Individual skill

tooluniverse-proteomics-analysis

Mass-spec proteomics analysis — protein identification, quantification (LFQ, TMT, iTRAQ), differential expression (tumor vs normal, treatment vs control), PTM identification, and pathway enrichment on protein lists. Use when you have proteomics MS output, asking about protein abundance differences, or doing systems-level proteomic interpretation.

plugins/tooluniverse/skills/tooluniverse-proteomics-analysis/SKILL.md

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Last checked: Nov 5, 2026

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Repository stars (not skill ratings): 1,779
Repository last updated: Nov 8, 2026
License: Apache-2.0

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CODE_REFERENCE.mddoc · 8,865 bytes
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REPORT_TEMPLATE.mddoc · 2,648 bytes
SKILL.mdcore · 12,467 bytes
SKILL.mdcore · 12,467 bytes

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# 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).

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