lib-deeptools

Convert BAM files to normalized bigWig tracks for NGS analysis.

Updated Mar 13, 2026
One-click install
npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-deeptools
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lib-deeptools
Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/lib-deeptools
Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-deeptools

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies complex Next-Generation Sequencing (NGS) data analysis, enabling users to efficiently process, visualize, and interpret genomic data from various experiments like ChIP-seq, RNA-seq, and ATAC-seq.

Core Features & Use Cases

  • Data Conversion & Normalization: Convert BAM files to normalized bigWig tracks, essential for visualization and comparison.
  • Quality Control: Perform comprehensive QC checks including sample correlation, PCA, and fingerprint analysis to ensure data reliability.
  • Visualization: Generate publication-quality heatmaps and profile plots around genomic features (e.g., TSS, peaks).
  • Use Case: Analyze ChIP-seq data by converting BAM files to bigWig, checking replicate correlation, and visualizing signal enrichment around known peak regions.

Quick Start

Use the lib-deeptools skill to convert a BAM file named 'sample.bam' into a normalized bigWig file named 'sample.bw' using RPGC normalization and an effective genome size of 2913022398.

Frequently Asked Questions about lib-deeptools

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

FAQPage Schema
How do I convert BAM files to bigWig for NGS data visualization?▼

To convert BAM files to bigWig for NGS data visualization, you apply normalization methods like RPGC with a specified effective genome size, generating normalized bigWig tracks essential for genome browser visualization and sample comparison.

What is the best way to visualize ChIP-seq signal enrichment around genomic features?▼

Visualizing ChIP-seq signal enrichment around genomic features is best achieved by generating publication-quality heatmaps and profile plots directly around specific regions like TSS or known peaks to interpret signal distribution effectively.

How do I perform quality control and check replicate correlation for RNA-seq?▼

Performing quality control and checking replicate correlation for RNA-seq involves running comprehensive QC metrics including sample correlation, PCA, and fingerprint analysis to ensure data reliability before downstream processing.

Can I process ATAC-seq data and generate profile plots without external dependencies?▼

Processing ATAC-seq data and generating profile plots requires Python libraries for bioinformatics data manipulation and visualization, meaning you need a Python environment configured for these specific genomic data tasks.

When do I need to normalize BAM files to bigWig tracks in genomics analysis?▼

You need to normalize BAM files to bigWig tracks in genomics analysis when comparing signal intensity across different NGS experiments, ensuring that sequencing depth variations do not skew your ChIP-seq, RNA-seq, or ATAC-seq visualizations.