Tooluniverse Proteomics Analysis

Handles the full mass spec proteomics workflow from MaxQuant/Spectronaut/DIA-NN output through differential expression, PTM analysis, and pathway enrichment. It's opinionated about minimums (3+ biological replicates, 2+ unique peptides, Benjamini-Hochberg correction) and includes a solid interpretation framework that walks through LFQ vs TMT tradeoffs and PTM localization scoring. The "check for pre-computed results first" rule at the top is smart since re-running raw proteomics can change numbers and burn turns. Integrates with other ToolUniverse skills for enrichment and PPI networks. If you're doing tumor vs normal protein expression or correlating proteomics with RNA-seq, this gives you the statistical testing and visualization pipeline without reinventing it.

skills.sh installs: 412
GitHub stars: 1.8K
last updated: Aug 21, 2026

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Install

$ npx -y skills add mims-harvard/tooluniverse --skill tooluniverse-proteomics-analysis --agent claude-code

Installs into .claude/skills of the current project.

Files

Proteomics Analysis

RULE ZERO — Check for pre-computed results FIRST

Before following any instruction below, scan the data folder for:

*_executed.ipynb → read with tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}' and cite its cell outputs as the authoritative answer
Pre-computed result files (CSV/TSV with names like *results*, *deseq*, *enrich*, *stats*, *_simplified.csv) → read directly and report the requested value
Canonical analysis scripts (analysis.R, run_*.py, find_*.R, *.Rmd) → execute as-is and read the output

Only follow this skill's re-analysis recipe below if none of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).

Comprehensive analysis of mass spectrometry-based proteomics data from protein identification through quantification, differential expression, post-translational modifications, and systems-level interpretation.