PhenoMeNal: A comprehensive and standardised e-infrastructure for analysing medical metabolic phenotype data

Información del proyecto

PhenoMeNal

Identificador del acuerdo de subvención: 654241

DOI

10.3030/654241

Proyecto cerrado

Fecha de la firma de la CE 27 Abril 2015

Fecha de inicio 1 Septiembre 2015

Fecha de finalización 31 Agosto 2018

Financiado con arreglo a

• EXCELLENT SCIENCE - Research Infrastructures

Coste total

€ 8 005 909,75

Aportación de la UE

€ 7 684 919,75

Coordinado por

EUROPEAN MOLECULAR BIOLOGY LABORATORY
Germany

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

Other (25)

D5.1 Build System with continuous integration, providing development snapshots of PhenoMeNal Virtual Machine Images

Build System with continuous integration, providing development snapshots of PhenoMeNal Virtual Machine Images

D7.1.1 Workshop 1 on best practices in handling sensitive human data, taking into account National and Institutional legal policies

Workshop 1 on best practices in handling sensitive human data, taking into account National and Institutional legal policies

D9.5.2 Updated Data Processing Virtual Machine Image 2

Updated Data Processing Virtual Machine Image 2

D9.5.1 Updated Preprocess Virtual Machine Image 1

Updated Preprocess Virtual Machine Image 1

D7.1.2 Workshop 2 on best practices in handling sensitive human data, taking into account National and Institutional legal policies

Workshop 2 on best practices in handling sensitive human data, taking into account National and Institutional legal policies

D8.4.2 Reference implementation guidelines and validation rules

Reference implementation guidelines and validation rules

D3.4.2 Two training workshops on omics data deposition, grid processing, dissemination and access

Two training workshops on omics data deposition, grid processing, dissemination and access.

D5.3 Operational grid/cloud allowing for combining data, tools, and compute VMIs. Most services available. Functional integration with EGI federated cloud/grid for compute resources. Demonstrated analysis on private/sensitive data in secure environment

Operational grid/cloud allowing for combining data, tools, and compute VMIs. Most services available. Functional integration with EGI federated cloud/grid for compute resources. Demonstrated analysis on private/sensitive data in secure environment

D9.2.1 PhenoMeNal-Preprocess Virtual Machine Image 1 to enable data producers to locally process raw data into standard formats supported in PhenoMeNal

PhenoMeNal-Preprocess Virtual Machine Image 1 to enable data producers to locally process raw data into standard formats supported in PhenoMeNal

D5.2 A beta-version of PhenoMeNal integration VMI capable of proof- of-concept integration with other VMIs. Initial services online supporting PhenoMeNal data standards

A beta-version of PhenoMeNal integration VMI capable of proof- of-concept integration with other VMIs. Initial services online supporting PhenoMeNal data standards

D6.4 Participating Biobanks and repositories connected to the VRC

Participating Biobanks and repositories connected to the VRC

D9.2.2 PhenoMeNal-Data Virtual Machine Image 2 to enable sharing and dissemination of standardised and processed omics data to participating online repositories, like MetaboLights

PhenoMeNal-Data Virtual Machine Image 2 to enable sharing and dissemination of standardised and processed omics data to participating online repositories, like MetaboLights

D8.4 Signal processing and analysis data exchange format

Signal processing and analysis data exchange format

D8.4.1 Specifications for derived data matrices specifications and terminology for description of analysis and statistical results

Specifications for derived data matrices specifications and terminology for description of analysis and statistical results

D8.3 nmrML, mzML data exchange formats and associated terminologies for instrument raw, with reference implementation guidelines and validation rules

nmrML, mzML data exchange formats and associated terminologies for instrument raw, with reference implementation guidelines and validation rules

D9.5.5 Updated Portal Virtual Machine Image 5

Updated Portal Virtual Machine Image 5

D9.2.4 Compute Virtual Machine Image 4 to enable standardised compute capabilities for all the grid supplying partners

Compute Virtual Machine Image 4 to enable standardised compute capabilities for all the grid supplying partners

D3.4.1 Two training workshops on omics data deposition, grid processing, dissemination and access

Two training workshops on omics data deposition, grid processing, dissemination and access.

