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Raycheva R, Kostadinov K, Mitova E, Iskrov G, Stefanov G, Vakevainen M, Elomaa K, Man YS, Gross E, Zschüntzsch J, Röttger R, Stefanov R. Landscape analysis of available European data sources amenable for machine learning and recommendations on usability for rare diseases screening. Orphanet J Rare Dis 2024; 19:147. [PMID: 38582900 PMCID: PMC10998425 DOI: 10.1186/s13023-024-03162-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/30/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Patient registries and databases are essential tools for advancing clinical research in the area of rare diseases, as well as for enhancing patient care and healthcare planning. The primary aim of this study is a landscape analysis of available European data sources amenable to machine learning (ML) and their usability for Rare Diseases screening, in terms of findable, accessible, interoperable, reusable(FAIR), legal, and business considerations. Second, recommendations will be proposed to provide a better understanding of the health data ecosystem. METHODS In the period of March 2022 to December 2022, a cross-sectional study using a semi-structured questionnaire was conducted among potential respondents, identified as main contact person of a health-related databases. The design of the self-completed questionnaire survey instrument was based on information drawn from relevant scientific publications, quantitative and qualitative research, and scoping review on challenges in mapping European rare disease (RD) databases. To determine database characteristics associated with the adherence to the FAIR principles, legal and business aspects of database management Bayesian models were fitted. RESULTS In total, 330 unique replies were processed and analyzed, reflecting the same number of distinct databases (no duplicates included). In terms of geographical scope, we observed 24.2% (n = 80) national, 10.0% (n = 33) regional, 8.8% (n = 29) European, and 5.5% (n = 18) international registries coordinated in Europe. Over 80.0% (n = 269) of the databases were still active, with approximately 60.0% (n = 191) established after the year 2000 and 71.0% last collected new data in 2022. Regarding their geographical scope, European registries were associated with the highest overall FAIR adherence, while registries with regional and "other" geographical scope were ranked at the bottom of the list with the lowest proportion. Responders' willingness to share data as a contribution to the goals of the Screen4Care project was evaluated at the end of the survey. This question was completed by 108 respondents; however, only 18 of them (16.7%) expressed a direct willingness to contribute to the project by sharing their databases. Among them, an equal split between pro-bono and paid services was observed. CONCLUSIONS The most important results of our study demonstrate not enough sufficient FAIR principles adherence and low willingness of the EU health databases to share patient information, combined with some legislation incapacities, resulting in barriers to the secondary use of data.
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Affiliation(s)
- Ralitsa Raycheva
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria.
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria.
| | - Kostadin Kostadinov
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Elena Mitova
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Georgi Iskrov
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Georgi Stefanov
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Merja Vakevainen
- Pfizer Biopharmaceuticals Group, Medical Affairs, Helsinki, Finland
| | | | - Yuen-Sum Man
- Global Medical Affairs Rare Disease, Novo Nordisk Health Care AG, Zurich, Switzerland
| | - Edith Gross
- EURORDIS - Rare Diseases Europe, 96 Rue Didot, Paris, 75014, France
| | - Jana Zschüntzsch
- Department of Neurology, University Medical Center, Göttingen, Germany
| | - Richard Röttger
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Rumen Stefanov
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
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Zuiderwijk A, Türk BO, Brazier F. Identifying the most important facilitators of open research data sharing and reuse in Epidemiology: A mixed-methods study. PLoS One 2024; 19:e0297969. [PMID: 38330007 PMCID: PMC10852342 DOI: 10.1371/journal.pone.0297969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 01/15/2024] [Indexed: 02/10/2024] Open
Abstract
To understand how open research data sharing and reuse can be further improved in the field of Epidemiology, this study explores the facilitating role that infrastructural and institutional arrangements play in this research discipline. It addresses two research questions: 1) What influence do infrastructural and institutional arrangements have on open research data sharing and reuse practices in the field of Epidemiology? And 2) how could infrastructural and institutional instruments used in Epidemiology potentially be useful to other research disciplines? First, based on a systematic literature review, a conceptual framework of infrastructural and institutional instruments for open research data facilitation is developed. Second, the conceptual framework is applied in interviews with Epidemiology researchers. The interviews show that two infrastructural and institutional instruments have a very high influence on open research data sharing and reuse practices in the field of Epidemiology, namely (a) access to a powerful search engine that meets open data search needs and (b) support by data stewards and data managers. Third, infrastructural and institutional instruments with a medium, high, or very high influence were discussed in a research workshop involving data stewards and research data officers from different research fields. This workshop suggests that none of the influential instruments identified in the interviews are specific to Epidemiology. Some of our findings thus seem to apply to multiple other disciplines. This study contributes to Science by identifying field-specific facilitators and challenges for open research data in Epidemiology, while at the same time revealing that none of the identified influential infrastructural and institutional instruments were specific to this field. Practically, this implies that open data infrastructure developers, policymakers, and research funding organizations may apply certain infrastructural and institutional arrangements to multiple research disciplines to facilitate and enhance open research data sharing and reuse.
