PyOpenMS provides Python bindings to the OpenMS library for computational mass spectrometry, enabling analysis of proteomics and metabolomics data. Use for handling mass spectrometry file formats, processing spectral data, detecting features, identifying peptides/proteins, and performing quantitative analysis.

Install using uv:

uv uv pip install pyopenms

Verify installation:

import pyopenms
print(pyopenms.__version__)

PyOpenMS organizes functionality into these domains:

Handle mass spectrometry file formats and convert between representations.

Supported formats: mzML, mzXML, TraML, mzTab, FASTA, pepXML, protXML, mzIdentML, featureXML, consensusXML, idXML

Basic file reading:

import pyopenms as ms

exp = ms.MSExperiment() ms.MzMLFile().load("data.mzML", exp)

for spectrum in exp: mz, intensity = spectrum.get_peaks() print(f"Spectrum: {len(mz)} peaks")

**For detailed file handling**: See `references/file_io.md`

Process raw spectral data with smoothing, filtering, centroiding, and normalization.

Basic spectrum processing:

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gaussian = ms.GaussFilter() params = gaussian.getParameters() params.setValue("gaussian_width", 0.1) gaussian.setParameters(params) gaussian.filterExperiment(exp)

**For algorithm details**: See `references/signal_processing.md`

Detect and link features across spectra and samples for quantitative analysis.

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ff = ms.FeatureFinder() ff.run("centroided", exp, features, params, ms.FeatureMap())

**For complete workflows**: See `references/feature_detection.md`

Integrate with search engines and process identification results.

Supported engines: Comet, Mascot, MSGFPlus, XTandem, OMSSA, Myrimatch

Basic identification workflow:

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protein_ids = [] peptide_ids = [] ms.IdXMLFile().load("identifications.idXML", protein_ids, peptide_ids)

fdr = ms.FalseDiscoveryRate() fdr.apply(peptide_ids)

**For detailed workflows**: See `references/identification.md`

Perform untargeted metabolomics preprocessing and analysis.

Typical workflow:

For complete metabolomics workflows: See

PyOpenMS uses these primary objects:

For detailed documentation: See

exp = ms.MSExperiment() ms.MzMLFile().load("sample.mzML", exp)

print(f"Number of spectra: {exp.getNrSpectra()}") print(f"Number of chromatograms: {exp.getNrChromatograms()}")

spec = exp.getSpectrum(0) print(f"MS level: {spec.getMSLevel()}") print(f"Retention time: {spec.getRT()}") mz, intensity = spec.get_peaks() print(f"Peaks: {len(mz)}")

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Most algorithms use a parameter system:

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algo = ms.GaussFilter() params = algo.getParameters()

for param in params.keys(): print(f"{param}: {params.getValue(param)}")

params.setValue("gaussian_width", 0.2) algo.setParameters(params)

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Convert data to pandas DataFrames for analysis:

import pyopenms as ms
import pandas as pd

fm = ms.FeatureMap() ms.FeatureXMLFile().load("features.featureXML", fm)

df = fm.get_df() print(df.head())

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PyOpenMS integrates with:

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