MyCompoundID: using an evidence-based metabolome library for metabolite identification

Area of Science:

• Metabolomics
• Systems Biology
• Biomedical Research

Background:

• Metabolite identification is a significant challenge in metabolomics.
• Unidentified metabolites hinder the integration of metabolome data with proteomic and genomic data for systems biology and medical research.

Purpose of the Study:

• To develop a web-based tool for enhanced metabolite identification.
• To create an expanded metabolome library for improved data interpretation.

Main Methods:

• Development of a web-based metabolite identification tool (http://www.mycompoundid.org).
• Construction of a novel metabolome library including known human endogenous metabolites and their predicted metabolic products (over 10 million compounds).
• Utilized liquid chromatography-mass spectrometry (LC-MS) and MS/MS for metabolite analysis.

Main Results:

• The developed tool identified at least twice as many metabolites in human urine and plasma samples compared to standard libraries.
• The evidence-based metabolome library (EML) demonstrated superior performance in identifying putative metabolites from human urine samples.
• Outperformed existing libraries like ChemPub and KEGG in metabolite identification.

Conclusions:

• The new web tool and evidence-based metabolome library significantly advance metabolite identification capabilities.
• This approach enhances the ability to link metabolome data with other omics data for deeper biological insights.
• Facilitates more comprehensive metabolomic analyses in biological and medical studies.