lib-matchms

To identify unknown compounds from mass spectrometry data, you compare your experimental spectra against curated spectral libraries. This Skill computes similarity scores to match unknowns against public libraries like GNPS, returning a ranked list of potential metabolite matches.

Supported mass spectrometry file formats for metabolomics data processing include MGF, mzML, and MSP. The Skill standardizes and cleans these file formats using extensive filtering functions to ensure reproducible analytical workflows.

To calculate spectral similarity scores for LC-MS/MS experiments, you apply algorithms like cosine or modified cosine. This process compares mass spectra peaks to compute scores, enabling compound identification against reference libraries.

Yes, you can clean and standardize mass spectra before compound identification. The Skill provides a comprehensive suite of filtering functions to preprocess mass spectrometry data, ensuring spectra are properly formatted for accurate library matching.

The best way to compare an unknown mass spectrum against a public library is to use spectral similarity calculations. This Skill matches your input spectra against library entries and generates a ranked list of potential compound matches with similarity scores.