Can this skill help with data normalization?

Yes, it provides specific guidance and command templates for RPGC, CPM, RPKM, and BPM normalization methods tailored to different experimental designs.

What sequencing types are supported by the deepTools skill?

This skill supports common high-throughput sequencing protocols including ChIP-seq, RNA-seq, ATAC-seq, and MNase-seq.

Can I use it to troubleshoot my NGS pipeline?

Yes, it includes validation scripts to check BAM indices, file integrity, and formatting before you run intensive computational tasks.

How does it handle different genome builds?

The skill includes a reference for effective genome sizes for major organisms like Human (hg38), Mouse (mm10), and Fruit Fly (dm6) to ensure accurate normalization.

Does it support publication-quality plotting?

Absolutely. It generates scripts for sophisticated visualizations like heatmaps around TSS, profile plots of signal enrichment, and PCA plots for sample comparison.

deepTools NGS Analysis

Name: deepTools NGS Analysis
Author: Microck

byMicrock

•

データサイエンスとML

Automates Next-Generation Sequencing (NGS) data processing, quality control, and publication-quality visualization.

概要

The deepTools Claude Code skill provides a specialized suite of capabilities for processing high-throughput sequencing data from ChIP-seq, RNA-seq, and ATAC-seq experiments. It streamlines complex bioinformatics workflows by helping users generate command-line templates for BAM to bigWig conversion, perform rigorous quality control assessments like PCA and correlation analysis, and create sophisticated genomic visualizations including heatmaps and TSS profiles. This skill is essential for researchers and bioinformaticians looking to maintain reproducibility and accuracy in their genomic data pipelines.

主な機能

Normalized coverage track generation (BAM to bigWig conversion)
Multi-sample correlation and PCA analysis for replicate validation
Genomic feature visualization including Heatmaps and Profile plots
Pre-configured effective genome sizes for major model organisms
Comprehensive QC workflows for ChIP-seq, ATAC-seq, and RNA-seq
81 GitHub stars

ユースケース

Generating normalized bigWig tracks for visualization in genome browsers
Assessing ChIP-seq enrichment strength using fingerprint and coverage plots
Comparing signal distribution across different treatment conditions or replicates

概要

主な機能

Normalized coverage track generation (BAM to bigWig conversion)
Multi-sample correlation and PCA analysis for replicate validation
Genomic feature visualization including Heatmaps and Profile plots
Pre-configured effective genome sizes for major model organisms
Comprehensive QC workflows for ChIP-seq, ATAC-seq, and RNA-seq
81 GitHub stars

ユースケース

Generating normalized bigWig tracks for visualization in genome browsers
Assessing ChIP-seq enrichment strength using fingerprint and coverage plots
Comparing signal distribution across different treatment conditions or replicates