How does it integrate with the Python data ecosystem?

PyOpenMS allows for easy conversion of mass spectrometry data structures into Pandas DataFrames and NumPy arrays for visualization and machine learning.

Which mass spectrometry file formats are supported?

The skill handles a wide variety of formats including mzML, mzXML, TraML, mzTab, FASTA, pepXML, protXML, and mzIdentML.

Does it support protein identification engines?

Yes, it integrates with results from major search engines like Comet, Mascot, MSGFPlus, and XTandem, and can apply False Discovery Rate (FDR) filtering.

What is the PyOpenMS skill used for?

It provides a specialized interface for Claude to perform computational mass spectrometry tasks, including signal processing, feature detection, and peptide identification.

Can I use this skill for metabolomics analysis?

Yes, it supports untargeted metabolomics workflows including preprocessing, retention time alignment, and compound annotation.

PyOpenMS Mass Spectrometry Analysis

Name: PyOpenMS Mass Spectrometry Analysis
Author: pur3v4d3r

bypur3v4d3r

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Data Science & ML

Processes and analyzes mass spectrometry data for proteomics and metabolomics research using the OpenMS framework.

The PyOpenMS skill provides a specialized Python interface for computational mass spectrometry, enabling Claude to assist with complex LC-MS/MS workflows. It supports a wide range of tasks including reading and converting standard file formats like mzML and FASTA, performing spectral signal processing, detecting features for quantification, and managing peptide or protein identification results. This skill is particularly useful for bioinformaticians and data scientists who need to build automated pipelines, integrate MS data with the Python scientific stack (Pandas, NumPy, Scikit-learn), or perform untargeted metabolomics analysis with high precision.

Key Features

01Advanced signal processing including smoothing, centroiding, and normalization

02Comprehensive file I/O for mzML, FASTA, mzIdentML, and other MS formats

031 GitHub stars

04Automated feature detection and linking for quantitative workflows

05Direct integration with Pandas DataFrames for downstream statistical analysis

06Peptide and protein identification management with FDR filtering

Use Cases

01Building automated LC-MS/MS data processing and conversion pipelines

02Analyzing and filtering peptide search engine results for proteomics experiments

03Performing untargeted metabolomics feature extraction and annotation

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add pur3v4d3r/pur3-pkb-codebase pyopenms

For use in Claude.ai and ChatGPT

Download Skill