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pyopenms

Process LC-MS mzML datasets for peak picking, feature detection, and format conversion.

7|Updated Apr 5, 2026
Search Tags:#metabolomics#pyopenms#feature-detection#openms#lc-ms#mzml#peak-picking
One-click install
npx skills add https://github.com/dailycafi/metabolism-skills --skill pyopenms-dailycafi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pyopenms
Source: https://github.com/dailycafi/metabolism-skills/tree/main/skills/ms-data-processing/pyopenms
Command: npx skills add https://github.com/dailycafi/metabolism-skills --skill pyopenms-dailycafi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Handling raw LC-MS and LC-MS/MS data requires many brittle steps—reading large mzML files, centroiding/peak picking, feature detection, alignment, adduct handling, and exporting interoperable OpenMS formats—which is time-consuming and error-prone for metabolomics and proteomics researchers. PyOpenMS provides Python bindings to OpenMS so users can script reproducible preprocessing and analysis pipelines that read/write mzML, perform signal processing, and produce feature and consensus maps for downstream statistics.

Core Features & Use Cases

  • File I/O & Format Conversion: Load and store mzML, mzXML, featureXML, consensusXML, idXML and other OpenMS formats for seamless data exchange.
  • Signal Processing & Peak Picking: Smoothing, centroiding, and multiple peak picking algorithms tailored for high-resolution and profile data.
  • Feature Detection & Linking: Detect chromatographic features, group isotopes, align retention times, and create consensus maps across samples for quantitative studies.
  • Identification & Annotation: Integrate identification results, apply FDR filtering, and annotate features with peptide/protein or compound matches.
  • Use Case: Run an end-to-end untargeted metabolomics preprocessing pipeline that includes peak picking, feature detection, adduct grouping, RT alignment, feature linking, and export of a quantitative feature table for statistical analysis.

Quick Start

Load a mzML file, run centroiding and feature detection with pyopenms, then save the detected features to a featureXML or consensusXML file for downstream analysis.

Frequently Asked Questions about pyopenms

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

FAQPage Schema
How do I perform peak picking and feature detection on mzML files for metabolomics?▼

Peak picking and feature detection on mzML files are performed by applying signal processing algorithms for centroiding and chromatographic extraction to identify and group isotopic features. This produces featureXML and consensusXML outputs for quantitative downstream analysis.

What is the best way to convert and process large LC-MS datasets into a pandas-ready table?▼

Processing large LC-MS datasets into a pandas-ready table involves reading mzML files, executing feature detection, and linking consensus features across samples. Detected chromatographic features are then exported as interoperable tables for statistical analysis.

Can I use Python to run retention time alignment and adduct detection across multiple LC-MS samples?▼

Retention time alignment and adduct detection across multiple LC-MS samples are executed through Python bindings to OpenMS algorithms. These steps group adducts and align retention times to create consensus maps for comparative quantitative studies.

Does this approach support reading and writing idXML and consensusXML formats for proteomics workflows?▼

Reading and writing idXML and consensusXML formats are fully supported for proteomics workflows. This enables seamless data exchange, integration of identification results, and FDR filtering to annotate features with peptide or protein matches.

How do I build an end-to-end untargeted metabolomics preprocessing pipeline?▼

An end-to-end untargeted metabolomics preprocessing pipeline is built by scripting sequential steps including peak picking, feature detection, adduct grouping, and RT alignment. This reproducible workflow exports a quantitative feature table for statistical analysis.

What signal processing steps are needed before feature detection on high-resolution LC-MS data?▼

Signal processing steps needed before feature detection on high-resolution LC-MS data include smoothing and centroiding profile data. These peak picking algorithms prepare raw signals for accurate chromatographic feature extraction and isotope grouping.

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