METLIN

The METLIN Metabolite and Chemical Entity Database is the largest repository of experimental tandem mass spectrometry data acquired from standards. The tandem mass spectrometry information on over 450,000 compounds (as of 1 July 2019) is provided to facilitate the identification of chemical entities from tandem mass spectrometry experiments. In addition to identification of known molecules it is also very useful for identifying unknowns using its similarity searching technology. All tandem mass spectrometry data comes from the experimental analysis of standards at multiple collision energies and in both positive and negative ionization modes. The METLIN Metabolite and Chemical Entity Database is the largest repository of experimental tandem mass spectrometry data acquired from standards. The tandem mass spectrometry information on over 450,000 compounds (as of 1 July 2019) is provided to facilitate the identification of chemical entities from tandem mass spectrometry experiments. In addition to identification of known molecules it is also very useful for identifying unknowns using its similarity searching technology. All tandem mass spectrometry data comes from the experimental analysis of standards at multiple collision energies and in both positive and negative ionization modes. METLIN serves as a data management system to assist in metabolite and chemical entity identification by providing public access to its repository of comprehensive MS/MS metabolite data. An annotated list of known compounds including metabolites and other chemical entities through their mass, chemical formula, and structure are available on the METLIN website. Each molecule is linked to outside resources such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) for further reference and inquiry. Available MS/MS data are expanding continuously. The METLIN database was developed and is maintained solely by the Siuzdak laboratory at The Scripps Research Institute. Since its initial implementation in 2005, the freely available METLIN website has collected comments and suggestions for improvements from users in the biotechnology, pharmaceutical and academic communities ultimately resulting in a dynamic, intuitive, and functionally useful metabolomics and chemical identification technology. The improved interface allows researchers to readily search the database and characterize metabolites and other compounds through features such as accurate mass, single and multiple fragment searching, neutral loss and full spectrum search capabilities. The powerful similarity searching is a feature introduced in 2010. These features are designed to facilitate the value of their metabolomics MS and MS/MS data and expedite the identification process of both known and unknown molecules.