The future of metabolomics in ELIXIR

Rijswijk, Merlijn van; Beirnaert, Charlie; Caron, Christophe; Cascante, Marta; Dominguez, Victoria M.; Dunn, Warwick B.; Ebbels, Timothy M. D.; Giacomoni, Franck; González-Beltrán, Alejandra; Hankemeier, Thomas; Haug, Kenneth; Izquierdo-García, José Luis; Jiménez, Rafael C.; Jourdan, Fabien; Kale, Namrata; Klapa, Maria I.; Kohlbacher, Oliver; Koort, Kairi; Kultima, Kim; Corguillé, Gildas Le; Moschonas, Nicholas Κ.; Neumann, Steffen; O′Donovan, Claire; Reczko, Martin; Rocca-Serra, Philippe; Rosato, Antonio; Salek, Reza M.; Sansone, Susanna-Assunta; Satagopam, Venkata; Schober, Daniel; Shimmo, Ruth; Spicer, Rachel; Spjuth, Ola; Thévenot, Etienne; Viant, Mark R.; Weber, Ralf; Willighagen, Egon; Zanetti, Gianluigi; Steinbeck, Christoph

doi:10.12688/f1000research.12342.1

Cited by 22 publications

(16 citation statements)

References 39 publications

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“…PhenoMeNal is part of a number of initiatives (BioMedBridges, COSMOS, and ELIXIR) to foster the role of metabolomics and to harmonize experimental data and metadata usage [15, 42]. Collaborations were established with EGI [43] and Indigo Datacloud [44] infrastructure providers and initiatives [45, 46] to ensure that PhenoMeNal uses technologies that are well supported and ensure their widespread usage, continuity, and further development.…”

Section: Resultsmentioning

confidence: 99%

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PhenoMeNal: processing and analysis of metabolomics data in the cloud

et al. 2018

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BackgroundMetabolomics 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.FindingsPhenoMeNal (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.ConclusionsPhenoMeNal 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.

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Section: Resultsmentioning

confidence: 99%

“…It is possible to combine data and metadata within an ELSI compliant framework [50] and in such cases users can follow the example of the European Genome Phenome Archive (EGA) [71]. In public installations of PhenoMeNal, the ELIXIR policy on privacy has been implemented within a technically secure environment to process data [42].…”

Section: Methodsmentioning

confidence: 99%

PhenoMeNal: processing and analysis of metabolomics data in the cloud

et al. 2018

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“…ELIXIR, the European infrastructure for biological data (https://elixir-europe.org), has also brought together several communities (e.g., plant sciences, metabolomics) with the common interest of dealing with the increasing complexity of data, ultimately making data easier to find, analyze and share. Challenges currently faced by the metabolomics community, namely (i) minimum information standards and early data capture; (ii) global spectral databases; (iii) tools and standards registries; (iv) compound identifier mapping; (v) omics data integration, and (vi) metabolite identification, have also been reported by ELIXIR in a dedicated workshop [105]. However, despite these initiatives, the compliance with these reporting standards still varies greatly across public repositories [106], and data and metadata sharing remain a critical issue in metabolomics publications [107,108].…”

Section: The Importance Of Forest Tree Metadata Standardizationmentioning

confidence: 99%

Experimental Design and Sample Preparation in Forest Tree Metabolomics

2019

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Appropriate experimental design and sample preparation are key steps in metabolomics experiments, highly influencing the biological interpretation of the results. The sample preparation workflow for plant metabolomics studies includes several steps before metabolite extraction and analysis. These include the optimization of laboratory procedures, which should be optimized for different plants and tissues. This is particularly the case for trees, whose tissues are complex matrices to work with due to the presence of several interferents, such as oleoresins, cellulose. A good experimental design, tree tissue harvest conditions, and sample preparation are crucial to ensure consistency and reproducibility of the metadata among datasets. In this review, we discuss the main challenges when setting up a forest tree metabolomics experiment for mass spectrometry (MS)-based analysis covering all technical aspects from the biological question formulation and experimental design to sample processing and metabolite extraction and data acquisition. We also highlight the importance of forest tree metadata standardization in metabolomics studies.

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“…Regardless of the reasons for this situation, a revision of the early work of the Metabolomics Standards Initiative seems timely. Minimum information standards and early data capture have also been identified by ELIXIR, the European infrastructure for biological data, as one of the challenges currently faced by the metabolomics community (van Rijswijk et al, 2017). Plant metabolomics can represent a powerful approach when applied to genotype‐phenotype association studies in the breeding of crop and forest species toward adaptation to climate change.…”

Section: Fair Forest Tree Metabolomicsmentioning

confidence: 99%

Mass spectrometry‐based forest tree metabolomics

Rodrigues

Miguel

Chaves

et al. 2019

Mass Spectrometry Reviews

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Research in forest tree species has advanced slowly when compared with other agricultural crops and model organisms, mainly due to the long‐life cycles, large genome sizes, and lack of genomic tools. Additionally, trees are complex matrices, and the presence of interferents (e.g., oleoresins and cellulose) challenges the analysis of tree tissues with mass spectrometry (MS)‐based analytical platforms. In this review, advances in MS‐based forest tree metabolomics are discussed. Given their economic and ecological significance, particular focus is given to Pinus, Quercus, and Eucalyptus forest tree species to better understand their metabolite responses to abiotic and biotic stresses in the current climate change scenario. Furthermore, MS‐based metabolomics technologies produce large and complex datasets that require expertize to adequately manage, process, analyze, and store the data in dedicated repositories. To ensure that the full potential of forest tree metabolomics data are translated into new knowledge, these data should comply with the FAIR principles (i.e., Findable, Accessible, Interoperable, and Re‐usable). It is essential that adequate standards are implemented to annotate metadata from forest tree metabolomics studies as is already required by many science and governmental agencies and some major scientific publishers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev 40:126–157, 2021.

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The future of metabolomics in ELIXIR

Cited by 22 publications

References 39 publications

PhenoMeNal: processing and analysis of metabolomics data in the cloud

PhenoMeNal: processing and analysis of metabolomics data in the cloud

Experimental Design and Sample Preparation in Forest Tree Metabolomics

Mass spectrometry‐based forest tree metabolomics

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