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Affiliation(s)
- Anneke Zuiderwijk
- Faculty of Technology, Policy and Management, Delft University of Technology, Delft, the Netherlands
| | - Berkay Onur Türk
- Education and Student Affairs, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Frances Brazier
- Faculty of Technology, Policy and Management, Delft University of Technology, Delft, the Netherlands
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Bernier A, Knoppers BM, Bermudez P, Beauvais MJS, Thorogood A. Open Data governance at the Canadian Open Neuroscience Platform (CONP): From the Walled Garden to the Arboretum. Gigascience 2024; 13:giad114. [PMID: 38217404 PMCID: PMC10787360 DOI: 10.1093/gigascience/giad114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/14/2023] [Accepted: 12/10/2023] [Indexed: 01/15/2024] Open
Abstract
Scientific research communities pursue dual imperatives in implementing strategies to share their data. These communities attempt to maximize the accessibility of biomedical data for downstream research use, in furtherance of open science objectives. Simultaneously, such communities safeguard the interests of research participants through data stewardship measures and the integration of suitable risk disclosures to the informed consent process. The Canadian Open Neuroscience Platform (CONP) convened an Ethics and Governance Committee composed of experts in bioethics, neuroethics, and law to develop holistic policy tools, organizational approaches, and technological supports to align the open governance of data with ethical and legal norms. The CONP has adopted novel platform governance methods that favor full data openness, legitimated through the use of robust deidentification processes and informed consent practices. The experience of the CONP is articulated as a potential template for other open science efforts to further build upon. This experience highlights informed consent guidance, deidentification practices, ethicolegal metadata, platform-level norms, and commercialization and publication policies as the principal pillars of a practicable approach to the governance of open data. The governance approach adopted by the CONP stands as a viable model for the broader neuroscience and open science communities to adopt for sharing data in full open access.
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Affiliation(s)
- Alexander Bernier
- Centre of Genomics and Policy, Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, 740, Dr Penfield Ave, suite 5200, Montréal, Québec H3A 0G1, Canada
| | - Bartha M Knoppers
- Centre of Genomics and Policy, Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, 740, Dr Penfield Ave, suite 5200, Montréal, Québec H3A 0G1, Canada
| | - Patrick Bermudez
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada
| | - Michael J S Beauvais
- Faculty of Law, University of Toronto, Falconer Hall, 84 Queens Park, Toronto, Ontario M5S 2C5, Canada
| | - Adrian Thorogood
- The Terry Fox Research Institute, 110 Pine Ave W, Montreal, Quebec H2W IR7, Canada
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Skantharajah N, Baichoo S, Boughtwood TF, Casas-Silva E, Chandrasekharan S, Dave SM, Fakhro KA, Falcon de Vargas AB, Gayle SS, Gupta VK, Hendricks-Sturrup R, Hobb AE, Li S, Llamas B, Lopez-Correa C, Machirori M, Melendez-Zajgla J, Millner MA, Page AJ, Paglione LD, Raven-Adams MC, Smith L, Thomas EM, Kumuthini J, Corpas M. Equity, diversity, and inclusion at the Global Alliance for Genomics and Health. CELL GENOMICS 2023; 3:100386. [PMID: 37868041 PMCID: PMC10589617 DOI: 10.1016/j.xgen.2023.100386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/24/2023]
Abstract
A lack of diversity in genomics for health continues to hinder equitable leadership and access to precision medicine approaches for underrepresented populations. To avoid perpetuating biases within the genomics workforce and genomic data collection practices, equity, diversity, and inclusion (EDI) must be addressed. This paper documents the journey taken by the Global Alliance for Genomics and Health (a genomics-based standard-setting and policy-framing organization) to create a more equitable, diverse, and inclusive environment for its standards and members. Initial steps include the creation of two groups: the Equity, Diversity, and Inclusion Advisory Group and the Regulatory and Ethics Diversity Group. Following a framework that we call "Reflected in our Teams, Reflected in our Standards," both groups address EDI at different stages in their policy development process.
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Affiliation(s)
- Neerjah Skantharajah
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Global Alliance for Genomics and Health, Toronto, ON, Canada
| | | | - Tiffany F. Boughtwood
- Australian Genomics, Parkville, VIC, Australia
- Murdoch Children’s Research Institute, Parkville, VIC, Australia
| | | | | | - Sanjay M. Dave
- Department of Biotechnology, Hemchandracharya North Gujarat University, Patan, Gujarat, India
| | - Khalid A. Fakhro
- Department of Human Genetics, Sidra Medicine, Doha, Qatar
- Department of Genetic Medicine, Weill Cornell Medical College, Doha, Qatar
| | - Aida B. Falcon de Vargas
- Hospital Vargas de Caracas, Vargas Medical School, Universidad Central de Venezuela, Caracas, Venezuela
- Hospital de Clínicas Caracas, Caracas, Venezuela
| | | | - Vivek K. Gupta
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia
| | | | | | - Stephanie Li
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- Broad Institute, Cambridge, MA, USA
| | - Bastien Llamas
- Australian Centre for Ancient DNA, School of Biological Sciences and The Environment Institute, University of Adelaide, Adelaide, SA, Australia
