Proteomics focuses on the large-scale study of proteins, including their identification, quantification, and functional analysis. This chapter covers computational methods for bottom-up proteomics data analysis using the R for Mass Spectrometry ecosystem.
The R for Mass Spectrometry ecosystem provides specialized packages for proteomics analysis:
Available proteomics files:1 : MRM-standmix-5.mzML.gz
2 : MS3TMT10_01022016_32917-33481.mzML.gz
3 : MS3TMT11.mzML
4 : TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML.gz
5 : TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzML.gz
Selected file: MRM-standmix-5.mzML.gz The typical bottom-up proteomics workflow involves:
flowchart TD
subgraph Sample["Sample Preparation"]
A[Protein Extraction] --> B[Reduction & Alkylation]
B --> C[Enzymatic Digestion<br/>Trypsin]
C --> D[Peptide Cleanup<br/>Desalting]
end
subgraph MS["LC-MS/MS Analysis"]
D --> E[LC Separation<br/>Reverse Phase]
E --> F[MS1 Scan<br/>Precursor Selection]
F --> G[MS2 Fragmentation<br/>HCD/CID/ETD]
G --> H[Raw Data<br/>mzML Files]
end
subgraph Search["Database Searching"]
H --> I[Spectra Object<br/>R/Spectra]
I --> J{Search Engine}
J --> K1[Mascot]
J --> K2[MaxQuant]
J --> K3[MSFragger]
K1 --> L[PSM Table<br/>PSMatch]
K2 --> L
K3 --> L
end
subgraph Inference["Protein Inference"]
L --> M[Filter PSMs<br/>FDR < 1%]
M --> N[Peptide Assembly<br/>Unique + Shared]
N --> O[Protein Grouping<br/>Parsimony Principle]
end
subgraph Quant["Quantification"]
O --> P{Quant Method?}
P -->|Label-Free| Q1[XIC Integration<br/>MS1 Intensity]
P -->|TMT/iTRAQ| Q2[Reporter Ions<br/>MS2 Intensity]
P -->|SILAC| Q3[Heavy/Light Ratio<br/>MS1 Intensity]
Q1 --> R[QFeatures Object]
Q2 --> R
Q3 --> R
end
subgraph Analysis["Statistical Analysis"]
R --> S[PSM → Peptide<br/>Aggregation]
S --> T[Peptide → Protein<br/>Summarization]
T --> U[Differential Analysis<br/>limma/DEqMS]
U --> V[Results<br/>Volcano/Heatmap]
end
style Sample fill:#D7E6FB,stroke:#27408B,stroke-width:3px,color:#102A43
style MS fill:#FBE0FA,stroke:#B000B0,stroke-width:3px,color:#102A43
style Search fill:#D7E6FB,stroke:#27408B,stroke-width:3px,color:#102A43
style Inference fill:#FBE0FA,stroke:#B000B0,stroke-width:3px,color:#102A43
style Quant fill:#D7E6FB,stroke:#27408B,stroke-width:3px,color:#102A43
style Analysis fill:#FBE0FA,stroke:#B000B0,stroke-width:3px,color:#102A43
Proteomics data has a hierarchical structure: - Spectra: Raw MS and MS/MS data - PSMs: Peptide-Spectrum Matches from database search - Peptides: Unique peptide sequences - Proteins: Protein groups inferred from peptides
Note: Using synthetic data due to mzR compatibility issues
Error details: BiocParallel errors
1 remote errors, element index: 1
0 unevaluated and other errors
first remote error:
Error in DataFrame(..., check.names = FALSE): different row counts implied by arguments
Dataset summary:Total spectra: 200 MS levels: 2, 1 Scan range: 1 200 RT range: 100 4500 seconds
MS2 spectra: 164 Precursor m/z range: 400.4 1595.69
Charge state distribution:
2 3 4
49 58 57 
Processed 50 MS/MS spectra
Created database with 100 proteins and 995 peptidesGenerated 0 PSMsThis chapter covered comprehensive proteomics data analysis workflows, including MS/MS data processing, protein identification, quantification, and differential expression analysis. These methods are essential for extracting biological insights from bottom-up proteomics experiments.