A structured proteomics analysis system for reproducible protein-level analysis, biological interpretation, and reporting.
The Proteomics System is the current addition to the CDI Omics Systems Architecture. It extends the architecture beyond transcript-level and community-level analysis into protein abundance, differential protein analysis, functional annotation, pathway interpretation, and reproducible reporting.
Biological Focus
Proteomics analysis enables the study of:
protein abundance
differential protein expression or abundance
protein identifiers and annotations
biological pathways
Gene Ontology terms
protein networks
functional interpretation
The goal is not simply to identify statistically significant proteins, but to understand how protein-level changes support biological mechanisms, pathway-level patterns, and defensible scientific conclusions.
Why Proteomics?
Proteomics is an important addition to the Omics Systems Architecture because proteins are closer to biological function than transcripts alone.
While RNA-Seq measures transcript-level activity and microbiome analysis evaluates microbial community structure, proteomics focuses on the molecules that often execute biological processes directly.
The Proteomics System introduces analytical concepts such as protein identifier cleaning, missing-value assessment, differential protein abundance, GO and pathway enrichment, protein interaction networks, and results-first biological interpretation.
Relationship to the Omics Systems Architecture
All Omics System Builds share a common analytical foundation.
Biological Question
↓
Experimental Design
↓
Data Generation
↓
Omics Data Processing
↓
Quality Control
↓
Feature Generation
↓
Domain-Specific Analysis
↓
Statistical Inference
↓
Biological Interpretation
↓
Reproducible Reporting
The Proteomics System extends this architecture by transforming protein abundance tables or differential protein results into interpretable biological evidence that can be quality checked, statistically evaluated, functionally annotated, and reported within a reproducible analytical framework.
Proteomics System Architecture
Code
flowchart TD A[Proteomics Result Tables] B[Input Assessment] C[Quality Control] D[Protein Identifier Cleaning] E[Differential Protein Abundance] F[Protein Ranking and Filtering] G[GO and Pathway Enrichment] H[Protein Network Interpretation] I[Biological Interpretation] J[Reproducible Reporting] A --> B B --> C C --> D D --> E E --> F F --> G G --> H H --> I I --> J style A fill:#dbeafe,stroke:#2563eb,stroke-width:2px,color:#0f172a style B fill:#e0f2fe,stroke:#0284c7,stroke-width:2px,color:#0f172a style C fill:#ecfeff,stroke:#0891b2,stroke-width:2px,color:#0f172a style D fill:#ede9fe,stroke:#7c3aed,stroke-width:2px,color:#0f172a style E fill:#f3e8ff,stroke:#9333ea,stroke-width:2px,color:#0f172a style F fill:#fae8ff,stroke:#c026d3,stroke-width:2px,color:#0f172a style G fill:#fef3c7,stroke:#d97706,stroke-width:2px,color:#0f172a style H fill:#ecfccb,stroke:#65a30d,stroke-width:2px,color:#0f172a style I fill:#f0fdf4,stroke:#16a34a,stroke-width:2px,color:#0f172a style J fill:#f8fafc,stroke:#334155,stroke-width:2px,color:#0f172a
flowchart TD
A[Proteomics Result Tables]
B[Input Assessment]
C[Quality Control]
D[Protein Identifier Cleaning]
E[Differential Protein Abundance]
F[Protein Ranking and Filtering]
G[GO and Pathway Enrichment]
H[Protein Network Interpretation]
I[Biological Interpretation]
J[Reproducible Reporting]
A --> B
B --> C
C --> D
D --> E
E --> F
F --> G
G --> H
H --> I
I --> J
style A fill:#dbeafe,stroke:#2563eb,stroke-width:2px,color:#0f172a
style B fill:#e0f2fe,stroke:#0284c7,stroke-width:2px,color:#0f172a
style C fill:#ecfeff,stroke:#0891b2,stroke-width:2px,color:#0f172a
style D fill:#ede9fe,stroke:#7c3aed,stroke-width:2px,color:#0f172a
style E fill:#f3e8ff,stroke:#9333ea,stroke-width:2px,color:#0f172a
style F fill:#fae8ff,stroke:#c026d3,stroke-width:2px,color:#0f172a
style G fill:#fef3c7,stroke:#d97706,stroke-width:2px,color:#0f172a
style H fill:#ecfccb,stroke:#65a30d,stroke-width:2px,color:#0f172a
style I fill:#f0fdf4,stroke:#16a34a,stroke-width:2px,color:#0f172a
style J fill:#f8fafc,stroke:#334155,stroke-width:2px,color:#0f172a
System Components
Input Assessment
Input assessment determines the structure and meaning of the proteomics data table.
