84 results on '"Jordi Rambla"'
Search Results
2. Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects
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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 more...
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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. more...
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- 2023
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3. Phenopacket-tools: Building and validating GA4GH Phenopackets.
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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 more...
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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. more...
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- 2023
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4. iASiS: Towards Heterogeneous Big Data Analysis for Personalized Medicine
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Krithara, Anastasia, Aisopos, Fotis, Rentoumi, Vassiliki, Nentidis, Anastasios, Bougatiotis, Konstantinos, Vidal, Maria-Esther, Menasalvas, Ernestina, Rodriguez-Gonzalez, Alejandro, Samaras, Eleftherios G., Garrard, Peter, Torrente, Maria, Pulla, Mariano Provencio, Dimakopoulos, Nikos, Mauricio, Rui, De Argila, Jordi Rambla, Tartaglia, Gian Gaetano, and Paliouras, George more...
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Computer Science - Artificial Intelligence ,Computer Science - Databases - Abstract
The vision of IASIS project is to turn the wave of big biomedical data heading our way into actionable knowledge for decision makers. This is achieved by integrating data from disparate sources, including genomics, electronic health records and bibliography, and applying advanced analytics methods to discover useful patterns. The goal is to turn large amounts of available data into actionable information to authorities for planning public health activities and policies. The integration and analysis of these heterogeneous sources of information will enable the best decisions to be made, allowing for diagnosis and treatment to be personalised to each individual. The project offers a common representation schema for the heterogeneous data sources. The iASiS infrastructure is able to convert clinical notes into usable data, combine them with genomic data, related bibliography, image data and more, and create a global knowledge base. This facilitates the use of intelligent methods in order to discover useful patterns across different resources. Using semantic integration of data gives the opportunity to generate information that is rich, auditable and reliable. This information can be used to provide better care, reduce errors and create more confidence in sharing data, thus providing more insights and opportunities. Data resources for two different disease categories are explored within the iASiS use cases, dementia and lung cancer., Comment: 6 pages, 2 figures, accepted at 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS) more...
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- 2024
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5. GA4GH: International policies and standards for data sharing across genomic research and healthcare
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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 more...
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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. more...
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- 2021
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6. International federation of genomic medicine databases using GA4GH standards
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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 more...
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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. more...
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- 2021
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7. The Data Use Ontology to streamline responsible access to human biomedical datasets
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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 more...
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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. more...
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- 2021
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8. Systematically linking tranSMART, Galaxy and EGA for reusing human translational research data [version 1; referees: 2 approved]
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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 more...
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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. more...
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- 2017
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9. Integration of EGA secure data access into Galaxy [version 1; referees: 2 approved]
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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 more...
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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. more...
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- 2016
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10. Consent Codes: Upholding Standard Data Use Conditions.
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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 more...
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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).
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- 2016
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11. AskBeacon - Performing genomic data exchange and analytics with natural language.
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Anuradha Wickramarachchi, Shakila Tonni, Sonali Majumdar, Sarvnaz Karimi, Sulev Kõks, Brendan Hosking, Jordi Rambla, Natalie A. Twine, Yatish Jain, and Denis C. Bauer
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- 2024
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12. iASiS: Towards Heterogeneous Big Data Analysis for Personalized Medicine.
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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 more...
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- 2024
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13. Consent Codes: Maintaining Consent in an Ever-expanding Open Science Ecosystem
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Dyke, Stephanie O. M., Connor, Kathleen, Nembaware, Victoria, Munung, Nchangwi S., Reinold, Kathy, Kerry, Giselle, Mbiyavanga, Mamana, Zass, Lyndon, Moldes, Mauricio, Das, Samir, Davis, John M., De Argila, Jordi Rambla, Spalding, J. Dylan, Evans, Alan C., Mulder, Nicola, and Karamchandani, Jason more...
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- 2023
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14. Consent Codes: Maintaining Consent in an Ever-expanding Open Science Ecosystem.
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Stephanie O. M. Dyke, Kathleen Connor, Victoria Nembaware, Nchangwi S. Munung, Kathy Reinold, Giselle Kerry, Mamana Mbiyavanga, Lyndon Zass, Mauricio Moldes, Samir Das, John Mike Davis, Jordi Rambla De Argila, J. Dylan Spalding, Alan C. Evans, Nicola J. Mulder, and Jason Karamchandani more...
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- 2023
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15. The European Genome-phenome Archive in 2021.
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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 more...
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- 2022
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16. RNAget: an API to securely retrieve RNA quantifications.
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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 more...
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- 2023
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17. Registered access: authorizing data access
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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 more...
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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. more...
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- 2018
18. iASiS: Towards Heterogeneous Big Data Analysis for Personalized Medicine.
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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 more...
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- 2019
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19. Genome-phenome explorer (GePhEx): a tool for the visualization and interpretation of phenotypic relationships supported by genetic evidence.
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Xavier Farré, Nino Spataro, Frédéric Haziza, Jordi Rambla, and Arcadi Navarro
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- 2020
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20. The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences.
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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 more...
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- 2020
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21. A quality control portal for sequencing data deposited at the European genome-phenome archive.
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Dietmar Fernández-Orth, Manuel Rueda, Babita Singh, Mauricio Moldes, Aina Jene, Marta Ferri, Claudia Vasallo, Lauren A. Fromont, Arcadi Navarro, and Jordi Rambla
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- 2022
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22. European Genome-Phenome Archive (EGA) - Granular Solutions for the Next 10 Years.
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Dietmar Fernández-Orth, Audald Lloret-Villas, and Jordi Rambla De Argila
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- 2019
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23. Beacon v2 Reference Implementation: a toolkit to enable federated sharing of genomic and phenotypic data.
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Manuel Rueda, Roberto Ariosa, Mauricio Moldes, and Jordi Rambla
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- 2022
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24. Accelerating FaST-LMM for Epistasis Tests.
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Héctor Martínez 0002, Sergio Barrachina 0001, Maribel Castillo, Enrique S. Quintana-Ortí, Jordi Rambla De Argila, Xavier Farré, and Arcadi Navarro
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- 2017
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25. Accelerating FaST-LMM for Epistasis Tests
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Martínez, Héctor, Barrachina, Sergio, Castillo, Maribel, Quintana-Ortí, Enrique S., De Argila, Jordi Rambla, Farré, Xavier, Navarro, Arcadi, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ibrahim, Shadi, editor, Choo, Kim-Kwang Raymond, editor, Yan, Zheng, editor, and Pedrycz, Witold, editor more...
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- 2017
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26. Ready-to-use public infrastructure for global SARS-CoV-2 monitoring
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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 more...
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- 2021
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27. FaST-LMM for Two-Way Epistasis Tests on High-Performance Clusters.
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Héctor Martínez 0002, Sergio Barrachina 0001, Maribel Castillo, Enrique S. Quintana-Ortí, Jordi Rambla De Argila, Xavier Farré, and Arcadi Navarro
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- 2018
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28. The European Genome-phenome Archive in 2021
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Claudia Vasallo, Frédéric Haziza, Gemma Milla, Daniel Barrowdale, Manuel Rueda, Roberto Ariosa, Jordi Rambla, Carles Garcia-Linares, Silvia Bahena, Emilio Garcia-Rios, Babita Singh, Coline Thomas, Anand Mohan, Mallory A. Freeberg, Teresa D’Altri, Helen Parkinson, Aravind Sankar, Sabela de la Torre, Arcadi Navarro, Ashutosh Shimpi, Mauricio Moldes, Umuthan Uyan, Paul Flicek, Oscar Martinez Llobet, Bela Juhasz, Giselle Kerry, Dona Shaju, Marcos Casado Barbero, Dietmar Fernandez-Orth, Aina Jene, Lauren A Fromont, Roderic Guigó, Jorge Izquierdo Ciges, Thomas M. Keane, and Anna Foix Romero more...