- ARC Centre of Excellence for Australian Biodiversity and Heritage, University of Adelaide, Adelaide, SA, Australia
- National Centre for Indigenous Genomics, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
- Indigenous Genomics, Telethon Kids Institute, Adelaide, SA, Australia
| | | | - Mavis Machirori
- Ada Lovelace Institute, London, UK
- PEALS, Newcastle University, Newcastle Upon Tyne, UK
| | | | - Mareike A. Millner
- Maastricht University, Health Law and Governance Group, Maastricht, the Netherlands
| | - Angela J.H. Page
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- Broad Institute, Cambridge, MA, USA
| | - Laura D. Paglione
- Spherical Cow Group, New York, NY, USA
- Laura Paglione LLC, New York, NY, USA
| | - Maili C. Raven-Adams
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- Wellcome Sanger Institute, Hinxton, UK
| | - Lindsay Smith
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Global Alliance for Genomics and Health, Toronto, ON, Canada
| | - Ericka M. Thomas
- The All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Judit Kumuthini
- South African National Bioinformatics Institute, University of Western Cape, Cape Town, South Africa
| | - Manuel Corpas
- School of Life Sciences, University of Westminster, London, UK
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Price E, Fedida LM, Pugacheva EM, Ji YJ, Loukinov D, Lobanenkov VV. An updated catalog of CTCF variants associated with neurodevelopmental disorder phenotypes. Front Mol Neurosci 2023; 16:1185796. [PMID: 37324587 PMCID: PMC10264798 DOI: 10.3389/fnmol.2023.1185796] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 05/02/2023] [Indexed: 06/17/2023] Open
Abstract
Introduction CTCF-related disorder (CRD) is a neurodevelopmental disorder (NDD) caused by monoallelic pathogenic variants in CTCF. The first CTCF variants in CRD cases were documented in 2013. To date, 76 CTCF variants have been further described in the literature. In recent years, due to the increased application of next-generation sequencing (NGS), growing numbers of CTCF variants are being identified, and multiple genotype-phenotype databases cataloging such variants are emerging. Methods In this study, we aimed to expand the genotypic spectrum of CRD, by cataloging NDD phenotypes associated with reported CTCF variants. Here, we systematically reviewed all known CTCF variants reported in case studies and large-scale exome sequencing cohorts. We also conducted a meta-analysis using public variant data from genotype-phenotype databases to identify additional CTCF variants, which we then curated and annotated. Results From this combined approach, we report an additional 86 CTCF variants associated with NDD phenotypes that have not yet been described in the literature. Furthermore, we describe and explain inconsistencies in the quality of reported variants, which impairs the reuse of data for research of NDDs and other pathologies. Discussion From this integrated analysis, we provide a comprehensive and annotated catalog of all currently known CTCF mutations associated with NDD phenotypes, to aid diagnostic applications, as well as translational and basic research.
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Torre-Healy LA, Kawalerski RR, Oh K, Chrastecka L, Peng XL, Aguirre AJ, Rashid NU, Yeh JJ, Moffitt RA. Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model. Commun Biol 2023; 6:163. [PMID: 36765128 PMCID: PMC9918476 DOI: 10.1038/s42003-023-04461-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 01/11/2023] [Indexed: 02/12/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease for which potent therapies have limited efficacy. Several studies have described the transcriptomic landscape of PDAC tumors to provide insight into potentially actionable gene expression signatures to improve patient outcomes. Despite centralization efforts from multiple organizations and increased transparency requirements from funding agencies and publishers, analysis of public PDAC data remains difficult. Bioinformatic pitfalls litter public transcriptomic data, such as subtle inclusion of low-purity and non-adenocarcinoma cases. These pitfalls can introduce non-specificity to gene signatures without appropriate data curation, which can negatively impact findings. To reduce barriers to analysis, we have created pdacR ( http://pdacR.bmi.stonybrook.edu , github.com/rmoffitt/pdacR), an open-source software package and web-tool with annotated datasets from landmark studies and an interface for user-friendly analysis in clustering, differential expression, survival, and dimensionality reduction. Using this tool, we present a multi-dataset analysis of PDAC transcriptomics that confirms the basal-like/classical model over alternatives.
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Affiliation(s)
- Luke A Torre-Healy
- Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, NY, USA
| | - Ryan R Kawalerski
- Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, NY, USA
- Department of Pathology, Stony Brook Medicine, Stony Brook, NY, USA
| | - Ki Oh
- Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, NY, USA
| | - Lucie Chrastecka
- Department of Pharmacological Sciences, Stony Brook Medicine, Stony Brook, NY, USA
| | - Xianlu L Peng
- Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
| | - Andrew J Aguirre
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Naim U Rashid
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jen Jen Yeh
- Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Surgery, University of North Carolina, Chapel Hill, NC, USA
| | - Richard A Moffitt
- Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, NY, USA.
- Department of Biomedical Informatics, Emory University, Atlanta, GA, USA.
- Department of Hematology and Medical Oncology, Emory University, Atlanta, GA, USA.
