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

Konstantinos Tzanakis; Tim W Nattkemper; Karsten Niehaus; Stefan P Albaum

doi:10.1186/s12859-022-04793-w

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

BMC Bioinformatics. 2022 Jul 8;23(1):267. doi: 10.1186/s12859-022-04793-w.

Authors

Konstantinos Tzanakis¹, Tim W Nattkemper², Karsten Niehaus³, Stefan P Albaum⁴

Affiliations

¹ International Research Training Group "Computational Methods for the Analysis of the Diversity and Dynamics of Genomes", Faculty of Technology, Bielefeld University, Bielefeld, Germany. ktzan@cebitec.uni-bielefeld.de.
² Biodata Mining Group, Center for Biotechnology (CeBiTec), Faculty of Technology, Bielefeld University, Bielefeld, Germany.
³ Proteome and Metabolome Research, Center for Biotechnology (CeBiTec), Faculty of Biology, Bielefeld University, Bielefeld, Germany.
⁴ Bioinformatics Resource Facility, Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany.

Abstract

Background: Modern mass spectrometry has revolutionized the detection and analysis of metabolites but likewise, let the data skyrocket with repositories for metabolomics data filling up with thousands of datasets. While there are many software tools for the analysis of individual experiments with a few to dozens of chromatograms, we see a demand for a contemporary software solution capable of processing and analyzing hundreds or even thousands of experiments in an integrative manner with standardized workflows.

Results: Here, we introduce MetHoS as an automated web-based software platform for the processing, storage and analysis of great amounts of mass spectrometry-based metabolomics data sets originating from different metabolomics studies. MetHoS is based on Big Data frameworks to enable parallel processing, distributed storage and distributed analysis of even larger data sets across clusters of computers in a highly scalable manner. It has been designed to allow the processing and analysis of any amount of experiments and samples in an integrative manner. In order to demonstrate the capabilities of MetHoS, thousands of experiments were downloaded from the MetaboLights database and used to perform a large-scale processing, storage and statistical analysis in a proof-of-concept study.

Conclusions: MetHoS is suitable for large-scale processing, storage and analysis of metabolomics data aiming at untargeted metabolomic analyses. It is freely available at: https://methos.cebitec.uni-bielefeld.de/ . Users interested in analyzing their own data are encouraged to apply for an account.

Keywords: Distributed analysis; Distributed storage; Large-scale metabolomics; Mass spectrometry data; Parallel processing.

MeSH terms

Electronic Data Processing
Mass Spectrometry
Metabolomics* / methods
Software*
Workflow

Grants and funding

GRK 1906/1/german-canadian dfg international research training group 'computational methods for the analysis of the diversity and dynamics of genomes' (didy)