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Genotype ,AcademicSubjects/SCI00010 ,Datasets as Topic ,Translational research ,Phenome ,Biology ,History, 21st Century ,Translational Research, Biomedical ,Resource (project management) ,Genetics ,Database Issue ,Humans ,Phenomics ,Value chain ,Register of data controllers ,computer.programming_language ,Metadata ,Genome, Human ,Information Dissemination ,History, 20th Century ,Data science ,Data access ,Phenotype ,Key (cryptography) ,Elixir (programming language) ,computer ,Confidentiality - Abstract
The European Genome-phenome Archive (EGA - https://ega-archive.org/) is a resource for long term secure archiving of all types of potentially identifiable genetic, phenotypic, and clinical data resulting from biomedical research projects. Its mission is to foster hosted data reuse, enable reproducibility, and accelerate biomedical and translational research in line with the FAIR principles. Launched in 2008, the EGA has grown quickly, currently archiving over 4,500 studies from nearly one thousand institutions. The EGA operates a distributed data access model in which requests are made to the data controller, not to the EGA, therefore, the submitter keeps control on who has access to the data and under which conditions. Given the size and value of data hosted, the EGA is constantly improving its value chain, that is, how the EGA can contribute to enhancing the value of human health data by facilitating its submission, discovery, access, and distribution, as well as leading the design and implementation of standards and methods necessary to deliver the value chain. The EGA has become a key GA4GH Driver Project, leading multiple development efforts and implementing new standards and tools, and has been appointed as an ELIXIR Core Data Resource., Graphical Abstract Graphical AbstractThe European Genome-phenome Archive serves human genomics and health communities by offering data submission, validation, discovery, and access services to researchers worldwide. Data reuse value is maximised by implementing community standards and ensuring interoperability of data and tools to accelerate biomedical and translational research. more...
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- 2021
29. Consent Codes: Maintaining Consent in an Ever-expanding Open Science Ecosystem
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Dyke, Stephanie O. M., primary, Connor, Kathleen, additional, Nembaware, Victoria, additional, Munung, Nchangwi S., additional, Reinold, Kathy, additional, Kerry, Giselle, additional, Mbiyavanga, Mamana, additional, Zass, Lyndon, additional, Moldes, Mauricio, additional, Das, Samir, additional, Davis, John M., additional, De Argila, Jordi Rambla, additional, Spalding, J. Dylan, additional, Evans, Alan C., additional, Mulder, Nicola, additional, and Karamchandani, Jason, additional more...
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- 2022
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30. Accelerating FaST-LMM for Epistasis Tests
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Martínez, Héctor, primary, Barrachina, Sergio, additional, Castillo, Maribel, additional, Quintana-Ortí, Enrique S., additional, De Argila, Jordi Rambla, additional, Farré, Xavier, additional, and Navarro, Arcadi, additional more...
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- 2017
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31. Beacon v2 and Beacon networks: A 'lingua franca' for federated data discovery in biomedical genomics, and beyond
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Jordi Rambla, Michael Baudis, Roberto Ariosa, Tim Beck, Lauren A. Fromont, Arcadi Navarro, Rahel Paloots, Manuel Rueda, Gary Saunders, Babita Singh, John D. Spalding, Juha Törnroos, Claudia Vasallo, Colin D. Veal, Anthony J. Brookes, Fundación 'la Caixa', UK Research and Innovation, European Commission, Swiss Institute of Bioinformatics, Swiss Personalized Health Network, University of Zurich, Rambla, Jordi, and Baudis, Michael more...
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2716 Genetics (clinical) ,Clinical genomics ,Programari ,Medicina ,Information Dissemination ,GA4GH ,REST API ,Genomics ,10124 Institute of Molecular Life Sciences ,Genòmica ,Phenotype ,Rare Diseases ,1311 Genetics ,Genetics ,570 Life sciences ,biology ,Humans ,Beacon ,Data sharing ,Software ,Genetics (clinical) ,Data discovery - Abstract
Beacon is a basic data discovery protocol issued by the Global Alliance for Genomics and Health (GA4GH). The main goal addressed by version 1 of the Beacon protocol was to test the feasibility of broadly sharing human genomic data, through providing simple “yes” or “no” responses to queries about the presence of a given variant in datasets hosted by Beacon providers. The popularity of this concept has fostered the design of a version 2, that better serves real-world requirements and addresses the needs of clinical genomics research and healthcare, as assessed by several contributing projects and organizations. Particularly, rare disease genetics and cancer research will benefit from new case level and genomic variant level requests and the enabling of richer phenotype and clinical queries as well as support for fuzzy searches. Beacon is designed as a “lingua franca” to bridge data collections hosted in software solutions with different and rich interfaces. Beacon version 2 works alongside popular standards like Phenopackets, OMOP, or FHIR, allowing implementing consortia to return matches in beacon responses and provide a handover to their preferred data exchange format. The protocol is being explored by other research domains and is being tested in several international projects., This study was funded by ELIXIR, the research infrastructure for life-science data and also by LaCaixa Foundation (grant number 004745/008034). Tim Beck was supported by a UKRI Innovation Fellowship at Health Data Research UK (MR/S003703/1). Anthony J. Brookes and Jordi Rambla were supported, in part, by the European Union's Horizon 2020 research and innovation program under the EJP RD COFUND-EJP #825575. Michael Baudis acknowledges funding under the BioMedIT Network project of Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN). more...
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- 2022
32. Consent Codes: Maintaining Consent in an Ever-expanding Open Science Ecosystem
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Stephanie O. M. Dyke, Kathleen Connor, Victoria Nembaware, Nchangwi S. Munung, Kathy Reinold, Giselle Kerry, Mamana Mbiyavanga, Lyndon Zass, Mauricio Moldes, Samir Das, John M. Davis, Jordi Rambla De Argila, J. Dylan Spalding, Alan C. Evans, Nicola Mulder, and Jason Karamchandani more...
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Consent ,Ethics ,General Neuroscience ,Data sharing ,Open science ,Data access ,Data management ,Software ,Information Systems - Abstract
We previously proposed a structure for recording consent-based data use 'categories' and 'requirements' - Consent Codes - with a view to supporting maximum use and integration of genomic research datasets, and reducing uncertainty about permissible re-use of shared data. Here we discuss clarifications and subsequent updates to the Consent Codes (v4) based on new areas of application (e.g., the neurosciences, biobanking, H3Africa), policy developments (e.g., return of research results), and further practical considerations, including developments in automated approaches to consent management. SOMD, SD, ACE and JK were supported by The Neuro Tanenbaum Open Science Institute, the Canadian Open Neuroscience Platform (funded in part by Brain Canada), and McGill Healthy Brains for Healthy Lives. NM and LZ are funded by the NIH under grant number U24HG006941. MM is funded by EUH2020 CINECA grant number 825775. NM, VN and NSM are funded by the NHLBI award number U24HL135600. JDS and GK are funded by the Wellcome Trust grant 360G-Wellcome-201535_Z_16_Z and previously the EU H2020 Corbel grant number 645248. more...
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- 2022
33. Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease
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Magdalena Arnal Segura, Giorgio Bini, Dietmar Fernandez Orth, Eleftherios Samaras, Maya Kassis, Fotis Aisopos, Jordi Rambla De Argila, George Paliouras, Peter Garrard, Claudia Giambartolomei, and Gian Gaetano Tartaglia more...
