Is PyOpenMS suitable for large-scale proteomics?

Yes, it is built for high-throughput analysis and provides the data structures needed to handle thousands of spectra and complex identification results efficiently.

Does it integrate with Python data science libraries?

Absolutely. PyOpenMS is designed to work seamlessly with Pandas for data manipulation, NumPy for array operations, and Scikit-learn for machine learning.

What search engines can I use for peptide identification?

The skill facilitates integration with several major engines, including Comet, Mascot, MSGFPlus, XTandem, OMSSA, and Myrimatch.

What mass spectrometry file formats are supported?

PyOpenMS supports a wide range of standard formats including mzML, mzXML, TraML, FASTA, pepXML, protXML, and mzIdentML.

Can I use this skill for metabolomics research?

Yes, it includes specialized support for untargeted metabolomics, including feature detection, alignment across samples, and annotation with compound databases.

PyOpenMS Mass Spectrometry Analysis

Name: PyOpenMS Mass Spectrometry Analysis
Author: x-cmd

byx-cmd

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Ciencia de Datos y ML

Simplifies mass spectrometry data processing and analysis within Python-based proteomics and metabolomics workflows.

Acerca de

PyOpenMS Analysis is a specialized Claude Code Skill that provides an expert interface for the OpenMS library, the gold standard for computational mass spectrometry. This skill empowers developers and bioinformaticians to build complex pipelines for raw spectral processing, feature detection, and peptide identification using Python. By offering standardized patterns for handling diverse file formats like mzML and FASTA, alongside deep integration with the Python data science stack (Pandas, NumPy, Scikit-learn), it accelerates the development of robust analytical workflows for large-scale proteomics and metabolomics research.

Características Principales

Comprehensive File I/O for mzML, FASTA, and mzIdentML
Direct data export to Pandas DataFrames for statistical analysis
Automated feature detection and retention time alignment
8 GitHub stars
Advanced signal processing including smoothing and centroiding
Peptide and protein identification with search engine integration

Casos de Uso

Building automated LC-MS/MS data processing pipelines
Implementing FDR filtering and quality control for protein identification
Analyzing untargeted metabolomics datasets with consensus mapping

Acerca de

Características Principales

Comprehensive File I/O for mzML, FASTA, and mzIdentML
Direct data export to Pandas DataFrames for statistical analysis
Automated feature detection and retention time alignment
8 GitHub stars
Advanced signal processing including smoothing and centroiding
Peptide and protein identification with search engine integration

Casos de Uso

Building automated LC-MS/MS data processing pipelines
Implementing FDR filtering and quality control for protein identification
Analyzing untargeted metabolomics datasets with consensus mapping