What is the primary file output for visualizations?

The skill generates high-quality PNG, PDF, or SVG images for plots, and compressed BigWig files for genome browser viewing.

Which sequencing types are supported by this skill?

This skill provides specialized workflows for ChIP-seq, RNA-seq, ATAC-seq, and MNase-seq data analysis.

Does it support parallel processing?

Yes, many deepTools commands within this skill utilize the --numberOfProcessors parameter to enable multi-core processing for faster results.

How does this skill handle file validation?

It includes a dedicated validation script to check for BAM indices, BED format correctness, and file accessibility before running intensive computations.

Can I use this for normalization across different genomes?

Yes, it includes a reference for effective genome sizes for Human (hg38), Mouse (mm10), Zebrafish, and more to support RPGC normalization.

deepTools NGS Analysis

Name: deepTools NGS Analysis
Author: Activer007

byActiver007

Data Science & ML

Processes and visualizes high-throughput sequencing data like ChIP-seq, RNA-seq, and ATAC-seq with professional-grade command-line tools.

About

deepTools is a specialized Claude Code skill for bioinformaticians and computational biologists, providing a robust suite of Python-based tools for Next-Generation Sequencing (NGS) analysis. It streamlines the transition from raw BAM alignments to publication-ready visualizations, offering automated workflows for quality control, normalization, and genomic feature analysis. Whether you are generating heatmaps around transcription start sites or performing PCA on genomic replicates, this skill ensures best practices and efficient command execution.

Key Features

Advanced visualization tools for generating multi-sample heatmaps and genomic profile plots.
Built-in validation scripts for BAM, BED, and BigWig file formats to ensure pipeline integrity.
Automated BAM to BigWig conversion with multiple normalization methods like RPGC and CPM.
Comprehensive QC workflows including PCA analysis, sample correlation, and fingerprint plots.
Specialized support for ATAC-seq Tn5 shift correction and strand-specific RNA-seq coverage.
0 GitHub stars

Use Cases

Generating normalized coverage tracks and enrichment signal matrices for ChIP-seq experiments.
Creating publication-quality heatmaps and profile plots across gene bodies or custom genomic regions.
Performing quality control assessments to compare biological replicates and sequencing depth.

About

Key Features

Advanced visualization tools for generating multi-sample heatmaps and genomic profile plots.
Built-in validation scripts for BAM, BED, and BigWig file formats to ensure pipeline integrity.
Automated BAM to BigWig conversion with multiple normalization methods like RPGC and CPM.
Comprehensive QC workflows including PCA analysis, sample correlation, and fingerprint plots.
Specialized support for ATAC-seq Tn5 shift correction and strand-specific RNA-seq coverage.
0 GitHub stars

Use Cases

Generating normalized coverage tracks and enrichment signal matrices for ChIP-seq experiments.
Creating publication-quality heatmaps and profile plots across gene bodies or custom genomic regions.
Performing quality control assessments to compare biological replicates and sequencing depth.