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Apolipoprotein E ,business.industry ,Late onset ,Genome-wide association study ,Disease ,Biology ,Machine learning ,computer.software_genre ,Psychiatry and Mental health ,Alzheimer, Malaltia d' ,Genòmica ,Expression quantitative trait loci ,Artificial intelligence ,Neurology (clinical) ,business ,Genotyping ,Gene ,computer ,Genètica ,Genetic association - Abstract
Introduction: Genome-wide association studies (GWAS) in late onset Alzheimer's disease (LOAD) provide lists of individual genetic determinants. However, GWAS do not capture the synergistic effects among multiple genetic variants and lack good specificity. Methods: We applied tree-based machine learning algorithms (MLs) to discriminate LOAD (>700 individuals) and age-matched unaffected subjects in UK Biobank with single nucleotide variants (SNVs) from Alzheimer's disease (AD) studies, obtaining specific genomic profiles with the prioritized SNVs. Results: MLs prioritized a set of SNVs located in genes PVRL2, TOMM40, APOE, and APOC1, also influencing gene expression and splicing. The genomic profiles in this region showed interaction patterns involving rs405509 and rs1160985, also present in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. rs405509 located in APOE promoter interacts with rs429358 among others, seemingly neutralizing their predisposing effect. Discussion: Our approach efficiently discriminates LOAD from controls, capturing genomic profiles defined by interactions among SNVs in a hot-spot region. Funding: the research leading to these results was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727658 (project iASiS), European Research Council ASTRA 855923, and the European Genome‐phenome Archive (EGA). Claudia Giambartolomei has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska‐Curie grant agreement No 754490–MINDED project. This research has been conducted using the UK Biobank Resource under Application Number 35916 more...
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- 2022
34. International federation of genomic medicine databases using GA4GH standards
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Edward S. Dove, Peter Goodhand, Angela Page, Mikael Linden, Heidi L. Rehm, Ewan Birney, Michael Brudno, Melissa S. Cline, Marc Fiume, Yann Joly, Tommi Nyrönen, Arcadi Navarro, Michael Baudis, Adrian Thorogood, Jordi Rambla, and University of Zurich more...
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Computer science ,Medicina ,data sharing ,Data security ,ComputingMilieux_LEGALASPECTSOFCOMPUTING ,computer.software_genre ,Article ,Bases de dades ,Resource (project management) ,federation ,Global network ,genomics ,Genomic medicine ,Data Protection Act 1998 ,data security ,data protection ,Database ,data commons ,GA4GH ,Precision medicine ,10124 Institute of Molecular Life Sciences ,Data sharing ,Genòmica ,networks ,standards ,Mandate ,570 Life sciences ,biology ,computer - 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. Funding: A.T. acknowledges funding support from Genome Canada, Genome Quebec, and the Canadian Institutes of Health Research. H.L.R. and A.J.H.P. acknowledge funding under NIH U41HG006834 and U24HG011025. M. Baudis acknowledges funding under the Bio-MedIT Network project of Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN). M.L. acknowledges funding from the CINECA project (H2020 No 825775). M.S.C. acknowledges funding under NIH/NCI U01CA242954, NIH/NHLBI Fellowship 5118777. M. Brudno is a CIFAR Canada AI Chair. P.G. acknowledges funding from CIHR, Genome Canada, Wellcome Trust, and NIH. T.H.N. was funded by the Academy of Finland grant no 319968 and ELIXIR Europe 2019–2023 program more...
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- 2021
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35. Inversions and genomic differentiation after secondary contact: When drift contributes to maintenance, not loss, of differentiation
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Arcadi Navarro, Jeffrey L. Feder, Jordi Rambla, Rui Faria, Marina Rafajlović, European Commission, Fundação para a Ciência e a Tecnologia (Portugal), Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Instituto Nacional de Bioinformática (España), Ministerio de Economía y Competitividad (España), Hasselblad Foundation, European Research Council, Swedish Research Council, National Science Foundation (US), and Department of Agriculture (US) more...
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0106 biological sciences ,0301 basic medicine ,Simulations ,Reproductive Isolation ,Genetic Speciation ,Chromosomal rearrangements ,Speciation ,European Regional Development Fund ,Library science ,Biology ,010603 evolutionary biology ,01 natural sciences ,Flux gènic ,03 medical and health sciences ,Genetics ,Computational Science and Engineering ,media_common.cataloged_instance ,Computer Simulation ,European union ,Selection ,Ecology, Evolution, Behavior and Systematics ,media_common ,Genètica de poblacions ,Models, Genetic ,European research ,Genetic Drift ,Adaptation, Physiological ,Gene flow ,Recombination ,030104 developmental biology ,Research council ,Chromosome Inversion ,General Agricultural and Biological Sciences ,Genètica - Abstract
Due to their effects on reducing recombination, chromosomal inversions may play an important role in speciation by establishing and/or maintaining linked blocks of genes causing reproductive isolation (RI) between populations. This view fits empirical data indicating that inversions typically harbor loci involved in RI. However, previous computer simulations of infinite populations with two to four loci involved in RI implied that, even with gene flux as low as 10–8 per gamete, per generation between alternative arrangements, inversions may not have large, qualitative advantages over collinear regions in maintaining population differentiation after secondary contact. Here, we report that finite population sizes can help counteract the homogenizing consequences of gene flux, especially when several fitness-related loci reside within the inversion. In these cases, the persistence time of differentiation after secondary contact can be similar to when gene flux is absent and notably longer than the persistence time without inversions. Thus, despite gene flux, population differentiation may be maintained for up to 100,000 generations, during which time new incompatibilities and/or local adaptations might accumulate and facilitate progress toward speciation. How often these conditions are met in nature remains to be determined., This study was supported by the European Regional Development Fund (FCOMP-01-0124-FEDER-014272), FCT – Foundation for Science and Technology (PTDC/BIA-EVF/113805/2009), Ministerio de Ciencia e Innovación, Spain (PGC2018-101927-B-I00, MINECO/FEDER, UE), by the Spanish National Institute of Bioinformatics (PT17/0009/0020), and by “Unidad de Excelencia María de Maeztu,” funded by the MINECO (ref: MDM-2014-0370). MR was funded by the Hasselblad Foundation (Grant for Female Scientists), European Research Council and the Swedish Research Councils VR and Formas (Linnaeus grant to the Centre for Marine Evolutionary Biology), and by an additional grant from Formas (to MR; grant number 2019-00882). JLF was funded by support from the National Science Foundation and United States Department of Agriculture NIFA program. RF was funded by FCT (SFRH/BPD/89313/2012) and by the European Union's Horizon 2020 research and innovation program, under the Marie Sklodowska-Curie grant agreement number 706376; and is currently funded by FEDER through the Operational Competitiveness Factors Program COMPETE and by National Funds through the FCT project “Hybrabbid” (PTDC/BIA-EVL/30628/2017 and POCI-01-0145-FEDER-030628). The simulations were performed on resources at Chalmers Centre for Computational Science and Engineering (C3SE), and at National Supercomputer Centre at Linköping University (NSC) provided by the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council through grant agreement no. 2018–05973. more...
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- 2021
36. Freely accessible ready to use global infrastructure for SARS-CoV-2 monitoring
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Marius van den Beek, Dave Bouvier, Sergei L Kosakovsky Pond, Andrew Lonie, Dannon Baker, Darren P. Martin, Anton Nekrutenko, Nathaniel Coraor, Wolfgang Maier, Jordi Rambla De Argila, Babita Singh, Frederik Coppens, Milad Miladi, Björn Grüning, Simon Gladman, Simon Bray, and Nathan P. Roach more...
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Databases, Factual ,Coronavirus disease 2019 (COVID-19) ,SARS-CoV-2 ,Computer science ,Test data generation ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,COVID-19 ,Genome, Viral ,Data science ,Article ,Documentation ,Workflow ,Resource (project management) ,Global health ,Humans ,Ready to use ,Pandemics - 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. more...
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- 2021
37. ELIXIR‐EXCELERATE: establishing Europe's data infrastructure for the life science research of the future
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Nils Peder Willassen, Jo McEntyre, Leslie Matalonga, Celia Miguel, Ilkka Lappalainen, John Hancock, Ivo Gut, Gary Saunders, Gabriella Rustici, Andrew Smith, Sirarat Sarntivijai, Robert Finn, Jennifer Harrow, Niklas Blomberg, Jordi Rambla, Steven Newhouse, Helen Parkinson, RS: NUTRIM - R1 - Obesity, diabetes and cardiovascular health, RS: FHML MaCSBio, and Bioinformatica more...
