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:
Sponsors
Agent CaffeineSponsoredClose your lid, come back to finished work
SnugSponsoredThe clipboard manager for the AI eraPromote your product hereReach 125k+ visitors monthly
WaxSponsoredBlock anything with supervision, without erasing your iPhonePromote your product hereReach 125k+ visitors monthly
knobsSponsoredSee your site on any phone, any day, in any languageDetails
- Repository
- github.com/k-dense-ai/scientific-agent-skills
- Categories
- AI & Agent Building, Data Science & ML, Marketing & SEO
- First seen
- Jun 3, 2026
More from k-dense-ai/scientific-agent-skills
All 137Recommended
More AI & Agent Building
deepchemk-dense-ai/scientific-agent-skillsMolecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive 2Kskills.sh installsbenchling-integrationk-dense-ai/scientific-agent-skillsBenchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when aut2Kskills.sh installsvaexk-dense-ai/scientific-agent-skillsUse this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations2Kskills.sh installs
laravel-inertia-reactasyrafhussin/agent-skillsLaravel + Inertia.js + React integration patterns. Use when building Inertia page components, handling forms with useForm, managing shared data, or implementing2Kskills.sh installsadaptyvk-dense-ai/scientific-agent-skillsHow to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mention2Kskills.sh installsarboretok-dense-ai/scientific-agent-skillsInfer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA2Kskills.sh installs