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Your search keyword '"Jordi Rambla"' showing total 24 results
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24 results on '"Jordi Rambla"'

2. Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects

Author

Haridimos Kondylakis, Varvara Kalokyri, Stelios Sfakianakis, Kostas Marias, Manolis Tsiknakis, Ana Jimenez-Pastor, Eduardo Camacho-Ramos, Ignacio Blanquer, J. Damian Segrelles, Sergio López-Huguet, Caroline Barelle, Magdalena Kogut-Czarkowska, Gianna Tsakou, Nikolaos Siopis, Zisis Sakellariou, Paschalis Bizopoulos, Vicky Drossou, Antonios Lalas, Konstantinos Votis, Pedro Mallol, Luis Marti-Bonmati, Leonor Cerdá Alberich, Karine Seymour, Samuel Boucher, Esther Ciarrocchi, Lauren Fromont, Jordi Rambla, Alexander Harms, Andrea Gutierrez, Martijn P. A. Starmans, Fred Prior, Josep Ll. Gelpi, and Karim Lekadir

Subjects
Artificial intelligence, Data anonymization, Data management, Diagnostic imaging, Neoplasms, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract

Abstract Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of ‘sick-care’ to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single–institution, size-limited, and annotation-limited datasets. Available public datasets (e.g., The Cancer Imaging Archive, TCIA, USA) are limited in scope, making model generalizability really difficult. In this direction, five European Union projects are currently working on the development of big data infrastructures that will enable European, ethically and General Data Protection Regulation-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will coexist. The vision is to create sustainable AI cloud-based platforms for the development, implementation, verification, and validation of trustable, usable, and reliable AI models for addressing specific unmet needs regarding cancer care provision. In this paper, we present an overview of the development efforts highlighting challenges and approaches selected providing valuable feedback to future attempts in the area. Key points • Artificial intelligence models for health imaging require access to large amounts of harmonized imaging data and metadata. • Main infrastructures adopted either collect centrally anonymized data or enable access to pseudonymized distributed data. • Developing a common data model for storing all relevant information is a challenge. • Trust of data providers in data sharing initiatives is essential. • An online European Union meta-tool-repository is a necessity minimizing effort duplication for the various projects in the area.

Published
2023
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3. Phenopacket-tools: Building and validating GA4GH Phenopackets.

Author

Daniel Danis, Julius O B Jacobsen, Alex H Wagner, Tudor Groza, Martha A Beckwith, Lauren Rekerle, Leigh C Carmody, Justin Reese, Harshad Hegde, Markus S Ladewig, Berthold Seitz, Monica Munoz-Torres, Nomi L Harris, Jordi Rambla, Michael Baudis, Christopher J Mungall, Melissa A Haendel, and Peter N Robinson

Subjects
Medicine, Science
Abstract

The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.

Published
2023
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4. GA4GH: International policies and standards for data sharing across genomic research and healthcare