D9.2.3 Services Virtual Machine Image 3 to facilitate the PhenoMeNal toolsets and pipelines, both locally and in the grid

Services Virtual Machine Image 3 to facilitate the PhenoMeNal toolsets and pipelines, both locally and in the grid

D9.5.4 Updated Compute Virtual Machine Image 4

Updated Compute Virtual Machine Image 4

D5.4 A federated cloud/grid system running on partners’ infrastructures for public data and tools. All services available. Operational installation at ICL clinical site for decision support

A federated cloud/grid system running on partners’ infrastructures for public data and tools. All services available. Operational installation at ICL clinical site for decision support

D9.2.5 Portal Virtual Machine Image 5 that is capable of integrating other PhenoMeNal-VMIs (in local federated clouds) and make all functionality available via command-line, Web-APIs, and graphical user interfaces

Portal Virtual Machine Image 5 that is capable of integrating other PhenoMeNal-VMIs (in local federated clouds) and make all functionality available via command-line, Web-APIs, and graphical user interfaces

D9.5.3 Updated Services Virtual Machine Image 3

Updated Services Virtual Machine Image 3

D7.4 Process to extract maximum information from sensitive datasets with minimum compromise, in collaboration with BBMRI and BioMedBridges

Workflows to extract maximum information from sensitive datasets with minimum compromise, in collaboration with BBMRI and BioMedBridges

D8.2 Modularized ISA model and format: biospecimen centric schema, corresponding xml schemas, reference implementation guidelines and validation rules

Modularized ISA model and format: biospecimen centric schema, corresponding xml schemas, reference implementation guidelines and validation rules

Documents, reports (9)

D9.3 Report API access to PhenoMeNal Resources

Report API access to PhenoMeNal Resources

D4.2 Report describing the activity and output of working groups

Establish and convene working groups involving the PhenoMeNal consortium as well as participants in other biomedical infrastructure and research projects. Report describing the activity and output of working groups.

D4.4 Report on State-of-The-Art and Perspectives in the field

Report on State-of-The-Art and Perspectives in the field

D4.1Report on requirements for relevant research centers producing and/or consuming metabolomics data with respect to computational aspects, data storage, and infrastructural needs

Reporting on requirements expressed/formalised by relevant biomedical infrastructures, both physical and electronic, with regard to data storage, retrieval, exchange, management and analysis.

D8.1 Report on community standards for reporting, access and integrity supported in the PhenoMeNal grid; to be disseminated in a dedicated BioSharing page and via the project website

Report on community standards for reporting, access and integrity supported in the PhenoMeNal grid; to be disseminated in a dedicated BioSharing page and via the project website

D9.4 Updated report on existing software tools, workflows and analytical pipelines supported in PhenoMeNal

Updated report on existing software tools, workflows and analytical pipelines supported in PhenoMeNal

D9.1 Report on existing software tools, workflows and analytical pipelines initially supported in the PhenoMeNal grid

Report on existing software tools, workflows and analytical pipelines initially supported in the PhenoMeNal grid

D7.2 Report on policies and procedures for sensitive human data management

Report on policies and procedures for sensitive human data management

D4.3 Consensus agreement document from the working groups

Consensus agreement document from the working groups

Open Research Data Pilot (3)

D1.5.3 Updated Data Management Plan (H2020 Open Research Data Pilot)

Updated Data Management Plan

D1.5.2 Updated Data Management Plan (H2020 Open Research Data Pilot)

Updated Data Management Plan

D1.5.1 Data Management Plan (H2020 Open Research Data Pilot)

Develop, and update, the Data Management Plan

Websites, patent fillings, videos etc. (5)

"D3.3.2 Web-based Tutorial release 2 about ""Metabolomics Data Deposition and Analysis through PhenoMeNal”, in the form of video clips"

