Can I perform normalization for different genome assemblies?

Yes, the skill includes reference data for effective genome sizes (e.g., hg38, mm10) required for RPGC and other normalization methods.

How does deepTools handle quality control?

It provides specific tools for checking library enrichment (plotFingerprint), sequencing depth (plotCoverage), and replicate similarity (plotCorrelation/plotPCA).

What sequencing types does this deepTools skill support?

The skill is optimized for high-throughput sequencing data, including ChIP-seq, RNA-seq, ATAC-seq, and MNase-seq.

What file formats are compatible with this skill?

The skill primarily works with BAM, bigWig, BED, and bedGraph files, and includes validation scripts for these formats.

Can I generate heatmaps directly through Claude with this skill?

Yes, the skill provides the command sequences for computeMatrix and plotHeatmap to visualize signal intensity over specific genomic regions.

deepTools NGS Analysis

Name: deepTools NGS Analysis
Author: x-cmd

byx-cmd

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数据科学与机器学习

Processes and visualizes high-throughput sequencing data (NGS) including ChIP-seq, RNA-seq, and ATAC-seq workflows.

关于

deepTools is a comprehensive suite of Python tools designed for the end-to-end analysis of Next-Generation Sequencing (NGS) data. It enables researchers to perform essential tasks such as converting BAM alignments to normalized coverage tracks, assessing sample quality through correlation and PCA, and generating publication-quality visualizations like heatmaps and profile plots. This skill provides Claude with the domain-specific knowledge to automate bioinformatics workflows, handle complex normalization methods (RPGC, CPM, RPKM), and troubleshoot common genomic data processing issues efficiently.

主要功能

Specialized workflows for ChIP-seq, ATAC-seq (Tn5 correction), and RNA-seq coverage.
Integrated file validation for BAM, BED, and bigWig formats to ensure workflow stability.
Comprehensive quality control including PCA, fingerprint plots, and sample correlation.
Automated heatmap and profile plot generation around genomic features like TSS.
8 GitHub stars
BAM to bigWig/bedGraph conversion with advanced normalization methods.

使用场景

Normalizing and comparing ChIP-seq treatment vs. control samples using log2 ratio tracks.
Assessing the consistency of biological replicates through Pearson correlation and PCA analysis.
Visualizing signal enrichment across gene bodies or specific genomic regions for publication-ready figures.

关于

主要功能

Specialized workflows for ChIP-seq, ATAC-seq (Tn5 correction), and RNA-seq coverage.
Integrated file validation for BAM, BED, and bigWig formats to ensure workflow stability.
Comprehensive quality control including PCA, fingerprint plots, and sample correlation.
Automated heatmap and profile plot generation around genomic features like TSS.
8 GitHub stars
BAM to bigWig/bedGraph conversion with advanced normalization methods.

使用场景

Normalizing and comparing ChIP-seq treatment vs. control samples using log2 ratio tracks.
Assessing the consistency of biological replicates through Pearson correlation and PCA analysis.
Visualizing signal enrichment across gene bodies or specific genomic regions for publication-ready figures.