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2019-20 coronavirus outbreak ,Biomedical Research ,Coronavirus disease 2019 (COVID-19) ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Information Storage and Retrieval ,Methods & Resources ,Biology ,Biological Science Disciplines ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,0302 clinical medicine ,Humans ,Molecular Biology ,030304 developmental biology ,computer.programming_language ,0303 health sciences ,General Immunology and Microbiology ,General Neuroscience ,Computational Biology ,S&S: Ethics ,Data science ,Europe ,Science research ,Commentary ,Elixir (programming language) ,computer ,030217 neurology & neurosurgery - Abstract
A new inter-governmental research infrastructure, ELIXIR, aims to unify bioinformatics resources and life science data across Europe, thereby facilitating their mining and (re-)use.© 2021 European Molecular Biology Laboratory. Published under the terms of the CC BY 4.0 license. more...
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- 2021
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38. The Data Use Ontology to streamline responsible access to human biomedical datasets
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Heidi J. Sofia, Kathy Reinold, Petr Holub, Gregory A. Rushton, Sarion R. Bowers, Melissa A. Konopko, Anthony J. Brookes, Chihiro Hata, Jaime M. Guidry Auvil, Giselle Kerry, Stephanie O. M. Dyke, Rebecca R. Boyles, Tony Burdett, Mallory A. Freeberg, Fabian Prasser, Soichi Ogishima, Jordi Rambla, Aina Jene, Matthew Brush, Mélanie Courtot, Ilia Tulchinsky, Esther van Enckevort, Minae Kawashima, Moran N. Cabili, Jonathan Lawson, Laura A.D. Paglione, Helen Parkinson, Tommi Nyrönen, Adrian Thorogood, Satoshi Nagaie, Craig Voisin, Anthony A. Philippakis, Pinar Alper, Haoyuan Li, Susheel Varma, John Dylan Spalding, Hayley L. Clissold, Gary I. Saunders, Natsuko Yamamoto, Nicola Mulder, Morris A. Swertz, Ravi N. Pandya, Melissa Haendel, Mikael Linden, Mizuki Morita, Vivian Ota Wang, Jean Muller, Chisato Yamasaki, Lyndon Zass, Francis Jeanson, Irene Kyomugisha, Stacey Donnelly, Tiffany Boughtwood, Laura Lyman Rodriguez, Jamal Nasir, Andrea Saltzman, and Shuichi Kawashima more...
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secondary data use ,Vocabulary ,Technology ,Computer science ,media_common.quotation_subject ,Data management ,data restrictions ,Ontology (information science) ,Biochemistry, biophysics & molecular biology [F05] [Life sciences] ,Ontologia ,Biochemistry, Genetics and Molecular Biology (miscellaneous) ,03 medical and health sciences ,Annotation ,0302 clinical medicine ,data access ,Conjunts de dades ,Genetics ,ontology ,Biochimie, biophysique & biologie moléculaire [F05] [Sciences du vivant] ,automated data access ,030304 developmental biology ,media_common ,FAIR ,Computer science [C05] [Engineering, computing & technology] ,0303 health sciences ,business.industry ,Authorization ,GA4GH ,standard ,Ontology language ,Sciences informatiques [C05] [Ingénierie, informatique & technologie] ,controlled access ,Data science ,3. Good health ,Data access ,Community awareness ,consent ,business ,030217 neurology & neurosurgery - 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., Graphical abstract, Highlights Biomedical advances depend on the efficient and compliant re-use of sensitive human data The Data Use Ontology standardizes terms and definitions for consented data uses The Data Use Ontology facilitates discovery of, request for, and access to datasets Over 200,000 datasets worldwide have been annotated using the Data Use Ontology, The GA4GH Data Use Ontology (DUO) provides unambiguous, machine-readable standard language for consent forms and the data sharing policies they represent. Lawson et al. describe the DUO standard and implementations throughout the data access workflow to expedite data access while maintaining or improving compliant processes. more...
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- 2021
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39. GA4GH: International policies and standards for data sharing across genomic research and healthcare
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Amber L. Johns, Ian Fore, Juha Törnroos, Melissa Haendel, Bimal Chaudhari, J. Patrick Woolley, Brian Walsh, Susan Fairley, Jonathan A. Tedds, Jessica Vamathevan, Martin Kuba, Clara L. Gaff, Ksenia Zaytseva, Sabine Oesterle, David Bujold, Sarion R. Bowers, Alexander Kanitz, Jordi Rambla, Anthony J. Brookes, Alice L. Mann, Gregory A. Rushton, Paul Flicek, Seik-Soon Khor, Khalid A. Fakhro, Aina Jene, Miro Cupak, Moran N. Cabili, Emilio Palumbo, Nathan C. Sheffield, Vivian Ota Wang, James K. Bonfield, Julius O.B. Jacobsen, Michael M. Hoffman, Neerjah Skantharajah, Ewan Birney, Rasko Leinonen, Anna Middleton, Anneke M. Lucassen, Ania Niewielska, Angela Page, Jeffrey Niu, Alastair A. Thomson, Elena M. Ghanaim, Albert V. Smith, Megan Doerr, Lena I. Dolman, Arcadi Navarro, Ada Hamosh, Sean Upchurch, Michael Baudis, Jerome Kelleher, Marc Fiume, Mikael Linden, Roderic Guigó, Orion J. Buske, Tristan H. Nelson, Kyle Ellrott, Lauren A. Fromont, Alex H. Wagner, Alexander Senf, Tommi Nyrönen, Michele Mattioni, David Haussler, Alejandro Metke-Jimenez, Francis Jeanson, Mélanie Courtot, David Hansen, Matthew H. Brush, Helen Parkinson, Peter Goodhand, Lindsay Smith, Jonathan Fuerth, Stephanie Li, Tim Beck, Debyani Chakravarty, Kristina Kekesi-Lafrance, Giselle Kerry, James A. Eddy, Torsten Schwede, Jaime M. Guidry Auvil, Xianglin Liu, Soichi Ogishima, Fiona Cunningham, Oliver Hofmann, Dean Hartley, Amy Nisselle, Katsushi Tokunaga, Alfonso Valencia, Hidewaki Nakagawa, Kurt W. Rodarmer, Lawrence J. Babb, Heidi J. Sofia, David Glazer, Angel Pizarro, Ammar Husami, Gil Alterovitz, Serena Scollen, J. Michael Cherry, Helen V. Firth, Zornitza Stark, Monica C. Munoz-Torres, Daniel L Cameron, Robert R. Freimuth, Manuel Rueda, Stephanie O.M. Dyke, Makoto Suematsu, Christina K. Yung, Rosalyn S. Ryan, Chisato Yamasaki, Michael S. Fitzsimons, Amanda B. Spurdle, Renee A. Rider, Karen Eilbeck, Ashley E. Hobb, Roman Valls Guimera, Calvin W. L. Ho, Robert L. Davies, Maxmillian P. Barkley, Malachi Griffith, Rishi Nag, Javier Lopez, Jacob Shujui Hsu, Isuru Udara Liyanage, Petr Holub, Dylan Spalding, Reece K. Hart, Barbara J. Wold, Fruzsina Molnár-Gábor, Sarah E. Hunt, Augusto Rendon, Danielle Denisko, Dipayan Gupta, Obi L. Griffith, Robert J. Carroll, Patrick Tan, Craig Voisin, Saumya Shekhar Jamuar, Mallory A. Freeberg, Michael Brudno, Andreas Prlic, Kenjiro Kosaki, Shu Hui Chen, Edward S. Dove, Tony Burdett, Anthony A. Philippakis, Richard Milne, Bartha Maria Knoppers, Kathryn North, David Torrents, Eva C. Winkler, Marc S. Williams, Melissa A. Konopko, Rachele M. Hendricks-Sturrup, Brian O'Connor, Grant M. Wood, Robert L. Grossman, Timothy L. Tickle, Michael F. Lin, Laura Lyman Rodriguez, Weiniu Gan, Laura A.D. Paglione, Justina Chung, Thomas M. Keane, Susan E. Wallace, Lyndon J. Zass, Heidi L. Rehm, Kazuto Kato, Alexander Bernier, Nicola Mulder, Jamal Nasir, Yann Joly, Junjun Zhang, Adrian Thorogood, Lincoln Stein, Guillaume Bourque, L. Jonathan Dursi, Tudor Groza, Jean-Pierre Hubaux, Coby Viner, Helen Schuilenburg, Sergi Beltran, Michael J.S. Beauvais, Hayley L. Clissold, Elizabeth L. Janes, Jacques S. Beckmann, Michael Lukowski, Melissa S. Cline, John F. Marshall, Alan F. Rubin, Tiffany Boughtwood, Peter N. Robinson, Robert C. Green, Robert Cook-Deegan, Esmeralda Casas-Silva, Jeremy Adams, Steven J.M. Jones, Gary I. Saunders, Danya F. Vears, Jonathan Lawson, Andrew D. Yates, David Bernick, Susheel Varma, Middleton, Anna [0000-0003-3103-8098], Milne, Richard [0000-0002-8770-2384], Apollo - University of Cambridge Repository, Abigail Wexner Research Institute, Academy of Finland, Medical Research Future Fund, BioBank Japan, Canada Foundation for Innovation, Canadian Institutes of Health Research, European Commission, German Research Foundation, Genome Canada, Google, Howard Hughes Medical Institute, Instituto de Salud Carlos III, Japan Agency for Medical Research and Development, Mayo Clinic, Fundación 'la Caixa', Ministère de l'Économie et de l'Innovation (Québec), Monarch Initiative, National Human Genome Research Institute (US), National University of Singapore, Agency for Science, Technology and Research A*STAR (Singapore), National Health and Medical Research Council (Australia), National Institutes of Health (US), National Institute of General Medical Sciences (US), Swiss Institute of Bioinformatics, State Secretariat for Education, Research and Innovation (Switzerland), Terry Fox Research Institute, Canada Research Chairs, European Molecular Biology Laboratory, Ministry of Research, Innovation and Science (Ontario), Ontario Genomics Institute, Natural Sciences and Engineering Research Council of Canada, Wellcome Trust, and National Taiwan University more...