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7
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Oldoni E, Saunders G, Bietrix F, Garcia Bermejo ML, Niehues A, ’t Hoen PAC, Nordlund J, Hajduch M, Scherer A, Kivinen K, Pitkänen E, Mäkela TP, Gut I, Scollen S, Kozera Ł, Esteller M, Shi L, Ussi A, Andreu AL, van Gool AJ. Tackling the translational challenges of multi-omics research in the realm of European personalised medicine: A workshop report. Front Mol Biosci 2022; 9:974799. [PMID: 36310597 PMCID: PMC9608444 DOI: 10.3389/fmolb.2022.974799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/08/2022] [Indexed: 11/13/2022] Open
Abstract
Personalised medicine (PM) presents a great opportunity to improve the future of individualised healthcare. Recent advances in -omics technologies have led to unprecedented efforts characterising the biology and molecular mechanisms that underlie the development and progression of a wide array of complex human diseases, supporting further development of PM. This article reflects the outcome of the 2021 EATRIS-Plus Multi-omics Stakeholder Group workshop organised to 1) outline a global overview of common promises and challenges that key European stakeholders are facing in the field of multi-omics research, 2) assess the potential of new technologies, such as artificial intelligence (AI), and 3) establish an initial dialogue between key initiatives in this space. Our focus is on the alignment of agendas of European initiatives in multi-omics research and the centrality of patients in designing solutions that have the potential to advance PM in long-term healthcare strategies.
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Affiliation(s)
- Emanuela Oldoni
- European Infrastructure for Translational Medicine (EATRIS), Amsterdam, Netherlands
- *Correspondence: Gary Saunders, ; Emanuela Oldoni,
| | - Gary Saunders
- European Infrastructure for Translational Medicine (EATRIS), Amsterdam, Netherlands
- *Correspondence: Gary Saunders, ; Emanuela Oldoni,
| | - Florence Bietrix
- European Infrastructure for Translational Medicine (EATRIS), Amsterdam, Netherlands
| | - Maria Laura Garcia Bermejo
- Biomarkers and Therapeutic Targets Group, Ramon and Cajal Health Research Institute (IRYCIS), Madrid, Spain
| | - Anna Niehues
- Translational Metabolomic Laboratory, Department of Laboratory Medicine, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Peter A. C. ’t Hoen
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Jessica Nordlund
- Department of Medical Sciences, Molecular Precision Medicine and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Marian Hajduch
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University and University Hospital in Olomouc, Olomouc, Czechia
| | - Andreas Scherer
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Katja Kivinen
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
- HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Esa Pitkänen
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
- HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Tomi Pekka Mäkela
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
- HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Ivo Gut
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | | | - Łukasz Kozera
- Biobanking and BioMolecular Resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI-ERIC), Graz, Austria
| | - Manel Esteller
- Josep Carreras Leukemia Research Institute (IJC), Badalona, Spain
- Centro de Investigacion Biomedica en Red Cancer (CIBERONC), Madrid, Spain
- Institucio Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Physiological Sciences Department, School of Medicine and Health Sciences, University of Barcelona (UB), Barcelona, Spain
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China
| | - Anton Ussi
- European Infrastructure for Translational Medicine (EATRIS), Amsterdam, Netherlands
| | - Antonio L. Andreu
- European Infrastructure for Translational Medicine (EATRIS), Amsterdam, Netherlands
| | - Alain J. van Gool
- Translational Metabolomic Laboratory, Department of Laboratory Medicine, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
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Garzón W, Benavides L, Gaignard A, Redon R, Südholt M. A taxonomy of tools and approaches for distributed genomic analyses. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.101024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022] Open
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Alharbi E, Gadiya Y, Henderson D, Zaliani A, Delfin-Rossaro A, Cambon-Thomsen A, Kohler M, Witt G, Welter D, Juty N, Jay C, Engkvist O, Goble C, Reilly DS, Satagopam V, Ioannidis V, Gu W, Gribbon P. Selection of data sets for FAIRification in drug discovery and development: Which, why, and how? Drug Discov Today 2022; 27:2080-2085. [PMID: 35595012 PMCID: PMC9236643 DOI: 10.1016/j.drudis.2022.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 04/28/2022] [Accepted: 05/10/2022] [Indexed: 11/30/2022]
Abstract
Research organisations are focussed on quantifying the costs and benefits of implementing FAIR. Criteria used for the selection of data for FAIRification can be opaque and inconsistent. FAIRification effort depends on individual skills, competencies, resources, and time available. FAIRification should satisfy reuse scenarios, and lead to scientific and economic impacts. Organisational challenges include providing training to individuals and developing a FAIR organisation culture.
Despite the intuitive value of adopting the Findable, Accessible, Interoperable, and Reusable (FAIR) principles in both academic and industrial sectors, challenges exist in resourcing, balancing long- versus short-term priorities, and achieving technical implementation. This situation is exacerbated by the unclear mechanisms by which costs and benefits can be assessed when decisions on FAIR are made. Scientific and research and development (R&D) leadership need reliable evidence of the potential benefits and information on effective implementation mechanisms and remediating strategies. In this article, we describe procedures for cost–benefit evaluation, and identify best-practice approaches to support the decision-making process involved in FAIR implementation.