Author

Heidi L. Rehm, Angela J.H. Page, Lindsay Smith, Jeremy B. Adams, Gil Alterovitz, Lawrence J. Babb, Maxmillian P. Barkley, Michael Baudis, Michael J.S. Beauvais, Tim Beck, Jacques S. Beckmann, Sergi Beltran, David Bernick, Alexander Bernier, James K. Bonfield, Tiffany F. Boughtwood, Guillaume Bourque, Sarion R. Bowers, Anthony J. Brookes, Michael Brudno, Matthew H. Brush, David Bujold, Tony Burdett, Orion J. Buske, Moran N. Cabili, Daniel L. Cameron, Robert J. Carroll, Esmeralda Casas-Silva, Debyani Chakravarty, Bimal P. Chaudhari, Shu Hui Chen, J. Michael Cherry, Justina Chung, Melissa Cline, Hayley L. Clissold, Robert M. Cook-Deegan, Mélanie Courtot, Fiona Cunningham, Miro Cupak, Robert M. Davies, Danielle Denisko, Megan J. Doerr, Lena I. Dolman, Edward S. Dove, L. Jonathan Dursi, Stephanie O.M. Dyke, James A. Eddy, Karen Eilbeck, Kyle P. Ellrott, Susan Fairley, Khalid A. Fakhro, Helen V. Firth, Michael S. Fitzsimons, Marc Fiume, Paul Flicek, Ian M. Fore, Mallory A. Freeberg, Robert R. Freimuth, Lauren A. Fromont, Jonathan Fuerth, Clara L. Gaff, Weiniu Gan, Elena M. Ghanaim, David Glazer, Robert C. Green, Malachi Griffith, Obi L. Griffith, Robert L. Grossman, Tudor Groza, Jaime M. Guidry Auvil, Roderic Guigó, Dipayan Gupta, Melissa A. Haendel, Ada Hamosh, David P. Hansen, Reece K. Hart, Dean Mitchell Hartley, David Haussler, Rachele M. Hendricks-Sturrup, Calvin W.L. Ho, Ashley E. Hobb, Michael M. Hoffman, Oliver M. Hofmann, Petr Holub, Jacob Shujui Hsu, Jean-Pierre Hubaux, Sarah E. Hunt, Ammar Husami, Julius O. Jacobsen, Saumya S. Jamuar, Elizabeth L. Janes, Francis Jeanson, Aina Jené, Amber L. Johns, Yann Joly, Steven J.M. Jones, Alexander Kanitz, Kazuto Kato, Thomas M. Keane, Kristina Kekesi-Lafrance, Jerome Kelleher, Giselle Kerry, Seik-Soon Khor, Bartha M. Knoppers, Melissa A. Konopko, Kenjiro Kosaki, Martin Kuba, Jonathan Lawson, Rasko Leinonen, Stephanie Li, Michael F. Lin, Mikael Linden, Xianglin Liu, Isuru Udara Liyanage, Javier Lopez, Anneke M. Lucassen, Michael Lukowski, Alice L. Mann, John Marshall, Michele Mattioni, Alejandro Metke-Jimenez, Anna Middleton, Richard J. Milne, Fruzsina Molnár-Gábor, Nicola Mulder, Monica C. Munoz-Torres, Rishi Nag, Hidewaki Nakagawa, Jamal Nasir, Arcadi Navarro, Tristan H. Nelson, Ania Niewielska, Amy Nisselle, Jeffrey Niu, Tommi H. Nyrönen, Brian D. O’Connor, Sabine Oesterle, Soichi Ogishima, Vivian Ota Wang, Laura A.D. Paglione, Emilio Palumbo, Helen E. Parkinson, Anthony A. Philippakis, Angel D. Pizarro, Andreas Prlic, Jordi Rambla, Augusto Rendon, Renee A. Rider, Peter N. Robinson, Kurt W. Rodarmer, Laura Lyman Rodriguez, Alan F. Rubin, Manuel Rueda, Gregory A. Rushton, Rosalyn S. Ryan, Gary I. Saunders, Helen Schuilenburg, Torsten Schwede, Serena Scollen, Alexander Senf, Nathan C. Sheffield, Neerjah Skantharajah, Albert V. Smith, Heidi J. Sofia, Dylan Spalding, Amanda B. Spurdle, Zornitza Stark, Lincoln D. Stein, Makoto Suematsu, Patrick Tan, Jonathan A. Tedds, Alastair A. Thomson, Adrian Thorogood, Timothy L. Tickle, Katsushi Tokunaga, Juha Törnroos, David Torrents, Sean Upchurch, Alfonso Valencia, Roman Valls Guimera, Jessica Vamathevan, Susheel Varma, Danya F. Vears, Coby Viner, Craig Voisin, Alex H. Wagner, Susan E. Wallace, Brian P. Walsh, Marc S. Williams, Eva C. Winkler, Barbara J. Wold, Grant M. Wood, J. Patrick Woolley, Chisato Yamasaki, Andrew D. Yates, Christina K. Yung, Lyndon J. Zass, Ksenia Zaytseva, Junjun Zhang, Peter Goodhand, Kathryn North, and Ewan Birney

Subjects
data sharing, data access, precision medicine, learning health system, genomics, standards, Genetics, QH426-470, Internal medicine, RC31-1245
Abstract

Summary: The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.

Published
2021
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5. International federation of genomic medicine databases using GA4GH standards

Author

Adrian Thorogood, Heidi L. Rehm, Peter Goodhand, Angela J.H. Page, Yann Joly, Michael Baudis, Jordi Rambla, Arcadi Navarro, Tommi H. Nyronen, Mikael Linden, Edward S. Dove, Marc Fiume, Michael Brudno, Melissa S. Cline, and Ewan Birney

Subjects
data sharing, federation, genomics, standards, networks, data security, Genetics, QH426-470, Internal medicine, RC31-1245
Abstract

We promote a shared vision and guide for how and when to federate genomic and health-related data sharing, enabling connections and insights across independent, secure databases. The GA4GH encourages a federated approach wherein data providers have the mandate and resources to share, but where data cannot move for legal or technical reasons. We recommend a federated approach to connect national genomics initiatives into a global network and precision medicine resource.