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Standards ,Knowledge management ,data sharing ,precision medicine ,Interoperability ,Technical standard ,data federation ,Article ,3105 Genetics ,03 medical and health sciences ,0302 clinical medicine ,data access ,learning health system ,Clinical Research ,Health care ,genomics ,Genetics ,Data federation ,Data access ,030304 developmental biology ,0303 health sciences ,business.industry ,Precision medicine ,Human Genome ,Learning health system ,3 Good Health and Well Being ,Bioethics ,Genomics ,Health Services ,3. Good health ,Data sharing ,Data aggregator ,Policy ,030220 oncology & carcinogenesis ,FOS: Biological sciences ,standards ,Business ,Generic health relevance ,bioethics ,policy ,31 Biological Sciences - Abstract
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., B.P.C. acknowledges funding from Abigail Wexner Research Institute at Nationwide Children’s Hospital; T.H. Nyrönen acknowledges funding from Academy of Finland grant #31996; A.M.-J., K.N., T.F.B., O.M.H., and Z.S. acknowledge funding from Australian Medical Research Future Fund; M.S. acknowledges funding from Biobank Japan; D. Bujold and S.J.M.J. acknowledge funding from Canada Foundation for Innovation; L.J.D. acknowledges funding from Canada Foundation for Innovation Cyber Infrastructure grant #34860; D. Bujold and G.B. acknowledge funding from CANARIE; L.J.D. acknowledges funding from CANARIE Research Data Management contract #RDM-090 (CHORD) and #RDM2-053 (ClinDIG); K.K.-L. acknowledges funding from CanSHARE; T.L.T. acknowledges funding from Chan Zuckerberg Initiative; T. Burdett acknowledges funding from Chan Zuckerberg Initiative grant #2017-171671; D. Bujold, G.B., and L.D.S. acknowledge funding from CIHR; L.J.D. acknowledges funding from CIHR grant #404896; M.J.S.B. acknowledges funding from CIHR grant #SBD-163124; M. Courtot and M. Linden acknowledge funding from CINECA project EU Horizon 2020 grant #825775; D. Bujold and G.B. acknowledge funding from Compute Canada; F.M.-G. acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – NFDI 1/1 “GHGA – German Human Genome-Phenome Archive; R.M.H.-S. acknowledges funding from Duke-Margolis Center for Health Policy; S.B. and A.J.B. acknowledge funding from EJP-RD EU Horizon 2020 grant #825575; A. Niewielska, A.K., D.S., G.I.S., J.A.T., J.R., M.A.K., M. Baudis, M. Linden, S.B., S.S., T.H. Nyrönen, and T.M.K. acknowledge funding from ELIXIR; A. Niewielska acknowledges funding from EOSC-Life EU Horizon 2020 grant #824087; J.-P.H. acknowledges funding from ETH Domain Strategic Focal Area “Personalized Health and Related Technologies (PHRT)” grant #2017-201; F.M.-G. acknowledges funding from EUCANCan EU Horizon 2020 grant #825835; B.M.K., D. Bujold, G.B., L.D.S., M.J.S.B., N.S., S.E.W., and Y.J. acknowledge funding from Genome Canada; B.M.K., M.J.S.B., S.E.W., and Y.J. acknowledge funding from Genome Quebec; F.M.-G. acknowledges funding from German Human Genome-Phenome Archive; C. Voisin acknowledges funding from Google; A.J.B. acknowledges funding from Health Data Research UK Substantive Site Award; D.H. acknowledges funding from Howard Hughes Medical Institute; S.B. acknowledges funding from Instituto de Salud Carlos III; S.-S.K. and K.T. acknowledge funding from Japan Agency for Medical Research and Development (AMED); S. Ogishima acknowledges funding from Japan Agency for Medical Research and Development (AMED) grant #20kk0205014h0005; C.Y. and K. Kosaki acknowledge funding from Japan Agency for Medical Research and Development (AMED) grant #JP18kk0205012; GEM Japan acknowledges funding from Japan Agency for Medical Research and Development (AMED) grants #19kk0205014h0004, #20kk0205014h0005, #20kk0205013h0005, #20kk0205012h0005, #20km0405401h0003, and #19km0405001h0104; J.R. acknowledges funding from La Caixa Foundation under project #LCF/PR/GN13/50260009; R.R.F. acknowledges funding from Mayo Clinic Center for Individualized Medicine; Y.J. and S.E.W. acknowledge funding from Ministère de l’Économie et de l’Innovation du Québec for the Can-SHARE Connect Project; S.E.W. and S.O.M.D. acknowledge funding from Ministère de l’Économie et de l’Innovation du Québec for the Can-SHARE grant #141210; M.A.H., M.C.M.-T., J.O.J., H.E.P., and P.N.R. acknowledge funding from Monarch Initiative grant #R24OD011883 and Phenomics First NHGRI grant #1RM1HG010860; A.L.M. and E.B. acknowledge funding from MRC grant #MC_PC_19024; P.T. acknowledges funding from National University of Singapore and Agency for Science, Technology and Research; J.M.C. acknowledges funding from NHGRI; A.H.W. acknowledges funding from NHGRI awards K99HG010157, R00HG010157, and R35HG011949; A.M.-J., K.N., D.P.H., O.M.H., T.F.B., and Z.S. acknowledge funding from NHMRC grants #GNT1113531 and #GNT2000001; D.L.C. acknowledges funding from NHMRC Ideas grant #1188098; A.B.S. acknowledges funding from NHMRC Investigator Fellowship grant #APP177524; J.M.C. and L.D.S. acknowledge funding from NIH; A.A.P. acknowledges funding from NIH Anvil; A.V.S. acknowledges funding from NIH contract #HHSN268201800002I (TOPMed Informatics Research Center); S.U. acknowledges funding from NIH ENCODE grant #UM1HG009443; M.C.M.-T. and M.A.H. acknowledge funding from NIH grant #1U13CA221044; R.J.C. acknowledges funding from NIH grants #1U24HG010262 and #1U2COD023196; M.G. acknowledges funding from NIH grant #R00HG007940; J.B.A., S.L., P.G., E.B., H.L.R., and L.S. acknowledge funding from NIH grant #U24HG011025; K.P.E. acknowledges funding from NIH grant #U2C-RM-160010; J.A.E. acknowledges funding from NIH NCATS grant #U24TR002306; M.M. acknowledges funding from NIH NCI contract #HHSN261201400008c and ID/IQ Agreement #17X146 under contract #HHSN2612015000031 and #75N91019D00024; R.M.C.-D. acknowledges funding from NIH NCI grant #R01CA237118; M. Cline acknowledges funding from NIH NCI grant #U01CA242954; K.P.E. acknowledges funding from NIH NCI ITCR grant #1U24CA231877-01; O.L.G. acknowledges funding from NIH NCI ITCR grant #U24CA237719; R.L.G. acknowledges funding from NIH NCI task order #17X147F10 under contract #HHSN261200800001E; A.F.R. acknowledges funding from NIH NHGRI grant #RM1HG010461; N.M. and L.J.Z. acknowledge funding from NIH NHGRI grant #U24HG006941; R.R.F., T.H. Nelson, L.J.B., and H.L.R. acknowledge funding from NIH NHGRI grant #U41HG006834; B.J.W. acknowledges funding from NIH NHGRI grant #UM1HG009443A; M. Cline acknowledges funding from NIH NHLBI BioData Catalyst Fellowship grant #5118777; M.M. acknowledges funding from NIH NHLBI BioData Catalyst Program grant #1OT3HL142478-01; N.C.S. acknowledges funding from NIH NIGMS grant #R35-GM128636; M.C.M.