pyopenms

name	pyopenms
description	Complete mass spectrometry analysis platform. Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. Best for proteomics, comprehensive MS data processing. For simple spectral comparison and metabolite ID use matchms.
license	3 clause BSD license
metadata	{"skill-author":"K-Dense Inc."}
verified	false
lastVerifiedAt	"2026-02-19T05:29:09.098Z"
source	builtin
trust_score	100
provenance_sha	fce23d13060b9215

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

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