Determining conserved metabolic biomarkers from a million database queries

Area of Science:

• Metabolomics
• Bioinformatics
• Biomarker Discovery

Background:

• Metabolite databases are crucial for cataloging commonly searched biomarkers in metabolome research.
• Advances in analytical technologies and bioinformatics enhance the utility of omics-scale metabolite profiling for biomarker discovery.
• Translating discovered biomarkers into clinically or biologically relevant indicators remains a challenge.

Purpose of the Study:

• To enhance the discovery of translatable metabolite biomarkers.
• To present search analytics from the METLIN metabolite database.
• To correlate common metabolites with data from XCMS Online for biomarker uniqueness assessment.

Main Methods:

• Analysis of over one million METLIN metabolite database queries.
• Cross-correlation of commonly searched metabolites with XCMS Online data.
• Utilizing search analytics to identify conserved metabolic responses and gauge biomarker uniqueness.

Main Results:

• Identification of commonly queried metabolites in the METLIN database.
• Correlation of METLIN data with XCMS Online, a cloud-based data processing and pathway analysis platform.
• Implications for understanding conserved metabolic responses to stressors and assessing biomarker uniqueness.

Conclusions:

• The analysis of METLIN and XCMS Online data provides insights into commonly relevant metabolites.
• This approach can help identify potential biomarkers that indicate conserved metabolic responses.
• The findings facilitate the evaluation of the relative uniqueness of potential biomarkers for improved translation.