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bio-proteomics-data-import

Maintainer FreedomIntelligence · Last updated April 1, 2026

Load and parse mass spectrometry data formats including mzML, mzXML, and quantification tool outputs like MaxQuant proteinGroups.txt. Use when starting a proteomics analysis with raw or processed MS data. Handles contaminant filtering and missing value assessment.

OpenClawNanoClawAnalysisReproductionbio-proteomics-data-import🧬 bioinformatics (gptomics bio-* suite)bioinformatics — proteomics & metabolomicsload

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-proteomics-data-import

Maintainer
FreedomIntelligence
License
MIT
Last updated
April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • Python: pyopenms.MzMLFile().load() for raw spectra, pandas.read_csv() for search engine outputs.
  • R: MSnbase::readMSData() for raw, read.delim() for MaxQuant/Proteome Discoverer.
  • Load my mass spec data into Python" → Parse mzML/mzXML raw files or MaxQuant proteinGroups.txt into data structures for programmatic access and downstream analysis. Python: pyopenms.MzMLFile().load() for raw spectra, pandas.read_csv() for search engine outputs R: MSnbase::readMSData() for raw, read.delim() for MaxQuant/Proteome Discoverer.
  • contam_col = 'Potential contaminant' if 'Potential contaminant' in protein_groups.columns else 'Contaminant' protein_groups = protein_groups[ (protein_groups.get(contam_col, '')!= '+') & (protein_groups.get('Reverse', '')!= '+') & (protein_groups.get('Only identified by site', '')!= '+') ].

Source Doc

Excerpt From SKILL.md

Loading mzML/mzXML Files with pyOpenMS

Goal: Parse raw mass spectrometry data files into memory for programmatic access.

Approach: Load mzML/mzXML into an MSExperiment object, then iterate spectra by MS level to access peaks and precursor info.

Loading MaxQuant Output

Goal: Import MaxQuant proteinGroups.txt with contaminant and decoy filtering.

Approach: Read the TSV file, remove reverse hits, contaminants, and site-only identifications, then extract intensity columns.

import pandas as pd

protein_groups = pd.read_csv('proteinGroups.txt', sep='\t', low_memory=False)

## Extract intensity columns (LFQ or iBAQ)

intensity_cols = [c for c in protein_groups.columns if c.startswith('LFQ intensity') or c.startswith('iBAQ ')]
if not intensity_cols:
    intensity_cols = [c for c in protein_groups.columns if c.startswith('Intensity ') and 'Intensity L' not in c]
intensities = protein_groups[['Protein IDs', 'Gene names'] + intensity_cols]

Use cases

  • Use when starting a proteomics analysis with raw or processed MS data.

Not for

  • Do not rely on this catalog entry alone for installation or maintenance details.

Upstream Related Skills

  • quantification - Process imported data for quantification
  • peptide-identification - Identify peptides from raw spectra
  • expression-matrix/counts-ingest - Similar data loading patterns

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