes, peaks) and render heatmaps/profile plots for ChIP-seq, RNA-seq, ATAC-seq, and related assays.
  • Compare samples for enrichment: Create log2 ratio tracks and enrichment plots for treatment vs control, including peak-region visualization.

Quick Start

Use the deeptools skill to generate a ChIP-seq QC workflow by running scripts/workflow_generator.py chipseq_qc with your input BAMs and genome effective size, then review correlation/PCA, coverage, fragment size, and fingerprint outputs.

Frequently Asked Questions about deeptools

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

FAQPage Schema
How do I convert NGS BAM files into bigWig coverage tracks with proper normalization?▼

Converting BAM files into bigWig coverage tracks requires applying normalization methods like RPGC, CPM, or RPKM. This produces normalized coverage tracks suitable for genome browser viewing and accurate sample comparisons.

What's the best way to generate publication-ready heatmaps and profile plots around genomic features?▼

Generating publication-ready heatmaps and profile plots involves building matrices around genomic features like TSS, genes, or peaks. This renders visualizations for ChIP-seq, RNA-seq, and ATAC-seq assays to show enrichment patterns.

How does ChIP-seq QC and replicate correlation analysis work for NGS data?▼

ChIP-seq QC and replicate correlation analysis assess data quality by generating diagnostic outputs like PCA, fingerprinting, and fragment-size checks. This validates experiment consistency and identifies potential outliers across sequencing samples.

Do I need to apply Tn5 shift preprocessing before converting ATAC-seq BAM to bigWig?▼

Applying Tn5 shift preprocessing is an optional step before converting ATAC-seq BAM to bigWig. This corrects the Tn5 transposase cut site bias, ensuring accurate coverage tracks and downstream genomic feature matrices.

Can I create log2 ratio tracks to compare treatment versus control enrichment?▼

You can create log2 ratio tracks and enrichment plots to compare treatment versus control samples. This visualizes differential enrichment directly within peak regions, highlighting condition-specific genomic binding changes.

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