# Pyopenms

> Plataforma completa de análisis de espectrometría de masas: detección de features, identificación de péptidos/proteínas y cuantificación para proteómica y metabolómica. Para matching simple de librerías, usa matchms.

Fuente: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/pyopenms
Markdown: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/pyopenms.md

Repositorio: https://github.com/K-Dense-AI/scientific-agent-skills
Autor: K-Dense-AI
Licencia: 3 clause BSD license
Actualizado: el mes pasado
Coste de contexto: 94 tok instalada, 2k tok al activarse, 36.6k tok con todos los archivos del bundle
Bundle: 23 archivos, 143 KB
Permisos que pide: read write edit bash

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add K-Dense-AI/scientific-agent-skills --skill pyopenms --agent claude-code
# Cursor
npx -y skills add K-Dense-AI/scientific-agent-skills --skill pyopenms --agent cursor
# Codex
npx -y skills add K-Dense-AI/scientific-agent-skills --skill pyopenms --agent codex
# Gemini CLI
npx -y skills add K-Dense-AI/scientific-agent-skills --skill pyopenms --agent gemini
# Windsurf
npx -y skills add K-Dense-AI/scientific-agent-skills --skill pyopenms --agent windsurf
# Cline
npx -y skills add K-Dense-AI/scientific-agent-skills --skill pyopenms --agent cline
```

## Qué hace

- Ofrece una plataforma completa de análisis de espectrometría de masas para proteómica y metabolómica
- Incluye scripts listos para detección de features, identificación de péptidos/proteínas y cuantificación label-free e isobárica
- Anota aductos y masa exacta contra HMDB y exporta a GNPS/SIRIUS/mzTab
- Cubre conversión y procesamiento de espectros (mzML/mzXML/MGF) y cálculo de masas/digestión in-silico de proteínas
- Documenta los cambios de API de la versión 3.5.0 que rompen tutoriales antiguos (FeatureFinder, IdXMLFile, MetaboliteFeatureDeconvolution)

## Cuándo usarla

- El usuario trabaja en flujos de proteómica o metabolómica con LC-MS/MS
- Necesita detectar o cuantificar features, o identificar péptidos y proteínas
- Quiere anotar aductos o masa exacta, o exportar a GNPS/SIRIUS
- Necesita convertir o inspeccionar archivos de espectrometría de masas (mzML, mzXML, featureXML, consensusXML, idXML)

## Cuándo no

- Comparación espectral simple o matching de librerías de moléculas pequeñas: el propio archivo recomienda usar matchms

## Qué la activa

- "Detecta features metabolómicos no dirigidos en este mzML"
- "Cuantifica estas muestras con align_link_quantify y exporta la matriz"
- "Calcula la masa monoisotópica de este péptido con oxidación"
- "Anota aductos en mis features de metabolómica"

## Antes de instalar

- Requiere Python 3.9+ y uv; los scripts y ejemplos están verificados con pyOpenMS 3.5.0 (uv pip install pyopenms).
- Necesita en el PATH: python
- makes network requests

## Archivos

- SKILL.md — 8 KB
- references/data_structures.md — 12 KB
- references/feature_detection.md — 14 KB
- references/file_io.md — 9 KB
- references/identification.md — 12 KB
- references/metabolomics.md — 16 KB
- references/signal_processing.md — 10 KB
- scripts/accurate_mass_search.py — 4 KB
- scripts/align_link_quantify.py — 5 KB
- scripts/consensus_to_matrix.py — 3 KB
- scripts/convert_format.py — 3 KB
- scripts/detect_adducts.py — 4 KB
- scripts/detect_features_centroided.py — 3 KB
- scripts/detect_features_metabo.py — 4 KB
- scripts/digest_protein.py — 3 KB
- scripts/export_gnps_sirius.py — 3 KB
- scripts/extract_chromatograms.py — 4 KB
- scripts/inspect_ms_data.py — 6 KB
- scripts/mass_calculator.py — 3 KB
- scripts/plot_ms_data.py — 4 KB
- scripts/process_identifications.py — 4 KB
- scripts/process_spectra.py — 5 KB
- scripts/theoretical_spectrum.py — 3 KB

## SKILL.md

Reproducido tal cual desde K-Dense-AI/scientific-agent-skills bajo 3 clause BSD license. Esta sección es el documento original y está en inglés.

# 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

```bash
uv pip install pyopenms
```

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

```python
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

```bash
# 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:

```python
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

```python
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

## Dónde encaja

- Categoría: [Investigación](https://skillsagentes.com/categorias/investigacion.md) — Investigación estructurada, búsqueda de fuentes y síntesis.
- Creador: [K-Dense-AI](https://skillsagentes.com/creators/k-dense-ai.md) — 163 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

## Otras skills del mismo repositorio

- [Citation Management](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/citation-management.md): Gestión integral de citas académicas: busca en OpenAlex, PubMed y Google Scholar, extrae metadatos precisos, valida citas y genera entradas BibTeX correctamente formateadas.
- [Scientific Slides](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scientific-slides.md): Crea decks de diapositivas y presentaciones para charlas de investigación: PowerPoint, presentaciones de conferencia, seminarios, defensas de tesis. Da estructura, plantillas, guía de tiempos y validación visual.
- [Literature Review](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/literature-review.md): Realiza revisiones bibliográficas sistemáticas y completas usando varias bases académicas (PubMed, arXiv, bioRxiv, Semantic Scholar). Genera markdown y PDF con citas verificadas en varios estilos (APA, Nature, Vancouver).
- [Infographics](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/infographics.md): Crea infografías profesionales con Nano Banana Pro AI y refinamiento iterativo inteligente. Usa Gemini 3.6 Flash para revisar la calidad e integra investigación con Perplexity Sonar. Soporta 10 tipos, 8 estilos y paletas para daltonismo.
- [Latex Posters](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/latex-posters.md): Crea pósteres de investigación profesionales en LaTeX con beamerposter, tikzposter o baposter, para conferencias y comunicación científica: layout, colores, columnas múltiples e integración de figuras.

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