Fragpipe Pyteomics Proteomics

FragPipe + Pyteomics Proteomics

Use this skill for bottom-up mass-spectrometry proteomics workflows where raw spectra need database searching, quantification, FDR control, or downstream Python parsing.

Routing

• Use pyopenms for OpenMS-native algorithms and feature maps.
• Use matchms for metabolomics spectra and spectral similarity.
• Use this skill for FragPipe/MSFragger-style proteomics searches or lightweight Python parsing with Pyteomics.

FragPipe Workflow

1. Collect inputs:
   • raw vendor files or converted mzML
   • FASTA database with contaminants/decoys
   • sample sheet and experimental design
2. Choose workflow:
   • DDA search with MSFragger and Philosopher.
   • Label-free quantification with IonQuant.
   • DIA analysis with DIA-NN when the experiment is DIA/SWATH-like.
3. Set search parameters deliberately:
   • enzyme, missed cleavages, precursor/fragment tolerance
   • fixed and variable modifications
   • isotope error and mass calibration
4. Validate outputs:
   • peptide-spectrum match FDR
   • peptide and protein FDR
   • missingness by run/sample
   • contaminant and decoy rates
5. Export clean tables for downstream analysis:
   • protein/peptide intensity matrices
   • annotation columns
   • QC summaries and parameter files

Pyteomics Patterns

Use Pyteomics for parsing and quick analysis:

from pyteomics import mzml, mgf, fasta

with mzml.read("run.mzML") as spectra:
    first_ms2 = next(s for s in spectra if s.get("ms level") == 2)

Good Pyteomics tasks:

• Inspect mzML, MGF, mzIdentML, mzTab, and FASTA files.
• Prototype peptide mass calculations and digestion logic.
• Join search outputs with metadata before using polars, pandas, or duckdb.

Reporting

Always report database, enzyme, modifications, FDR thresholds, quantification method, normalization method, and software versions. These details are required for reproducible proteomics analysis.