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Affiliation(s)
- Ebtisam Alharbi
- Department of Computer Science, The University of Manchester, Oxford Road, Manchester, UK
| | - Yojana Gadiya
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525 Hamburg, and Theodor Stern Kai 7, 60590 Frankfurt, Germany; Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590 Frankfurt, Germany
| | - David Henderson
- Bayer AG, Research & Development, Pharmaceuticals, Müllerstrasse 178, 13353 Berlin, Germany
| | - Andrea Zaliani
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525 Hamburg, and Theodor Stern Kai 7, 60590 Frankfurt, Germany; Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590 Frankfurt, Germany
| | | | | | - Manfred Kohler
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525 Hamburg, and Theodor Stern Kai 7, 60590 Frankfurt, Germany; Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590 Frankfurt, Germany
| | - Gesa Witt
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525 Hamburg, and Theodor Stern Kai 7, 60590 Frankfurt, Germany; Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590 Frankfurt, Germany
| | - Danielle Welter
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367 Belval, Luxembourg
| | - Nick Juty
- Department of Computer Science, The University of Manchester, Oxford Road, Manchester, UK
| | - Caroline Jay
- Department of Computer Science, The University of Manchester, Oxford Road, Manchester, UK
| | - Ola Engkvist
- Discovery Sciences, R&D, AstraZeneca, SE-43183 Mölndal, Sweden
| | - Carole Goble
- Department of Computer Science, The University of Manchester, Oxford Road, Manchester, UK
| | - Dorothy S Reilly
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367 Belval, Luxembourg
| | - Vassilios Ioannidis
- SIB Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Amphipole, 1015 Lausanne, Switzerland.
| | - Wei Gu
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland.
| | - Philip Gribbon
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525 Hamburg, and Theodor Stern Kai 7, 60590 Frankfurt, Germany; Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590 Frankfurt, Germany.
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10
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van der Velde KJ, Singh G, Kaliyaperumal R, Liao X, de Ridder S, Rebers S, Kerstens HHD, de Andrade F, van Reeuwijk J, De Gruyter FE, Hiltemann S, Ligtvoet M, Weiss MM, van Deutekom HWM, Jansen AML, Stubbs AP, Vissers LELM, Laros JFJ, van Enckevort E, Stemkens D, 't Hoen PAC, Beliën JAM, van Gijn ME, Swertz MA. FAIR Genomes metadata schema promoting Next Generation Sequencing data reuse in Dutch healthcare and research. Sci Data 2022; 9:169. [PMID: 35418585 PMCID: PMC9008059 DOI: 10.1038/s41597-022-01265-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 03/25/2022] [Indexed: 11/08/2022] Open
Abstract
The genomes of thousands of individuals are profiled within Dutch healthcare and research each year. However, this valuable genomic data, associated clinical data and consent are captured in different ways and stored across many systems and organizations. This makes it difficult to discover rare disease patients, reuse data for personalized medicine and establish research cohorts based on specific parameters. FAIR Genomes aims to enable NGS data reuse by developing metadata standards for the data descriptions needed to FAIRify genomic data while also addressing ELSI issues. We developed a semantic schema of essential data elements harmonized with international FAIR initiatives. The FAIR Genomes schema v1.1 contains 110 elements in 9 modules. It reuses common ontologies such as NCIT, DUO and EDAM, only introducing new terms when necessary. The schema is represented by a YAML file that can be transformed into templates for data entry software (EDC) and programmatic interfaces (JSON, RDF) to ease genomic data sharing in research and healthcare. The schema, documentation and MOLGENIS reference implementation are available at https://fairgenomes.org .
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Affiliation(s)
- K Joeri van der Velde
- University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
- University of Groningen and University Medical Center Groningen, Department of Genetics, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Gurnoor Singh
- Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Center for Molecular and Biomolecular Informatics, Geert Grooteplein 28, 6525 GA, Nijmegen, The Netherlands
| | - Rajaram Kaliyaperumal
- Leiden University Medical Center, Department of Human Genetics, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
| | - XiaoFeng Liao
- Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Center for Molecular and Biomolecular Informatics, Geert Grooteplein 28, 6525 GA, Nijmegen, The Netherlands
| | - Sander de Ridder
- Amsterdam University Medical Center, University of Amsterdam, Department of Pathology, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Susanne Rebers
- The Netherlands Cancer Institute, Division of Molecular Pathology, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Hindrik H D Kerstens
- Prinses Máxima Center for Pediatric Oncology, Kemmeren group, Heidelberglaan 25, 3584 CS, Utrecht, The Netherlands
| | - Fernanda de Andrade
- University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Jeroen van Reeuwijk
- Radboud University Medical Center, Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Fini E De Gruyter
- University Medical Center Utrecht, Department of Genetics, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Saskia Hiltemann
- Erasmus Medical Center, Department of Pathology, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Maarten Ligtvoet
- Nictiz - Dutch competence centre for electronic exchange of health and care information, Oude Middenweg 55, 2491 AC, The Hague, The Netherlands
| | - Marjan M Weiss
- Radboud University Medical Center, Department of Human Genetics, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Hanneke W M van Deutekom
- University Medical Center Utrecht, Department of Genetics, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Anne M L Jansen
- University Medical Center Utrecht, Department of Pathology, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Andrew P Stubbs
- Erasmus Medical Center, Department of Pathology, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Lisenka E L M Vissers
- Radboud University Medical Center, Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Jeroen F J Laros
- Leiden University Medical Center, Department of Human Genetics, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
- Leiden University Medical Center, Department of Clinical Genetics, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
- Rijksinstituut voor Volksgezondheid en Milieu, Antonie van Leeuwenhoeklaan 9, 3721 MA, Bilthoven, The Netherlands
| | - Esther van Enckevort
- University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Daphne Stemkens
- VSOP - Patient Alliance for Rare and Genetic Diseases The Netherlands, Koninginnelaan 23, 3762 DA, Soest, The Netherlands
| | - Peter A C 't Hoen
- Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Center for Molecular and Biomolecular Informatics, Geert Grooteplein 28, 6525 GA, Nijmegen, The Netherlands
| | - Jeroen A M Beliën
- Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Department of Pathology, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Mariëlle E van Gijn
- University of Groningen and University Medical Center Groningen, Department of Genetics, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Morris A Swertz
- University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.
