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,

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 PTM Analysis : Phospho site localization, PTM enrichment, kinase prediction Protein RNA Integration : Correlation analysis, translation efficiency Pathway Enrichment : Over representation and GSEA for protein sets PPI Analysis : Protein complex detection, interaction networks via STRING/IntAct Workflow Overview See [PHASE DETAILS.md](PHASE DETAILS.md) for detailed procedures per phase. Integration with ToolUniverse Skill Used For Phase tooluniverse gene enrichment Pathway enrichment Phase 5 tooluniverse protein interactions PPI networks Phase 6 tooluniverse rnaseq deseq2 RNA seq for integration Phase 7 tooluniverse multi omics integration Cross omics analysis Phase 7 tooluniverse target research Protein annotation Phase 8 Quantified Minimums 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) Discovery study, flexible design → Label free (LFQ) (intensity based; median/quantile normalization; more missing values; wider dynamic range) 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. FDR cutoff : Benjamini Hochberg correction mandatory. FDR < 0.05 standard; FDR < 0.01 stringent. Never report unadjusted p values alone. Protein Identification Reasoning 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. 1. 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). 2. 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. 3. 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. 4. 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. Post Translational Modification (PTM) Analysis Reasoning PTMs (phosphorylation, ubiquitination, acetylation, glycosylation) add biological complexity beyond protein abundance. 1. 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. 2. 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. 3. 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. 4. 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. Differential Expression Thresholds Strong : padj < 0.01, FC 2.0, ≥5 unique peptides, <20% missing Moderate : padj 0.01 0.05, FC 1.5 2.0, 2 5 peptides, 20 50% missing Weak/unreliable : padj 0.05 0.1, FC 1.2 1.5, 1 2 peptides (single peptide proteins are unreliable), 50% missing (imputation needed) Evidence Grading T1 : Validated by orthogonal method (Western blot, PRM) + functional study T2 : Significant DE (padj < 0.05, FC 1.5) in 2+ biological replicates T3 : Significant DE in 1 experiment, or significant but low FC T4 : Identified but not quantified, or single peptide identification Synthesis Questions 1. How many proteins are differentially expressed? ( 500 DE proteins suggests global perturbation; <50 suggests targeted effect) 2. Are key pathway proteins concordantly regulated? (all subunits of a complex changing = high confidence) 3. Do proteomics results correlate with transcriptomics? (low correlation is common — post translational regulation) 4. Are PTM changes driving the phenotype? (check phosphoproteomics if available) 5. What is the coverage relative to the expected proteome? (human: ~10K quantified is good; <3K is limited) Limitations Platform specific : Optimized for MS based proteomics (not Western blot quantification) Missing values : High missing rate ( 50% per protein) limits statistical power PTM analysis : Requires enrichment protocols for comprehensive PTM profiling Absolute quantification : Relative abundance only (unless TMT/SILAC used) Protein isoforms : Typically collapsed to gene level Dynamic range : MS has limited dynamic range vs mRNA sequencing References Methods : MaxQuant (doi:10.1038/nbt.1511), Limma for proteomics (doi:10.1093/nar/gkv007), DEP workflow (doi:10.1038/nprot.2018.107) Databases : [STRING](https://string db.org), [PhosphoSitePlus](https://www.phosphosite.org), [CORUM](https://mips.helmholtz muenchen.de/corum) Reference Files [PHASE DETAILS.md](PHASE DETAILS.md) Detailed procedures for each analysis phase, including report template

tooluniverse-proteomics-analysis