Agent skill
bio-proteomics-data-import
Load and parse mass spectrometry data formats including mzML, mzXML, and quantification tool outputs like MaxQuant proteinGroups.txt. Handles common preprocessing steps like contaminant filtering and missing value assessment.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/data-import
SKILL.md
Mass Spectrometry Data Import
Loading mzML/mzXML Files with pyOpenMS
from pyopenms import MSExperiment, MzMLFile, MzXMLFile
exp = MSExperiment()
MzMLFile().load('sample.mzML', exp)
for spectrum in exp:
if spectrum.getMSLevel() == 1:
mz, intensity = spectrum.get_peaks()
elif spectrum.getMSLevel() == 2:
precursor = spectrum.getPrecursors()[0]
precursor_mz = precursor.getMZ()
Loading MaxQuant Output
import pandas as pd
protein_groups = pd.read_csv('proteinGroups.txt', sep='\t', low_memory=False)
# Filter contaminants and reverse hits
protein_groups = protein_groups[
(protein_groups['Potential contaminant'] != '+') &
(protein_groups['Reverse'] != '+') &
(protein_groups['Only identified by site'] != '+')
]
# Extract intensity columns
intensity_cols = [c for c in protein_groups.columns if c.startswith('LFQ intensity')]
intensities = protein_groups[['Protein IDs', 'Gene names'] + intensity_cols]
Loading Spectronaut/DIA-NN Output
diann_report = pd.read_csv('report.tsv', sep='\t')
# Pivot to protein-level matrix
protein_matrix = diann_report.pivot_table(
index='Protein.Group', columns='Run', values='PG.MaxLFQ', aggfunc='first'
)
R: Loading with MSnbase
library(MSnbase)
raw_data <- readMSData('sample.mzML', mode = 'onDisk')
spectra <- spectra(raw_data)
header_info <- fData(raw_data)
Missing Value Assessment
def assess_missing_values(df, intensity_cols):
missing_per_protein = df[intensity_cols].isna().sum(axis=1)
missing_per_sample = df[intensity_cols].isna().sum(axis=0)
total_missing = df[intensity_cols].isna().sum().sum()
total_values = df[intensity_cols].size
missing_pct = 100 * total_missing / total_values
return {'per_protein': missing_per_protein, 'per_sample': missing_per_sample, 'total_pct': missing_pct}
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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