pyopenms

K-Dense-AI/scientific-agent-skillsBiology Medicine and BioinformaticsReview advisedSnapshot 6df3afabd13a

Installs: 0

Summary

Complete mass spectrometry analysis platform. Use for proteomics and metabolomics workflows—feature detection, peptide/protein identification, label-free and isobaric quantification, adduct/accurate-mass annotation, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. For simple spectral comparison and small-molecule library matching use matchms.

SKILL.md

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.

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.

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.

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

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.

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.

# 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

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)

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, ...

pyopenms

Summary

SKILL.md

PyOpenMS

Overview

Installation

Scripts (start here)

Inspect & convert

Feature detection & quantification

Annotation

Identification

Chemistry

Targeted & visualization

Common script recipes

Key 3.5.0 API notes

Core data structures

Parameter management

Export to pandas

Integration with other tools

Resources

References

Related skills