bio-proteomics-ptm-analysis
Post-translational modification analysis including phosphorylation, acetylation, and ubiquitination. Covers site localization, motif analysis, and quantitative PTM analysis. Use when analyzing phosphoproteomic data or other modification-enriched samples.
Best use case
bio-proteomics-ptm-analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Post-translational modification analysis including phosphorylation, acetylation, and ubiquitination. Covers site localization, motif analysis, and quantitative PTM analysis. Use when analyzing phosphoproteomic data or other modification-enriched samples.
Teams using bio-proteomics-ptm-analysis should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/bio-proteomics-ptm-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How bio-proteomics-ptm-analysis Compares
| Feature / Agent | bio-proteomics-ptm-analysis | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Post-translational modification analysis including phosphorylation, acetylation, and ubiquitination. Covers site localization, motif analysis, and quantitative PTM analysis. Use when analyzing phosphoproteomic data or other modification-enriched samples.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
Best AI Skills for ChatGPT
Find the best AI skills to adapt into ChatGPT workflows for research, writing, summarization, planning, and repeatable assistant tasks.
AI Agent for Product Research
Browse AI agent skills for product research, competitive analysis, customer discovery, and structured product decision support.
AI Agent for SaaS Idea Validation
Use AI agent skills for SaaS idea validation, market research, customer discovery, competitor analysis, and documenting startup hypotheses.
SKILL.md Source
## Version Compatibility
Reference examples tested with: numpy 1.26+, pandas 2.2+, scipy 1.12+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Post-Translational Modification Analysis
**"Analyze phosphorylation sites from my proteomics data"** → Identify and quantify post-translational modifications including phosphorylation, acetylation, and ubiquitination with site localization and motif analysis.
- Python: `pyopenms` for PTM-aware search, `scipy` for site-level statistics
- CLI: MaxQuant with variable modifications for enrichment-based PTM analysis
## Common PTMs and Mass Shifts
```python
PTM_MASSES = {
'Phosphorylation': 79.966331, # STY
'Oxidation': 15.994915, # M
'Acetylation': 42.010565, # K, N-term
'Methylation': 14.015650, # KR
'Dimethylation': 28.031300, # KR
'Trimethylation': 42.046950, # K
'Ubiquitination': 114.042927, # K (GlyGly remnant)
'Deamidation': 0.984016, # NQ
'Carbamidomethyl': 57.021464, # C (fixed mod from IAA)
}
```
## Processing MaxQuant PTM Output
**Goal:** Extract high-confidence phosphorylation sites from MaxQuant output with proper filtering and site annotation.
**Approach:** Load the Phospho(STY)Sites table, remove reverse hits and contaminants, filter by localization probability, and construct gene-level site identifiers.
```python
import pandas as pd
import numpy as np
# Phospho(STY)Sites.txt from MaxQuant
phospho = pd.read_csv('Phospho (STY)Sites.txt', sep='\t', low_memory=False)
# Filter valid sites
phospho = phospho[
(phospho['Reverse'] != '+') &
(phospho['Potential contaminant'] != '+')
]
# Filter by localization probability
phospho_confident = phospho[phospho['Localization prob'] >= 0.75]
print(f'Confident sites (prob >= 0.75): {len(phospho_confident)}')
# Extract site information
phospho_confident['site'] = phospho_confident.apply(
lambda r: f"{r['Gene names']}_{r['Amino acid']}{r['Position']}", axis=1
)
```
## Site Localization Scoring
```python
def calculate_ascore_simple(peak_matches_with_ptm, peak_matches_without_ptm, total_peaks):
'''Simplified A-score calculation'''
if peak_matches_without_ptm >= peak_matches_with_ptm:
return 0
p = peak_matches_with_ptm / total_peaks if total_peaks > 0 else 0
if p <= 0 or p >= 1:
return 0
from scipy.stats import binom
p_value = 1 - binom.cdf(peak_matches_with_ptm - 1, total_peaks, 0.5)
return -10 * np.log10(p_value) if p_value > 0 else 100
```