-T., M.A.H., P.N.R., and R.R.F. acknowledge funding from NIH NLM contract #75N97019P00280; E.B. and A.L.M. acknowledge funding from NIHR; R.G. acknowledges funding from Project Ris3CAT VEIS; S.B. acknowledges funding from RD-Connect, Seventh Framework Program grant #305444; J.K. acknowledges funding from Robertson Foundation; S.B. and A.J.B. acknowledge funding from Solve-RD, EU Horizon 2020 grant #779257; T.S. and S. Oesterle acknowledge funding from Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN), supported by the Swiss State Secretariat for Education, Research and Innovation SERI; S.J.M.J. acknowledges funding from Terry Fox Research Institute; A.E.H., M.P.B., M. Cupak, M.F., and J.F. acknowledge funding from the Digital Technology Supercluster; D.F.V. acknowledges funding from the Australian Medical Research Future Fund, as part of the Genomics Health Futures Mission grant #76749; M. Baudis acknowledges funding from the BioMedIT Network project of Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN); B.M.K. acknowledges funding from the Canada Research Chair in Law and Medicine and CIHR grant #SBD-163124; D.S., G.I.S., M.A.K., S.B., S.S., and T.H. Nyrönen acknowledge funding from the EU Horizon 2020 Beyond 1 Million Genomes (B1MG) Project grant #951724; P.F., A.D.Y., F.C., H.S., I.U.L., D. Gupta, M. Courtot, S.E.H., T. Burdett, T.M.K., and S.F. acknowledge funding from the European Molecular Biology Laboratory; Y.J. and S.E.W. acknowledge funding from the Government of Canada; P.G. acknowledges funding from the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-206); J.Z. acknowledges funding from the Government of Ontario; C.K.Y. acknowledges funding from the Government of Ontario, Canada Foundation for Innovation; C. Viner and M.M.H. acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (grant #RGPIN-2015-03948 to M.M.H. and Alexander Graham Bell Canada Graduate Scholarship to C.V.); K.K.-L. acknowledges funding from the Program for Integrated Database of Clinical and Genomic Information; J.K. acknowledges funding from the Robertson Foundation; D.F.V. acknowledges funding from the Victorian State Government through the Operational Infrastructure Support (OIS) Program; A.M.L., R.N., and H.V.F. acknowledge funding from Wellcome (collaborative award); F.C., H.S., P.F., and S.E.H. acknowledge funding from Wellcome Trust grant #108749/Z/15/Z; A.D.Y., H.S., I.U.L., M. Courtot, H.E.P., P.F., and T.M.K. acknowledge funding from Wellcome Trust grant #201535/Z/16/Z; A.M., J.K.B., R.J.M., R.M.D., and T.M.K. acknowledge funding from Wellcome Trust grant #206194; E.B., P.F., P.G., and S.F. acknowledge funding from Wellcome Trust grant #220544/Z/20/Z; A. Hamosh acknowledges funding from NIH NHGRI grant U41HG006627 and U54HG006542; J.S.H. acknowledges funding from National Taiwan University #91F701-45C and #109T098-02; the work of K.W.R. was supported by the Intramural Research Program of the National Library of Medicine, NIH. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. H.V.F. acknowledges funding from Wellcome Grant 200990/A/16/Z ‘Designing, developing and delivering integrated foundations for genomic medicine'. more...
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- 2021
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40. Ready-to-use public infrastructure for global SARS-CoV-2 monitoring
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Marius van den Beek, Milad Miladi, Nathan Coraor, Björn Grüning, Jordi Rambla De Argila, Babita Singh, Simon Gladman, Nathan P. Roach, Sergei L Kosakovsky Pond, Darren P. Martin, Dave Bouvier, Anton Nekrutenko, Frederik Coppens, Wolfgang Maier, Andrew Lonie, Simon Bray, and Dannon Baker more...
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2019-20 coronavirus outbreak ,Public infrastructure ,Coronavirus disease 2019 (COVID-19) ,Computer science ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Pandemic ,Biomedical Engineering ,Molecular Medicine ,Ready to use ,Bioengineering ,Applied Microbiology and Biotechnology ,Data science ,Biotechnology - Published
- 2021
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41. Assessing the role of inversions in maintaining genomic differentiation after secondary contact: local adaptation, genetic incompatibilities, and drift
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Rui Faria, Jeffrey L. Feder, Marina Rafajlović, Arcadi Navarro, and Jordi Rambla
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education.field_of_study ,Empirical data ,Evolutionary biology ,Population ,Genetic algorithm ,Inversion (meteorology) ,Reproductive isolation ,Biology ,education ,Gene ,Flux (metabolism) ,Local adaptation - Abstract
Due to their effects on reducing recombination, chromosomal inversions may play an important role in speciation by establishing and/or maintaining linked blocks of genes causing reproductive isolation (RI) between populations. These views fit empirical data indicating that inversions typically harbour loci involved in RI. However, previous computer simulations of infinite populations with 2-4 loci involved in RI implied that, even with gene flux as low as 10−8between alternative arrangements, inversions may not have large, qualitative advantages over collinear regions in maintaining population differentiation after secondary contact. Here, we report that finite population sizes can help counteract the homogenizing consequences of gene flux, especially when several fitness-related loci reside within the inversion. In these cases, the persistence time of differentiation after secondary contact can be similar to when gene flux is absent, and notably longer than the persistence time without inversions. Thus, despite gene flux, population differentiation may be maintained for up to 100,000 generations, during which time new incompatibilities and/or local adaptations might accumulate and facilitate progress towards speciation. How often these conditions are met in nature remains to be determined. more...
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- 2020
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42. Genome-phenome explorer (GePhEx): a tool for the visualization and interpretation of phenotypic relationships supported by genetic evidence
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Nino Spataro, Arcadi Navarro, Jordi Rambla, Frédéric Haziza, Xavier Farré, Ministerio de Economía y Competitividad (España), Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), European Commission, Generalitat de Catalunya, Instituto de Salud Carlos III, Instituto Nacional de Bioinformática (España), and Red Española de Esclerosis Múltiple more...
