Computational metabolomics at scale: from open data to insight

Area of Science:

• Metabolomics
• Computational Biology
• Bioinformatics

Background:

• Metabolomics technologies have advanced significantly, enabling large-scale detection of diverse metabolites.
• Increasing volumes of metabolomics data are being deposited in public repositories for reuse and integration.
• Current computational tools and infrastructure are insufficient to fully leverage the wealth of available metabolomics data for scientific insight.

Purpose of the Study:

• To review discussions from the Dagstuhl Seminar 24181 on Computational Metabolomics.
• To focus on public data availability, open data standards, data/knowledge integration, and education in metabolomics.
• To promote awareness and adoption of open science resources in computational metabolomics.

Main Methods:

• Review of discussions and outcomes from the Dagstuhl Seminar 24181.
• Synthesis of current challenges and opportunities in computational metabolomics.
• Identification of key areas for future development in data infrastructure and standards.

Main Results:

• The field possesses a wealth of metabolomics data but lacks a consistent, adopted data and analytics infrastructure.
• Significant potential exists to harness new computational technologies for metabolomics data interpretation.
• Key areas for advancement include public data availability, open standards, data integration, and education.

Conclusions:

• There is a critical need for a robust computational infrastructure to translate metabolomics data into scientific understanding.
• Adoption of open science resources and standards is crucial for advancing the field.
• Further development is required in data integration, standardization, and educational initiatives for computational metabolomics.