MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data.

Area of Science:

• Biochemistry
• Bioinformatics
• Computational Biology

Background:

• Mass spectrometry has generated vast amounts of metabolomics data, necessitating efficient analysis tools.
• Existing software often struggles with processing large numbers of datasets from diverse studies.
• There is a need for integrated solutions to handle thousands of metabolomics experiments cohesively.

Purpose of the Study:

• To introduce MetHoS, an automated web-based platform for large-scale metabolomics data management and analysis.
• To provide a scalable solution for processing, storing, and analyzing numerous mass spectrometry-based metabolomics datasets.
• To enable integrative analysis across diverse metabolomics studies.

Main Methods:

• MetHoS utilizes Big Data frameworks for parallel and distributed processing, storage, and analysis.
• The platform is designed for high scalability, capable of handling extensive datasets across computer clusters.
• Thousands of experiments from the MetaboLights database were used for a large-scale proof-of-concept study.

Main Results:

• MetHoS successfully processed, stored, and statistically analyzed thousands of metabolomics experiments.
• The platform demonstrated scalability and efficiency in handling large volumes of mass spectrometry data.
• Proof-of-concept study confirmed the platform's capability for integrative, large-scale metabolomics analysis.

Conclusions:

• MetHoS is well-suited for large-scale processing, storage, and analysis of metabolomics data.
• The platform facilitates untargeted metabolomic analyses on extensive datasets.
• MetHoS is freely available, encouraging its adoption for metabolomics research.