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.