Proteomics Analysis

Comprehensive analysis of mass spectrometry-based proteomics data from protein identification through quantification, differential expression, post-translational modifications, and systems-level interpretation.

When to Use This Skill

Triggers:

User has proteomics data (MS output files)
Questions about protein abundance or expression
Differential protein expression analysis requests
PTM analysis (phosphorylation, acetylation, ubiquitination)
Protein-RNA correlation analysis
Multi-omics integration involving proteomics
Protein complex or interaction analysis
Proteomics biomarker discovery

Example Questions This Skill Solves:

"Analyze this MaxQuant output for differential protein expression"
"Which proteins are significantly upregulated in disease vs control?"
"Correlate protein abundance with mRNA expression"
"What post-translational modifications change between conditions?"
"Identify protein complexes in my co-IP MS data"
"Which pathways are enriched in differentially expressed proteins?"
"Find protein biomarkers for disease classification"
"Compare protein and RNA levels to identify translation-regulated genes"

Core Capabilities

Capability	Description
Data Import	MaxQuant, Spectronaut, DIA-NN, Proteome Discoverer, FragPipe outputs
Quality Control	Missing value analysis, intensity distributions, sample clustering
Normalization	Median, quantile, TMM, VSN normalization methods
Imputation	MinProb, KNN, QRILC for missing values
Differential Expression	Limma, DEP, MSstats for statistical testing
PTM Analysis	Phospho-site localization, PTM enrichment, kinase prediction
Protein-RNA Integration	Correlation analysis, translation efficiency
Pathway Enrichment	Over-representation and GSEA for protein sets
PPI Analysis	Protein complex detection, interaction networks via STRING/IntAct
Reporting	Comprehensive reports with volcano plots, heatmaps, pathway diagrams

Workflow Overview

Input: MS Proteomics Data
    |
    v
Phase 1: Data Import & QC
    |-- Load MaxQuant/Spectronaut/DIA-NN output
    |-- Parse protein groups, intensities, modifications
    |-- Quality control plots (missing values, intensity distributions)
    |-- Sample correlation and PCA
    |
    v
Phase 2: Preprocessing
    |-- Filter low-confidence proteins
    |-- Handle missing values (imputation or filtering)
    |-- Log-transform intensities
    |-- Normalize across samples
    |
    v
Phase 3: Differential Expression Analysis
    |-- Statistical testing (limma, t-test, ANOVA)
    |-- Multiple testing correction (BH, Bonferroni)
    |-- Fold change calculation
    |-- Significance thresholds (p < 0.05, |log2FC| > 1)
    |
    v
Phase 4: PTM Analysis (if applicable)
    |-- Identify modified peptides
    |-- Localization probability filtering
    |-- PTM site quantification
    |-- Kinase-substrate prediction
    |-- PTM enrichment analysis
    |
    v
Phase 5: Functional Enrichment
    |-- Gene Ontology enrichment
    |-- KEGG/Reactome pathway enrichment
    |-- Protein complex enrichment (CORUM)
    |-- Tissue-specific enrichment
    |
    v
Phase 6: Protein-Protein Interactions
    |-- Query STRING for interaction networks
    |-- Identify protein complexes
    |-- Network clustering and modules
    |-- Hub protein identification
    |
    v
Phase 7: Multi-Omics Integration (optional)
    |-- Correlate with RNA-seq data
    |-- Identify translation-regulated proteins
    |-- Compare with variant/CNV data
    |-- Integrate with metabolomics
    |
    v
Phase 8: Generate Report
    |-- Summary statistics
    |-- Volcano plots and heatmaps
    |-- Pathway diagrams
    |-- Protein network visualizations
    |-- Multi-omics integration plots

Phase Details

Phase 1: Data Import & Quality Control

Objective: Load proteomics data and assess data quality.

Supported input formats:

MaxQuant (most common):

```
proteinGroups.txt
```
- Protein-level quantification
```
evidence.txt
```
- Peptide-level data
```
Phospho (STY)Sites.txt
```
- Phosphorylation sites
```
modificationSpecificPeptides.txt
```
- Other PTMs

Spectronaut:

```
*_Report.tsv
```
- Protein/peptide quantification
DIA-based quantification

DIA-NN:

```
report.tsv
```
- Protein groups
```
report.pr_matrix.tsv
```
- Protein matrix

Proteome Discoverer:

```
*_Proteins.txt
```
```
*_PSMs.txt
```

Data loading:

python

def load_maxquant_proteins(protein_groups_file):
    """
    Load MaxQuant proteinGroups.txt file.