## Motif Analysis
```python
from collections import Counter
def extract_motifs(sites_df, sequence_col, position_col, window=7):
'''Extract sequence windows around modification sites'''
motifs = []
for _, row in sites_df.iterrows():
seq = row[sequence_col]
pos = row[position_col] - 1 # 0-indexed
start = max(0, pos - window)
end = min(len(seq), pos + window + 1)
# Pad if at sequence boundary
motif = '_' * (window - (pos - start)) + seq[start:end] + '_' * (window - (end - pos - 1))
motifs.append(motif)
return motifs
def count_amino_acids_by_position(motifs, center=7):
'''Count amino acid frequencies by position'''
position_counts = {i: Counter() for i in range(-center, center + 1)}
for motif in motifs:
for i, aa in enumerate(motif):
position_counts[i - center][aa] += 1
return position_counts
```
## R: Site-Level Quantification with MSstatsPTM
```r
library(MSstatsPTM)
# Prepare input from MaxQuant
ptm_input <- MaxQtoMSstatsPTMFormat(
evidence = read.table('evidence.txt', sep = '\t', header = TRUE),
annotation = read.csv('annotation.csv'),
fasta = 'uniprot_human.fasta',
mod_type = 'Phospho'
)
# Process data
processed_ptm <- dataSummarizationPTM(ptm_input, method = 'msstats')
# Differential PTM analysis (adjusting for protein-level changes)
ptm_results <- groupComparisonPTM(processed_ptm, contrast.matrix = comparison_matrix)
```
## Related Skills
- peptide-identification - Identify modified peptides
- quantification - Quantify PTM sites
- pathway-analysis/go-enrichment - Enrichment of modified proteinsRelated Skills
tooluniverse-variant-analysis
Production-ready VCF processing, variant annotation, mutation analysis, and structural variant (SV/CNV) interpretation for bioinformatics questions. Parses VCF files (streaming, large files), classifies mutation types (missense, nonsense, synonymous, frameshift, splice, intronic, intergenic) and structural variants (deletions, duplications, inversions, translocations), applies VAF/depth/quality/consequence filters, annotates with ClinVar/dbSNP/gnomAD/CADD via ToolUniverse, interprets SV/CNV clinical significance using ClinGen dosage sensitivity scores, computes variant statistics, and generates reports. Solves questions like "What fraction of variants with VAF < 0.3 are missense?", "How many non-reference variants remain after filtering intronic/intergenic?", "What is the pathogenicity of this deletion affecting BRCA1?", or "Which dosage-sensitive genes overlap this CNV?". Use when processing VCF files, annotating variants, filtering by VAF/depth/consequence, classifying mutations, interpreting structural variants, assessing CNV pathogenicity, comparing cohorts, or answering variant analysis questions.
tooluniverse-structural-variant-analysis
Comprehensive structural variant (SV) analysis skill for clinical genomics. Classifies SVs (deletions, duplications, inversions, translocations), assesses pathogenicity using ACMG-adapted criteria, evaluates gene disruption and dosage sensitivity, and provides clinical interpretation with evidence grading. Use when analyzing CNVs, large deletions/duplications, chromosomal rearrangements, or any structural variants requiring clinical interpretation.
tooluniverse-spatial-omics-analysis
Computational analysis framework for spatial multi-omics data integration. Given spatially variable genes (SVGs), spatial domain annotations, tissue type, and disease context from spatial transcriptomics/proteomics experiments (10x Visium, MERFISH, DBiTplus, SLIDE-seq, etc.), performs comprehensive biological interpretation including pathway enrichment, cell-cell interaction inference, druggable target identification, immune microenvironment characterization, and multi-modal integration. Produces a detailed markdown report with Spatial Omics Integration Score (0-100), domain-by-domain characterization, and validation recommendations. Uses 70+ ToolUniverse tools across 9 analysis phases. Use when users ask about spatial transcriptomics analysis, spatial omics interpretation, tissue heterogeneity, spatial gene expression patterns, tumor microenvironment mapping, tissue zonation, or cell-cell communication from spatial data.