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Statistics and Probability ,Linkage disequilibrium ,Computer science ,Locus (genetics) ,Disease ,Computational biology ,Phenome ,Biochemistry ,Genome ,DNA sequencing ,03 medical and health sciences ,0302 clinical medicine ,Humans ,Phenomics ,Molecular Biology ,Gene ,030304 developmental biology ,Genetic association ,0303 health sciences ,High-Throughput Nucleotide Sequencing ,Genetic architecture ,3. Good health ,Computer Science Applications ,Computational Mathematics ,Phenotype ,Computational Theory and Mathematics ,Software ,030217 neurology & neurosurgery ,Genome-Wide Association Study - Abstract
[Motivation] Association studies based on SNP arrays and Next Generation Sequencing technologies have enabled the discovery of thousands of genetic loci related to human diseases. Nevertheless, their biological interpretation is still elusive, and their medical applications limited. Recently, various tools have been developed to help bridging the gap between genomes and phenomes. To our knowledge, however none of these tools allows users to retrieve the phenotype-wide list of genetic variants that may be linked to a given disease or to visually explore the joint genetic architecture of different pathologies., [Results] We present the Genome-Phenome Explorer (GePhEx), a web-tool easing the visual exploration of phenotypic relationships supported by genetic evidences. GePhEx is primarily based on the thorough analysis of linkage disequilibrium between disease-associated variants and also considers relationships based on genes, pathways or drug-targets, leveraging on publicly available variant-disease associations to detect potential relationships between diseases. We demonstrate that GePhEx does retrieve well-known relationships as well as novel ones, and that, thus, it might help shedding light on the patho-physiological mechanisms underlying complex diseases. To this end, we investigate the potential relationship between schizophrenia and lung cancer, first detected using GePhEx and provide further evidence supporting a functional link between them., This work was supported by Ministerio de Economía y Competitividad (MINECO): BFU2015-68649-P (MINECO/FEDER, UE), and by the Agencia Estatal de investigación: AEI-PGC2018-101927-B-I00 (FEDER/UE), by Direcció General de Recerca, Generalitat de Catalunya (2017SGR880) and by the Spanish National Institute of Bioinformatics (PT17/0009/0020), the REEM (RD16/00150017) of the Instituto de Salud Carlos III. This research has also received funding from the European Union's Horizon 2020 research and innovation programme 2014–2020 under Grant Agreement N°. 634143 (MedBioinformatics). more...
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- 2020
43. FaST-LMM for Two-Way Epistasis Tests on High-Performance Clusters
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Xavier Farré, Jordi Rambla De Argila, Sergio Barrachina, Héctor Martínez, Enrique S. Quintana-Ortí, Arcadi Navarro, and Maribel Castillo
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epistasis ,0301 basic medicine ,Bipolar Disorder ,GPUs ,Computer science ,Parallel computing ,030105 genetics & heredity ,Polymorphism, Single Nucleotide ,03 medical and health sciences ,Software ,Genetics ,Humans ,Computer Simulation ,Sensitivity (control systems) ,Genome-wide association studies (GWAS) ,multicore processors ,Molecular Biology ,Multi-core processor ,Models, Genetic ,business.industry ,Multicore processors ,Computational Biology ,Epistasis, Genetic ,ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES ,Computational Mathematics ,030104 developmental biology ,FaST-LMM ,Computational Theory and Mathematics ,Modeling and Simulation ,Epistasis ,Genome-Wide Association Studies (GWAS) ,clusters of computers ,business ,Algorithms ,Clusters of computers - Abstract
[EN] We introduce a version of the epistasis test in FaST-LMM for clusters of multithreaded processors. This new software maintains the sensitivity of the original FaST-LMM while delivering acceleration that is close to linear on 12-16 nodes of two recent platforms, with respect to improved implementation of FaST-LMM presented in an earlier work. This efficiency is attained through several enhancements on the original single-node version of FaST-LMM, together with the development of a message passing interface (MPI)-based version that ensures a balanced distribution of the workload as well as a multigraphics processing unit (GPU) module that can exploit the presence of multiple GPUs per node., The researchers from the Universitat Jaume I were supported by projects TIN2014-53495-R and TIN2017-82972-R of the MINECO and FEDER. more...
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- 2018
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44. Freely accessible ready to use global infrastructure for SARS-CoV-2 monitoring
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Maier, Wolfgang, primary, Bray, Simon, additional, van den Beek, Marius, additional, Bouvier, Dave, additional, Coraor, Nathaniel, additional, Miladi, Milad, additional, Singh, Babita, additional, De Argila, Jordi Rambla, additional, Baker, Dannon, additional, Roach, Nathan, additional, Gladman, Simon, additional, Coppens, Frederik, additional, Martin, Darren P, additional, Lonie, Andrew, additional, Grüning, Björn, additional, Kosakovsky Pond, Sergei L., additional, and Nekrutenko, Anton, additional more...
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- 2021
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45. Leveraging European infrastructures to access 1 million human genomes by 2022
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David Salgado, Helen Parkinson, Jaap Heringa, Ivo Gut, Gert Matthijs, Paul Flicek, Ewan Birney, Peter Goodhand, Andres Metspalu, Ilkka Lappalainen, Aarno Palotie, Rachel Drysdale, Michael Baudis, Bengt Persson, Steven Newhouse, Serena Scollen, Jan O. Korbel, Regina Becker, Nick Juty, Angela Page, Jef Hooyberghs, Francesco Florindi, Susheel Varma, Marc Van den Bulcke, Jordi Rambla, Alfonso Valencia, Brane Leskošek, Morris A. Swertz, Petr Holub, Thomas Keane, Salvador Capella-Gutierrez, Cath Brooksbank, Tommi Nyrönen, Niklas Blomberg, Christophe Béroud, Michaela Th. Mayrhofer, Søren Brunak, Arcadi Navarro, Gary Saunders, Erik Steinfelder, Sergi Beltran, Computer Science, and AIMMS more...
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Biomedical Research ,Big data ,Declaration ,Genomics ,Human genomics ,Biology ,Article ,03 medical and health sciences ,0302 clinical medicine ,SDG 17 - Partnerships for the Goals ,SDG 3 - Good Health and Well-being ,Human Genome Project ,Health care ,Genetics ,Humans ,Clinical genetics ,Molecular Biology ,Genetics (clinical) ,030304 developmental biology ,0303 health sciences ,Genome, Human ,business.industry ,Precision medicine ,Data science ,Europe ,Data sharing ,Next-generation sequencing ,Human genome ,business ,Medical genomics ,030217 neurology & neurosurgery - Abstract
Human genomics is undergoing a step change from being a predominantly research-driven activity to one driven through health care as many countries in Europe now have nascent precision medicine programmes. To maximize the value of the genomic data generated, these data will need to be shared between institutions and across countries. In recognition of this challenge, 21 European countries recently signed a declaration to transnationally share data on at least 1 million human genomes by 2022. In this Roadmap, we identify the challenges of data sharing across borders and demonstrate that European research infrastructures are well-positioned to support the rapid implementation of widespread genomic data access. more...
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- 2019
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46. European Genome-Phenome Archive (EGA) - Granular Solutions for the Next 10 Years
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Jordi Rambla de Argila, Audald Lloret-Villas, and Dietmar Fernandez-Orth
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0303 health sciences ,business.industry ,Computer science ,Phenome ,Genome ,Data science ,Data sharing ,03 medical and health sciences ,0302 clinical medicine ,Data access ,Data visualization ,Data quality ,Community awareness ,Elixir (programming language) ,business ,computer ,030217 neurology & neurosurgery ,030304 developmental biology ,computer.programming_language - Abstract
The European Genome-phenome Archive (EGA) is a repository that facilitates access and management for long-term archival of human biomolecular data. The EGA is co-managed by the European Bioinformatics Institute (EBI) and the Centre for Genomic Regulation (CRG). As the omics community awareness of data sharing and reproducibility increases, complex services and granular solutions are needed from the repositories such as EGA. Not only will we introduce the EGA environment but we will also present advanced features designed for a wide range of users. These new tools and technologies include the EGA Beacon (developed within the GA4GH and ELIXIR framework), infrastructures for data access and retrieval, as well as data quality control and visualisation projects. more...
