Proteomics

JoBBurt/proteomics-skill

Proteomics analysis toolkit for label-free quantitative proteomics. Invokes R scripts for normalization, visualization (volcano, heatmap, PCA, LOPIT), pathway analysis (KEGG, ConsensusPathDB), and protein list cross-referencing (MISEV2018, SASP, Matrisome). USE WHEN user says 'analyze proteomics', 'volcano plot', 'normalize protein data', 'pathway enrichment', 'check EV markers', 'SASP analysis', 'matrisome', OR mentions q-value, fold-change, or protein quantification.

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SKILL.md

name: Proteomics description: Proteomics analysis toolkit for label-free quantitative proteomics. Invokes R scripts for normalization, visualization (volcano, heatmap, PCA, LOPIT), pathway analysis (KEGG, ConsensusPathDB), and protein list cross-referencing (MISEV2018, SASP, Matrisome). USE WHEN user says 'analyze proteomics', 'volcano plot', 'normalize protein data', 'pathway enrichment', 'check EV markers', 'SASP analysis', 'matrisome', OR mentions q-value, fold-change, or protein quantification.

Proteomics

Quantitative proteomics analysis toolkit combining R script invocation with embedded methodology knowledge. Fully portable - all scripts and reference data included.

Skill Directory: ~/.claude/Skills/Proteomics/

Workflow Routing

When executing a workflow, output this notification:

Running the **WorkflowName** workflow from the **Proteomics** skill...

Workflow	Trigger	File
Normalize	"normalize data", "apply normalization", "median/quantile/loess normalize"	`workflows/Normalize.md`
VolcanoPlot	"volcano plot", "create volcano", "visualize fold change"	`workflows/VolcanoPlot.md`
Heatmap	"heatmap", "PCA", "correlation plot", "sample clustering"	`workflows/Heatmap.md`
PathwayAnalysis	"pathway analysis", "KEGG enrichment", "ConsensusPathDB", "GO enrichment"	`workflows/PathwayAnalysis.md`
ProteinListQuery	"check EV markers", "MISEV proteins", "exosome markers", "blood contaminants"	`workflows/ProteinListQuery.md`
ExcelWorkup	"create Excel report", "filter by q-value", "generate data tables"	`workflows/ExcelWorkup.md`
Matrisome	"matrisome analysis", "ECM proteins", "extracellular matrix"	`workflows/Matrisome.md`
SaspAnalysis	"SASP analysis", "senescence factors", "core SASP"	`workflows/SaspAnalysis.md`

Examples

Example 1: Generate Volcano Plot

User: "Create a volcano plot for my proteomics comparison data"
-> Invokes VolcanoPlot workflow
-> Asks for data file location and parameters (q-value, fold-change threshold)
-> Either invokes Plot_Workup_V10.R or generates custom ggplot2 code
-> Outputs TIFF files to output/ directory

Example 2: Check for EV Markers

User: "Which MISEV2018 EV markers are in my dataset?"
-> Invokes ProteinListQuery workflow
-> Reads user's protein list
-> Cross-references against data/MISEV2018_EV_Markers.txt
-> Returns categorized matches (Category 1-5, tetraspanins, annexins, etc.)

Example 3: Full Analysis Pipeline

User: "Run a complete proteomics analysis on my kidney data"
-> Sequences multiple workflows:
  1. Normalize (median normalization)
  2. Heatmap (PCA, sample correlation)
  3. VolcanoPlot (for each comparison)
  4. Matrisome (ECM protein analysis)
  5. SaspAnalysis (if relevant)
  6. ExcelWorkup (generate report)
-> Creates organized output/ directory structure

Example 4: Pathway Enrichment

User: "Run KEGG pathway analysis on my significantly altered proteins"
-> Invokes PathwayAnalysis workflow
-> Filters to q < 0.01, |log2FC| > 0.58
-> Runs clusterProfiler or ConsensusPathDB
-> Generates dotplot visualization

R Script Quick Reference

All scripts are in the skill's rscripts/ directory.