    Returns:
    - DataFrame with proteins as rows, samples as columns
    - Metadata (protein names, gene names, sequence coverage)
    """
    import pandas as pd

    # Read file
    df = pd.read_csv(protein_groups_file, sep='\t')

    # Extract intensity columns (LFQ or raw)
    intensity_cols = [col for col in df.columns if 'LFQ intensity' in col or 'Intensity ' in col]

    # Create intensity matrix
    intensity_matrix = df[intensity_cols].copy()
    intensity_matrix.columns = [col.replace('LFQ intensity ', '').replace('Intensity ', '')
                                 for col in intensity_cols]

    # Add protein metadata
    metadata = df[['Protein IDs', 'Gene names', 'Fasta headers',
                   'Peptides', 'Sequence coverage [%]']].copy()

    return intensity_matrix, metadata

Quality Control:

Missing value assessment:

python

def assess_missing_values(intensity_matrix):
    """
    Calculate percentage of missing values per protein and sample.
    """
    # Per protein
    missing_per_protein = (intensity_matrix == 0).sum(axis=1) / intensity_matrix.shape[1]

    # Per sample
    missing_per_sample = (intensity_matrix == 0).sum(axis=0) / intensity_matrix.shape[0]

    # Visualize
    plot_missing_value_heatmap(intensity_matrix)

    return missing_per_protein, missing_per_sample

Intensity distribution:

python

def plot_intensity_distributions(intensity_matrix):
    """
    Plot log10 intensity distributions per sample.
    Check for consistent distributions across samples.
    """
    import matplotlib.pyplot as plt
    import numpy as np

    log_intensities = np.log10(intensity_matrix.replace(0, np.nan))

    # Boxplot per sample
    log_intensities.plot(kind='box')
    plt.ylabel('log10 Intensity')
    plt.title('Intensity Distribution per Sample')

    # Should see similar median and spread across samples

Sample correlation:

python

def plot_sample_correlation(intensity_matrix):
    """
    Calculate and visualize sample-sample correlation.
    Expect: High correlation within replicates, lower between conditions.
    """
    # Log-transform and remove zeros
    log_data = np.log2(intensity_matrix.replace(0, np.nan))

    # Correlation matrix
    corr_matrix = log_data.corr(method='pearson')

    # Heatmap
    import seaborn as sns
    sns.heatmap(corr_matrix, annot=True, cmap='RdYlBu_r', vmin=0.8, vmax=1.0)

PCA:

python

def perform_pca(intensity_matrix, sample_groups):
    """
    Principal component analysis for sample clustering.
    """
    from sklearn.decomposition import PCA

    # Prepare data (log, impute, scale)
    log_data = np.log2(intensity_matrix.replace(0, np.nan))
    # Simple imputation with minimum value
    imputed = log_data.fillna(log_data.min().min())

    # PCA
    pca = PCA(n_components=2)
    pca_result = pca.fit_transform(imputed.T)

    # Plot with group colors
    plt.scatter(pca_result[:, 0], pca_result[:, 1], c=sample_groups)
    plt.xlabel(f'PC1 ({pca.explained_variance_ratio_[0]:.1%})')
    plt.ylabel(f'PC2 ({pca.explained_variance_ratio_[1]:.1%})')

Phase 2: Preprocessing & Normalization

Objective: Clean data and normalize across samples for fair comparison.

Filtering:

python

def filter_proteins(intensity_matrix, metadata, min_valid=3):
    """
    Filter out low-confidence proteins.

    Criteria:
    - At least 2 unique peptides (from metadata)
    - At least min_valid samples with detected intensity
    - Remove contaminants and reverse sequences
    """
    # Filter by peptide count
    valid_proteins = metadata['Peptides'] >= 2

    # Filter by detection in samples
    n_detected = (intensity_matrix > 0).sum(axis=1)
    valid_detection = n_detected >= min_valid

    # Remove contaminants (from MaxQuant)
    is_contaminant = metadata['Protein IDs'].str.contains('CON__', na=False)
    is_reverse = metadata['Protein IDs'].str.contains('REV__', na=False)

    # Combined filter
    keep = valid_proteins & valid_detection & ~is_contaminant & ~is_reverse

    return intensity_matrix[keep], metadata[keep]

Missing value imputation:

python

def impute_missing_values(intensity_matrix, method='MinProb'):
    """
    Impute missing protein intensities.

