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Your search keyword '"Spjuth, Ola"' showing total 9 results
Start Over Author "Spjuth, Ola" Topic metabolomics
9 results on '"Spjuth, Ola"'

1. Metabolomics: The Stethoscope for the Twenty-First Century.

Author

Ashrafian H, Sounderajah V, Glen R, Ebbels T, Blaise BJ, Kalra D, Kultima K, Spjuth O, Tenori L, Salek RM, Kale N, Haug K, Schober D, Rocca-Serra P, O'Donovan C, Steinbeck C, Cano I, de Atauri P, and Cascante M

Subjects
Humans, Metabolomics, Precision Medicine, Stethoscopes
Abstract

Metabolomics encompasses the systematic identification and quantification of all metabolic products in the human body. This field could provide clinicians with novel sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualized level. This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice, and discuss the translational challenges that the field faces. We searched PubMed, MEDLINE, and EMBASE for primary and secondary research articles regarding clinical applications of metabolomics. Metabolic profiling can be performed using mass spectrometry and nuclear magnetic resonance-based techniques using a variety of biological samples. This is carried out in vivo or in vitro following careful sample collection, preparation, and analysis. The potential clinical applications constitute disruptive innovations in their respective specialities, particularly oncology and metabolic medicine. Outstanding issues currently preventing widespread clinical use are scalability of data interpretation, standardization of sample handling practice, and e-infrastructure. Routine utilization of metabolomics at a patient and population level will constitute an integral part of future healthcare provision., (© 2020 The Author(s) Published by S. Karger AG, Basel.)

Published
2021
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2. Interoperable and scalable data analysis with microservices: applications in metabolomics.

Author

Emami Khoonsari P, Moreno P, Bergmann S, Burman J, Capuccini M, Carone M, Cascante M, de Atauri P, Foguet C, Gonzalez-Beltran AN, Hankemeier T, Haug K, He S, Herman S, Johnson D, Kale N, Larsson A, Neumann S, Peters K, Pireddu L, Rocca-Serra P, Roger P, Rueedi R, Ruttkies C, Sadawi N, Salek RM, Sansone SA, Schober D, Selivanov V, Thévenot EA, van Vliet M, Zanetti G, Steinbeck C, Kultima K, and Spjuth O

Subjects
Computational Biology, Software, Workflow, Data Analysis, Metabolomics
Abstract

Motivation: Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator., Results: We developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science., Availability and Implementation: The PhenoMeNal consortium maintains a web portal (https://portal.phenomenal-h2020.eu) providing a GUI for launching the Virtual Research Environment. The GitHub repository https://github.com/phnmnl/ hosts the source code of all projects., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press.)

Published
2019
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3. PhenoMeNal: processing and analysis of metabolomics data in the cloud.

Author

Peters K, Bradbury J, Bergmann S, Capuccini M, Cascante M, de Atauri P, Ebbels TMD, Foguet C, Glen R, Gonzalez-Beltran A, Günther UL, Handakas E, Hankemeier T, Haug K, Herman S, Holub P, Izzo M, Jacob D, Johnson D, Jourdan F, Kale N, Karaman I, Khalili B, Emami Khonsari P, Kultima K, Lampa S, Larsson A, Ludwig C, Moreno P, Neumann S, Novella JA, O'Donovan C, Pearce JTM, Peluso A, Piras ME, Pireddu L, Reed MAC, Rocca-Serra P, Roger P, Rosato A, Rueedi R, Ruttkies C, Sadawi N, Salek RM, Sansone SA, Selivanov V, Spjuth O, Schober D, Thévenot EA, Tomasoni M, van Rijswijk M, van Vliet M, Viant MR, Weber RJM, Zanetti G, and Steinbeck C

Subjects
Cloud Computing, Humans, Workflow, Metabolomics methods, Software
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., (© The Author(s) 2018. Published by Oxford University Press.)

