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proteomics-ptm

Analyze mass spectrometry data for PTM site localization, type distribution, and motifs.

155|26|Updated Mar 14, 2026
Search Tags:#proteomics#motif analysis#PTM#phosphorylation#site localization#MSstatsPTM
One-click install
npx skills add https://github.com/TianGzlab/OmicsClaw --skill proteomics-ptm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: proteomics-ptm
Source: https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-ptm
Command: npx skills add https://github.com/TianGzlab/OmicsClaw --skill proteomics-ptm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the complex analysis of post-translational modifications (PTMs) in proteomics data, enabling researchers to understand protein regulation beyond simple abundance.

Core Features & Use Cases

  • PTM Site Localization: Assess the confidence of PTM site identification using localization probability scores.
  • PTM Type Analysis: Quantify and categorize different types of PTMs (e.g., phosphorylation, acetylation).
  • Motif Analysis: Identify sequence motifs enriched around PTM sites to infer kinase specificity or functional context.
  • Differential PTM Analysis: Quantify changes in PTM levels, accounting for protein abundance variations.
  • Use Case: After a mass spectrometry experiment, use this Skill to identify all phosphorylated sites, determine their localization confidence, and find enriched motifs around these sites to hypothesize regulatory pathways.

Quick Start

Run proteomics-ptm analysis on the demo data.

Frequently Asked Questions about proteomics-ptm

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

FAQPage Schema
How do I analyze post-translational modification sites from mass spectrometry data?▼

Post-translational modification analysis processes mass spectrometry data to score PTM site localization confidence, quantify PTM types like phosphorylation, and identify enriched sequence motifs to infer protein regulation.

What is motif analysis in proteomics and how does it infer kinase specificity?▼

Motif analysis in proteomics identifies enriched amino acid sequence patterns around PTM sites. By analyzing these sequence motifs, researchers can infer kinase specificity and hypothesize functional regulatory pathways for modified proteins.

How do I assess the confidence of phosphorylation site localization?▼

Phosphorylation site localization confidence is assessed using localization probability scores. This analysis evaluates PTM identification results from mass spectrometry data to determine the exact modified residue and quantify identification certainty.

Can I quantify changes in PTM levels while accounting for protein abundance variations?▼

Differential PTM analysis quantifies changes in modification levels while accounting for protein abundance variations. It processes PTM identification results to provide accurate quantitative insights into protein regulation dynamics.

Does MSstatsPTM work with numpy and pandas for PTM type distribution analysis?▼

PTM type distribution analysis utilizes numpy, pandas, and scipy dependencies to categorize and quantify different modification types. This environment supports processing PTM identification results to quantify modifications like phosphorylation and acetylation.

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