Compare mass spectra against spectral libraries to identify unknown compounds.
npx skills add https://github.com/dralkh/seerai --skill matchms-dralkh
Please help me install this Agent Skill. Skill: matchms Source: https://github.com/dralkh/seerai/tree/main/skills/matchms Command: npx skills add https://github.com/dralkh/seerai --skill matchms-dralkh
Matchms removes the manual burden of cleaning mass spectrometry data and comparing spectra against reference libraries, making compound identification faster, more consistent, and reproducible.
Use the matchms skill to load two spectra collections, preprocess them consistently, and return the highest-scoring library matches for each unknown sample.
High-intent search queries and answers about installing and using this skill.
Compound identification from mass spectrometry spectral libraries involves comparing unknown spectra against reference libraries. This skill preprocesses spectra, calculates similarity scores, and ranks top hits to identify compounds consistently and reproducibly.
Spectral matching and library search support MGF, MSP, mzML, mzXML, JSON, USI, and pickle file formats. You can import, process, and export mass spectrometry data across these common formats for reproducible metabolomics analysis.
Preprocessing mass spectrometry data requires harmonizing metadata, normalizing intensities, filtering peaks, and enforcing quality checks. This skill automates these steps to ensure spectra are consistently prepared before calculating similarity scores.
Similarity scoring methods for comparing mass spectra include cosine, modified cosine, neutral loss, fingerprint, and metadata-based approaches. These methods calculate spectral similarity to rank library matches for unknown compound identification.
Yes, metabolomics library search pipelines support searching unknown compounds, ranking top hits, and filtering by precursor mass. This skill streamlines identification workflows by applying similarity scoring and mass filters to reference libraries.
Harmonized spectrum metadata is required for mass spectrometry compound identification because it ensures consistent preprocessing, accurate similarity scoring, and reproducible library search results across different spectral data formats and analysis pipelines.
jackspaceProcess MS/MS spectra with filtering, similarity scoring, and metadata harmonization pipelines.
jasrajtulsiProcess mass spectrometry spectra for similarity scoring and compound identification.
mkurmanCompare mass spectra to reference libraries using multiple similarity metrics.
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