Published
2019
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6. Integration of magnetic resonance imaging and protein and metabolite CSF measurements to enable early diagnosis of secondary progressive multiple sclerosis

Author

Herman, Stephanie, Emami Khoonsari, Payam, Tolf, Andreas, Steinmetz, Julia, Zetterberg, Henrik, Åkerfeldt, Torbjörn, Jakobsson, Per-Johan, Larsson, Anders, Spjuth, Ola, Burman, Joachim, and Kultima, Kim

Subjects
disease progression, Neurology, Neurologi, biomarker, multiple sclerosis, data integration, metabolomics
Abstract

Molecular networks in neurological diseases are complex. Despite this fact, contemporary biomarkers are in most cases interpreted in isolation, leading to a significant loss of information and power. We present an analytical approach to scrutinize and combine information from biomarkers originating from multiple sources with the aim of discovering a condensed set of biomarkers that in combination could distinguish the progressive degenerative phenotype of multiple sclerosis (SPMS) from the relapsing-remitting phenotype (RRMS). Methods: Clinical and magnetic resonance imaging (MRI) data were integrated with data from protein and metabolite measurements of cerebrospinal fluid, and a method was developed to sift through all the variables to establish a small set of highly informative measurements. This prospective study included 16 SPMS patients, 30 RRMS patients and 10 controls. Protein concentrations were quantitated with multiplexed fluorescent bead-based immunoassays and ELISA. The metabolome was recorded using liquid chromatography-mass spectrometry. Clinical follow-up data of the SPMS patients were used to assess disease progression and development of disability. Results: Eleven variables were in combination able to distinguish SPMS from RRMS patients with high confidence superior to any single measurement. The identified variables consisted of three MRI variables: the size of the spinal cord and the third ventricle and the total number of T1 hypointense lesions; six proteins: galectin-9, monocyte chemoattractant protein-1 (MCP-1), transforming growth factor alpha (TGF-α), tumor necrosis factor alpha (TNF-α), soluble CD40L (sCD40L) and platelet-derived growth factor AA (PDGF-AA); and two metabolites: 20β-dihydrocortisol (20β-DHF) and indolepyruvate. The proteins myelin basic protein (MBP) and macrophage-derived chemokine (MDC), as well as the metabolites 20β-DHF and 5,6-dihydroxyprostaglandin F1a (5,6-DH-PGF1), were identified as potential biomarkers of disability progression. Conclusion: Our study demonstrates, in a limited but well-defined and data-rich cohort, the importance and value of combining multiple biomarkers to aid diagnostics and track disease progression.

Published
2018

7. RDFIO: extending Semantic MediaWiki for interoperable biomedical data management

Author

Lampa, Samuel, Willighagen, Egon, Kohonen, Pekka, King, Ali, Vrandečić, Denny, Grafström, Roland, Spjuth, Ola, Bioinformatica, RS: NUTRIM - R1 - Obesity, diabetes and cardiovascular health, and RS: NUTRIM - R4 - Gene-environment interaction

Subjects
Bioinformatics (Computational Biology), Internet, Wiki, INFORMATION, CHALLENGES, BIOLOGICAL DATABASES, Information Storage and Retrieval, lcsh:Computer applications to medicine. Medical informatics, SPARQL, WEB, RDF, DRUG DISCOVERY, MediaWiki, Wikidata, User-Computer Interface, Rare Diseases, SYSTEMS, Bioinformatik (beräkningsbiologi), lcsh:R858-859.7, Humans, Metabolomics, Semantic MediaWiki, Intersectoral Collaboration, Software, Semantic Web
Abstract

Background: Biological sciences are characterised not only by an increasing amount but also the extreme complexity of its data. This stresses the need for efficient ways of integrating these data in a coherent description of biological systems. In many cases, biological data needs organization before integration. This is not seldom a collaborative effort, and it is thus important that tools for data integration support a collaborative way of working. Wiki systems with support for structured semantic data authoring, such as Semantic MediaWiki, provide a powerful solution for collaborative editing of data combined with machine-readability, so that data can be handled in an automated fashion in any downstream analyses. Semantic MediaWiki lacks a built-in data import function though, which hinders efficient round-tripping of data between interoperable Semantic Web formats such as RDF and the internal wiki format.Results: To solve this deficiency, the RDFIO suite of tools is presented, which supports importing of RDF data into Semantic MediaWiki, with metadata needed to export it again in the same RDF format, or ontology. Additionally, the new functionality enables mash-ups of automated data imports combined with manually created data presentations. The application of the suite of tools is demonstrated by importing drug discovery related data about rare diseases from Orphanet and acid dissociation constants from Wikidata. The RDFIO suite of tools is freely available for download via pharmb.io/project/rdfio.Conclusions: Through a set of biomedical demonstrators, it is demonstrated how the new functionality enables a number of usage scenarios where the interoperability of SMW and the wider Semantic Web is leveraged for biomedical data sets, to create an easy to use and flexible platform for exploring and working with biomedical data.

