proteomics Plugin
Turn search-engine output tables into high-confidence biological insights: reading, QC, normalization, imputation, batch correction, differential expression, and biological interpretation.
So installierst du das proteomics Plugin
Führe beide Befehle in Claude Code aus. Der erste fügt den Marketplace einmalig hinzu; der zweite installiert das Plugin.
/plugin marketplace add MannLabs/proteomics-agent-skills
/plugin install proteomics@proteomics-agent-skillsWas ist in proteomics enthalten
11 Teile wurden aus den eigenen Dateien des Plugins gelesen.
Skills 11
Claude lädt es, wenn die Aufgabe passt- analyzing-proteomics-data
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.
- applying-code-standards
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.
- correcting-proteomics-batch-effects
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.
- formulating-biological-findings
Drawing findings from proteomics analysis. Use when computational data analysis is complete. Use for investigating biological or clinical impact.
- imputing-proteomics-data
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.
- interpreting-biological-results
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.
- normalizing-proteomics-data
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
- performing-proteomics-quality-control
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.
- performing-statistical-analysis
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.
- reading-proteomics-data
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.
- using-proteomics-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.
Verwendung von proteomics in Claude Code
Skills benötigen keinen Command. Claude liest sie, wenn deine Anfrage dem entspricht, was sie beschreiben.
Aktualisieren, deaktivieren oder entfernen
- Plugin aktualisieren (shell)
- claude plugin update proteomics@proteomics-agent-skills
- marketplace aktualisieren
- /plugin marketplace update proteomics-agent-skills
- Vorerst deaktivieren
- /plugin disable proteomics@proteomics-agent-skills
- Entfernen
- /plugin uninstall proteomics@proteomics-agent-skills
Skills in proteomics
- analyzing-proteomics-dataMannLabs/proteomics-agent-skills
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.
4 Dateien17Stars - interpreting-biological-resultsMannLabs/proteomics-agent-skills
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.
3 Dateien17Stars - performing-proteomics-quality-controlMannLabs/proteomics-agent-skills
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.
2 Dateien17Stars - reading-proteomics-dataMannLabs/proteomics-agent-skills
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.
3 Dateien17Stars - applying-code-standardsMannLabs/proteomics-agent-skills
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.
17Stars - correcting-proteomics-batch-effectsMannLabs/proteomics-agent-skills
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.
17Stars - formulating-biological-findingsMannLabs/proteomics-agent-skills
Drawing findings from proteomics analysis. Use when computational data analysis is complete. Use for investigating biological or clinical impact.
17Stars - imputing-proteomics-dataMannLabs/proteomics-agent-skills
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.
17Stars - normalizing-proteomics-dataMannLabs/proteomics-agent-skills
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
17Stars - performing-statistical-analysisMannLabs/proteomics-agent-skills
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.
17Stars - using-proteomics-skillsMannLabs/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.
17Stars
Fragen zum proteomics Plugin
Wie installiere ich das proteomics Plugin?
Führe in Claude Code /plugin marketplace add MannLabs/proteomics-agent-skills aus, dann /plugin install proteomics@proteomics-agent-skills.
Was enthält das proteomics Plugin?
proteomics bündelt 11 Skills.
Wie aktualisiere oder entferne ich das proteomics Plugin?
Aktualisiere es mit claude plugin update proteomics@proteomics-agent-skills in deiner shell oder über den Installed-Tab von /plugin. Entferne es mit /plugin uninstall proteomics@proteomics-agent-skills.
Unter welcher Lizenz steht das proteomics Plugin?
Apache-2.0, wie im Manifest des Plugins angegeben.