PyOpenMS

Overview

PyOpenMS provides Python bindings to the OpenMS library for computational mass spectrometry, enabling analysis of proteomics and metabolomics data. Use it to read/write MS file formats, process raw spectra, detect and quantify features, identify peptides and proteins, and run end-to-end LC-MS/MS pipelines.

This skill ships ready-to-run scripts in scripts/ covering the most common high-level workflows. Prefer running a script over writing new code—each is a parameterized CLI tool that handles loading, processing, and export. Drop into the Python API (and the references/) only when no script fits.

Installation

uv pip install pyopenms

Verify (note: __version__ works, but the bundled binary prints a one-line memory-status notice on import that is harmless):

import pyopenms as ms
print(ms.__version__)  # 3.5.0

Scripts (start here)

Run with python scripts/<name>.py --help for full options. All accept standard MS file formats and write featureXML/consensusXML/CSV/mzTab/PNG as appropriate.

Inspect & convert

Script	What it does
`inspect_ms_data.py`	Summarize any mzML/mzXML/featureXML/consensusXML/idXML (counts, RT/m/z ranges, TIC, metadata); optional per-spectrum CSV.
`convert_format.py`	Convert between mzML/mzXML/MGF with optional MS-level, RT, and intensity filtering.
`process_spectra.py`	Configurable signal-processing chain: smoothing (Gauss/SGolay), centroiding (PeakPickerHiRes), normalization, S/N and intensity thresholds.

Feature detection & quantification

Script	What it does
`detect_features_metabo.py`	Untargeted metabolomics feature finding: MassTraceDetection → ElutionPeakDetection → FeatureFindingMetabo.
`detect_features_centroided.py`	Peptide/centroided feature detection via FeatureFinderAlgorithmPicked.
`align_link_quantify.py`	Multi-sample pipeline: detect (or load) features → RT alignment → consensus linking → quant matrix CSV.
`consensus_to_matrix.py`	consensusXML → wide intensity matrix + metadata, with optional median/quantile normalization and long format.

Annotation

Script	What it does
`detect_adducts.py`	Group adducts/charge variants of the same neutral mass (MetaboliteFeatureDeconvolution).
`accurate_mass_search.py`	Annotate features against HMDB by accurate mass (AccurateMassSearchEngine → mzTab/CSV).
`export_gnps_sirius.py`	Export GNPS FBMN inputs (MGF + quant table) or a SIRIUS `.ms` file.

Identification

Script	What it does
`process_identifications.py`	Re-index against FASTA, estimate FDR/q-values, filter (FDR/length/best-per-spectrum), export idXML + CSV.

Chemistry

Script	What it does
`mass_calculator.py`	Monoisotopic/average mass, charged m/z, formula, and isotope pattern for peptides or empirical formulas.
`digest_protein.py`	In-silico protease digestion of FASTA/sequence → theoretical peptides with masses and m/z.
`theoretical_spectrum.py`	Generate annotated theoretical fragment spectra (b/y/a/c/x/z, losses) for a peptide.

Targeted & visualization

Script	What it does
`extract_chromatograms.py`	Build TIC/BPC and XIC traces for target m/z (CSV + optional plot).
`plot_ms_data.py`	Quick plots: single spectrum, TIC, 2D feature map, MS1 signal map.

Common script recipes

# Inspect a file
python scripts/inspect_ms_data.py sample.mzML --spectra-csv spectra.csv

# Untargeted metabolomics: features for one sample
python scripts/detect_features_metabo.py sample.mzML --out-csv features.csv

# Full multi-sample quantification study
python scripts/align_link_quantify.py s1.mzML s2.mzML s3.mzML --out-prefix study
python scripts/consensus_to_matrix.py study.consensusXML --out quant.csv --normalize median

# Peptide chemistry
python scripts/mass_calculator.py --peptide "PEPTIDEM(Oxidation)K" --charges 1 2 3 --isotopes 5
python scripts/digest_protein.py proteins.fasta --enzyme Trypsin --missed 2 --out peptides.csv

# Identification post-processing
python scripts/process_identifications.py search.idXML --fasta db.fasta --fdr 0.01 --out filtered.idXML --csv hits.csv

Key 3.5.0 API notes

These changed from older OpenMS releases—older tutorials and code will break:

Feature finding: FeatureFinder("centroided") was removed. Use FeatureFinderAlgorithmPicked (proteomics/centroided) or the MassTraceDetection → ElutionPeakDetection → FeatureFindingMetabo pipeline (metabolomics). See detect_features_*.py.
idXML I/O: IdXMLFile().load/store require a ms.PeptideIdentificationList() for peptide IDs (a plain Python list raises "can not handle type"). Protein IDs remain a plain list.
Adduct decharging: the class is MetaboliteFeatureDeconvolution, and adducts use Elements:Charge:Probability syntax (e.g. H:+:0.4, H-2O-1:0:0.05)—not bracket notation like [M+H]+.
DataFrame columns: FeatureMap.get_df() uses lowercase rt/mz (not RT). ConsensusMap provides get_intensity_df() and get_metadata_df().
Bundled data caveat: the pip wheel ships HMDBMappingFile.tsv but not HMDB2StructMapping.tsv; accurate_mass_search.py detects this and explains how to supply it.

Core data structures

MSExperiment – collection of spectra and chromatograms
MSSpectrum / MSChromatogram – a single spectrum / chromatographic trace
Feature / FeatureMap – a detected LC-MS peak / collection of features
ConsensusMap – features linked across samples (the quant table)
PeptideIdentification / ProteinIdentification – search results
AASequence / EmpiricalFormula – sequence and formula chemistry

For details: see references/data_structures.md.

Parameter management

Most algorithms expose an OpenMS Param object:

algo = ms.FeatureFindingMetabo()
p = algo.getDefaults()
for key in p.keys():
    print(key.decode(), "=", p.getValue(key), "|", p.getDescription(key))
p.setValue("charge_lower_bound", 1)
algo.setParameters(p)

Export to pandas

fm = ms.FeatureMap(); ms.FeatureXMLFile().load("features.featureXML", fm)
df = fm.get_df()             # columns include lowercase rt, mz, intensity, charge, quality

cm = ms.ConsensusMap(); ms.ConsensusXMLFile().load("study.consensusXML", cm)
intensities = cm.get_intensity_df()   # features x samples
metadata = cm.get_metadata_df()       # rt, mz, charge, quality, ...

Integration with other tools

Pandas (DataFrames), NumPy (peak arrays), scikit-learn (ML), Matplotlib/Seaborn (plots), and downstream tools via export: GNPS (FBMN), SIRIUS, and mzTab.

Resources

Official docs (3.5.0): https://pyopenms.readthedocs.io/en/release-3.5.0/
OpenMS: https://www.openms.org
GitHub: https://github.com/OpenMS/OpenMS

References

references/file_io.md – 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

Pyopenms