Published
2017

8. Biochemical Differences in Cerebrospinal Fluid between Secondary Progressive and Relapsing–Remitting Multiple Sclerosis.

Author

Herman, Stephanie, Åkerfeldt, Torbjörn, Spjuth, Ola, Burman, Joachim, and Kultima, Kim

Subjects
CEREBROSPINAL fluid examination, MULTIPLE sclerosis, CEREBROSPINAL fluid, CEREBRAL atrophy, DISCRIMINANT analysis, MASS spectrometry, DISEASE progression
Abstract

To better understand the pathophysiological differences between secondary progressive multiple sclerosis (SPMS) and relapsing-remitting multiple sclerosis (RRMS), and to identify potential biomarkers of disease progression, we applied high-resolution mass spectrometry (HRMS) to investigate the metabolome of cerebrospinal fluid (CSF). The biochemical differences were determined using partial least squares discriminant analysis (PLS-DA) and connected to biochemical pathways as well as associated to clinical and radiological measures. Tryptophan metabolism was significantly altered, with perturbed levels of kynurenate, 5-hydroxytryptophan, 5-hydroxyindoleacetate, and N-acetylserotonin in SPMS patients compared with RRMS and controls. SPMS patients had altered kynurenine compared with RRMS patients, and altered indole-3-acetate compared with controls. Regarding the pyrimidine metabolism, SPMS patients had altered levels of uridine and deoxyuridine compared with RRMS and controls, and altered thymine and glutamine compared with RRMS patients. Metabolites from the pyrimidine metabolism were significantly associated with disability, disease activity and brain atrophy, making them of particular interest for understanding the disease mechanisms and as markers of disease progression. Overall, these findings are of importance for the characterization of the molecular pathogenesis of SPMS and support the hypothesis that the CSF metabolome may be used to explore changes that occur in the transition between the RRMS and SPMS pathologies. [ABSTRACT FROM AUTHOR]

Published
2019
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9. The future of metabolomics in ELIXIR