"Web-based Tutorial release 2 about ""Metabolomics Data Deposition and Analysis through PhenoMeNal”, in the form of video clips"

"D3.3.1 Web-based Tutorial release 1 about ""Metabolomics Data Deposition and Analysis through PhenoMeNal”, in the form of video clips"

"Web-based Tutorial release 1 about ""Metabolomics Data Deposition and Analysis through PhenoMeNal”, in the form of video clips"

D6.3 Online user feedback form

An Online feedback form will be available for user requests and initiate direct communication with interested parties.

D6.2 PhenoMeNal VRC (static) portal publicly available

PhenoMeNal VRC (static) portal publicly available

D6.5 Training and online tutorial for the general use of the PhenoMeNal

Training and online tutorial for the general use of the PhenoMeNal

Publicaciones

Peer reviewed articles (21)

A design framework and exemplar metrics for FAIRness

Autores: Mark D. Wilkinson, Susanna-Assunta Sansone, Erik Schultes, Peter Doorn, Luiz Olavo Bonino da Silva Santos, Michel Dumontier
Publicado en: Scientific Data, Edición 5, 2018, Página(s) 180118, ISSN 2052-4463
Editor: Nature Scientific Data
DOI: 10.1038/sdata.2018.118

From correlation to causation: analysis of metabolomics data using systems biology approaches

Autores: Antonio Rosato, Leonardo Tenori, Marta Cascante, Pedro Ramon De Atauri Carulla, Vitor A. P. Martins dos Santos, Edoardo Saccenti
Publicado en: Metabolomics, Edición 14/4, 2018, ISSN 1573-3882
Editor: Springer Verlag
DOI: 10.1007/s11306-018-1335-y

The future of metabolomics in ELIXIR

Autores: Van Rijswijk, Merlijn; Beirnaert, Charlie; Caron, Christophe; Cascante, Marta; Dominguez, Victoria; Dunn, Warwick B.; Ebbels, Timothy M. D.; Giacomoni, Franck; Gonzalez-beltran, Alejandra; Hankemeier, Thomas; Haug, Kenneth; Izquierdo-garcia, Jose L.; Jimenez, Rafael C.; Jourdan, Fabien; Kale, Namrata; Klapa, Maria I.; Kohlbacher, Oliver; Koort, Kairi; Kultima, Kim; Le Corguillé, Gildas; Moreno, P
Publicado en: F1000Research, 6, Edición 8, 2017, ISSN 2046-1402
Editor: F1000 Research Ltd.
DOI: 10.17863/CAM.17780

Bayesian estimation of the number of protonation sites for urinary metabolites from NMR spectroscopic data

Autores: Lifeng Ye, Maria De Iorio, Timothy M. D. Ebbels
Publicado en: Metabolomics, Edición 14/5, 2018, ISSN 1573-3882
Editor: Springer Verlag
DOI: 10.1007/s11306-018-1351-y

Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy

Autores: Rico Rueedi, Roger Mallol, Johannes Raffler, David Lamparter, Nele Friedrich, Peter Vollenweider, Gérard Waeber, Gabi Kastenmüller, Zoltán Kutalik, Sven Bergmann
Publicado en: PLOS Computational Biology, Edición 13/12, 2017, Página(s) e1005839, ISSN 1553-7358
Editor: PLOS
DOI: 10.1371/journal.pcbi.1005839

MetExploreViz: web component for interactive metabolic network visualization

Autores: Maxime Chazalviel, Clément Frainay, Nathalie Poupin, Florence Vinson, Benjamin Merlet, Yoann Gloaguen, Ludovic Cottret, Fabien Jourdan
Publicado en: Bioinformatics, Edición 34/2, 2017, Página(s) 312-313, ISSN 1367-4803
Editor: Oxford University Press
DOI: 10.1093/bioinformatics/btx588

Bayesian inference for multiple Gaussian graphical models with application to metabolic association networks

