deeptools

Convert BAM files to normalized bigWig tracks and generate quality control visualizations.

1|Updated Mar 12, 2026
One-click install
npx skills add https://github.com/yf8578/clawomics --skill deeptools-yf8578
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: deeptools
Source: https://github.com/yf8578/clawomics/tree/main/skills/deeptools
Command: npx skills add https://github.com/yf8578/clawomics --skill deeptools-yf8578

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a comprehensive suite of command-line tools for analyzing high-throughput sequencing data, enabling quality control, normalization, comparison, and visualization of complex genomic datasets.

Core Features & Use Cases

  • Data Conversion: Convert BAM alignments to normalized coverage tracks (bigWig/bedGraph).
  • Quality Control: Assess sample quality using methods like correlation, PCA, and fingerprints.
  • Visualization: Generate publication-quality heatmaps and profile plots around genomic features.
  • Use Case: Analyze ChIP-seq data by converting BAM files to bigWig tracks, performing quality control checks, and generating heatmaps of signal enrichment around known peaks.

Quick Start

Use the deeptools skill to generate a quality control heatmap for ChIP-seq data.

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 bigWig for ChIP-seq visualization?

This Skill provides scripts to convert BAM files to bigWig for ChIP-seq visualization. It generates normalized coverage tracks from BAM alignments, outputting bigWig or bedGraph formats for downstream signal visualization.

What is the best way to perform quality control on RNA-seq data?

Quality control on RNA-seq data is performed using correlation, PCA, and fingerprint assessments. This Skill executes these quality control checks to evaluate high-throughput sequencing sample reliability and identify outliers.

How do I generate heatmaps of signal enrichment around genomic features?

Generate heatmaps of signal enrichment around genomic features using this Skill's visualization scripts. It creates publication-quality heatmaps and profile plots from high-throughput sequencing data to visualize signal distribution patterns.

Can I use this Skill for ATAC-seq workflows and normalization?

Yes, you can use this Skill for ATAC-seq workflows and normalization. It explicitly supports ATAC-seq processing, providing specific scripts and reference documentation for normalization and effective genome size calculations.

Does this Skill support sample comparisons for high-throughput sequencing data?

Yes, this Skill supports sample comparisons for high-throughput sequencing data. It provides command-line tools that enable direct sample comparisons alongside quality control assessments and normalization features.