Author

van Rijswijk, M, Beirnaert, C, Caron, C, Cascante, M, Dominguez, V, Dunn, WB, Ebbels, TMD, Giacomoni, F, Gonzalez-Beltran, A, Hankemeier, T, Haug, K, Izquierdo-Garcia, JL, Jimenez, RC, Jourdan, F, Kale, N, Klapa, MI, Kohlbacher, O, Koort, K, Kultima, K, Le Corguillé, G, Moreno, P, Moschonas, NK, Neumann, S, O'Donovan, C, Reczko, M, Rocca-Serra, P, Rosato, A, Salek, RM, Sansone, S-A, Satagopam, V, Schober, D, Shimmo, R, Spicer, RA, Spjuth, O, Thévenot, EA, Viant, MR, Weber, RJM, Willighagen, EL, Zanetti, G, Steinbeck, C, Dutch Techcentre for Life Sciences [Utrecht], Netherlands Metabolomics Centre, Department of Mathematics and Computer Science, ADReM, University of Antwerp (UA), French Institute of Bioinformatics (ELIXIR-FR), Department of Biochemistry and Molecular Biomedicine, Faculty of Science and Technology, Biochemistry and Molecular Biology, University of Barcelona, Birmingham Metabolomics Training Centre, University of Birmingham, Department of Surgery and Cancer, Imperial College London, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), MetaboHUB, Oxford e-Research Centre, University of Oxford, Leiden Academic Centre for Drug Research (LACDR), Universiteit Leiden, European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Centro Nacional de Investigaciones Cardiovasculares, Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), ELIXIR Hub [Cambridge], Métabolisme et Xénobiotiques (ToxAlim-MeX), ToxAlim (ToxAlim), Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT), Forth ICE-HT, Institute of Chemical Engineering Sciences, Foundation for Research and Technology - Hellas (FORTH), Max Planck Institute for Developmental Biology, Max-Planck-Gesellschaft, Department of Computer Science, Duke University [Durham], Center for Bioinformatics, University of Tübingen, The Centre of Excellence in Neural and Behavioural Sciences, Tallinn University, School of Natural Sciences and Health, Department of Medical Sciences (University of Miyasaki), University of Miyasaki, ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (ABIMS), Fédération de recherche de Roscoff (FR2424), Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC), Centre National de la Recherche Scientifique (CNRS), Department of General Biology, Universidade Federal de Minas Gerais [Belo Horizonte] (UFMG), University of Patras, Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry (IPB), BSRC 'Alexander Fleming', Magnetic Resonance Center/Interuniversity Consortium of Magnetic Resonance Metalloproteins, Università degli Studi di Firenze = University of Florence (UniFI), Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg [Luxembourg], Université du Luxembourg (Uni.lu), Department of Pharmaceutical Biosciences, Uppsala University, Laboratoire Sciences des Données et de la Décision (LS2D), Département Métrologie Instrumentation & Information (DM2I), Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Department of Bioinformatics, BiGCaT, Maastricht University [Maastricht], NUTRIM, Data-Intensive Computing, CRS4 Bioinformat, Friedrich-Schiller-Universität = Friedrich Schiller University Jena [Jena, Germany], ANR-11-INBS-0010,METABOHUB,Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation(2011), European Project: 654241,H2020,H2020-EINFRA-2014-2,PhenoMeNal(2015), European Commission, Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Leiden University, Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (FR2424), Université Pierre et Marie Curie (Paris 6), Federal University of Minas Gerais Belo Horizonte, University of Patras [Patras], University of Florence (UNIFI), Laboratoire d'analyse des données et d'intelligence des systèmes (LADIS), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Université Paris-Saclay-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Maastricht University, Friedrich-Schiller-Universität Jena, ANR-11-INBS-0010/11-INBS-0010,METABOHUB,Développement d’une infrastructure française distribuée pour la métabolomique dédiée à l’innovation(2011), Commission of the European Communities, European Molecular Biology Laboratory, Apollo-University Of Cambridge Repository, van Rijswijk, Merlijn [0000-0002-1067-7766], Beirnaert, Charlie [0000-0003-3007-2569], Gonzalez-Beltran, Alejandra [0000-0003-3499-8262], Haug, Kenneth [0000-0003-3168-4145], Jimenez, Rafael C [0000-0001-5404-7670], Kale, Namrata [0000-0002-4255-8104], Klapa, Maria I [0000-0002-2047-3185], Moschonas, Nicholas K [0000-0002-2556-537X], Rosato, Antonio [0000-0001-6172-0368], Salek, Reza M [0000-0001-8604-1732], Sansone, Susanna-Assunta [0000-0001-5306-5690], Schober, Daniel [0000-0001-8014-6648], Spicer, Rachel A [0000-0002-2807-8796], Spjuth, Ola [0000-0002-8083-2864], Thévenot, Etienne A [0000-0003-1019-4577], Willighagen, Egon L [0000-0001-7542-0286], Steinbeck, Christoph [0000-0001-6966-0814], Apollo - University of Cambridge Repository, RS: NUTRIM - R1 - Obesity, diabetes and cardiovascular health, Bioinformatica, RS: NUTRIM - R4 - Gene-environment interaction, University of Oxford [Oxford], Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Università degli Studi di Firenze = University of Florence [Firenze], Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées

Subjects
0301 basic medicine, databases, Data management, computational workflows, infrastructure en ligne, Cloud computing, Computational biology, Biology, General Biochemistry, Genetics and Molecular Biology, Chemical Biology of the Cell, analyse métabolomique, 03 medical and health sciences, statistical analysis, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], [INFO.INFO-DL]Computer Science [cs]/Digital Libraries [cs.DL], Use case, General Pharmacology, Toxicology and Pharmaceutics, signal processing, computer.programming_language, bioinformatics infrastructure, training, 030102 biochemistry & molecular biology, General Immunology and Microbiology, metabolomics, bioinformatics, distributed computing, cloud, business.industry, cloud computing, pipeline, Articles, General Medicine, Opinion Article, Data science, metabolomics, Open data, Identification (information), 030104 developmental biology, Workflow, multi-omics approaches, Elixir (programming language), [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, data standards, computer, Omics technologies
Abstract

We are grateful to the proteomics community for sharing their experiences from their meeting “The future of proteomics in ELIXIR” on March 1–2 2017 in Tübingen, allowing us to build on this and organise our workshop as a one-day event.The meeting was funded by PhenoMeNal, European Commission's Horizon2020 programme, grant agreement number 654241; Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the "Future of metabolomics in ELIXIR" was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases

Published
2017
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