- University of Groningen and University Medical Center Groningen, Department of Genetics, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.
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11
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Forero DA, Curioso WH, Patrinos GP. The importance of adherence to international standards for depositing open data in public repositories. BMC Res Notes 2021; 14:405. [PMID: 34727971 PMCID: PMC8561348 DOI: 10.1186/s13104-021-05817-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 10/22/2021] [Indexed: 12/14/2022] Open
Abstract
There has been an important global interest in Open Science, which include open data and methods, in addition to open access publications. It has been proposed that public availability of raw data increases the value and the possibility of confirmation of scientific findings, in addition to the potential of reducing research waste. Availability of raw data in open repositories facilitates the adequate development of meta-analysis and the cumulative evaluation of evidence for specific topics. In this commentary, we discuss key elements about data sharing in open repositories and we invite researchers around the world to deposit their data in them.
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Affiliation(s)
- Diego A Forero
- Health and Sport Sciences Research Group, School of Health and Sport Sciences, Fundación Universitaria del Área Andina, Bogotá, Colombia. .,Professional Program in Respiratory Therapy, School of Health and Sport Sciences, Fundación Universitaria del Área Andina, Bogotá, Colombia.
| | - Walter H Curioso
- Vicerrectorado de Investigación, Universidad Continental, Lima, Peru
| | - George P Patrinos
- Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece.,Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, UAE.,Zayed Center for Health Sciences, United Arab Emirates University, Al-Ain, UAE
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12
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Alharbi E, Skeva R, Juty N, Jay C, Goble C. Exploring the Current Practices, Costs and Benefits of FAIR
Implementation in Pharmaceutical Research and Development: A Qualitative
Interview Study. DATA INTELLIGENCE 2021. [DOI: 10.1162/dint_a_00109] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
The findable, accessible, interoperable, reusable (FAIR) principles for scientific data management and stewardship aim to facilitate data reuse at scale by both humans and machines. Research and development (R&D) in the pharmaceutical industry is becoming increasingly data driven, but managing its data assets according to FAIR principles remains costly and challenging. To date, little scientific evidence exists about how FAIR is currently implemented in practice, what its associated costs and benefits are, and how decisions are made about the retrospective FAIRification of data sets in pharmaceutical R&D. This paper reports the results of semi-structured interviews with 14 pharmaceutical professionals who participate in various stages of drug R&D in seven pharmaceutical businesses. Inductive thematic analysis identified three primary themes of the benefits and costs of FAIRification, and the elements that influence the decision-making process for FAIRifying legacy data sets. Participants collectively acknowledged the potential contribution of FAIRification to data reusability in diverse research domains and the subsequent potential for cost-savings. Implementation costs, however, were still considered a barrier by participants, with the need for considerable expenditure in terms of resources, and cultural change. How decisions were made about FAIRification was influenced by legal and ethical considerations, management commitment, and data prioritisation. The findings have significant implications for those in the pharmaceutical R&D industry who are engaged in driving FAIR implementation, and for external parties who seek to better understand existing practices and challenges.
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Affiliation(s)
- Ebtisam Alharbi
- School of Computer Science, University of Manchester, Manchester, Manchester M13 9PL, UK
- College of Computer and Information Systems, Umm Al-Qura University, Mecca, Makkah 21421, Saudi Arabia
| | - Rigina Skeva
- School of Computer Science, University of Manchester, Manchester, Manchester M13 9PL, UK
| | - Nick Juty
- School of Computer Science, University of Manchester, Manchester, Manchester M13 9PL, UK
| | - Caroline Jay
- School of Computer Science, University of Manchester, Manchester, Manchester M13 9PL, UK
| | - Carole Goble
- School of Computer Science, University of Manchester, Manchester, Manchester M13 9PL, UK
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13
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Orchestrating and sharing large multimodal data for transparent and reproducible research. Nat Commun 2021; 12:5797. [PMID: 34608132 PMCID: PMC8490371 DOI: 10.1038/s41467-021-25974-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 09/08/2021] [Indexed: 11/08/2022] Open
Abstract
Reproducibility is essential to open science, as there is limited relevance for findings that can not be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, scrutinized, challenged, and built upon. However, the intrinsic complexity and continuous growth of biomedical data makes it increasingly difficult to process, analyze, and share with the community in a FAIR (findable, accessible, interoperable, and reusable) manner. To overcome these issues, we created a cloud-based platform called ORCESTRA ( orcestra.ca ), which provides a flexible framework for the reproducible processing of multimodal biomedical data. It enables processing of clinical, genomic and perturbation profiles of cancer samples through automated processing pipelines that are user-customizable. ORCESTRA creates integrated and fully documented data objects with persistent identifiers (DOI) and manages multiple dataset versions, which can be shared for future studies.
