matchms

Automate mass spectrometry data processing by importing spectral files, filtering spectra, and calculating similarity scores against reference libraries. This approach matches unknown compounds and builds reproducible workflows, saving hours of manual analysis.

Calculate spectral similarity for mass spectrometry data using Python libraries to compare spectra with various similarity metrics. This process computes similarity scores for metabolite identification and spectral library matching.

Build reproducible metabolomics workflows in Python by chaining mass spectrometry data import, spectrum filtering, and spectral similarity calculation steps. This ensures multi-step analysis can be replicated for consistent results.

Yes, rdkit is required to process chemical structures during metabolomics analysis. It works alongside matchms and numpy to handle mass spectrometry data, calculate spectral similarities, and perform chemical structure processing.

Spectral library searching works by calculating similarity metrics between your experimental mass spectrometry spectra and reference library spectra. The computed similarity scores match unknown compounds, automating metabolite identification.

Import and export mass spectrometry data formats like MGF files for metabolomics analysis. The process handles spectral data filtering and processing to prepare inputs for reproducible workflows and metabolite identification.