pyopenms

Solid

Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.

AI & Automation 2,210 stars 164 forks Updated 1 weeks ago Apache-2.0

Install

View on GitHub

Quality Score: 91/100

Stars 20%
100
Recency 20%
90
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# PyOpenMS ## Overview PyOpenMS provides Python bindings to the OpenMS library for computational mass spectrometry, enabling analysis of proteomics and metabolomics data. Use for handling mass spectrometry file formats, processing spectral data, detecting features, identifying peptides/proteins, and performing quantitative analysis. ## Installation Install using uv: ```bash uv uv pip install pyopenms ``` Verify installation: ```python import pyopenms print(pyopenms.__version__) ``` ## Core Capabilities PyOpenMS organizes functionality into these domains: ### 1. File I/O and Data Formats Handle mass spectrometry file formats and convert between representations. **Supported formats**: mzML, mzXML, TraML, mzTab, FASTA, pepXML, protXML, mzIdentML, featureXML, consensusXML, idXML Basic file reading: ```python import pyopenms as ms # Read mzML file exp = ms.MSExperiment() ms.MzMLFile().load("data.mzML", exp) # Access spectra for spectrum in exp: mz, intensity = spectrum.get_peaks() print(f"Spectrum: {len(mz)} peaks") ``` **For detailed file handling**: See `references/file_io.md` ### 2. Signal Processing Process raw spectral data with smoothing, filtering, centroiding, and normalization. Basic spectrum processing: ```python # Smooth spectrum with Gaussian filter gaussian = ms.GaussFilter() params = gaussian.getParameters() params.setValue("gaussian_width", 0.1) gaussian.setParameters(params) gaussian.filterExperiment(exp) ``` **For algorithm details**:...

Details

Author
foryourhealth111-pixel
Repository
foryourhealth111-pixel/Vibe-Skills
Created
3 months ago
Last Updated
1 weeks ago
Language
Python
License
Apache-2.0

Similar Skills

Semantically similar based on skill content — not just same category

Data & Documents Solid

pyopenms

Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.

27,705 Updated today
davila7
Data & Documents Listed

pyopenms

Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.

335 Updated today
aiskillstore
Data & Documents Solid

pyopenms

Complete mass spectrometry analysis platform. Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. Best for proteomics, comprehensive MS data processing. For simple spectral comparison and metabolite ID use matchms.

26,817 Updated today
K-Dense-AI
AI & Automation Solid

matchms

Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.

2,210 Updated 1 weeks ago
foryourhealth111-pixel
Data & Documents Solid

matchms

Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.

26,817 Updated today
K-Dense-AI