Agilent METLIN Metabolomics database and library

Importance of the topic

Metabolite identification is essential in discovery metabolomics to understand global metabolic changes in biological systems. The reliability of identification hinges on database quality, and a highly curated library like Agilent METLIN increases confidence and accuracy.

Objectives and Study Overview

This white paper introduces the Agilent METLIN Metabolomics database and library within integrated MassHunter workflows. It outlines how researchers can achieve confident metabolite annotation through curated data and orthogonal criteria.

Methodology and Instrumentation

The study employs accurate mass measurement, isotope pattern matching, retention time alignment, collision cross section data and authentic MS/MS spectra. Key software tools include MassHunter Qualitative Analysis, Molecular Structure Correlator, Profinder, ID Browser and PCDL Manager. Custom pathways can be created via Pathways to PCDL.

Main Results and Discussion

Analysis of the database revealed broad coverage of metabolite classes including organic acids, steroids, amino acids and nucleosides. Incorporating orthogonal data such as isotope pattern and retention time alongside MS/MS spectra elevated confidence in identifications. Comparison with public databases highlighted the superior mass accuracy and completeness of METLIN entries. Authentic MS/MS spectra demonstrated closer alignment to experimental fragmentation than in silico predictions.

Benefits and Practical Applications

The highly curated content reduces false positive identifications and streamlines workflows in target discovery, flux analysis and quality control. Users benefit from rapid annotation of diverse metabolites and the ability to extend the library with custom entries.

Future Trends and Applications

Expansion of lipid annotations, integration with pathway analysis tools, application of machine learning for spectral prediction and increased support for fluxomics will further enhance metabolite identification platforms. Collaboration across community databases can enrich spectral libraries and improve global metabolomics initiatives.

Conclusion

The Agilent METLIN Metabolomics database and library provides a robust foundation for confident compound annotation in discovery metabolomics. Its curated spectra, orthogonal matching criteria and integration within MassHunter workflows support accurate, reproducible results across research and industrial applications.

References

No literature references were provided in the original document.