Common input types include:
protein abundance tables
peptide abundance tables
protein group tables
differential protein abundance results
annotation or enrichment result tables
This step identifies key columns such as protein identifiers, gene symbols, protein names, abundance values, log fold changes, p-values, adjusted p-values, and comparison labels.
Quality Control
Quality control evaluates whether the proteomics results are suitable for downstream interpretation.
Common checks include:
missing-value assessment
protein identifier completeness
sample or comparison consistency
abundance distribution review
duplicate protein identifier detection
significance column validation
Protein Identifier Cleaning
Protein identifier cleaning prepares protein IDs for annotation, enrichment, and biological interpretation.
This may include:
standardizing protein identifiers
separating protein groups
mapping protein IDs to gene symbols
checking identifier compatibility with enrichment tools
preparing clean annotation-ready tables
Differential Protein Abundance
Differential protein abundance analysis identifies proteins associated with biological conditions or experimental contrasts.
For a results-first implementation, this step often begins from an existing differential protein table containing:
protein identifier
gene symbol
log2 fold change
p-value
adjusted p-value
comparison or contrast
Protein Ranking and Filtering
Protein ranking and filtering prioritize proteins for interpretation.
Common criteria include:
adjusted p-value threshold
log2 fold-change threshold
direction of change
biological relevance
annotation availability
GO and Pathway Enrichment
Functional enrichment translates protein-level results into broader biological themes.
Common interpretation layers include:
Gene Ontology biological process
molecular function
cellular component
pathway enrichment
functional category summaries
Protein Network Interpretation
Protein network interpretation evaluates how differentially abundant proteins relate to each other biologically.
Common approaches include:
protein-protein interaction networks
functional modules
pathway-linked clusters
network-supported biological hypotheses
Biological Interpretation
Biological interpretation connects statistical evidence, protein annotation, enrichment patterns, and network context into a coherent scientific explanation.
The goal is to move from a list of proteins toward a defensible biological narrative.
Reproducible Reporting
Reproducible reporting connects workflow decisions, code, outputs, interpretation, and conclusions in a transparent analytical document.
Typical tools include:
Quarto
GitHub
reproducible computational environments
Core Technologies
Examples of technologies commonly used within the Proteomics System include:
R
tidyverse
readr
dplyr
stringr
tibble
ggplot2
clusterProfiler
STRING
Quarto
GitHub
These technologies support the workflow, but the primary focus of the Proteomics System is protein-level reasoning, functional interpretation, and reproducibility.
Expected Outputs
A complete Proteomics System should produce:
proteomics input inspection summaries
detected column summaries
missing-value reports
cleaned protein identifier tables
ranked protein tables
filtered differential protein results
GO or pathway enrichment summaries
protein network interpretation outputs
biological interpretation evidence tables
reproducible analytical reports
Status
Current addition / active implementation
The Proteomics System is the current addition to the CDI Omics Systems Architecture. It expands the ecosystem from RNA-Seq and Microbiome systems into protein-level biological analysis and interpretation.
The Proteomics System illustrates the Omics Systems approach to protein-level biological analysis.
Rather than treating differential protein tables, protein identifiers, enrichment results, networks, and reporting as separate activities, the system connects them into a unified analytical framework.
The result is a workflow that links:
proteomics result tables
↓
protein-level statistical evidence
↓
functional and network interpretation
↓
reproducible reporting
in a transparent, reproducible, and scientifically defensible manner.