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14
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Boegel S, Castle JC, Schwarting A. Current status of use of high throughput nucleotide sequencing in rheumatology. RMD Open 2021; 7:rmdopen-2020-001324. [PMID: 33408124 PMCID: PMC7789458 DOI: 10.1136/rmdopen-2020-001324] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 09/15/2020] [Accepted: 11/24/2020] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE Here, we assess the usage of high throughput sequencing (HTS) in rheumatic research and the availability of public HTS data of rheumatic samples. METHODS We performed a semiautomated literature review on PubMed, consisting of an R-script and manual curation as well as a manual search on the Sequence Read Archive for public available HTS data. RESULTS Of the 699 identified articles, rheumatoid arthritis (n=182 publications, 26%), systemic lupus erythematous (n=161, 23%) and osteoarthritis (n=152, 22%) are among the rheumatic diseases with the most reported use of HTS assays. The most represented assay is RNA-Seq (n=457, 65%) for the identification of biomarkers in blood or synovial tissue. We also find, that the quality of accompanying clinical characterisation of the sequenced patients differs dramatically and we propose a minimal set of clinical data necessary to accompany rheumatological-relevant HTS data. CONCLUSION HTS allows the analysis of a broad spectrum of molecular features in many samples at the same time. It offers enormous potential in novel personalised diagnosis and treatment strategies for patients with rheumatic diseases. Being established in cancer research and in the field of Mendelian diseases, rheumatic diseases are about to become the third disease domain for HTS, especially the RNA-Seq assay. However, we need to start a discussion about reporting of clinical characterisation accompany rheumatological-relevant HTS data to make clinical meaningful use of this data.
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Affiliation(s)
- Sebastian Boegel
- Department of Internal Medicine, University Center of Autoimmunity, University Medical Center Mainz, Mainz, Germany
| | | | - Andreas Schwarting
- Department of Internal Medicine, University Center of Autoimmunity, University Medical Center Mainz, Mainz, Germany.,Division of Rheumatology and Clinical Immunology, University Hospital Mainz, Mainz, Germany.,Acura Rheumatology Center Rhineland Palatinate, Bad Kreuznach, Germany
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15
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O'Doherty KC, Shabani M, Dove ES, Bentzen HB, Borry P, Burgess MM, Chalmers D, De Vries J, Eckstein L, Fullerton SM, Juengst E, Kato K, Kaye J, Knoppers BM, Koenig BA, Manson SM, McGrail KM, McGuire AL, Meslin EM, Nicol D, Prainsack B, Terry SF, Thorogood A, Burke W. Toward better governance of human genomic data. Nat Genet 2021; 53:2-8. [PMID: 33414545 PMCID: PMC8450011 DOI: 10.1038/s41588-020-00742-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
In this Commentary, we argue that in line with the dramatic increase in the collection, storage, and curation of human genomic data for biomedical research, genomic data repositories and consortia have adopted governance frameworks to address the dual objectives of enabling wide access while protecting against possible harms. However, there are ongoing debates in the scientific community about the merits and limitations of different governance frameworks in achieving these twin aims; and indeed, best practices and points for consideration are notably absent when it comes to devising a governance framework for genomic databases. Based on our collective experience of devising and assessing governance frameworks, our Commentary identifies five key functions of “good governance” (or what makes “better governance”) and three areas where trade-offs should be considered when specifying policies within those functions. We apply these functions as a benchmark to describe, as an example, the governance frameworks of six large-scale international genomic projects.
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Affiliation(s)
| | - Mahsa Shabani
- Metamedica, Faculty of Law and Criminology, Ghent University, Ghent, Belgium
| | - Edward S Dove
- School of Law, University of Edinburgh, Edinburgh, UK.
| | - Heidi Beate Bentzen
- Center for Medical Ethics, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Pascal Borry
- Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | - Michael M Burgess
- School of Population and Public Health, University of British Columbia, Kelowna, British Columbia, Canada
| | - Don Chalmers
- Faculty of Law, University of Tasmania, Hobart, Tasmania, Australia
| | - Jantina De Vries
- Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Lisa Eckstein
- Faculty of Law, University of Tasmania, Hobart, Tasmania, Australia
| | | | - Eric Juengst
- Department of Social Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Kazuto Kato
- Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Jane Kaye
- Faculty of Law, University of Oxford, Oxford, UK
| | | | | | - Spero M Manson
- Colorado School of Public Health, University of Colorado, Aurora, CO, USA
| | - Kimberlyn M McGrail
- School of Population and Public Health, University of British Columbia, Kelowna, British Columbia, Canada
| | - Amy L McGuire
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, USA
| | - Eric M Meslin
- Council of Canadian Academies, Ottawa, Ontario, Canada
| | - Dianne Nicol
- Faculty of Law, University of Tasmania, Hobart, Tasmania, Australia
| | - Barbara Prainsack
- Department of Political Science, University of Vienna, Vienna, Austria
- Department of Global Health & Social Medicine, King's College London, London, UK
| | | | - Adrian Thorogood
- Centre of Genomics and Policy, McGill University, Montreal, Québec, Canada
| | - Wylie Burke
- Department of Bioethics and Humanities, University of Washington, Seattle, WA, USA
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16
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Egli A. Digitalization, clinical microbiology and infectious diseases. Clin Microbiol Infect 2020; 26:1289-1290. [PMID: 32622954 PMCID: PMC7330545 DOI: 10.1016/j.cmi.2020.06.031] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 06/20/2020] [Indexed: 01/11/2023]
Affiliation(s)
- A Egli
- Clinical Bacteriology and Mycology, University Hospital Basel, Basel, Switzerland; Applied Microbiology Research, Department of Biomedicine, University of Basel, Basel, Switzerland.
