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Bottom-Up Proteomics Data Analysis Pipeline: From Sample Prep to Pathway Interpretation

Bottom-up proteomics data analysis pipeline cover

Bottom-up proteomics digests proteins to peptides, identifies them by LC-MS/MS, and infers protein abundance. A standardized pipeline keeps large studies reproducible from extraction through pathway interpretation.

Key Takeaways

  • Digestion QC precedes downstream claims.
  • DDA for discovery depth; DIA for scalable quantification.
  • Document search, FDR, and inference settings.
  • PCA and normalization catch batch effects.
  • Enrichment translates lists into hypotheses.

Pipeline overview
Figure 1. Each stage affects the next.

Related Services

Bottom-Up Proteomics Service

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Bioinformatics Customized Service

Proteomics Analysis Services, Biopharmaceutical Characterization Services, Bioinformatics Services

Sample Preparation and LC-MS/MS

Extract and quantify protein; digest with trypsin; separate peptides by nanoLC; acquire DDA or DIA on high-resolution MS.

Raw Data Processing

Convert raw files; search databases at ~1% FDR; infer proteins; quantify label-free, TMT, or DIA-based.

DDA vs DIA
Figure 2. Choose acquisition before injection.

QC and Interpretation

Normalize; PCA for batch checks; differential testing; GO/KEGG/Reactome and PPI networks.

QC and enrichment
Figure 3. Close the loop from spectra to biology.

FAQ

What FDR is standard?

1% peptide/protein FDR is common in discovery studies.

Conclusion

Standardize each transition and bottom-up data support durable biological conclusions.

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