Deeptools
If you're working with ChIP-seq, RNA-seq, or ATAC-seq data, this wraps the deepTools Python toolkit for converting BAM files to normalized bigWig tracks, running quality control checks, and generating heatmaps around genomic features like TSS or peaks. The skill includes workflow generators that scaffold complete analysis pipelines (ChIP QC, coverage generation, visualization) and validation scripts to check your inputs before running long jobs. The normalization guidance is solid, it covers RPGC vs CPM vs RPKM clearly, and the fingerprint plots for assessing ChIP enrichment are genuinely useful. This is a workhorse toolkit if you're past alignment and need to actually look at your data.
- skills.sh installs
- 2K
- GitHub stars
- 56.6K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add k-dense-ai/scientific-agent-skills --skill deeptools --agent claude-codeInstalls into .claude/skills of the current project.
Files
deepTools: NGS Data Analysis Toolkit
Overview
deepTools is a comprehensive suite of Python command-line tools designed for processing and analyzing high-throughput sequencing data. Use deepTools to perform quality control, normalize data, compare samples, and generate publication-quality visualizations for ChIP-seq, RNA-seq, ATAC-seq, MNase-seq, and other NGS experiments.
Core capabilities:
- Convert BAM alignments to normalized coverage tracks (bigWig/bedGraph)
- Quality control assessment (fingerprint, correlation, coverage)
- Sample comparison and correlation analysis
- Heatmap and profile plot generation around genomic features
- Enrichment analysis and peak region visualization
When to Use This Skill
This skill should be used when:
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- Repository
- github.com/k-dense-ai/scientific-agent-skills
- Categories
- AI & Agent Building, Data Science & ML, Marketing & SEO
- First seen
- Jun 3, 2026
