What file formats does this skill support?

It supports a wide range of scientific formats including mzML, mzXML, TraML, mzTab, FASTA, pepXML, protXML, and mzIdentML.

What is PyOpenMS used for?

PyOpenMS is a Python interface for the OpenMS library, used specifically for computational mass spectrometry, including proteomics and metabolomics data analysis.

Can I use PyOpenMS with Pandas?

Yes, this skill includes functionality to export mass spectrometry data structures directly into Pandas DataFrames for easy statistical manipulation and visualization.

Does it support peptide identification?

Yes, it integrates with various search engines like Comet, Mascot, and MSGFPlus to process and filter peptide and protein identifications using FDR (False Discovery Rate) methods.

PyOpenMS Mass Spectrometry Analysis

Name: PyOpenMS Mass Spectrometry Analysis
Author: Activer007

byActiver007

Data Science & ML

Facilitates mass spectrometry data analysis, proteomics, and metabolomics workflows using Python-based OpenMS bindings.

About

PyOpenMS provides specialized Python bindings for the OpenMS library, empowering researchers and developers to perform complex mass spectrometry tasks directly within Claude Code. It streamlines the processing of proteomics and metabolomics datasets by providing tools for file conversion, signal processing, feature detection, and protein identification. This skill is ideal for bioinformaticians and data scientists needing to automate LC-MS/MS workflows, manage diverse data formats like mzML or FASTA, and integrate spectral data with modern data science tools like Pandas and NumPy for advanced statistical analysis.

Key Features

Advanced signal processing including smoothing, filtering, and centroiding
Comprehensive file I/O for mzML, mzXML, FASTA, and mzIdentML
Seamless integration with Pandas and NumPy for downstream data analysis
0 GitHub stars
Automated peptide and protein identification and FDR filtering
High-performance feature detection and quantification workflows

Use Cases

Building untargeted metabolomics preprocessing pipelines
Converting and manipulating specialized mass spectrometry file formats
Processing and analyzing LC-MS/MS proteomics experiments

About

Key Features

Advanced signal processing including smoothing, filtering, and centroiding
Comprehensive file I/O for mzML, mzXML, FASTA, and mzIdentML
Seamless integration with Pandas and NumPy for downstream data analysis
0 GitHub stars
Automated peptide and protein identification and FDR filtering
High-performance feature detection and quantification workflows

Use Cases

Building untargeted metabolomics preprocessing pipelines
Converting and manipulating specialized mass spectrometry file formats
Processing and analyzing LC-MS/MS proteomics experiments