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PhenoMeNal: processing and analysis of metabolomics data in the cloud

Kristian Peters 1 James Bradbury 2 Sven Bergmann 3, 4 Marco Capuccini 5, 6 Marta Cascante 7, 8 Pedro de Atauri 7, 8 Timothy M. D. Ebbels 9 Carles Foguet 7, 8 Robert Glen 9, 10 Alejandra Gonzalez-Beltran 11 Ulrich L. Günther 12 Evangelos Handakas 9 Thomas Hankemeier 13 Kenneth Haug 14 Stephanie Herman 6, 15 Petr Holub 16 Massimiliano Izzo 11 Daniel Jacob 17 David Johnson 11, 18 Fabien Jourdan 19 Namrata Kale 14 Ibrahim Karaman 20 Bita Khalili 3, 4 Payam Emami Khonsari 15 Kim Kultima 15 Samuel Lampa 6 Anders Larsson 6, 5 Christian Ludwig Pablo Moreno 14 Steffen Neumann 21, 22 Jon Ander Novella 6, 5 Claire O'Donovan 14 Jake T. M. Pearce 9 Alina Peluso 9 Marco Enrico Piras 23 Luca Pireddu 23 Michelle A. C. Reed 12 Philippe Rocca-Serra 11 Pierrick Roger 24 Antonio Rosato 25, 26, 27 Rico Rueedi 3, 4 Christoph Ruttkies 22 Noureddin Sadawi 9, 28 Reza M. Salek 14 Susanna-Assunta Sansone 11 Vitaly Selivanov 7, 8 Ola Spjuth 6 Daniel Schober 22 Etienne A. Thevenot 29 Mattia Tomasoni 3, 4 Merlijn van Rijswijk 30, 31 Michael van Vliet 13 Mark R. Viant 2, 12 Ralf J. M. Weber 2, 12 Gianluigi Zanetti 23 Christoph Steinbeck 32
1 Department of Stress and Developmental Biology
2 School of Biosciences
3 UNIL - Université de Lausanne
4 SIB - Swiss Institute of Bioinformatics [Lausanne]
5 Uppsala University
6 Department of Pharmaceutical Biosciences
7 Department of Biochemistry and Molecular Biomedicine
8 ISC - Instituto de Salud Carlos III [Madrid]
9 Department of Surgery & Cancer
10 CAM - University of Cambridge [UK]
11 Department of Engineering Science
12 University of Birmingham
13 Leiden University
14 European Molecular Biology Laboratory European Bioinformatics Institute
15 Department of Medical Sciences, Clinical Chemistry
16 BBMRI-ERIC
17 BFP - Biologie du fruit et pathologie
18 Department of Informatics and Media
19 ToxAlim - ToxAlim
20 School of Public Health - Department of Epidemiology and Biostatistics
21 iDiv - German Centre for Integrative Biodiversity Research
22 IPB - Leibniz Institute of Plant Biochemistry
23 CRS4 Bioinformat
24 CEA, LIST, Lab Data Anal & Syst Intelligence, MetaboHUB, F-91191 Gif Sur Yvette, France
25 Univ Florence, Magnet Resonance Ctr CERM, I-50019 Florence, Italy
26 CIRMMP, I-50019 Florence, Italy
27 Univ Florence, Dept Chem, I-50019 Florence, Italy
28 Brunel University London [Uxbridge]
29 CEA - Commissariat à l'énergie atomique et aux énergies alternatives
30 Netherlands Metabolomics Centre
31 Life Sciences
32 Institute of Analytical Chemistry
Abstract : Background Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. Findings PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. Conclusions PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and omics research domains.
Mots-clés : metabolomics e-infrastructures galaxy statistics
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Kristian Peters, James Bradbury, Sven Bergmann, Marco Capuccini, Marta Cascante, et al.. PhenoMeNal: processing and analysis of metabolomics data in the cloud. GigaScience, BioMed Central, 2019, 8 (2), 12 p. ⟨10.1093/gigascience/giy149⟩. ⟨hal-02627233⟩

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