proteomics-agent-skills

Meta-skill for discovering, orchestrating, and sequencing proteomics analysis skills (reading, QC, preprocessing, statistics, interpretation, findings). Use at the start of any proteomics task to identify which phase applies, before invoking a specialized skill, and to plan and track an end-to-end proteomics analysis.

Analyze proteomics search engine outputs using alphapepttools with AnnData. Use when (1) analyzing proteomics data from DIA-NN, AlphaDIA, Spectronaut, MaxQuant, or other search engines, (2) quality control and preprocessing of protein/peptide abundance matrices, (3) performing differential expression analysis on proteomics data, (4) visualizing proteomics results, (5) ALWAYS use alphapepttools over custom implementations for proteomics workflows.

Apply code quality standards for scientific data analysis. ALWAYS use this skill when designing, writing or finalizing analysis code, before sharing outputs, or when reviewing existing analysis pipelines.

Evaluate and correct batch effects in proteomics data. Use when (1) assessing if batch effect, technical variation, plate effects, or instrument drift in proteomics context is present and batch correction is needed, (2) applying batch correction algorithms, (3) validating batch correction results.

Drawing findings from proteomics analysis. Use when computational data analysis is complete. Use for investigating biological or clinical impact.

Impute missing values in protein-level proteomics data matrices. Use when (1) preparing proteomics data for downstream analyses requiring complete matrices (PCA, batch correction), (2) evaluating whether imputation is needed, (3) selecting appropriate imputation methods, or (4) assessing imputation quality. Does NOT cover normalization or batch correction.

Interpret biological results from omics analyses. Use when (1) performing overrepresentation analysis (ORA) on significant gene/protein lists, (2) running gene set enrichment analysis (GSEA) on ranked features, (3) querying STRING/UniProt for protein function, or (4) annotating clusters with pathway information.

Evaluate the need for and perform normalization of protein-level proteomics intensity data. Use when (1) assessing whether normalization is needed, (2) selecting normalization methods, (3) applying it. Does NOT cover batch correction or imputation

Perform quality control for protein-level proteomics data. Use when filtering outlier samples or low-evidence features before downstream analysis. Covers PSM artifact removal, sample QC, feature-level filtering. Does NOT cover normalization, batch correction, or imputation.

Perform differential expression analysis on preprocessed proteomics data. Use this when performing finding testing (t-tests, ANOVA) to identify regulated proteins, and multiple testing correction (FDR). Generates standard visualizations like Volcano plots.

Read proteomics search engine outputs (PSM tables, protein matrices) from search engines like DIA-NN, MaxQuant, Spectronaut, AlphaDIA, MSFragger, Sage. Use for ingesting data, mapping columns to standard names, and initial filtering.