CORDIS provides links to public deliverables and publications of HORIZON projects.
Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .
Deliverables
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 (opens in new window)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 (opens in new window)Updated Data Processing Virtual Machine Image 2
D9.5.1 Updated Preprocess Virtual Machine Image 1 (opens in new window)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 (opens in new window)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 (opens in new window)Reference implementation guidelines and validation rules
D3.4.2 Two training workshops on omics data deposition, grid processing, dissemination and access (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)Modularized ISA model and format: biospecimen centric schema, corresponding xml schemas, reference implementation guidelines and validation rules
Report API access to PhenoMeNal Resources
D4.2 Report describing the activity and output of working groups (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)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 (opens in new window)Report on policies and procedures for sensitive human data management
D4.3 Consensus agreement document from the working groups (opens in new window)Consensus agreement document from the working groups
Updated Data Management Plan
D1.5.2 Updated Data Management Plan (H2020 Open Research Data Pilot) (opens in new window)Updated Data Management Plan
D1.5.1 Data Management Plan (H2020 Open Research Data Pilot) (opens in new window)Develop, and update, the Data Management Plan
"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" (opens in new window)"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 (opens in new window)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 (opens in new window)PhenoMeNal VRC (static) portal publicly available
D6.5 Training and online tutorial for the general use of the PhenoMeNal (opens in new window)Training and online tutorial for the general use of the PhenoMeNal
Publications
Author(s):
Mark D. Wilkinson, Susanna-Assunta Sansone, Erik Schultes, Peter Doorn, Luiz Olavo Bonino da Silva Santos, Michel Dumontier
Published in:
Scientific Data, Issue 5, 2018, Page(s) 180118, ISSN 2052-4463
Publisher:
Nature Scientific Data
DOI:
10.1038/sdata.2018.118
Author(s):
Antonio Rosato, Leonardo Tenori, Marta Cascante, Pedro Ramon De Atauri Carulla, Vitor A. P. Martins dos Santos, Edoardo Saccenti
Published in:
Metabolomics, Issue 14/4, 2018, ISSN 1573-3882
Publisher:
Springer Verlag
DOI:
10.1007/s11306-018-1335-y
Author(s):
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
Published in:
F1000Research, 6, Issue 8, 2017, ISSN 2046-1402
Publisher:
F1000 Research Ltd.
DOI:
10.17863/CAM.17780
Author(s):
Lifeng Ye, Maria De Iorio, Timothy M. D. Ebbels
Published in:
Metabolomics, Issue 14/5, 2018, ISSN 1573-3882
Publisher:
Springer Verlag
DOI:
10.1007/s11306-018-1351-y
Author(s):
Rico Rueedi, Roger Mallol, Johannes Raffler, David Lamparter, Nele Friedrich, Peter Vollenweider, Gérard Waeber, Gabi Kastenmüller, Zoltán Kutalik, Sven Bergmann
Published in:
PLOS Computational Biology, Issue 13/12, 2017, Page(s) e1005839, ISSN 1553-7358
Publisher:
PLOS
DOI:
10.1371/journal.pcbi.1005839
Author(s):
Maxime Chazalviel, Clément Frainay, Nathalie Poupin, Florence Vinson, Benjamin Merlet, Yoann Gloaguen, Ludovic Cottret, Fabien Jourdan
Published in:
Bioinformatics, Issue 34/2, 2017, Page(s) 312-313, ISSN 1367-4803
Publisher:
Oxford University Press
DOI:
10.1093/bioinformatics/btx588
Author(s):
Linda S. L. Tan, Ajay Jasra, Maria De Iorio, Timothy M. D. Ebbels
Published in:
The Annals of Applied Statistics, Issue 11/4, 2017, Page(s) 2222-2251, ISSN 1932-6157
Publisher:
Institute of Mathematical Statistics
DOI:
10.1214/17-AOAS1076
Author(s):
Panteleimon G. Takis, Hartmut Schäfer, Manfred Spraul, Claudio Luchinat
Published in:
Nature Communications, Issue 8/1, 2017, ISSN 2041-1723
Publisher:
Nature Publishing Group
DOI:
10.1038/s41467-017-01587-0
Author(s):
Stephanie Herman, Payam Emami Khoonsari, Obaid Aftab, Shibu Krishnan, Emil Strömbom, Rolf Larsson, Ulf Hammerling, Ola Spjuth, Kim Kultima, Mats Gustafsson
Published in:
Metabolomics, Issue 13/7, 2017, ISSN 1573-3882
Publisher:
Springer Verlag
DOI:
10.1007/s11306-017-1213-z
Author(s):
Spicer, Rachel; Salek, RM; Moreno, P; Cañueto, C; Steinbeck, C
Published in:
Metabolomics, Issue 5, 2017, ISSN 1573-3882
Publisher:
Springer Verlag
DOI:
10.17863/CAM.13427
Author(s):
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
Published in:
Scientific Reports, Issue 8/1, 2018, ISSN 2045-2322
Publisher:
Nature Publishing Group
DOI:
10.1038/s41598-018-20834-y
Author(s):
Nils ePaulhe; Benjamin eMerlet; Yoann eGloaguen; Clément eFrainay; Nathalie ePoupin; Fabien eJourdan; Maxime eChazalviel; Florence eVinson; Franck eGiacomoni
Published in:
Frontiers in Molecular Biosciences, Vol 3 (2016), Issue 3, 2016, ISSN 2296-889X
Publisher:
Holtzbrinck Publishing Group
DOI:
10.3389/fmolb.2016.00002
Author(s):
Edoardo Saccenti, Giulia Menichetti, Veronica Ghini, Daniel Remondini, Leonardo Tenori, Claudio Luchinat
Published in:
Journal of Proteome Research, Issue 15/9, 2016, Page(s) 3298-3307, ISSN 1535-3893
Publisher:
American Chemical Society
DOI:
10.1021/acs.jproteome.6b00454
Author(s):
Kenneth Haug, Reza M Salek, Christoph Steinbeck
Published in:
Current Opinion in Chemical Biology, Issue 36, 2017, Page(s) 58-63, ISSN 1367-5931
Publisher:
Elsevier BV
DOI:
10.1016/j.cbpa.2016.12.024
Author(s):
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
Published in:
Analytical Chemistry, Issue 88/10, 2016, Page(s) 5179-5188, ISSN 0003-2700
Publisher:
American Chemical Society
DOI:
10.1021/acs.analchem.6b00188
Author(s):
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
Published in:
Metabolomics, Issue 12/1, 2016, ISSN 1573-3882
Publisher:
Springer Verlag
DOI:
10.1007/s11306-015-0879-3
Author(s):
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
Published in:
Journal of Proteome Research, Issue 15/12, 2016, Page(s) 4188-4194, ISSN 1535-3893
Publisher:
American Chemical Society
DOI:
10.1021/acs.jproteome.6b00125
Author(s):
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
Published in:
BMC Bioinformatics, Issue 18/1, 2017, ISSN 1471-2105
Publisher:
BioMed Central
DOI:
10.1186/s12859-017-1513-3
Author(s):
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
Published in:
PLOS ONE, Issue 11/4, 2016, Page(s) e0154556, ISSN 1932-6203
Publisher:
Public Library of Science
DOI:
10.1371/journal.pone.0154556
Author(s):
Stefano Cacciatore, Leonardo Tenori, Claudio Luchinat, Phillip R. Bennett, David A. MacIntyre
Published in:
Bioinformatics, 2016, Page(s) btw705, ISSN 1367-4803
Publisher:
Oxford University Press
DOI:
10.1093/bioinformatics/btw705
Author(s):
Panteleimon G. Takis, Leonardo Tenori, Enrico Ravera, Claudio Luchinat
Published in:
Analytical Chemistry, Issue 89/2, 2017, Page(s) 1054-1058, ISSN 0003-2700
Publisher:
American Chemical Society
DOI:
10.1021/acs.analchem.6b04318
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