Can I use this for quantitative proteomics?

Yes, the skill provides patterns for feature detection, consensus mapping, and quantitative analysis of proteomics datasets.

Which file formats does this skill support?

It supports standard mass spectrometry formats including mzML, mzXML, TraML, FASTA, pepXML, protXML, and mzIdentML.

What is the PyOpenMS Claude Skill?

It is a specialized capability that helps Claude assist with mass spectrometry data analysis using the pyopenms Python library and OpenMS framework.

Does it integrate with Pandas?

Yes, the skill includes guidance for exporting mass spectrometry data structures directly into Pandas DataFrames for statistical analysis.

Is it suitable for metabolomics?

Absolutely, it supports untargeted metabolomics workflows including retention time alignment, feature linking, and compound database annotation.

PyOpenMS Mass Spectrometry

Name: PyOpenMS Mass Spectrometry
Author: K-Dense-AI

byK-Dense-AI

•

2,188

데이터 과학 및 ML

Facilitates advanced mass spectrometry data analysis using the Python interface to the OpenMS library for proteomics and metabolomics.

소개

This skill empowers Claude to perform complex computational mass spectrometry tasks by leveraging the pyopenms library. It provides domain-specific guidance for managing intricate file formats like mzML and FASTA, implementing signal processing algorithms, and conducting comprehensive proteomics and metabolomics workflows. Whether you are detecting features across spectra, identifying peptides with search engines like Comet, or performing quantitative analysis, this skill ensures best practices in scientific data processing and seamless integration with the Python data science ecosystem including Pandas and NumPy.

주요 기능

Advanced signal processing capabilities for smoothing, filtering, and centroiding spectral data
Integration with major identification engines like Comet and Mascot for peptide and protein discovery
Built-in workflows for untargeted metabolomics preprocessing and retention time alignment
2,188 GitHub stars
Comprehensive support for mass spectrometry data formats including mzML, mzXML, and FASTA
Automated feature detection and linking across multiple samples for quantitative analysis

사용 사례

Developing custom scientific pipelines that integrate mass spectrometry data with machine learning models
Analyzing large-scale LC-MS/MS proteomics datasets to identify and quantify proteins
Preprocessing raw mass spectrometry files for metabolomics research and compound annotation

소개

주요 기능

Advanced signal processing capabilities for smoothing, filtering, and centroiding spectral data
Integration with major identification engines like Comet and Mascot for peptide and protein discovery
Built-in workflows for untargeted metabolomics preprocessing and retention time alignment
2,188 GitHub stars
Comprehensive support for mass spectrometry data formats including mzML, mzXML, and FASTA
Automated feature detection and linking across multiple samples for quantitative analysis

사용 사례

Developing custom scientific pipelines that integrate mass spectrometry data with machine learning models
Analyzing large-scale LC-MS/MS proteomics datasets to identify and quantify proteins
Preprocessing raw mass spectrometry files for metabolomics research and compound annotation