    Methods:
    - MinProb: Random from minimum observed + normal noise (for MNAR)
    - KNN: K-nearest neighbors imputation
    - QRILC: Quantile regression-based imputation
    """
    if method == 'MinProb':
        # Assume missing = low abundance (MNAR assumption)
        min_val = intensity_matrix[intensity_matrix > 0].min().min()
        width = 0.3  # Standard deviation of noise
        shift = 1.8  # Downshift from minimum

        # Replace zeros with random low values
        imputed = intensity_matrix.copy()
        missing_mask = imputed == 0
        n_missing = missing_mask.sum().sum()

        random_vals = np.random.normal(
            loc=min_val - shift,
            scale=width,
            size=n_missing
        )
        imputed.values[missing_mask.values] = random_vals

        return imputed

    elif method == 'KNN':
        from sklearn.impute import KNNImputer
        imputer = KNNImputer(n_neighbors=5)
        imputed = pd.DataFrame(
            imputer.fit_transform(intensity_matrix.replace(0, np.nan)),
            index=intensity_matrix.index,
            columns=intensity_matrix.columns
        )
        return imputed

Normalization:

python

def normalize_intensities(intensity_matrix, method='median'):
    """
    Normalize protein intensities across samples.

    Methods:
    - median: Divide by median intensity per sample
    - quantile: Quantile normalization (same distribution)
    - TMM: Trimmed mean of M-values (from edgeR)
    - VSN: Variance-stabilizing normalization
    """
    if method == 'median':
        # Median normalization
        medians = intensity_matrix.median(axis=0)
        global_median = medians.median()
        norm_factors = global_median / medians
        normalized = intensity_matrix * norm_factors
        return normalized

    elif method == 'quantile':
        # Quantile normalization
        from sklearn.preprocessing import quantile_transform
        normalized = pd.DataFrame(
            quantile_transform(intensity_matrix, axis=1),
            index=intensity_matrix.index,
            columns=intensity_matrix.columns
        )
        return normalized

Phase 3: Differential Expression Analysis

Objective: Identify proteins with significant abundance changes between conditions.

Statistical testing with limma:

python

def differential_expression_limma(log2_intensities, group1_samples, group2_samples):
    """
    Perform differential expression using limma-like approach.

    Returns:
    - log2 fold changes
    - p-values
    - adjusted p-values (BH)
    """
    from scipy import stats

    results = []

    for protein in log2_intensities.index:
        # Extract intensities for each group
        group1 = log2_intensities.loc[protein, group1_samples]
        group2 = log2_intensities.loc[protein, group2_samples]

        # Calculate statistics
        mean1 = group1.mean()
        mean2 = group2.mean()
        log2fc = mean2 - mean1

        # t-test
        t_stat, p_value = stats.ttest_ind(group1, group2, equal_var=False)

        results.append({
            'protein': protein,
            'log2FC': log2fc,
            'mean_group1': mean1,
            'mean_group2': mean2,
            'p_value': p_value,
            't_statistic': t_stat
        })

    results_df = pd.DataFrame(results)

    # Multiple testing correction (Benjamini-Hochberg)
    from statsmodels.stats.multitest import multipletests
    results_df['adj_p_value'] = multipletests(results_df['p_value'], method='fdr_bh')[1]

    # Classify significance
    results_df['significant'] = (
        (results_df['adj_p_value'] < 0.05) &
        (np.abs(results_df['log2FC']) > 1.0)
    )

    return results_df

Volcano plot:

python

def plot_volcano(de_results, title='Volcano Plot'):
    """
    Visualize differential expression results.
    """
    import matplotlib.pyplot as plt

    plt.figure(figsize=(8, 6))

    # Non-significant
    non_sig = de_results[~de_results['significant']]
    plt.scatter(non_sig['log2FC'], -np.log10(non_sig['p_value']),
                c='gray', alpha=0.5, s=10)

    # Significant
    sig = de_results[de_results['significant']]
    plt.scatter(sig['log2FC'], -np.log10(sig['p_value']),
                c='red', alpha=0.7, s=20)

    # Thresholds
    plt.axhline(-np.log10(0.05), color='blue', linestyle='--', label='p=0.05')
    plt.axvline(-1, color='blue', linestyle='--')
    plt.axvline(1, color='blue', linestyle='--', label='|log2FC|=1')

    plt.xlabel('log2 Fold Change')
    plt.ylabel('-log10(p-value)')
    plt.title(title)
    plt.legend()

Phase 4: PTM Analysis

Objective: Analyze post-translational modifications (phosphorylation, acetylation, etc.)