Published
2021
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6. The Data Use Ontology to streamline responsible access to human biomedical datasets

Author

Jonathan Lawson, Moran N. Cabili, Giselle Kerry, Tiffany Boughtwood, Adrian Thorogood, Pinar Alper, Sarion R. Bowers, Rebecca R. Boyles, Anthony J. Brookes, Matthew Brush, Tony Burdett, Hayley Clissold, Stacey Donnelly, Stephanie O.M. Dyke, Mallory A. Freeberg, Melissa A. Haendel, Chihiro Hata, Petr Holub, Francis Jeanson, Aina Jene, Minae Kawashima, Shuichi Kawashima, Melissa Konopko, Irene Kyomugisha, Haoyuan Li, Mikael Linden, Laura Lyman Rodriguez, Mizuki Morita, Nicola Mulder, Jean Muller, Satoshi Nagaie, Jamal Nasir, Soichi Ogishima, Vivian Ota Wang, Laura D. Paglione, Ravi N. Pandya, Helen Parkinson, Anthony A. Philippakis, Fabian Prasser, Jordi Rambla, Kathy Reinold, Gregory A. Rushton, Andrea Saltzman, Gary Saunders, Heidi J. Sofia, John D. Spalding, Morris A. Swertz, Ilia Tulchinsky, Esther J. van Enckevort, Susheel Varma, Craig Voisin, Natsuko Yamamoto, Chisato Yamasaki, Lyndon Zass, Jaime M. Guidry Auvil, Tommi H. Nyrönen, and Mélanie Courtot

Subjects
data access, consent, FAIR, ontology, GA4GH, standard, Genetics, QH426-470, Internal medicine, RC31-1245
Abstract

Summary: Human biomedical datasets that are critical for research and clinical studies to benefit human health also often contain sensitive or potentially identifying information of individual participants. Thus, care must be taken when they are processed and made available to comply with ethical and regulatory frameworks and informed consent data conditions. To enable and streamline data access for these biomedical datasets, the Global Alliance for Genomics and Health (GA4GH) Data Use and Researcher Identities (DURI) work stream developed and approved the Data Use Ontology (DUO) standard. DUO is a hierarchical vocabulary of human and machine-readable data use terms that consistently and unambiguously represents a dataset’s allowable data uses. DUO has been implemented by major international stakeholders such as the Broad and Sanger Institutes and is currently used in annotation of over 200,000 datasets worldwide. Using DUO in data management and access facilitates researchers’ discovery and access of relevant datasets. DUO annotations increase the FAIRness of datasets and support data linkages using common data use profiles when integrating the data for secondary analyses. DUO is implemented in the Web Ontology Language (OWL) and, to increase community awareness and engagement, hosted in an open, centralized GitHub repository. DUO, together with the GA4GH Passport standard, offers a new, efficient, and streamlined data authorization and access framework that has enabled increased sharing of biomedical datasets worldwide.

Published
2021
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7. Systematically linking tranSMART, Galaxy and EGA for reusing human translational research data [version 1; referees: 2 approved]

Author

Chao Zhang, Jochem Bijlard, Christine Staiger, Serena Scollen, David van Enckevort, Youri Hoogstrate, Alexander Senf, Saskia Hiltemann, Susanna Repo, Wibo Pipping, Mariska Bierkens, Stefan Payralbe, Bas Stringer, Jaap Heringa, Andrew Stubbs, Luiz Olavo Bonino Da Silva Santos, Jeroen Belien, Ward Weistra, Rita Azevedo, Kees van Bochove, Gerrit Meijer, Jan-Willem Boiten, Jordi Rambla, Remond Fijneman, J. Dylan Spalding, and Sanne Abeln

