tooluniverse-proteomics-analysis

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

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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:

Quality Control:

1. Missing value assessment:

2. Intensity distribution:

3. Sample correlation:

4. PCA:

Phase 2: Preprocessing & Normalization

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

Filtering:

Missing value imputation:

Normalization: