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Manen-Freixa L, Antolin AA. Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery. Expert Opin Drug Discov 2024; 19:1043-1069. [PMID: 39004919 DOI: 10.1080/17460441.2024.2376643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 07/02/2024] [Indexed: 07/16/2024]
Abstract
INTRODUCTION Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology. AREAS COVERED This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples. EXPERT OPINION Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.
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Affiliation(s)
- Leticia Manen-Freixa
- Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
| | - Albert A Antolin
- Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
- Center for Cancer Drug Discovery, The Division of Cancer Therapeutics, The Institute of Cancer Research, London, UK
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2
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Brown CM, Marrink SJ. Modeling membranes in situ. Curr Opin Struct Biol 2024; 87:102837. [PMID: 38744147 DOI: 10.1016/j.sbi.2024.102837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/26/2024] [Accepted: 04/23/2024] [Indexed: 05/16/2024]
Abstract
Molecular dynamics simulations of cellular membranes have come a long way-from simple model lipid bilayers to multicomponent systems capturing the crowded and complex nature of real cell membranes. In this opinionated minireview, we discuss the current challenge to simulate the dynamics of membranes in their native environment, in situ, with the prospect of reaching the level of whole cells and cell organelles using an integrative modeling framework.
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Affiliation(s)
- Chelsea M Brown
- Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands. https://twitter.com/chelseabrowncg
| | - Siewert J Marrink
- Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands. s.j.marrinkrug.nl
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3
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Hussein R, Balaur I, Burmann A, Ćwiek-Kupczyńska H, Gadiya Y, Ghosh S, Jayathissa P, Katsch F, Kremer A, Lähteenmäki J, Meng Z, Morasek K, C. Rancourt R, Satagopam V, Sauermann S, Scheider S, Stamm T, Muehlendyck C, Gribbon P. Getting ready for the European Health Data Space (EHDS): IDERHA's plan to align with the latest EHDS requirements for the secondary use of health data. OPEN RESEARCH EUROPE 2024; 4:160. [PMID: 39185338 PMCID: PMC11342032 DOI: 10.12688/openreseurope.18179.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/18/2024] [Indexed: 08/27/2024]
Abstract
Objective The European Health Data Space (EHDS) shapes the digital transformation of healthcare in Europe. The EHDS regulation will also accelerate the use of health data for research, innovation, policy-making, and regulatory activities for secondary use of data (known as EHDS2). The Integration of heterogeneous Data and Evidence towards Regulatory and HTA Acceptance (IDERHA) project builds one of the first pan-European health data spaces in alignment with the EHDS2 requirements, addressing lung cancer as a pilot. Methods In this study, we conducted a comprehensive review of the EHDS regulation, technical requirements for EHDS2, and related projects. We also explored the results of the Joint Action Towards the European Health Data Space (TEHDAS) to identify the framework of IDERHA's alignment with EHDS2. We also conducted an internal webinar and an external workshop with EHDS experts to share expertise on the EHDS requirements and challenges. Results We identified the lessons learned from the existing projects and the minimum-set of requirements for aligning IDERHA infrastructure with EHDS2, including user journey, concepts, terminologies, and standards. The IDERHA framework (i.e., platform architecture, standardization approaches, documentation, etc.) is being developed accordingly. Discussion The IDERHA's alignment plan with EHDS2 necessitates the implementation of three categories of standardization for: data discoverability: Data Catalog Vocabulary (DCAT-AP), enabling semantics interoperability: Observational Medical Outcomes Partnership (OMOP), and health data exchange (DICOM and FHIR). The main challenge is that some standards are still being refined, e.g., the extension of the DCAT-AP (HealthDCAT-AP). Additionally, extensions to the Observational Health Data Sciences and Informatics (OHDSI) OMOP Common Data Model (CDM) to represent the patient-generated health data are still needed. Finally, proper mapping between standards (FHIR/OMOP) is a prerequisite for proper data exchange. Conclusions The IDERHA's plan and our collaboration with other EHDS initiatives/projects are critical in advancing the implementation of EHDS2.
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Affiliation(s)
- Rada Hussein
- Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria
| | - Irina Balaur
- Luxembourg Centre for Systems Biology, University of Luxembourg, Luxembourg, Luxembourg
| | - Anja Burmann
- Fraunhofer Institute for Software and Systems Engineering, Dortmund, Germany
| | | | - Yojana Gadiya
- Discovery Research ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD), Frankfurt, Germany
- Bonn-Aachen International Center for Information Technology, University of Bonn, Bonn, Germany
| | - Soumyabrata Ghosh
- Luxembourg Centre for Systems Biology, University of Luxembourg, Luxembourg, Luxembourg
| | - Prabath Jayathissa
- Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria
| | - Florian Katsch
- Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria
- Institute of Medical Information Management, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
- Institute of Outcomes Research, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
| | | | | | - Zhaoling Meng
- Clinical Modeling and Evidence Integration, Sanofi, Cambridge, MA, USA
| | - Kathrin Morasek
- Institute of Outcomes Research, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
- Ludwig Boltzmann Institute for Arthritis and Rehabilitation, Vienna, Austria
| | | | - Venkata Satagopam
- Luxembourg Centre for Systems Biology, University of Luxembourg, Luxembourg, Luxembourg
| | - Stefan Sauermann
- Faculty Life Science Engineering, FH Technikum Wien, Vienna, Austria
| | - Simon Scheider
- Fraunhofer Institute for Software and Systems Engineering, Dortmund, Germany
- Chair for Industrial Information Management, TU Dortmund, Dortmund, Germany
| | - Tanja Stamm
- Institute of Outcomes Research, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
- Ludwig Boltzmann Institute for Arthritis and Rehabilitation, Vienna, Austria
| | | | - Philip Gribbon
- Discovery Research ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD), Frankfurt, Germany
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4
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Gioti A, Theodosopoulou D, Bravakos P, Magoulas A, Kotoulas G. The bioinformatics landscape in environmental omics: Lessons from a national ELIXIR survey. iScience 2024; 27:110062. [PMID: 38947499 PMCID: PMC11214481 DOI: 10.1016/j.isci.2024.110062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 03/31/2024] [Accepted: 05/17/2024] [Indexed: 07/02/2024] Open
Abstract
As a research infrastructure with a mission to provide services for bioinformatics, ELIXIR aims to identify and inform its target audiences. Here, we present a survey on a community of researchers studying the environment with omics approaches in Greece, one of the youngest member countries of ELIXIR. Personal interviews followed by quantitative and qualitative analysis were employed to document interactions and practices of the community and to perform a gap analysis for the transition toward multiomics and systems biology. Environmental omics in Greece mostly concerns production of data, in large majority on microbes and non-model organisms. Our survey highlighted (1) the popularity and suitability of targeted hands-on training events; (2) data quality and management issues as important elements for the transition to multiomics, and (3) lack of knowledge and misconceptions regarding interoperability, metadata standards, and pre-registration. The publicly available collected answers represent a valuable resource in view of future strategic planning.
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Affiliation(s)
- Anastasia Gioti
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, 71500 Gournes, Crete, Greece
| | - Danai Theodosopoulou
- Faculty of Medicine & Health Sciences, University of Nottingham, Nottingham NG7 2UH, UK
| | - Panos Bravakos
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, 71500 Gournes, Crete, Greece
| | - Antonios Magoulas
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, 71500 Gournes, Crete, Greece
| | - Georgios Kotoulas
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, 71500 Gournes, Crete, Greece
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Soiland-Reyes S, Goble C, Groth P. Evaluating FAIR Digital Object and Linked Data as distributed object systems. PeerJ Comput Sci 2024; 10:e1781. [PMID: 38855229 PMCID: PMC11157569 DOI: 10.7717/peerj-cs.1781] [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: 06/28/2023] [Accepted: 12/06/2023] [Indexed: 06/11/2024]
Abstract
FAIR Digital Object (FDO) is an emerging concept that is highlighted by European Open Science Cloud (EOSC) as a potential candidate for building an ecosystem of machine-actionable research outputs. In this work we systematically evaluate FDO and its implementations as a global distributed object system, by using five different conceptual frameworks that cover interoperability, middleware, FAIR principles, EOSC requirements and FDO guidelines themself. We compare the FDO approach with established Linked Data practices and the existing Web architecture, and provide a brief history of the Semantic Web while discussing why these technologies may have been difficult to adopt for FDO purposes. We conclude with recommendations for both Linked Data and FDO communities to further their adaptation and alignment.
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Affiliation(s)
- Stian Soiland-Reyes
- Department of Computer Science, The University of Manchester, Manchester, UK
- Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
| | - Carole Goble
- Department of Computer Science, The University of Manchester, Manchester, UK
| | - Paul Groth
- Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
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6
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Ouwendijk WJD, Roychoudhury P, Cunningham AL, Jerome KR, Koelle DM, Kinchington PR, Mohr I, Wilson AC, Verjans GGMGM, Depledge DP. Reanalysis of single-cell RNA sequencing data does not support herpes simplex virus 1 latency in non-neuronal ganglionic cells in mice. J Virol 2024; 98:e0185823. [PMID: 38445887 PMCID: PMC11019907 DOI: 10.1128/jvi.01858-23] [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: 11/28/2023] [Accepted: 02/14/2024] [Indexed: 03/07/2024] Open
Abstract
Most individuals are latently infected with herpes simplex virus type 1 (HSV-1), and it is well-established that HSV-1 establishes latency in sensory neurons of peripheral ganglia. However, it was recently proposed that latent HSV-1 is also present in immune cells recovered from the ganglia of experimentally infected mice. Here, we reanalyzed the single-cell RNA sequencing (scRNA-Seq) data that formed the basis for that conclusion. Unexpectedly, off-target priming in 3' scRNA-Seq experiments enabled the detection of non-polyadenylated HSV-1 latency-associated transcript (LAT) intronic RNAs. However, LAT reads were near-exclusively detected in mixed populations of cells undergoing cell death. Specific loss of HSV-1 LAT and neuronal transcripts during quality control filtering indicated widespread destruction of neurons, supporting the presence of contaminating cell-free RNA in other cells following tissue processing. In conclusion, the reported detection of latent HSV-1 in non-neuronal cells is best explained using compromised scRNA-Seq datasets.IMPORTANCEMost people are infected with herpes simplex virus type 1 (HSV-1) during their life. Once infected, the virus generally remains in a latent (silent) state, hiding within the neurons of peripheral ganglia. Periodic reactivation (reawakening) of the virus may cause fresh diseases such as cold sores. A recent study using single-cell RNA sequencing (scRNA-Seq) proposed that HSV-1 can also establish latency in the immune cells of mice, challenging existing dogma. We reanalyzed the data from that study and identified several flaws in the methodologies and analyses performed that invalidate the published conclusions. Specifically, we showed that the methodologies used resulted in widespread destruction of neurons which resulted in the presence of contaminants that confound the data analysis. We thus conclude that there remains little to no evidence for HSV-1 latency in immune cells.
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Affiliation(s)
- Werner J. D. Ouwendijk
- HerpesLabNL, Department of Viroscience, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Pavitra Roychoudhury
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Anthony L. Cunningham
- Centre for Virus Research, The Westmead Institute for Medical Research, Sydney, New South Wales, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Keith R. Jerome
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - David M. Koelle
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Medicine, University of Washington, Seattle, Washington, USA
- Department of Global Health, University of Washington, Seattle, Washington, USA
- Department of Translational Research, Benaroya Research Institute, Seattle, Washington, USA
| | - Paul R. Kinchington
- Department of Ophthalmology and of Molecular Microbiology and Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ian Mohr
- Department of Microbiology, New York University School of Medicine, New York, New York, USA
| | - Angus C. Wilson
- Department of Microbiology, New York University School of Medicine, New York, New York, USA
| | | | - Daniel P. Depledge
- Department of Microbiology, New York University School of Medicine, New York, New York, USA
- Institute of Virology, Hannover Medical School, Hannover, Germany
- German Center for Infection Research (DZIF) partner site Hannover-Braunschweig, Hannover, Germany
- Cluster of Excellence RESIST (EXC 2155), Hannover Medical School, Hannover, Germany
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Holt KD, Roman G, McIntosh L, Kleinsorge J, Holden-Wiltse J, Bennett NM. RocHealthData.org: Development and usage of a publicly available, geographic source of social determinants of health data. J Clin Transl Sci 2024; 8:e41. [PMID: 38476248 PMCID: PMC10928699 DOI: 10.1017/cts.2024.484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 12/27/2023] [Accepted: 02/02/2024] [Indexed: 03/14/2024] Open
Abstract
Access to local, population specific, and timely data is vital in understanding factors that impact population health. The impact of place (neighborhood, census tract, and city) is particularly important in understanding the Social Determinants of Health. The University of Rochester Medical Center's Clinical and Translational Science Institute created the web-based tool RocHealthData.org to provide access to thousands of geographically displayed publicly available health-related datasets. The site has also hosted a variety of locally curated datasets (eg., COVID-19 vaccination rates and community-derived health indicators), helping set community priorities and impacting outcomes. Usage statistics (available through Google Analytics) show returning visitors with a lower bounce rate (leaving a site after a single page access) and spent longer at the site than new visitors. Of the currently registered 1033 users, 51.7% were from within our host university, 20.1% were from another educational institution, and 28.2% identified as community members. Our assessments indicate that these data are useful and valued across a variety of domains. Continuing site improvement depends on new sources of locally relevant data, as well as increased usage of data beyond our local region.
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Affiliation(s)
- Kathleen D. Holt
- Clinical and Translational Science Institute, University of Rochester, Rochester, NY, USA
- Center for Community Health and Prevention, University of Rochester, Rochester, NY, USA
| | - Gretchen Roman
- Department of Family Medicine, University of Rochester, Rochester, NY, USA
| | - Laura McIntosh
- Center for Community Health and Prevention, University of Rochester, Rochester, NY, USA
| | - Jamie Kleinsorge
- Center for Applied Research and Engagement Systems, University of Missouri, Columbia, MO, USA
| | - Jeanne Holden-Wiltse
- Clinical and Translational Science Institute, University of Rochester, Rochester, NY, USA
| | - Nancy M. Bennett
- Clinical and Translational Science Institute, University of Rochester, Rochester, NY, USA
- Department of Medicine, School of Medicine and Dentistry, Rochester, NY, USA
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Niehues A, de Visser C, Hagenbeek FA, Kulkarni P, Pool R, Karu N, Kindt ASD, Singh G, Vermeiren RRJM, Boomsma DI, van Dongen J, ’t Hoen PAC, van Gool AJ. A multi-omics data analysis workflow packaged as a FAIR Digital Object. Gigascience 2024; 13:giad115. [PMID: 38217405 PMCID: PMC10787363 DOI: 10.1093/gigascience/giad115] [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: 06/13/2023] [Revised: 11/14/2023] [Accepted: 12/10/2023] [Indexed: 01/15/2024] Open
Abstract
BACKGROUND Applying good data management and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object. FINDINGS We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub. CONCLUSIONS Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.
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Affiliation(s)
- Anna Niehues
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands
| | - Casper de Visser
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Purva Kulkarni
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands
- Department of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Naama Karu
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, 2333 AL Leiden, The Netherlands
| | - Alida S D Kindt
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, 2333 AL Leiden, The Netherlands
| | - Gurnoor Singh
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Robert R J M Vermeiren
- Department of Child and Adolescent Psychiatry, LUMC-Curium, Leiden University Medical Center, 2342 AK Oegstgeest, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, 1081 BT Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, 1081 BT Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Peter A C ’t Hoen
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Alain J van Gool
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands
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David R, Rybina A, Burel J, Heriche J, Audergon P, Boiten J, Coppens F, Crockett S, Exter K, Fahrner S, Fratelli M, Goble C, Gormanns P, Grantner T, Grüning B, Gurwitz KT, Hancock JM, Harmse H, Holub P, Juty N, Karnbach G, Karoune E, Keppler A, Klemeier J, Lancelotti C, Legras J, Lister AL, Longo DL, Ludwig R, Madon B, Massimi M, Matser V, Matteoni R, Mayrhofer MT, Ohmann C, Panagiotopoulou M, Parkinson H, Perseil I, Pfander C, Pieruschka R, Raess M, Rauber A, Richard AS, Romano P, Rosato A, Sánchez‐Pla A, Sansone S, Sarkans U, Serrano‐Solano B, Tang J, Tanoli Z, Tedds J, Wagener H, Weise M, Westerhoff HV, Wittner R, Ewbank J, Blomberg N, Gribbon P. "Be sustainable": EOSC-Life recommendations for implementation of FAIR principles in life science data handling. EMBO J 2023; 42:e115008. [PMID: 37964598 PMCID: PMC10690449 DOI: 10.15252/embj.2023115008] [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: 07/14/2023] [Revised: 09/12/2023] [Accepted: 09/18/2023] [Indexed: 11/16/2023] Open
Abstract
The main goals and challenges for the life science communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large-scale data-driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable life science resources based on the collaborative, cross-disciplinary work done within the EOSC-Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European life science research infrastructures, it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC-Life provides a model for sustainable data management according to FAIR (findability, accessibility, interoperability, and reusability) principles, including solutions for sensitive- and industry-related resources, by means of cross-disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences.