Subjects
Bioinformatics, Medicine, Science
Abstract

The availability of high-throughput molecular profiling techniques has provided more accurate and informative data for regular clinical studies. Nevertheless, complex computational workflows are required to interpret these data. Over the past years, the data volume has been growing explosively, requiring robust human data management to organise and integrate the data efficiently. For this reason, we set up an ELIXIR implementation study, together with the Translational research IT (TraIT) programme, to design a data ecosystem that is able to link raw and interpreted data. In this project, the data from the TraIT Cell Line Use Case (TraIT-CLUC) are used as a test case for this system. Within this ecosystem, we use the European Genome-phenome Archive (EGA) to store raw molecular profiling data; tranSMART to collect interpreted molecular profiling data and clinical data for corresponding samples; and Galaxy to store, run and manage the computational workflows. We can integrate these data by linking their repositories systematically. To showcase our design, we have structured the TraIT-CLUC data, which contain a variety of molecular profiling data types, for storage in both tranSMART and EGA. The metadata provided allows referencing between tranSMART and EGA, fulfilling the cycle of data submission and discovery; we have also designed a data flow from EGA to Galaxy, enabling reanalysis of the raw data in Galaxy. In this way, users can select patient cohorts in tranSMART, trace them back to the raw data and perform (re)analysis in Galaxy. Our conclusion is that the majority of metadata does not necessarily need to be stored (redundantly) in both databases, but that instead FAIR persistent identifiers should be available for well-defined data ontology levels: study, data access committee, physical sample, data sample and raw data file. This approach will pave the way for the stable linkage and reuse of data.

Published
2017
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8. Integration of EGA secure data access into Galaxy [version 1; referees: 2 approved]

Author

Youri Hoogstrate, Chao Zhang, Alexander Senf, Jochem Bijlard, Saskia Hiltemann, David van Enckevort, Susanna Repo, Jaap Heringa, Guido Jenster, Remond J.A. Fijneman, Jan-Willem Boiten, Gerrit A. Meijer, Andrew Stubbs, Jordi Rambla, Dylan Spalding, and Sanne Abeln

Subjects
Bioinformatics, Medicine, Science
Abstract

High-throughput molecular profiling techniques are routinely generating vast amounts of data for translational medicine studies. Secure access controlled systems are needed to manage, store, transfer and distribute these data due to its personally identifiable nature. The European Genome-phenome Archive (EGA) was created to facilitate access and management to long-term archival of bio-molecular data. Each data provider is responsible for ensuring a Data Access Committee is in place to grant access to data stored in the EGA. Moreover, the transfer of data during upload and download is encrypted. ELIXIR, a European research infrastructure for life-science data, initiated a project (2016 Human Data Implementation Study) to understand and document the ELIXIR requirements for secure management of controlled-access data. As part of this project, a full ecosystem was designed to connect archived raw experimental molecular profiling data with interpreted data and the computational workflows, using the CTMM Translational Research IT (CTMM-TraIT) infrastructure http://www.ctmm-trait.nl as an example. Here we present the first outcomes of this project, a framework to enable the download of EGA data to a Galaxy server in a secure way. Galaxy provides an intuitive user interface for molecular biologists and bioinformaticians to run and design data analysis workflows. More specifically, we developed a tool -- ega_download_streamer - that can download data securely from EGA into a Galaxy server, which can subsequently be further processed. This tool will allow a user within the browser to run an entire analysis containing sensitive data from EGA, and to make this analysis available for other researchers in a reproducible manner, as shown with a proof of concept study. The tool ega_download_streamer is available in the Galaxy tool shed: https://toolshed.g2.bx.psu.edu/view/yhoogstrate/ega_download_streamer.

Published
2016
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9. Consent Codes: Upholding Standard Data Use Conditions.

Author

Stephanie O M Dyke, Anthony A Philippakis, Jordi Rambla De Argila, Dina N Paltoo, Erin S Luetkemeier, Bartha M Knoppers, Anthony J Brookes, J Dylan Spalding, Mark Thompson, Marco Roos, Kym M Boycott, Michael Brudno, Matthew Hurles, Heidi L Rehm, Andreas Matern, Marc Fiume, and Stephen T Sherry

Subjects
Genetics, QH426-470
Abstract

A systematic way of recording data use conditions that are based on consent permissions as found in the datasets of the main public genome archives (NCBI dbGaP and EMBL-EBI/CRG EGA).

Published
2016
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11. iASiS: Towards Heterogeneous Big Data Analysis for Personalized Medicine.