Phosphoproteomics workflow:

python

def analyze_phosphosites(phospho_sites_file, intensity_matrix):
    """
    Analyze phosphorylation site changes.

    Input: MaxQuant Phospho (STY)Sites.txt
    Output: Differential phosphorylation per site
    """
    # Load phospho data
    phospho = pd.read_csv(phospho_sites_file, sep='\t')

    # Filter by localization probability
    phospho_confident = phospho[phospho['Localization prob'] > 0.75]

    # Extract site information
    phospho_confident['site'] = (
        phospho_confident['Gene names'] + '_' +
        phospho_confident['Amino acid'] +
        phospho_confident['Position'].astype(str)
    )

    # Quantification (similar to protein-level analysis)
    # ... perform differential analysis ...

    return phospho_results

Kinase-substrate prediction:

python

def predict_kinases(phospho_sites):
    """
    Predict upstream kinases for phosphorylation sites.

    Uses ToolUniverse PhosphoSitePlus or KEA3 tools.
    """
    from tooluniverse import ToolUniverse
    tu = ToolUniverse()

    # For each significant phosphosite
    kinase_predictions = []
    for site in phospho_sites:
        # Query kinase-substrate databases
        # (would use actual ToolUniverse tool here)
        result = tu.run_one_function({
            "name": "phosphosite_plus_query",  # hypothetical
            "arguments": {"site": site}
        })
        kinase_predictions.append(result)

    return kinase_predictions

Phase 5: Functional Enrichment

Objective: Interpret biological meaning of protein changes via pathway analysis.

Gene Ontology enrichment:

python

def pathway_enrichment_proteins(de_proteins, organism='human'):
    """
    Perform pathway enrichment for differentially expressed proteins.

    Uses ToolUniverse gene-enrichment skill.
    """
    from tooluniverse import ToolUniverse
    tu = ToolUniverse()

    # Extract gene names for significant proteins
    sig_proteins = de_proteins[de_proteins['significant']]
    gene_list = sig_proteins['gene_name'].tolist()

    # Run enrichment via ToolUniverse
    enrichment = tu.run_one_function({
        "name": "enrichr_enrich",
        "arguments": {
            "gene_list": ",".join(gene_list),
            "library": "KEGG_2021_Human"
        }
    })

    return enrichment

Protein complex enrichment:

python

def protein_complex_enrichment(protein_list):
    """
    Test for enrichment of known protein complexes (CORUM database).
    """
    # Query CORUM or use ToolUniverse
    # Identify if proteins are part of known complexes
    pass

Phase 6: Protein-Protein Interactions

Objective: Identify interaction networks and protein complexes.

STRING network analysis:

python

def build_protein_network(protein_list, confidence=0.7):
    """
    Build PPI network using STRING database.

    Uses ToolUniverse STRING tools.
    """
    from tooluniverse import ToolUniverse
    tu = ToolUniverse()

    # Get interactions
    interactions = tu.run_one_function({
        "name": "string_get_interactions",
        "arguments": {
            "proteins": ",".join(protein_list),
            "species": 9606,  # human
            "score_threshold": int(confidence * 1000)
        }
    })

    # Build network graph
    import networkx as nx
    G = nx.Graph()

    for interaction in interactions['data']:
        G.add_edge(
            interaction['protein1'],
            interaction['protein2'],
            score=interaction['score']
        )

    return G

Module detection:

python

def detect_protein_modules(network_graph):
    """
    Identify tightly connected protein modules (complexes).
    """
    from networkx.algorithms import community

    # Detect communities
    communities = community.greedy_modularity_communities(network_graph)

    # Annotate modules with enriched functions
    modules = []
    for i, comm in enumerate(communities):
        module_proteins = list(comm)
        # Run enrichment for this module
        enrichment = pathway_enrichment_proteins(module_proteins)
        modules.append({
            'module_id': i,
            'proteins': module_proteins,
            'size': len(module_proteins),
            'top_function': enrichment['top_terms'][0]
        })

    return modules

Phase 7: Multi-Omics Integration

Objective: Integrate proteomics with transcriptomics and other omics.