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Dumschott K, Dörpholz H, Laporte MA, Brilhaus D, Schrader A, Usadel B, Neumann S, Arnaud E, Kranz A. Ontologies for increasing the FAIRness of plant research data. FRONTIERS IN PLANT SCIENCE 2023; 14:1279694. [PMID: 38098789 PMCID: PMC10720748 DOI: 10.3389/fpls.2023.1279694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/15/2023] [Indexed: 12/17/2023]
Abstract
The importance of improving the FAIRness (findability, accessibility, interoperability, reusability) of research data is undeniable, especially in the face of large, complex datasets currently being produced by omics technologies. Facilitating the integration of a dataset with other types of data increases the likelihood of reuse, and the potential of answering novel research questions. Ontologies are a useful tool for semantically tagging datasets as adding relevant metadata increases the understanding of how data was produced and increases its interoperability. Ontologies provide concepts for a particular domain as well as the relationships between concepts. By tagging data with ontology terms, data becomes both human- and machine- interpretable, allowing for increased reuse and interoperability. However, the task of identifying ontologies relevant to a particular research domain or technology is challenging, especially within the diverse realm of fundamental plant research. In this review, we outline the ontologies most relevant to the fundamental plant sciences and how they can be used to annotate data related to plant-specific experiments within metadata frameworks, such as Investigation-Study-Assay (ISA). We also outline repositories and platforms most useful for identifying applicable ontologies or finding ontology terms.
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Affiliation(s)
- Kathryn Dumschott
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics) & Bioeconomy Science Center (BioSC), CEPLAS, Forschungszentrum Jülich, Jülich, Germany
| | - Hannah Dörpholz
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics) & Bioeconomy Science Center (BioSC), CEPLAS, Forschungszentrum Jülich, Jülich, Germany
| | - Marie-Angélique Laporte
- Digital Solutions Team, Digital Inclusion Lever, Bioversity International, Montpellier Office, Montpellier, France
| | - Dominik Brilhaus
- Data Science and Management & Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Andrea Schrader
- Data Science and Management & Cluster of Excellence on Plant Sciences (CEPLAS), University of Cologne, Cologne, Germany
| | - Björn Usadel
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics) & Bioeconomy Science Center (BioSC), CEPLAS, Forschungszentrum Jülich, Jülich, Germany
- Institute for Biological Data Science & Cluster of Excellence on Plant Sciences (CEPLAS), Faculty of Mathematics and Life Sciences, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Steffen Neumann
- Program Center MetaCom, Leibniz Institute of Plant Biochemistry, Halle, Germany
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Germany
| | - Elizabeth Arnaud
- Digital Solutions Team, Digital Inclusion Lever, Bioversity International, Montpellier Office, Montpellier, France
| | - Angela Kranz
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics) & Bioeconomy Science Center (BioSC), CEPLAS, Forschungszentrum Jülich, Jülich, Germany
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Caliskan A, Dangwal S, Dandekar T. Metadata integrity in bioinformatics: Bridging the gap between data and knowledge. Comput Struct Biotechnol J 2023; 21:4895-4913. [PMID: 37860229 PMCID: PMC10582761 DOI: 10.1016/j.csbj.2023.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 10/04/2023] [Accepted: 10/04/2023] [Indexed: 10/21/2023] Open
Abstract
In the fast-evolving landscape of biomedical research, the emergence of big data has presented researchers with extraordinary opportunities to explore biological complexities. In biomedical research, big data imply also a big responsibility. This is not only due to genomics data being sensitive information but also due to genomics data being shared and re-analysed among the scientific community. This saves valuable resources and can even help to find new insights in silico. To fully use these opportunities, detailed and correct metadata are imperative. This includes not only the availability of metadata but also their correctness. Metadata integrity serves as a fundamental determinant of research credibility, supporting the reliability and reproducibility of data-driven findings. Ensuring metadata availability, curation, and accuracy are therefore essential for bioinformatic research. Not only must metadata be readily available, but they must also be meticulously curated and ideally error-free. Motivated by an accidental discovery of a critical metadata error in patient data published in two high-impact journals, we aim to raise awareness for the need of correct, complete, and curated metadata. We describe how the metadata error was found, addressed, and present examples for metadata-related challenges in omics research, along with supporting measures, including tools for checking metadata and software to facilitate various steps from data analysis to published research.
