Can I use this skill for quantitative proteomics?

Yes, PyOpenMS includes robust tools for feature detection, linking across samples, and protein quantification.

What file formats does the PyOpenMS skill support?

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

Is this skill suitable for metabolomics research?

Yes, it includes specific workflows for untargeted metabolomics, covering retention time alignment, feature linking, and compound database annotation.

Does it integrate with other Python data science libraries?

Absolutely. It provides native methods to convert mass spectrometry objects directly into Pandas DataFrames and NumPy arrays for downstream analysis.

How do I install the necessary pyopenms dependencies?

You can install the library using 'uv pip install pyopenms' or 'pip install pyopenms' within your Python environment.

PyOpenMS for Mass Spectrometry

Name: PyOpenMS for Mass Spectrometry
Author: Microck

byMicrock

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데이터 과학 및 ML

Analyzes proteomics and metabolomics data using the OpenMS library for comprehensive mass spectrometry workflows.

소개

PyOpenMS is a specialized Claude Code Skill that provides high-level Python bindings for the OpenMS library, facilitating advanced computational mass spectrometry. It enables researchers and developers to build sophisticated pipelines for signal processing, feature detection, and protein identification across a wide array of industry-standard file formats like mzML and FASTA. By integrating seamlessly with the Python data science stack—including pandas, NumPy, and scikit-learn—it simplifies the transition from raw spectral data to actionable biological insights in proteomics and metabolomics research.

주요 기능

Automated feature detection and linking for quantitative analysis
81 GitHub stars
Peptide and protein identification with FDR filtering support
Comprehensive file handling for mzML, mzXML, FASTA, and mzIdentML
Advanced signal processing including smoothing, filtering, and centroiding
Seamless data conversion to Pandas DataFrames and NumPy arrays

사용 사례

Building automated LC-MS/MS data processing and analysis pipelines
Identifying and quantifying proteins in complex biological samples
Preprocessing and annotating untargeted metabolomics datasets

소개

주요 기능

Automated feature detection and linking for quantitative analysis
81 GitHub stars
Peptide and protein identification with FDR filtering support
Comprehensive file handling for mzML, mzXML, FASTA, and mzIdentML
Advanced signal processing including smoothing, filtering, and centroiding
Seamless data conversion to Pandas DataFrames and NumPy arrays

사용 사례

Building automated LC-MS/MS data processing and analysis pipelines
Identifying and quantifying proteins in complex biological samples
Preprocessing and annotating untargeted metabolomics datasets