Protein-RNA correlation:

python

def correlate_protein_rna(protein_data, rna_data, common_samples):
    """
    Correlate protein and mRNA levels for each gene.

    Expected: r ~ 0.4-0.6 (moderate correlation)
    Discordance indicates post-transcriptional regulation
    """
    from scipy.stats import spearmanr

    # Find common genes
    common_genes = set(protein_data.index) & set(rna_data.index)

    correlations = {}
    for gene in common_genes:
        protein = protein_data.loc[gene, common_samples]
        rna = rna_data.loc[gene, common_samples]

        r, p = spearmanr(protein, rna)
        correlations[gene] = {
            'r': r,
            'p': p,
            'regulation': classify_regulation(r, protein.mean(), rna.mean())
        }

    return correlations

def classify_regulation(r, protein_level, rna_level):
    """
    Classify regulatory mechanism based on correlation and levels.
    """
    if r > 0.6 and protein_level > 0 and rna_level > 0:
        return 'transcriptional_upregulation'
    elif r > 0.6 and protein_level < 0 and rna_level < 0:
        return 'transcriptional_downregulation'
    elif r < 0.2 and protein_level > 0 and rna_level < 0:
        return 'translational_upregulation'
    elif r < 0.2 and protein_level < 0 and rna_level > 0:
        return 'protein_degradation'
    else:
        return 'mixed_regulation'

Integration with multi-omics skill:

python

def integrate_with_multiomics(protein_data, rna_data, methylation_data):
    """
    Pass proteomics data to multi-omics integration skill.

    Enables comprehensive analysis across all molecular layers.
    """
    # Prepare for multi-omics skill
    omics_data = {
        'proteomics': protein_data,
        'rnaseq': rna_data,
        'methylation': methylation_data
    }

    # Invoke multi-omics integration skill
    from tooluniverse import ToolUniverse
    # (Would use Skill tool to invoke tooluniverse-multi-omics-integration)

    return integrated_analysis

Phase 8: Report Generation

Generate comprehensive proteomics report:

markdown

# Proteomics Analysis Report

## Dataset Summary
- **Samples**: 20 (10 disease, 10 control)
- **Proteins Identified**: 5,432
- **Proteins Quantified**: 4,987 (at least 3 samples)
- **Platform**: Orbitrap Fusion Lumos, MaxQuant 2.0

## Quality Control
- **Missing Values**: 15% average per protein
- **Sample Correlation**: 0.92-0.98 within groups
- **PCA**: Clear separation between disease and control (PC1: 35% variance)

## Differential Expression
- **Significant Proteins**: 432 (adj. p < 0.05, |log2FC| > 1)
  - Upregulated: 245 proteins
  - Downregulated: 187 proteins
- **Top upregulated**: MYC (log2FC=3.2), EGFR (log2FC=2.8)
- **Top downregulated**: TP53 (log2FC=-2.5), BRCA1 (log2FC=-2.1)

## Phosphoproteomics
- **Phosphosites Quantified**: 8,543
- **Differentially Phosphorylated**: 234 sites (p < 0.05)
- **Top Predicted Kinases**: CDK1, MAPK1, AKT1

## Pathway Enrichment
### Top Pathways (Upregulated)
1. **Cell Cycle** (p=1e-15) - 45 proteins, including cyclins, CDKs
2. **DNA Replication** (p=1e-12) - 23 proteins
3. **Glycolysis** (p=1e-10) - 18 proteins

### Top Pathways (Downregulated)
1. **Apoptosis** (p=1e-14) - 32 proteins, including caspases
2. **DNA Repair** (p=1e-11) - 28 proteins
3. **Oxidative Phosphorylation** (p=1e-9) - 25 proteins

## Protein Network Analysis
- **Network**: 432 nodes, 1,245 edges (STRING confidence > 0.7)
- **Modules Detected**: 8 functional modules
  - Module 1: Cell cycle (85 proteins)
  - Module 2: Metabolism (62 proteins)
  - Module 3: Translation (48 proteins)

## Protein-RNA Correlation
- **Overall Correlation**: r = 0.54 (moderate, expected)
- **High Correlation**: 2,134 genes (r > 0.6) - transcriptional regulation
- **Low Correlation**: 456 genes (r < 0.2) - post-transcriptional regulation
- **Translation-Regulated**: 89 proteins (high protein, low RNA)

## Biological Interpretation
Disease state shows increased proliferation (MYC, cyclins) with concurrent
suppression of apoptosis and DNA repair (TP53, BRCA1). Metabolic shift toward
glycolysis evident at protein level. Post-transcriptional upregulation of
translation machinery suggests adaptation to proliferative demands.

