PyOpenMS Overview

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.

Installation

Install using uv:

uv uv pip install pyopenms

Verify installation:

import pyopenms print(pyopenms.version)

Core Capabilities

PyOpenMS organizes functionality into these domains:

1. File I/O and Data Formats

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

Read mzML file

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

Access spectra

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

For detailed file handling: See references/file_io.md

1. Signal Processing

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

Basic spectrum processing:

Smooth spectrum with Gaussian filter

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

1. Feature Detection

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

Detect features

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

For complete workflows: See references/feature_detection.md

1. Peptide and Protein Identification

Integrate with search engines and process identification results.

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

Basic identification workflow:

Load identification data

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

Apply FDR filtering

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

For detailed workflows: See references/identification.md

1. Metabolomics Analysis

Perform untargeted metabolomics preprocessing and analysis.

Typical workflow:

Load and process raw data Detect features Align retention times across samples Link features to consensus map Annotate with compound databases

For complete metabolomics workflows: See references/metabolomics.md

Data Structures

PyOpenMS uses these primary objects:

MSExperiment: Collection of spectra and chromatograms MSSpectrum: Single mass spectrum with m/z and intensity pairs MSChromatogram: Chromatographic trace Feature: Detected chromatographic peak with quality metrics FeatureMap: Collection of features PeptideIdentification: Search results for peptides ProteinIdentification: Search results for proteins

For detailed documentation: See references/data_structures.md

Common Workflows Quick Start: Load and Explore Data import pyopenms as ms

Load mzML file

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

Get basic statistics

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

Examine first spectrum

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)}")

Parameter Management

Most algorithms use a parameter system:

Get algorithm parameters

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

View available parameters

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

Modify parameters

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

Export to Pandas

Convert data to pandas DataFrames for analysis:

import pyopenms as ms import pandas as pd

Load feature map

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

Convert to DataFrame

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

Integration with Other Tools

PyOpenMS integrates with:

Pandas: Export data to DataFrames NumPy: Work with peak arrays Scikit-learn: Machine learning on MS data Matplotlib/Seaborn: Visualization R: Via rpy2 bridge Resources Official documentation: https://pyopenms.readthedocs.io OpenMS documentation: https://www.openms.org GitHub: https://github.com/OpenMS/OpenMS References references/file_io.md - Comprehensive file format handling references/signal_processing.md - Signal processing algorithms references/feature_detection.md - Feature detection and linking references/identification.md - Peptide and protein identification references/metabolomics.md - Metabolomics-specific workflows references/data_structures.md - Core objects and data structures