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17
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Brewster C, Nouwt B, Raaijmakers S, Verhoosel J. Ontology-based Access Control for FAIR Data. DATA INTELLIGENCE 2020. [DOI: 10.1162/dint_a_00029] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
This paper focuses on fine-grained, secure access to FAIR data, for which we propose ontology-based data access policies. These policies take into account both the FAIR aspects of the data relevant to access (such as provenance and licence), expressed as metadata, and additional metadata describing users. With this tripartite approach (data, associated metadata expressing FAIR information, and additional metadata about users), secure and controlled access to object data can be obtained. This yields a security dimension to the “A” (accessible) in FAIR, which is clearly needed in domains like security and intelligence. These domains need data to be shared under tight controls, with widely varying individual access rights. In this paper, we propose an approach called Ontology-Based Access Control (OBAC), which utilizes concepts and relations from a data set's domain ontology. We argue that ontology-based access policies contribute to data reusability and can be reconciled with privacy-aware data access policies. We illustrate our OBAC approach through a proof-of-concept and propose that OBAC to be adopted as a best practice for access management of FAIR data.
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Affiliation(s)
- Christopher Brewster
- Data Science Department at TNO, Kampweg 55, Soesterberg 3769 DE, The Netherlands
- Institute of Data Science, Maastricht University, Maastricht 6229 ER, The Netherlands
| | - Barry Nouwt
- Data Science Department at TNO, Kampweg 55, Soesterberg 3769 DE, The Netherlands
| | - Stephan Raaijmakers
- Data Science Department at TNO, Kampweg 55, Soesterberg 3769 DE, The Netherlands
| | - Jack Verhoosel
- Data Science Department at TNO, Kampweg 55, Soesterberg 3769 DE, The Netherlands
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18
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Zhong X, Heinicke F, Lie BA, Rayner S. Accurate Adapter Information Is Crucial for Reproducibility and Reusability in Small RNA Seq Studies. Noncoding RNA 2019; 5:ncrna5040049. [PMID: 31661777 PMCID: PMC6958438 DOI: 10.3390/ncrna5040049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 10/23/2019] [Accepted: 10/25/2019] [Indexed: 11/16/2022] Open
Abstract
A necessary pre-processing data analysis step is the removal of adapter sequences from the raw reads. While most adapter trimming tools require adapter sequence as an essential input, adapter information is often incomplete or missing. This can impact quantification of features, reproducibility of the study and might even lead to erroneous conclusions. Here, we provide examples to highlight the importance of specifying the adapter sequence by demonstrating the effect of using similar but different adapter sequences and identify additional potential sources of errors in the adapter trimming step. Finally, we propose solutions by which users can ensure their small RNA-seq data is fully annotated with adapter information.
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Affiliation(s)
- Xiangfu Zhong
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, 0450 Oslo, Norway.
| | - Fatima Heinicke
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, 0450 Oslo, Norway.
| | - Benedicte A Lie
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, 0450 Oslo, Norway.
| | - Simon Rayner
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, 0450 Oslo, Norway.
- Hybrid Technology Hub-Centre of Excellence, Institute of Basic Medical Sciences, University of Oslo, 0372 Oslo, Norway.
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19
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Lu A, Kuhn P, Deichaite I. Time for a change: considering the rights of study participants to ownership of their personal research-grade genomic data. CONVERGENT SCIENCE PHYSICAL ONCOLOGY 2018. [DOI: 10.1088/2057-1739/aaf822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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20
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Abstract
Genomics and molecular imaging, along with clinical and translational research have transformed biomedical science into a data-intensive scientific endeavor. For researchers to benefit from Big Data sets, developing long-term biomedical digital data preservation strategy is very important. In this opinion article, we discuss specific actions that researchers and institutions can take to make research data a continued resource even after research projects have reached the end of their lifecycle. The actions involve utilizing an Open Archival Information System model comprised of six functional entities: Ingest, Access, Data Management, Archival Storage, Administration and Preservation Planning. We believe that involvement of data stewards early in the digital data life-cycle management process can significantly contribute towards long term preservation of biomedical data. Developing data collection strategies consistent with institutional policies, and encouraging the use of common data elements in clinical research, patient registries and other human subject research can be advantageous for data sharing and integration purposes. Specifically, data stewards at the onset of research program should engage with established repositories and curators to develop data sustainability plans for research data. Placing equal importance on the requirements for initial activities (e.g., collection, processing, storage) with subsequent activities (data analysis, sharing) can improve data quality, provide traceability and support reproducibility. Preparing and tracking data provenance, using common data elements and biomedical ontologies are important for standardizing the data description, making the interpretation and reuse of data easier. The Big Data biomedical community requires scalable platform that can support the diversity and complexity of data ingest modes (e.g. machine, software or human entry modes). Secure virtual workspaces to integrate and manipulate data, with shared software programs (e.g., bioinformatics tools), can facilitate the FAIR (Findable, Accessible, Interoperable and Reusable) use of data for near- and long-term research needs.
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Affiliation(s)
- Vivek Navale
- Center for Information Technology, National Institutes of Health, Bethesda, Maryland, 20892, USA
| | - Matthew McAuliffe
- Center for Information Technology, National Institutes of Health, Bethesda, Maryland, 20892, USA
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