Autores: Linda S. L. Tan, Ajay Jasra, Maria De Iorio, Timothy M. D. Ebbels
Publicado en: The Annals of Applied Statistics, Edición 11/4, 2017, Página(s) 2222-2251, ISSN 1932-6157
Editor: Institute of Mathematical Statistics
DOI: 10.1214/17-AOAS1076

Deconvoluting interrelationships between concentrations and chemical shifts in urine provides a powerful analysis tool

Autores: Panteleimon G. Takis, Hartmut Schäfer, Manfred Spraul, Claudio Luchinat
Publicado en: Nature Communications, Edición 8/1, 2017, ISSN 2041-1723
Editor: Nature Publishing Group
DOI: 10.1038/s41467-017-01587-0

Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions

Autores: Stephanie Herman, Payam Emami Khoonsari, Obaid Aftab, Shibu Krishnan, Emil Strömbom, Rolf Larsson, Ulf Hammerling, Ola Spjuth, Kim Kultima, Mats Gustafsson
Publicado en: Metabolomics, Edición 13/7, 2017, ISSN 1573-3882
Editor: Springer Verlag
DOI: 10.1007/s11306-017-1213-z

Navigating freely-available software tools for metabolomics analysis

Autores: Spicer, Rachel; Salek, RM; Moreno, P; Cañueto, C; Steinbeck, C
Publicado en: Metabolomics, Edición 5, 2017, ISSN 1573-3882
Editor: Springer Verlag
DOI: 10.17863/CAM.13427

Plasma and urinary metabolomic profiles of Down syndrome correlate with alteration of mitochondrial metabolism

Autores: Maria Caracausi, Veronica Ghini, Chiara Locatelli, Martina Mericio, Allison Piovesan, Francesca Antonaros, Maria Chiara Pelleri, Lorenza Vitale, Rosa Anna Vacca, Federica Bedetti, Maria Chiara Mimmi, Claudio Luchinat, Paola Turano, Pierluigi Strippoli, Guido Cocchi
Publicado en: Scientific Reports, Edición 8/1, 2018, ISSN 2045-2322
Editor: Nature Publishing Group
DOI: 10.1038/s41598-018-20834-y

A computational solution to automatically map metabolite libraries in the context of genome scale metabolic networks

Autores: Nils ePaulhe; Benjamin eMerlet; Yoann eGloaguen; Clément eFrainay; Nathalie ePoupin; Fabien eJourdan; Maxime eChazalviel; Florence eVinson; Franck eGiacomoni
Publicado en: Frontiers in Molecular Biosciences, Vol 3 (2016), Edición 3, 2016, ISSN 2296-889X
Editor: Holtzbrinck Publishing Group
DOI: 10.3389/fmolb.2016.00002

Entropy-Based Network Representation of the Individual Metabolic Phenotype

Autores: Edoardo Saccenti, Giulia Menichetti, Veronica Ghini, Daniel Remondini, Leonardo Tenori, Claudio Luchinat
Publicado en: Journal of Proteome Research, Edición 15/9, 2016, Página(s) 3298-3307, ISSN 1535-3893
Editor: American Chemical Society
DOI: 10.1021/acs.jproteome.6b00454

Global open data management in metabolomics

Autores: Kenneth Haug, Reza M Salek, Christoph Steinbeck
Publicado en: Current Opinion in Chemical Biology, Edición 36, 2017, Página(s) 58-63, ISSN 1367-5931
Editor: Elsevier BV
DOI: 10.1016/j.cbpa.2016.12.024

Power Analysis and Sample Size Determination in Metabolic Phenotyping

Autores: Benjamin J. Blaise, Gonçalo Correia, Adrienne Tin, J. Hunter Young, Anne-Claire Vergnaud, Matthew Lewis, Jake T. M. Pearce, Paul Elliott, Jeremy K. Nicholson, Elaine Holmes, Timothy M. D. Ebbels
Publicado en: Analytical Chemistry, Edición 88/10, 2016, Página(s) 5179-5188, ISSN 0003-2700
Editor: American Chemical Society
DOI: 10.1021/acs.analchem.6b00188