tooluniverse-proteomics-analysis
Analyze mass spectrometry proteomics data including protein quantification, differential expression, post-translational modifications (PTMs), and protein-protein interactions. Processes MaxQuant, Spectronaut, DIA-NN, and other MS platform outputs. Performs normalization, statistical analysis, pathway enrichment, and integration with transcriptomics. Use when analyzing proteomics data, comparing protein abundance between conditions, identifying PTM changes, studying protein complexes, integrating protein and RNA data, discovering protein biomarkers, or conducting quantitative proteomics experiments.
protein-interaction-network-analysis
Analyze protein-protein interaction networks using STRING, BioGRID, and SASBDB databases. Maps protein identifiers, retrieves interaction networks with confidence scores, performs functional enrichment analysis (GO/KEGG/Reactome), and optionally includes structural data. No API key required for core functionality (STRING). Use when analyzing protein networks, discovering interaction partners, identifying functional modules, or studying protein complexes.
tooluniverse-metabolomics-analysis
Analyze metabolomics data including metabolite identification, quantification, pathway analysis, and metabolic flux. Processes LC-MS, GC-MS, NMR data from targeted and untargeted experiments. Performs normalization, statistical analysis, pathway enrichment, metabolite-enzyme integration, and biomarker discovery. Use when analyzing metabolomics datasets, identifying differential metabolites, studying metabolic pathways, integrating with transcriptomics/proteomics, discovering metabolic biomarkers, performing flux balance analysis, or characterizing metabolic phenotypes in disease, drug response, or physiological conditions.
tooluniverse-immune-repertoire-analysis
Comprehensive immune repertoire analysis for T-cell and B-cell receptor sequencing data. Analyze TCR/BCR repertoires to assess clonality, diversity, V(D)J gene usage, CDR3 characteristics, convergence, and predict epitope specificity. Integrate with single-cell data for clonotype-phenotype associations. Use for adaptive immune response profiling, cancer immunotherapy research, vaccine response assessment, autoimmune disease studies, or repertoire diversity analysis in immunology research.
tooluniverse-image-analysis
Production-ready microscopy image analysis and quantitative imaging data skill for colony morphometry, cell counting, fluorescence quantification, and statistical analysis of imaging-derived measurements. Processes ImageJ/CellProfiler output (area, circularity, intensity, cell counts), performs Dunnett's test, Cohen's d effect size, power analysis, Shapiro-Wilk normality tests, two-way ANOVA, polynomial regression, natural spline regression with confidence intervals, and comparative morphometry. Supports CSV/TSV measurement tables, multi-channel fluorescence data, colony swarming assays, and neuron counting datasets. Use when analyzing microscopy measurement data, colony area/circularity, cell count statistics, swarming assays, co-culture ratio optimization, or answering questions about imaging-derived quantitative data.
tooluniverse-crispr-screen-analysis
Comprehensive CRISPR screen analysis for functional genomics. Analyze pooled or arrayed CRISPR screens (knockout, activation, interference) to identify essential genes, synthetic lethal interactions, and drug targets. Perform sgRNA count processing, gene-level scoring (MAGeCK, BAGEL), quality control, pathway enrichment, and drug target prioritization. Use for CRISPR screen analysis, gene essentiality studies, synthetic lethality detection, functional genomics, drug target validation, or identifying genetic vulnerabilities.
statistical-analysis
Statistical analysis toolkit. Hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, Bayesian stats, power analysis, assumption checks, APA reporting, for academic research.
single-trajectory-analysis
Guide to reproducing OmicVerse trajectory workflows spanning PAGA, Palantir, VIA, velocity coupling, and fate scoring notebooks.
single-cell-downstream-analysis
Checklist-style reference for OmicVerse downstream tutorials covering AUCell scoring, metacell DEG, and related exports.