Match mass spectra across libraries using multiple similarity metrics.
npx skills add https://github.com/OwnLabAI/ownlab --skill matchms-ownlabai
Please help me install this Agent Skill. Skill: matchms Source: https://github.com/OwnLabAI/ownlab/tree/main/mart/skills/scientific-skills/matchms Command: npx skills add https://github.com/OwnLabAI/ownlab --skill matchms-ownlabai
Spectral data processing and similarity analysis for mass spectrometry can be labor-intensive and error-prone without standardized tooling, hindering reproducibility and faster discovery.
Load a library and query dataset, apply standard preprocessing, and compute pairwise similarity with CosineGreedy.
High-intent search queries and answers about installing and using this skill.
To match mass spectrometry spectra, import your library and query datasets from formats like MGF or mzML, apply standard preprocessing, and compute pairwise similarity using metrics such as CosineGreedy to annotate metabolites. This builds reusable workflows for scalable spectral library matching.
Spectral library matching supports CosineGreedy, CosineHungarian, ModifiedCosine, and NeutralLossesCosine metrics, alongside metadata-based scores. These options allow flexible similarity calculations across query spectra and spectral libraries for compound annotation tasks.
Yes, you can import, preprocess, and harmonize spectra from mzML, mzXML, MGF, MSP, and GNPS JSON formats. This standardizes mass spectrometry data for consistent similarity analysis and metabolite identification workflows.
Yes, this spectral analysis requires a Python runtime. Optional dependencies like rdkit are needed specifically for molecular fingerprinting tasks, while core spectral library matching and preprocessing functions operate within the standard environment.
The best way to build scalable metabolomics workflows is to standardize import and preprocessing of mass spectrometry data, then apply multiple similarity metrics for library matching. This end-to-end approach ensures reproducible compound annotation and method development.
Spectral data processing lacks reproducibility without standardized tooling for mass spectrometry harmonization. Applying consistent preprocessing and defined similarity metrics across MGF and mzML inputs ensures error-free, reproducible metabolite identification and library matching.
rubenslivImport mass spectrometry spectra from mzML, mgf, MSP, and GNPS JSON formats.
tassiovaleCompare mass spectra and identify compounds using spectral libraries.
mkurmanCompare mass spectra to reference libraries using multiple similarity metrics.
jasrajtulsiProcess mass spectrometry spectra for similarity scoring and compound identification.
