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.
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.
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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).
Comprehensive analysis of mass spectrometry-based proteomics data from protein identification through quantification, differential expression, post-translational modifications, and systems-level interpretation.
When to Use This Skill
Triggers: User has proteomics MS output files, asks about protein abundance/expression, differential protein expression, PTM analysis, protein-RNA correlation, multi-omics integration involving proteomics, protein complex/interaction analysis, or proteomics biomarker discovery.
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
Core Capabilities
Data Import: MaxQuant, Spectronaut, DIA-NN, Proteome Discoverer, FragPipe outputs
Quality Control: Missing value analysis, intensity distributions, sample clustering
Normalization: Median, quantile, TMM, VSN — choice depends on experimental design (see Interpretation Framework)
Imputation: MinProb (MNAR), KNN (MAR), QRILC for missing values
Differential Expression: Limma, DEP, MSstats for statistical testing
At least 500 proteins quantified (human: 3,000+ is reasonable; 10,000+ is deep coverage)
At least 3 biological replicates per condition (non-negotiable for reliable statistics)
Filter to proteins with 2+ unique peptides (single-peptide IDs are not reported as DE)
Statistical test: limma or t-test with Benjamini-Hochberg multiple testing correction
Pathway enrichment: at least one method (GO, KEGG, or Reactome)
Report must include: QC summary, DE results with volcano plot, pathway analysis, visualizations
Interpretation Framework
Starting Point: Experimental Design
Quantitative proteomics compares protein abundance. LOOK UP DON'T GUESS — always verify the experimental method, platform, and replicate count before choosing an analysis strategy.
Quantification strategy decision tree:
Cell culture, high accuracy needed → SILAC (ratios within same MS run, most accurate, but culture-only)
Multiple conditions, multiplexing needed → TMT/iTRAQ (up to 18-plex in one run; TMM/VSN normalization; beware ratio compression artifact that reduces observed fold-changes)
Replicates: n < 3 = unreliable fold changes (variance cannot be estimated). Minimum n = 3 biological replicates; n >= 4 preferred for clinical samples. Never report significance from duplicates.
Protein identification from MS data follows a logical chain. LOOK UP DON'T GUESS — search UniProt and STRING for protein annotation rather than inferring function from name alone.
Peptide mass fingerprinting (PMF): Intact protein digested → measured peptide masses compared against theoretical digest of all database proteins. A match requires >=4 peptides covering >=15% of the protein sequence. Single-peptide hits are unreliable (could match multiple proteins).
Tandem MS (MS/MS): Fragment ion spectra matched to peptide sequences via search engines (Andromeda, SEQUEST, X!Tandem). Each peptide-spectrum match (PSM) scored; only PSMs above FDR threshold count. Unique peptides (mapping to one protein) are essential — shared peptides cannot distinguish between protein isoforms.
Protein inference: Multiple peptides → protein groups. When peptides are shared between homologs, report the protein group (not individual proteins). Use proteins_api_search or UniProt_search to resolve ambiguous protein groups.
Coverage matters: 2+ unique peptides is the minimum for a confident protein ID. Proteins identified by a single unique peptide should be flagged as tentative.
Site localization: A phospho-site is confidently localized only if the localization probability > 0.75 (MaxQuant) or ptmRS score > 75 (Proteome Discoverer). Ambiguous sites should not be reported as specific residue modifications.
Enrichment is required: Without phospho-enrichment (TiO2, IMAC), only the most abundant phosphopeptides are detected (~1% of phosphoproteome). An absence of a phospho-site in non-enriched data does not mean it is absent biologically.
Kinase prediction: If phospho-sites are identified, predict upstream kinases using motif analysis. Cross-reference with OpenTargets_get_target_safety_profile_by_ensemblID for kinase-disease associations. LOOK UP kinase-substrate relationships in PhosphoSitePlus rather than guessing from sequence motif alone.
Stoichiometry: A protein can be 5% or 95% phosphorylated at a given site — this matters enormously for function but is hard to measure. Report whether data supports stoichiometry estimation or only site identification.
