How does it prevent common bioinformatics errors?

The skill includes validation scripts and specific guidance on parameters, such as preventing read extension in RNA-seq analysis to avoid splice-junction errors.

Can I generate publication-ready plots directly?

Absolutely. It provides automated commands for generating high-quality heatmaps, profile plots, PCA, and correlation matrices in standard image formats.

Does it handle genome-specific normalization?

Yes, the skill includes pre-configured effective genome sizes for several model organisms (including GRCh38, mm10, and dm6) to ensure accurate RPGC and CPM normalization.

What types of sequencing data can I analyze with this skill?

This skill is designed for high-throughput sequencing data analysis, specifically optimized for ChIP-seq, RNA-seq, ATAC-seq, and MNase-seq workflows.

NGS Data Analysis (deepTools)

Name: NGS Data Analysis (deepTools)
Author: Activer007

byActiver007

データサイエンスとML

Performs comprehensive next-generation sequencing (NGS) data processing, quality control, and visualization for genomic experiments.

概要

The deepTools skill for Claude Code streamlines the analysis of high-throughput sequencing data such as ChIP-seq, RNA-seq, and ATAC-seq. It provides a structured framework for converting BAM files to normalized coverage tracks, performing critical quality control assessments like PCA and fingerprinting, and generating publication-quality heatmaps and profile plots. By integrating domain-specific best practices for normalization, read filtering, and effective genome sizes, this skill helps bioinformaticians maintain rigorous analysis standards while automating complex command-line workflows and file validation tasks.

主な機能

0 GitHub stars
Specialized presets for ChIP-seq, RNA-seq, and ATAC-seq processing
Normalized BAM to bigWig coverage track conversion
Heatmap and profile generation around genomic features (TSS, peaks)
Automated QC workflows including PCA, correlation, and fingerprint plots
Built-in effective genome size references for common organisms (Human, Mouse, etc.)

ユースケース

Generating normalized coverage tracks for genome browsers like IGV or UCSC
Assessing ChIP-seq enrichment quality and replicate correlation before downstream analysis
Visualizing signal density and distribution across gene bodies or regulatory elements

概要

主な機能

0 GitHub stars
Specialized presets for ChIP-seq, RNA-seq, and ATAC-seq processing
Normalized BAM to bigWig coverage track conversion
Heatmap and profile generation around genomic features (TSS, peaks)
Automated QC workflows including PCA, correlation, and fingerprint plots
Built-in effective genome size references for common organisms (Human, Mouse, etc.)

ユースケース

Generating normalized coverage tracks for genome browsers like IGV or UCSC
Assessing ChIP-seq enrichment quality and replicate correlation before downstream analysis
Visualizing signal density and distribution across gene bodies or regulatory elements