How does the skill handle different normalization methods?

The skill includes documentation and implementation patterns for RPGC, CPM, RPKM, and BPM normalization strategies tailored to specific experimental designs.

Can I use this skill to generate publication-ready plots?

Yes, the skill provides expert guidance for using plotHeatmap, plotProfile, and other tools to create high-quality genomic visualizations.

What sequencing types does this deepTools skill support?

It supports a wide range of NGS data types, including ChIP-seq, RNA-seq, ATAC-seq, and MNase-seq, with specific workflows for each.

Does it provide effective genome sizes for normalization?

Yes, it includes a quick reference for common organisms like Human (hg38), Mouse (mm10), and Zebrafish (GRCz11) to ensure accurate RPGC normalization.

deepTools NGS Analysis

Name: deepTools NGS Analysis
Author: Activer007

byActiver007

Data Science & ML

Processes and visualizes high-throughput sequencing data through specialized workflows for ChIP-seq, RNA-seq, and ATAC-seq.

About

deepTools is a comprehensive suite of Python-based command-line tools designed for the rigorous analysis of high-throughput sequencing data. This skill provides Claude with domain-specific knowledge to automate quality control, normalize coverage tracks, and generate publication-quality visualizations such as heatmaps and profile plots. It is particularly useful for bioinformaticians working with ChIP-seq, RNA-seq, and ATAC-seq data who need to convert BAM alignments to bigWig formats, assess sample correlations, and visualize signal enrichment around genomic features.

Key Features

Comprehensive QC tools including PCA, correlation, and fingerprint plots
Support for RPGC, CPM, RPKM, and BPM normalization methods
0 GitHub stars
Automated BAM to normalized bigWig coverage track conversion
Heatmap and profile plot generation around TSS and peak regions
Specialized Tn5 offset correction and analysis for ATAC-seq

Use Cases

Visualizing signal enrichment patterns around specific genomic coordinates
Generating strand-specific coverage tracks for RNA-seq visualization
Performing quality control and replicate correlation on ChIP-seq samples

About

Key Features

Comprehensive QC tools including PCA, correlation, and fingerprint plots
Support for RPGC, CPM, RPKM, and BPM normalization methods
0 GitHub stars
Automated BAM to normalized bigWig coverage track conversion
Heatmap and profile plot generation around TSS and peak regions
Specialized Tn5 offset correction and analysis for ATAC-seq

Use Cases

Visualizing signal enrichment patterns around specific genomic coordinates
Generating strand-specific coverage tracks for RNA-seq visualization
Performing quality control and replicate correlation on ChIP-seq samples