Script	Purpose	Key Parameters
`Plot_Workup_V10.R`	Full visualization pipeline	`organism`, `batch`, `myFC`, `myQval`, `mypattern`
`Excel_Workup_v05.R`	Excel report generation	`myoutput`, `batch`, `myFC`, q-value flags
`normalization/Step_1_Normalization.R`	Data normalization	Input matrix (iMat)
`ConsensusPathDB_23_0411_v03.R`	Pathway dotplots	`input_dir`, `output_dir`, `q.val`, `t.level`
`toolkit.R`	Library loading	Called at start of analysis
`barplots.R`	Bar plot utility	Various

Standard Parameters

Parameter	Typical Values	Description
q-value	0.05, 0.01, 0.001	Statistical significance threshold
Fold Change	0.58 (1.5x), 1.0 (2x)	Log2 fold change cutoff
Organism	"human", "mouse"	Species for reference lists
Pattern	`"JB\\d_\\d+"`	Regex for sample ID extraction

Reference Data Available

All protein lists are in the skill's data/ directory.

List	File	Contents
MISEV2018 EV Markers	`MISEV2018_EV_Markers.txt`	500+ proteins, Category 1-5
EV Categories	`MISEV2018_EV_Categories.txt`	Category definitions
Exosome Markers	`Exosome_Protein_Markers.txt`	CD63, CD81, CD9, TSG101, etc.
Blood Contaminants	`Top_10_Blood_Proteins.txt`	Albumin, IgG, fibrinogen, etc.
Apolipoproteins	`Apolipoproteins.txt`	APOA1, APOB, etc.
Human Core SASP	`Human_Core_SASP.csv`	175 SASP factors with IR/RAS/ATV scores
Mouse Core SASP	`Mouse_Core_SASP.csv`	Mouse SASP orthologs
Human Matrisome	`matrisome_hs_masterlist.csv`	ECM proteins by category
Mouse Matrisome	`matrisome_mm_masterlist.csv`	Mouse ECM proteins

Required Data Structure

For running the full analysis scripts, data should be organized as:

[PROJECT_DIR]/
├── data/
│   ├── [batch]_Protein_Report_2pep.csv    # Protein intensities
│   ├── [batch]_candidates_2pep.csv         # Comparison results
│   └── [batch]_ConditionSetup.csv          # Sample metadata
└── output/
    ├── Data_Tables/                        # Excel reports
    └── [plots will be saved here]

Invocation Pattern

To run R scripts from this skill:

cd [PROJECT_WORKING_DIR]
Rscript ~/.claude/Skills/Proteomics/rscripts/[SCRIPT_NAME].R

Important: Scripts expect:

Working directory set to project folder
data/ subdirectory with input files
output/ subdirectory for results
Reference data paths point to skill's data/ directory (may need adjustment)

When NOT to Use This Skill

General R coding questions -> Use standard Claude
Non-proteomics data analysis -> Use appropriate tools
Genomics/transcriptomics -> Different methodology
Statistical consulting without data -> Explain methodology, don't run

README

Proteomics Skill

A Claude Code skill for label-free quantitative proteomics analysis. Provides workflows for data normalization, visualization (volcano plots, heatmaps, PCA), pathway enrichment analysis (KEGG, ConsensusPathDB), and protein list cross-referencing.

Installation

Clone this repository directly to your Claude Code Skills directory:

git clone https://github.com/jobburt-labs/proteomics-skill.git ~/.claude/Skills/Proteomics

R Dependencies

This skill requires R with the following packages installed:

CRAN Packages

install.packages(c(
  "tidyverse",
  "ggplot2",
  "openxlsx",
  "pheatmap",
  "gplots",
  "corrplot",
  "RColorBrewer",
  "VennDiagram",
  "eulerr",
  "scales",
  "stringr"
))

Bioconductor Packages

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install(c(
  "limma",
  "preprocessCore",
  "vsn",
  "clusterProfiler",
  "org.Hs.eg.db",
  "org.Mm.eg.db",
  "DOSE",
  "enrichplot",
  "ReactomePA",
  "pathview",
  "pRoloc",
  "pRolocdata"
))

One-liner Installation

# Install all dependencies at once
install.packages(c("tidyverse", "ggplot2", "openxlsx", "pheatmap", "gplots", "corrplot", "RColorBrewer", "VennDiagram", "eulerr", "scales", "stringr"))

if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install(c("limma", "preprocessCore", "vsn", "clusterProfiler", "org.Hs.eg.db", "org.Mm.eg.db", "DOSE", "enrichplot", "ReactomePA", "pathview", "pRoloc", "pRolocdata"))

Python Dependencies (Optional)