## Potential Biomarkers
Top 10 proteins for disease classification (Random Forest AUC=0.95):
1. MYC (protein)
2. EGFR (protein)
3. CDK1 (phospho-T161)
4. TP53 (protein)
5. BRCA1 (protein)

Integration with ToolUniverse

Skills Coordinated:

Skill	Used For	Phase
`tooluniverse-gene-enrichment`	Pathway enrichment	Phase 5
`tooluniverse-protein-interactions`	PPI networks	Phase 6
`tooluniverse-rnaseq-deseq2`	RNA-seq for integration	Phase 7
`tooluniverse-multi-omics-integration`	Cross-omics analysis	Phase 7
`tooluniverse-target-research`	Protein annotation	Phase 8

Example Use Cases

Use Case 1: Cancer Proteomics

Question: "Analyze proteomics data from breast cancer vs normal tissue"

Workflow:

Load MaxQuant proteinGroups.txt
QC and filter (keep proteins with 2+ peptides, detected in 3+ samples)
Impute missing, normalize by median
Differential expression (limma): 432 significant proteins
Pathway enrichment: Cell cycle, metabolism upregulated
STRING network: Identify hub proteins (MYC, EGFR)
Integrate with TCGA RNA-seq: Find translation-regulated genes
Report: Comprehensive analysis with biomarkers

Use Case 2: Phosphoproteomics Signaling

Question: "What kinase signaling is activated in response to drug treatment?"

Workflow:

Load Phospho (STY)Sites.txt from MaxQuant
Filter by localization probability > 0.75
Differential phosphorylation analysis
Kinase prediction for significant sites
Identify MAPK1, CDK1, AKT1 as top kinases
Pathway enrichment: MAPK, PI3K/AKT pathways
Report: Drug activates growth signaling

Use Case 3: Protein-RNA Integration

Question: "Which proteins are regulated post-transcriptionally?"

Workflow:

Load proteomics (MaxQuant) and RNA-seq (DESeq2) data
Match samples, extract common genes
Correlate protein and RNA for each gene
Identify low-correlation genes (r < 0.2)
Classify: translation upregulation, protein degradation
Enrichment: Find pathways enriched in post-transcriptional regulation
Report: 89 translation-regulated proteins, RNA-binding proteins enriched

Quantified Minimums

Component	Requirement
Proteins quantified	At least 500 proteins
Replicates	At least 3 per condition
Filtering	2+ unique peptides per protein
Statistical test	limma or t-test with multiple testing correction
Pathway enrichment	At least one method (GO, KEGG, or Reactome)
Report	Summary, QC, DE results, pathways, visualizations

Limitations

Platform-specific: Optimized for MS-based proteomics (not Western blot quantification)
Missing values: High missing rate (>50% per protein) limits statistical power
PTM analysis: Requires enrichment protocols for comprehensive PTM profiling
Absolute quantification: Relative abundance only (unless TMT/SILAC used)
Protein isoforms: Typically collapsed to gene level
Dynamic range: MS has limited dynamic range vs mRNA sequencing

References

Methods:

MaxQuant: https://doi.org/10.1038/nbt.1511
Limma for proteomics: https://doi.org/10.1093/nar/gkv007
DEP workflow: https://doi.org/10.1038/nprot.2018.107

Databases:

STRING: https://string-db.org
PhosphoSitePlus: https://www.phosphosite.org
CORUM: https://mips.helmholtz-muenchen.de/corum

tooluniverse-proteomics-analysis

NPX Install

Tags

SKILL.md Content

Proteomics Analysis

When to Use This Skill

Core Capabilities

Workflow Overview

Phase Details

Phase 1: Data Import & Quality Control

Phase 2: Preprocessing & Normalization

Phase 3: Differential Expression Analysis

Phase 4: PTM Analysis

Phase 5: Functional Enrichment

Phase 6: Protein-Protein Interactions

Phase 7: Multi-Omics Integration

Phase 8: Report Generation

Integration with ToolUniverse

Example Use Cases

Use Case 1: Cancer Proteomics

Use Case 2: Phosphoproteomics Signaling

Use Case 3: Protein-RNA Integration

Quantified Minimums

Limitations

References