Author

Anastasia Krithara, Fotis Aisopos, Vassiliki Rentoumi, Anastasios Nentidis, Konstantinos Bougiatiotis, Maria-Esther Vidal, Ernestina Menasalvas, Alejandro Rodríguez González, Eleftherios Samaras, Peter Garrard, Maria Torrente, Mariano Provencio Pulla, Nikos Dimakopoulos, Rui Mauricio, Jordi Rambla De Argila, Gian Gaetano Tartaglia, and George Paliouras

Published
2024
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14. The European Genome-phenome Archive in 2021.

Author

Mallory Ann Freeberg, Lauren A. Fromont, Teresa D'altri, Anna Foix Romero, Jorge Izquierdo Ciges, Aina Jene, Giselle Kerry, Mauricio Moldes, Roberto Ariosa, Silvia Bahena, Daniel Barrowdale, Marcos Casado Barbero, Dietmar Fernández-Orth, Carles Garcia-Linares, Emilio Garcia-Rios, Frédéric Haziza, Bela Juhasz, Oscar Martinez Llobet, Gemma Milla, Anand Mohan, Manuel Rueda, Aravind Sankar, Dona Shaju, Ashutosh Shimpi, Babita Singh, Coline Thomas, Sabela de la Torre, Umuthan Uyan, Claudia Vasallo, Paul Flicek, Roderic Guigó, Arcadi Navarro, Helen E. Parkinson, Thomas M. Keane, and Jordi Rambla

Published
2022
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15. RNAget: an API to securely retrieve RNA quantifications.

Author

Sean Upchurch, Emilio Palumbo, Jeremy Adams, David Bujold, Guillaume Bourque, Jared Nedzel, Keenan Graham, Meenakshi S. Kagda, Pedro Assis, Benjamin C. Hitz, Emilio Righi, Roderic Guigó, Barbara J. Wold, Alvis Brazma, Julia Burchard, Joe Capka, Michael Cherry, Laura Clarke, Brian Craft, Manolis Dermitzakis, Mark Diekhans, John Dursi, Michael Sean Fitzsimons, Zac Flaming, Romina Garrido, Alfred Gil, Paul Godden, Matt Green, Mitch Guttman, Brian Haas, Max Haeussler, Bo Li, Sten Linnarsson, Adam Lipski, David Liu, Simonne Longerich, David Lougheed, Jonathan Manning, John C. Marioni, Christopher Meyer, Stephen B. Montgomery, Alyssa Morrow, Alfonso Muñoz-Pomer Fuentes, Jared L. Nedzel, David Nguyen, Kevin Osborn, Francis Ouellette, Irene Papatheodorou, Dmitri D. Pervouchine, Arun K. Ramani, Jordi Rambla, Bashir Sadjad, David Steinberg, Jeremiah Talkar, Timothy Tickle, Kathy Tzeng, Saman Vaisipour, Sean Watford, Barbara Wold, Zhenyu Zhang, and Jing Zhu

Published
2023
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16. Registered access: authorizing data access

Author

Dyke, Stephanie OM, Linden, Mikael, Lappalainen, Ilkka, De Argila, Jordi Rambla, Carey, Knox, Lloyd, David, Spalding, J Dylan, Cabili, Moran N, Kerry, Giselle, Foreman, Julia, Cutts, Tim, Shabani, Mahsa, Rodriguez, Laura L, Haeussler, Maximilian, Walsh, Brian, Jiang, Xiaoqian, Wang, Shuang, Perrett, Daniel, Boughtwood, Tiffany, Matern, Andreas, Brookes, Anthony J, Cupak, Miro, Fiume, Marc, Pandya, Ravi, Tulchinsky, Ilia, Scollen, Serena, Törnroos, Juha, Das, Samir, Evans, Alan C, Malin, Bradley A, Beck, Stephan, Brenner, Steven E, Nyrönen, Tommi, Blomberg, Niklas, Firth, Helen V, Hurles, Matthew, Philippakis, Anthony A, Rätsch, Gunnar, Brudno, Michael, Boycott, Kym M, Rehm, Heidi L, Baudis, Michael, Sherry, Stephen T, Kato, Kazuto, Knoppers, Bartha M, Baker, Dixie, and Flicek, Paul

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Clinical Sciences, Genetics, 8.3 Policy, ethics, and research governance, Health and social care services research, Generic health relevance, Good Health and Well Being, Access to Information, Genetics, Medical, Genomics, Humans, Information Dissemination, Licensure, Practice Guidelines as Topic, Genetics & Heredity, Clinical sciences
Abstract

The Global Alliance for Genomics and Health (GA4GH) proposes a data access policy model-"registered access"-to increase and improve access to data requiring an agreement to basic terms and conditions, such as the use of DNA sequence and health data in research. A registered access policy would enable a range of categories of users to gain access, starting with researchers and clinical care professionals. It would also facilitate general use and reuse of data but within the bounds of consent restrictions and other ethical obligations. In piloting registered access with the Scientific Demonstration data sharing projects of GA4GH, we provide additional ethics, policy and technical guidance to facilitate the implementation of this access model in an international setting.