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- 2019
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47. Federated discovery and sharing of genomic data using Beacons
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Maximilian Haeussler, Stephen Keenan, Heinz Stockinger, Ilkka Lappalainen, Stephen T. Sherry, Mikael Linden, J. Dylan Spalding, David Lloyd, Serena Scollen, Saif Ur-Rehman, Miroslav Cupak, Sabela de la Torre, Susheel Varma, Paul Flicek, Anthony J. Brookes, Michael Baudis, Stephanie O.M. Dyke, Lena Dolman, Gary Saunders, Marc Fiume, Knox Carey, Juha Törnroos, Peter Goodhand, Angela Page, David Haussler, Jordi Rambla, University of Zurich, and Fiume, Marc more...
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Genetic testing ,Computer science ,Genomic data ,Information Dissemination ,Biomedical Engineering ,2204 Biomedical Engineering ,Information Storage and Retrieval ,Bioengineering ,Balises ,Applied Microbiology and Biotechnology ,Article ,World Wide Web ,03 medical and health sciences ,0302 clinical medicine ,Genetics research ,2402 Applied Microbiology and Biotechnology ,Humans ,Protocol (object-oriented programming) ,030304 developmental biology ,0303 health sciences ,1502 Bioengineering ,Extramural ,Genomics ,Publisher Correction ,10124 Institute of Molecular Life Sciences ,3. Good health ,Beacon ,Data sharing ,Genòmica ,1313 Molecular Medicine ,1305 Biotechnology ,570 Life sciences ,biology ,Molecular Medicine ,030217 neurology & neurosurgery ,Biotechnology - Abstract
M.F. and S.O.M.D. are supported by Genome Quebec, Genome Canada, the Government of Canada, and the Ministère de l’Économie, Innovation et Exportation du Québec (Can-SHARE grant 141210); S.O.M.D. is supported by the Canadian Institutes of Health Research (grants EP1-120608; EP2-120609); M.H. is supported by BD2K NIH/NCI 5U54HG007990-02; S. Scollen, S.V., M.B., I.L., J.T., S.U.-R., S.d.l.T., M.L., H.S. and the EGA are supported by ELIXIR, the research infrastructure for life-science data. This work was supported by ELIXIR-EXCELERATE, funded by the European Commission within the Research Infrastructures programme of Horizon 2020, grant agreement number 676559 (J.D.S., I.L.), the Wellcome Trust grant numbers WT201535/Z/16/Z (P.F.) and WT098051 (S.K., D.L., P.F.). A.J.B. is supported by the European Union FP7 Programme ‘EMIF’ IMI-JU grant no. 115372, and H2020 Programme ‘GCOF’ grant no. 643439 more...
- Published
- 2019
48. Registered access: authorizing data access
- Author
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Dyke, Stephanie O M, Linden, Mikael, Lappalainen, Ilkka, De Argila, Jordi Rambla, et al, Baudis, Michael, University of Zurich, and Dyke, Stephanie O M
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2716 Genetics (clinical) ,1311 Genetics ,570 Life sciences ,biology ,10124 Institute of Molecular Life Sciences - Published
- 2018
49. Analysis of Five Gene Sets in Chimpanzees Suggests Decoupling between the Action of Selection on Protein-Coding and on Noncoding Elements
- Author
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Daniel L. Halligan, Gabriel Santpere, Jose Maria Heredia-Genestar, Arcadi Navarro, Elena Carnero-Montoro, François Serra, Natalia Petit, Christina Hvilsom, Jordi Rambla, Elena Bosch, Hernán Dopazo, Ministerio de Ciencia e Innovación (España), Generalitat de Catalunya, Instituto de Salud Carlos III, and European Commission more...
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Ximpanzés -- Genètica ,Untranslated region ,Chimpanzee ,Pan troglodytes ,Natural selection ,Biochemical pathways ,Fraction of adaptive subsubstitution (a) and adaptive substitution rate (oa) ,Fraction of adaptive substitution (α) and adaptive substitution rate (ω α) ,Biology ,medicine.disease_cause ,Polymorphism, Single Nucleotide ,Evolution, Molecular ,Open Reading Frames ,Effective population size ,Untranslated Regions ,Genetics ,medicine ,Animals ,Humans ,Parkinson ,Selection, Genetic ,Chimpanzees ,Promoter Regions, Genetic ,Gene ,Ecology, Evolution, Behavior and Systematics ,Selection (genetic algorithm) ,Complement (set theory) ,Mutation ,Seleccion Positiva ,Intron ,Complement System Proteins ,Parkinson, Malaltia de -- Aspectes genètics ,Actins ,Introns ,Alzheimer, Malaltia d' -- Aspectes genètics ,fraction of adaptive substitution (α) and adaptive substitution rate (ωα) ,Genes ,Evolutionary biology ,Alzheimer ,Distribution of fitness effects ,Genomica ,Research Article - Abstract
Santpere, Gabriel et al., We set out to investigate potential differences and similarities between the selective forces acting upon the coding and noncoding regions of five different sets of genes defined according to functional and evolutionary criteria: 1) two reference gene sets presenting accelerated and slow rates of protein evolution (the Complement and Actin pathways); 2) a set of genes with evidence of accelerated evolution in at least one of their introns; and 3) two gene sets related to neurological function (Parkinson’s and Alzheimer’s diseases). To that effect, we combine human–chimpanzee divergence patterns with polymorphism data obtained from target resequencing 20 central chimpanzees, our closest relatives with largest long-term effective population size. By using the distribution of fitness effect-alpha extension of the McDonald–Kreitman test, we reproduce inferences of rates of evolution previously based only on divergence data on both coding and intronic sequences and also obtain inferences for other classes of genomic elements (untranslated regions, promoters, and conserved noncoding sequences). Our results suggest that 1) the distribution of fitness effect-alpha method successfully helps distinguishing different scenarios of accelerated divergence (adaptation or relaxed selective constraints) and 2) the adaptive history of coding and noncoding sequences within the gene sets analyzed is decoupled., This work was supported by Ministerio de Ciencia e Innovación, Spain (SAF2011-29239 to E.B. and BFU2012-38236 to A.N.), Direcció General de Recerca, Generalitat de Catalunya (2014SGR1311 and 2014SGR866), the Spanish National Institute of Bioinfomatics of the Instituto de Salud Carlos III (PT13/0001/0026), and FEDER (Fondo Europeo de Desarrollo Regional)/FSE (Fondo Social Europeo). more...
- Published
- 2015
- Full Text
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50. Accelerating FaST-LMM for Epistasis Tests
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Xavier Farré, Sergio Barrachina, Jordi Rambla De Argila, Héctor Martínez, Maribel Castillo, Arcadi Navarro, and Enrique S. Quintana-Ortí
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0301 basic medicine ,Coprocessor ,Acceleration factor ,Computer science ,business.industry ,0206 medical engineering ,02 engineering and technology ,Python (programming language) ,Supercomputer ,Graphics accelerator ,Computational science ,03 medical and health sciences ,030104 developmental biology ,Software ,Epistasis ,Graphics ,business ,computer ,020602 bioinformatics ,computer.programming_language - Abstract
We introduce an enhanced version of FaST-LMM that maintains the sensitivity of this software when applied to identify epistasis interactions while delivering an acceleration factor that is close to 7.5\(\times \) on a server equipped with a state-of-the-art graphics coprocessor. This performance boost is obtained from the combined effects of integrating a dictionary for faster storage of the test results; a re-organization of the original FaST-LMM Python code; and off-loading of compute-intensive parts to the graphics accelerator. more...
- Published
- 2017
- Full Text
- View/download PDF
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