For ConsensusPathDB SOAP client:

pip install zeep  # SOAP client for Python 3

Directory Structure

Proteomics/
├── SKILL.md                    # Main skill definition
├── README.md                   # This file
├── DataFormats.md              # Input/output file specifications
├── NormalizationMethods.md     # Statistical normalization reference
├── VisualizationPatterns.md    # ggplot2 plotting patterns
├── PathwayAnalysisGuide.md     # Pathway enrichment methodology
├── workflows/
│   ├── Normalize.md            # Data normalization
│   ├── VolcanoPlot.md          # Volcano plot generation
│   ├── Heatmap.md              # Heatmap/PCA/correlation
│   ├── PathwayAnalysis.md      # KEGG/ConsensusPathDB
│   ├── ProteinListQuery.md     # Protein list cross-reference
│   ├── ExcelWorkup.md          # Excel report generation
│   ├── Matrisome.md            # ECM/Matrisome analysis
│   └── SaspAnalysis.md         # SASP factor analysis
├── rscripts/
│   ├── Plot_Workup_V10.R       # Visualization pipeline
│   ├── Excel_Workup_v05.R      # Excel report generation
│   ├── ConsensusPathDB_23_0411_v03.R
│   ├── toolkit.R               # Library loading
│   ├── barplots.R              # Bar plot utility
│   └── normalization/
│       ├── Step_1_Normalization.R
│       └── 2201_Label_Free_Functions.R
├── python/
│   ├── client.py               # ConsensusPathDB SOAP client
│   ├── cpdb_services.py
│   └── cpdb_services_types.py
├── data/
│   ├── MISEV2018_EV_Markers.txt
│   ├── MISEV2018_EV_Categories.txt
│   ├── Exosome_Protein_Markers.txt
│   ├── Top_10_Blood_Proteins.txt
│   ├── Apolipoproteins.txt
│   ├── Human_Core_SASP.csv
│   ├── Mouse_Core_SASP.csv
│   ├── matrisome_hs_masterlist.csv
│   └── matrisome_mm_masterlist.csv
└── tools/
    └── .gitkeep

Workflows

Workflow	Trigger	Description
Normalize	"normalize", "median normalization"	Apply normalization methods
VolcanoPlot	"volcano plot", "fold change plot"	Generate volcano plots
Heatmap	"heatmap", "PCA", "correlation"	PCA, heatmaps, correlation plots
PathwayAnalysis	"pathway", "KEGG", "enrichment"	Pathway enrichment analysis
ProteinListQuery	"EV markers", "check against"	Cross-reference protein lists
ExcelWorkup	"Excel report", "filter proteins"	Generate filtered Excel output
Matrisome	"matrisome", "ECM proteins"	ECM/Matrisome analysis
SaspAnalysis	"SASP", "senescence"	Core SASP factor analysis

Usage Examples

"Create a volcano plot for my proteomics data"
"Normalize my protein intensities using quantile normalization"
"Which of my proteins are MISEV2018 EV markers?"
"Run KEGG pathway analysis on significantly altered proteins"
"Check for SASP factors in my aging samples"
"Generate a heatmap of my top differentially expressed proteins"

Reference Data

Dataset	Description	Organisms
MISEV2018_EV_Markers	Extracellular vesicle marker proteins	Human
Core_SASP	Senescence-associated secretory phenotype factors	Human, Mouse
MatrisomeDB	Extracellular matrix proteins	Human, Mouse
Apolipoproteins	Blood contamination markers	Human

License

This skill is provided as-is for proteomics data analysis.

Installation

Option 1: Use slash command in Claude Code

/install-skill https://github.com/JoBBurt/proteomics-skill

Option 2: Clone to skills directory

# Global (all projects)

git clone https://github.com/JoBBurt/proteomics-skill ~/.claude/skills/proteomics-skill

# Project-specific

git clone https://github.com/JoBBurt/proteomics-skill .claude/skills/proteomics-skill

Add MCP server to .cursor/mcp.json:

{
  "mcpServers": {
    "skillz": {
      "command": "npx",
      "args": ["-y", "skillz-mcp", "https://github.com/JoBBurt/proteomics-skill"]
    }
  }
}

Restart Cursor after adding the configuration.

Option 1: Use Gemini CLI command

gemini extensions install https://github.com/JoBBurt/proteomics-skill

Option 2: Clone to extensions directory

git clone https://github.com/JoBBurt/proteomics-skill ~/.gemini/extensions/proteomics-skill