Published
2018

18. The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences.

Author

Rachel Drysdale, Charles E. Cook, Robert Petryszak, Vivienne Baillie Gerritsen, Mary Barlow, Elisabeth Gasteiger, Franziska Gruhl, Jürgen Haas, Jerry Lanfear, Rodrigo Lopez, Nicole Redaschi, Heinz Stockinger, Daniel Teixeira, Aravind Venkatesan, Alex Bateman, Alan J. Bridge, Guy Cochrane, Robert D. Finn, Frank Oliver Glöckner, Marc Hanauer, Thomas M. Keane, Andrew Leach, Luana Licata, Per Oksvold, Sandra E. Orchard, Christine A. Orengo, Helen E. Parkinson, Bengt Persson, Pablo Porras, Jordi Rambla, Ana Rath, Charlotte Rodwell, Ugis Sarkans, Dietmar Schomburg, Ian Sillitoe, J. Dylan Spalding, Mathias Uhlén, Sameer Velankar, Juan Antonio Vizcaíno, Kalle von Feilitzen, Christian von Mering, Andrew D. Yates, Niklas Blomberg, Christine Durinx, and Johanna R. McEntyre

Published
2020
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21. Ready-to-use public infrastructure for global SARS-CoV-2 monitoring

Author

Maier, Wolfgang, Bray, Simon, van den Beek, Marius, Bouvier, Dave, Coraor, Nathan, Miladi, Milad, Singh, Babita, De Argila, Jordi Rambla, Baker, Dannon, Roach, Nathan, Gladman, Simon, Coppens, Frederik, Martin, Darren P., Lonie, Andrew, Grüning, Björn, Kosakovsky Pond, Sergei L., and Nekrutenko, Anton

Published
2021
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23. Freely accessible ready to use global infrastructure for SARS-CoV-2 monitoring.

Author

Maier W, Bray S, van den Beek M, Bouvier D, Coraor N, Miladi M, Singh B, De Argila JR, Baker D, Roach N, Gladman S, Coppens F, Martin DP, Lonie A, Grüning B, Kosakovsky Pond SL, and Nekrutenko A

Abstract

The COVID-19 pandemic is the first global health crisis to occur in the age of big genomic data.Although data generation capacity is well established and sufficiently standardized, analytical capacity is not. To establish analytical capacity it is necessary to pull together global computational resources and deliver the best open source tools and analysis workflows within a ready to use, universally accessible resource. Such a resource should not be controlled by a single research group, institution, or country. Instead it should be maintained by a community of users and developers who ensure that the system remains operational and populated with current tools. A community is also essential for facilitating the types of discourse needed to establish best analytical practices. Bringing together public computational research infrastructure from the USA, Europe, and Australia, we developed a distributed data analysis platform that accomplishes these goals. It is immediately accessible to anyone in the world and is designed for the analysis of rapidly growing collections of deep sequencing datasets. We demonstrate its utility by detecting allelic variants in high-quality existing SARS-CoV-2 sequencing datasets and by continuous reanalysis of COG-UK data. All workflows, data, and documentation is available at https://covid19.galaxyproject.org .

Published
2021
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24. The European Genome-phenome Archive of human data consented for biomedical research.

Author

Lappalainen I, Almeida-King J, Kumanduri V, Senf A, Spalding JD, Ur-Rehman S, Saunders G, Kandasamy J, Caccamo M, Leinonen R, Vaughan B, Laurent T, Rowland F, Marin-Garcia P, Barker J, Jokinen P, Torres AC, de Argila JR, Llobet OM, Medina I, Puy MS, Alberich M, de la Torre S, Navarro A, Paschall J, and Flicek P

Subjects
Access to Information, Biomedical Research, Europe, Genome, Human, Humans, Databases, Genetic
Published
2015
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