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A universal language for finding mass spectrometry data patterns
Tito Damiani1, Alan K Jarmusch2, Allegra T Aron3
1Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
Nature Methods
|May 12, 2025
Summary
Untargeted mass spectrometry data is often underutilized due to software limitations. A new Mass Spectrometry Query Language (MassQL) enables direct querying of this data, unlocking new discoveries in metabolomics and beyond.
Area of Science:
- Analytical Chemistry
- Bioinformatics
- Metabolomics
Background:
- Vast amounts of untargeted mass spectrometry data remain underutilized.
- Current software tools lack the flexibility and scalability for effective data reanalysis.
- Limited accessibility hinders downstream interpretation and discovery from raw mass spectrometry datasets.
Purpose of the Study:
- Introduce a novel language, Mass Spectrometry Query Language (MassQL), and its software ecosystem.
- Enable direct querying of mass spectrometry data using user-defined patterns.
- Enhance the reanalysis and interpretation of public untargeted metabolomics data.
Main Methods:
- Development of Mass Spectrometry Query Language (MassQL) for querying mass spectrometry data.
- Creation of an accompanying software ecosystem to support MassQL.
- Demonstration of MassQL capabilities with real-world examples and public datasets.
Main Results:
- MassQL enables expressive, user-defined queries on mass spectrometry data.
- The MassQL ecosystem facilitates direct data access and analysis.
- Implementation in multiple software tools enhances data mining capabilities.
Conclusions:
- MassQL addresses limitations in mass spectrometry data utilization.
- It empowers scientists to redefine chemical diversity and make new discoveries.
- The widespread adoption of MassQL improves interoperability and reproducibility in data analysis.
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