alterlab-pyopenms

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 (pyOpenMS 3.x — examples here are verified against 3.5):

uv pip install "pyopenms>=3.4"

Verify installation:

import pyopenms
print(pyopenms.__version__)

Version note: pyOpenMS 3.x removed the old FeatureFinder facade. Use FeatureFinderAlgorithmPicked (the former "centroided" algorithm) or, for metabolomics, the MassTraceDetection → ElutionPeakDetection → FeatureFindingMetabo chain. See references/feature_detection.md.

When to Use This Skill

Use this skill when the user wants to:

Read, convert, or inspect MS file formats (mzML, mzXML, idXML, featureXML, consensusXML, mzTab).
Process raw spectra — smoothing, baseline removal, centroiding/peak picking, normalization.
Run feature detection and label-free quantification on LC-MS runs.
Do peptide/protein identification and FDR control (search-engine adapters, PeptideIndexer, FalseDiscoveryRate) or build a full LC-MS/MS pipeline.
Do untargeted metabolomics feature finding (MassTraceDetection -> ElutionPeakDetection -> FeatureFindingMetabo) and adduct/formula work.
Drive OpenMS algorithms programmatically with Param objects instead of TOPP command lines.

Does NOT Trigger

Scenario	Use Instead
Spectral similarity scoring / library matching of small-molecule MS/MS	`alterlab-matchms`
Cheminformatics on the identified molecules (descriptors, substructures, SMILES)	`alterlab-rdkit`
Statistics on an already-quantified abundance matrix (tests, PCA, clustering)	`alterlab-statistical-analysis`
Protein sequence/structure work rather than the mass spectra	`alterlab-biopython` or `alterlab-alphafold`
Transcript quantification from RNA-seq reads	`alterlab-rnaseq-quant`

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

2. 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

3. Feature Detection

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

# Detect features in centroided data (pyOpenMS 3.x API)
ff = ms.FeatureFinderAlgorithmPicked()
params = ff.getParameters()          # defaults for the "centroided" algorithm
ff.setParameters(params)

features = ms.FeatureMap()
seeds = ms.FeatureMap()              # empty seeds = detect de novo
ff.run(exp, features, params, seeds)

For complete workflows: See references/feature_detection.md

4. 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.
# pyOpenMS 3.x: protein_ids is a plain list, peptide_ids MUST be a
# PeptideIdentificationList (a plain [] is rejected by load()).
protein_ids = []
peptide_ids = ms.PeptideIdentificationList()
ms.IdXMLFile().load("identifications.idXML", protein_ids, peptide_ids)

# Compute q-values (target-decoy FDR), then filter at 1%.
# fdr.apply() requires target/decoy hits annotated with a 'target_decoy'
# meta value (run PeptideIndexer on a concatenated target-decoy search first).
fdr = ms.FalseDiscoveryRate()
fdr.apply(peptide_ids)               # rewrites scores to q-values (lower = better)
ms.IDFilter().filterHitsByScore(peptide_ids, 0.01)
ms.IDFilter().removeEmptyIdentifications(peptide_ids)

For detailed workflows: See references/identification.md

5. 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