/pyopenms

name: pyopenms
description: Processes mass spectrometry data with pyOpenMS. Supports proteomics and metabolomics workflows—feature detection, peptide/protein identification, label-free 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.
license: 3 clause BSD license
allowed-tools: Read Write Edit Bash
compatibility: Requires CPython 3.11+ and pyOpenMS 3.6.0; pandas and NumPy for tables, Matplotlib for plots. Wheels support macOS 15+ arm64, Linux glibc 2.34+ x86-64/arm64, and Windows x86-64. Search-engine executables are separate.
metadata:
  version: "3.0"
  last-reviewed: "2026-10-01"
  upstream-version: "3.6.0"
  skill-author: K-Dense Inc.

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 venv --python 3.13
uv pip install "pyopenms==3.6.0" pandas numpy matplotlib

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__)  # 3.6.0

Scripts (start here)

Run with `python scripts/<name>.py --help` for full options. Input formats differ by script; inspect its help. Commands below run from the skill directory with the environment activated. Native regression tests use tiny synthetic files; instrument-specific detection/search performance is not validated.

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 local formula/structure TSVs 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

Identification confidence

`--fdr` estimates top-hit PSM q-values from one comparable search run and rejects missing labels, non-finite scores, mixed score types/directions, and absent target or decoy top hits. It does not recalibrate existing q-values. Before using it, verify target/decoy annotations, score direction, and the search database used to generate the hits. The script applies `FalseDiscoveryRate` to peptide identifications; its threshold does not establish protein-level FDR. Report the tested unit (PSM, unique peptide, or protein), pooling/search settings, decoy strategy, and threshold expl