Data standards can boost metabolomics research, and if there is a will, there is a way

Autores: Philippe Rocca-Serra, Reza M. Salek, Masanori Arita, Elon Correa, Saravanan Dayalan, Alejandra Gonzalez-Beltran, Tim Ebbels, Royston Goodacre, Janna Hastings, Kenneth Haug, Albert Koulman, Macha Nikolski, Matej Oresic, Susanna-Assunta Sansone, Daniel Schober, James Smith, Christoph Steinbeck, Mark R. Viant, Steffen Neumann
Publicado en: Metabolomics, Edición 12/1, 2016, ISSN 1573-3882
Editor: Springer Verlag
DOI: 10.1007/s11306-015-0879-3

Workflow for Integrated Processing of Multicohort Untargeted 1 H NMR Metabolomics Data in Large-Scale Metabolic Epidemiology

Autores: Ibrahim Karaman, Diana L. S. Ferreira, Claire L. Boulangé, Manuja R. Kaluarachchi, David Herrington, Anthony C. Dona, Raphaële Castagné, Alireza Moayyeri, Benjamin Lehne, Marie Loh, Paul S. de Vries, Abbas Dehghan, Oscar H. Franco, Albert Hofman, Evangelos Evangelou, Ioanna Tzoulaki, Paul Elliott, John C. Lindon, Timothy M. D. Ebbels
Publicado en: Journal of Proteome Research, Edición 15/12, 2016, Página(s) 4188-4194, ISSN 1535-3893
Editor: American Chemical Society
DOI: 10.1021/acs.jproteome.6b00125

MIDcor, an R-program for deciphering mass interferences in mass spectra of metabolites enriched in stable isotopes

Autores: Vitaly A. Selivanov, Adrián Benito, Anibal Miranda, Esther Aguilar, Ibrahim Halil Polat, Josep J. Centelles, Anusha Jayaraman, Paul W. N. Lee, Silvia Marin, Marta Cascante
Publicado en: BMC Bioinformatics, Edición 18/1, 2017, ISSN 1471-2105
Editor: BioMed Central
DOI: 10.1186/s12859-017-1513-3

The Ontology for Biomedical Investigations

Autores: Anita Bandrowski, Ryan Brinkman, Mathias Brochhausen, Matthew H. Brush, Bill Bug, Marcus C. Chibucos, Kevin Clancy, Mélanie Courtot, Dirk Derom, Michel Dumontier, Liju Fan, Jennifer Fostel, Gilberto Fragoso, Frank Gibson, Alejandra Gonzalez-Beltran, Melissa A. Haendel, Yongqun He, Mervi Heiskanen, Tina Hernandez-Boussard, Mark Jensen, Yu Lin, Allyson L. Lister, Phillip Lord, James Malone, Elisabe
Publicado en: PLOS ONE, Edición 11/4, 2016, Página(s) e0154556, ISSN 1932-6203
Editor: Public Library of Science
DOI: 10.1371/journal.pone.0154556

KODAMA: an R package for knowledge discovery and data mining

Autores: Stefano Cacciatore, Leonardo Tenori, Claudio Luchinat, Phillip R. Bennett, David A. MacIntyre
Publicado en: Bioinformatics, 2016, Página(s) btw705, ISSN 1367-4803
Editor: Oxford University Press
DOI: 10.1093/bioinformatics/btw705

Gelified Biofluids for High-Resolution Magic Angle Spinning 1 H NMR Analysis: The Case of Urine

Autores: Panteleimon G. Takis, Leonardo Tenori, Enrico Ravera, Claudio Luchinat
Publicado en: Analytical Chemistry, Edición 89/2, 2017, Página(s) 1054-1058, ISSN 0003-2700
Editor: American Chemical Society
DOI: 10.1021/acs.analchem.6b04318