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pyopenms

Convert LC-MS raw files into features and identifications using PyOpenMS.

21|2|Updated Dec 8, 2025
Search Tags:#python#metabolomics#mass spectrometry#proteomics#feature detection#lc-ms/ms#fdr filtering
One-click install
npx skills add https://github.com/silverstein/claude-scientific-skills-desktop --skill pyopenms-silverstein
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pyopenms
Source: https://github.com/silverstein/claude-scientific-skills-desktop/tree/main/corpus/pyopenms
Command: npx skills add https://github.com/silverstein/claude-scientific-skills-desktop --skill pyopenms-silverstein

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

PyOpenMS streamlines the end-to-end analysis of LC-MS/MS and metabolomics datasets by providing Python bindings for OpenMS workflows, so you can go from raw instrument files to features, identifications, and quantitative results without manual tool switching.

Core Features & Use Cases

  • Mass spectrometry file handling: Read and convert common formats such as mzML, mzXML, mzTab, FASTA, pepXML, protXML, and mzIdentML for proteomics and metabolomics pipelines.
  • Signal processing and preprocessing: Apply smoothing, filtering, peak picking/centroiding, and normalization to make spectra analysis-ready.
  • Feature detection and linking: Detect chromatographic features and group/link them across samples to build consensus feature maps for quantification.
  • Identification workflows and FDR filtering: Load identification results and perform peptide/protein post-processing such as q-value inspection and false discovery rate control.
  • Metabolomics-oriented processing: Run untargeted pipelines including peak picking, adduct detection, RT alignment, consensus building, and downstream table export.

Quick Start

Use the pyopenms skill to load an mzML file, detect features, and output a featureXML file containing the detected chromatographic peaks.

Frequently Asked Questions about pyopenms

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I convert raw LC-MS/MS files into analysis-ready features for metabolomics?▼

To convert raw LC-MS/MS files into analysis-ready features, read mzML or mzXML data into MSExperiment objects, apply signal processing like peak picking, and use feature detection algorithms to output featureXML maps for quantification.

What is the best way to perform FDR filtering on peptide and protein identifications in Python?▼

FDR filtering on peptide and protein identifications is performed by loading idXML files and applying OpenMS post-processing algorithms to calculate q-values and control the false discovery rate for proteomics workflows.

Can I use Python to link chromatographic features across multiple samples for untargeted metabolomics?▼

You can link chromatographic features across multiple samples in Python by detecting peaks in individual featureXML maps and applying consensus building algorithms to generate a consensusXML file for untargeted metabolomics quantification.

Does pyopenms support reading and converting common mass spectrometry file formats like mzML and FASTA?▼

pyopenms supports reading and converting common mass spectrometry file formats including mzML, mzXML, mzTab, FASTA, pepXML, protXML, and mzIdentML for proteomics and metabolomics pipelines.

How do I apply signal processing like smoothing and centroiding to mass spectrometry spectra in Python?▼

Apply signal processing like smoothing and centroiding to mass spectrometry spectra by loading MSExperiment data and executing OpenMS preprocessing algorithms to normalize and filter the spectra into analysis-ready representations.

Why do I need RT alignment and adduct detection for untargeted metabolomics pipelines?▼

RT alignment and adduct detection are needed for untargeted metabolomics pipelines to correct chromatographic shifts across samples and group related ions, enabling accurate consensus building and downstream table export.

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