skills.rest
JoBBurt
JoBBurtCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

Proteomics

Normalize, visualize, and analyze label-free quantitative proteomics data in R.

2|1|Updated Dec 27, 2025
Search Tags:#data analysis#visualization#pathway analysis#normalization#R#proteomics
One-click install
npx skills add https://github.com/JoBBurt/proteomics-skill --skill proteomics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Proteomics
Source: https://github.com/JoBBurt/proteomics-skill/tree/main
Command: npx skills add https://github.com/JoBBurt/proteomics-skill --skill proteomics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tidyverse, ggplot2, openxlsx, pheatmap, gplots, corrplot, RColorBrewer, VennDiagram, eulerr, scales, stringr, limma, preprocessCore, vsn, clusterProfiler, org.Hs.eg.db, org.Mm.eg.db, DOSE, enrichplot, ReactomePA, pathview, pRoloc, pRolocdata, zeep, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit for analyzing quantitative proteomics data, enabling researchers to normalize data, visualize results, and perform pathway enrichment analysis.

Core Features & Use Cases

  • Data Normalization: Apply various statistical methods (Median, Quantile, VSN, etc.) to correct for technical variations.
  • Visualization: Generate publication-quality plots like volcano plots, heatmaps, and PCA plots.
  • Pathway Analysis: Perform enrichment analysis using KEGG, GO, and ConsensusPathDB databases.
  • Use Case: A researcher has raw proteomics data and needs to identify differentially expressed proteins between two conditions, visualize the results, and understand the biological pathways involved.

Quick Start

Use the Proteomics skill to create a volcano plot for my proteomics comparison data.

Frequently Asked Questions about Proteomics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I normalize quantitative proteomics data in R?▼

You can normalize quantitative proteomics data in R using methods like Median, Quantile, or VSN to correct for technical variations. The toolkit applies these statistical techniques to prepare your data for downstream analysis.

How do I generate a volcano plot from label-free proteomics data?▼

To generate a volcano plot from label-free proteomics data, the toolkit uses R visualization packages to plot differential expression. This helps you quickly identify and visualize significantly changed proteins between conditions.

Can I perform KEGG and GO pathway enrichment analysis on a protein list?▼

Yes, you can perform KEGG and GO pathway enrichment analysis on a protein list. The toolkit leverages databases like clusterProfiler and ReactomePA to identify enriched biological pathways from your differentially expressed proteins.

What is the best way to cross-reference proteomics results against the MISEV2018 database?▼

The best way to cross-reference proteomics results is by matching your protein lists directly against curated databases like MISEV2018, SASP, and Matrisome. This identifies known proteins within specific biological contexts.

Does this proteomics analysis approach support heatmap and PCA visualizations?▼

Yes, this proteomics analysis approach supports heatmap and PCA visualizations. It generates publication-quality plots to help you explore sample clustering and protein expression patterns across conditions.

Related Skills

Explore More Data & Analytics Skills→
KetomihineKetomihine
1

sc-best-practices-complete-100percent

Guide single-cell analysis workflows from preprocessing to differential abundance testing.

Community
Basic
dailycafidailycafi
7

bio-multi-omics-mixomics-analysis

Integrate and classify multi-omics datasets with mixOmics supervised methods.

Community
Advanced
ya-wayya-way

bio-pathway-reactome

Analyzes Reactome pathway enrichment for gene lists using enrichPathway and gsePathway.

Community
Advanced
MubasherMohammedMubasherMohammed

proteomics-de

Automate differential expression analysis of proteomics data with Python libraries.

Community
Advanced