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Affiliation(s)
- Aylin Caliskan
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Seema Dangwal
- Stanford Cardiovascular Institute, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305-5101, United States
| | - Thomas Dandekar
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
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Welter D, Rocca-Serra P, Grouès V, Sallam N, Ancien F, Shabani A, Asariardakani S, Alper P, Ghosh S, Burdett T, Sansone SA, Gu W, Satagopam V. The Translational Data Catalog - discoverable biomedical datasets. Sci Data 2023; 10:470. [PMID: 37474618 PMCID: PMC10359386 DOI: 10.1038/s41597-023-02258-0] [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: 10/17/2022] [Accepted: 05/22/2023] [Indexed: 07/22/2023] Open
Abstract
The discoverability of datasets resulting from the diverse range of translational and biomedical projects remains sporadic. It is especially difficult for datasets emerging from pre-competitive projects, often due to the legal constraints of data-sharing agreements, and the different priorities of the private and public sectors. The Translational Data Catalog is a single discovery point for the projects and datasets produced by a number of major research programmes funded by the European Commission. Funded by and rooted in a number of these European private-public partnership projects, the Data Catalog is built on FAIR-enabling community standards, and its mission is to ensure that datasets are findable and accessible by machines. Here we present its creation, content, value and adoption, as well as the next steps for sustainability within the ELIXIR ecosystem.
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Affiliation(s)
- Danielle Welter
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367, Belval, Luxembourg
- Luxembourg National Data Service (PNED G.I.E), 6 avenue des Hauts-Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg
| | - Philippe Rocca-Serra
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX13QG, Oxford, UK
- AstraZeneca, Data Office, Data Science & AI unit R&D, 136 Hills Rd, Cambridge, UK
| | - Valentin Grouès
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367, Belval, Luxembourg
| | - Nirmeen Sallam
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367, Belval, Luxembourg
| | - François Ancien
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367, Belval, Luxembourg
| | - Abetare Shabani
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367, Belval, Luxembourg
| | - Saeideh Asariardakani
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367, Belval, Luxembourg
| | - Pinar Alper
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367, Belval, Luxembourg
- Luxembourg National Data Service (PNED G.I.E), 6 avenue des Hauts-Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg
| | - Soumyabrata Ghosh
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367, Belval, Luxembourg
| | - Tony Burdett
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - Susanna-Assunta Sansone
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX13QG, Oxford, UK
| | - Wei Gu
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367, Belval, Luxembourg.
- Luxembourg National Data Service (PNED G.I.E), 6 avenue des Hauts-Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg.
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367, Belval, Luxembourg.
- Frankfurt Institute for Advanced Studies (FIAS), Ruth-Moufang-Straße 1, D-60438, Frankfurt am Main, Germany.
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Ouwendijk WJ, Roychoudhury P, Cunningham AL, Jerome KR, Koelle DM, Kinchington PR, Mohr I, Wilson AC, Verjans GM, Depledge DP. Reanalysis of single-cell RNA sequencing data does not support herpes simplex virus 1 latency in non-neuronal ganglionic cells in mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.17.549345. [PMID: 37503290 PMCID: PMC10370134 DOI: 10.1101/2023.07.17.549345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Most individuals are latently infected with herpes simplex virus type 1 (HSV-1) and it is well-established that HSV-1 establishes latency in sensory neurons of peripheral ganglia. However, it was recently proposed that latent virus is also present in immune cells recovered from ganglia in a mouse model used for studying latency. Here, we reanalyzed the single-cell RNA sequencing (scRNA-Seq) data that formed the basis for this conclusion. Unexpectedly, off-target priming in 3' scRNA-Seq experiments enabled the detection of non-polyadenylated HSV-1 latency-associated transcript (LAT) intronic RNAs. However, LAT reads were nearexclusively detected in a mixed population of cells undergoing cell death. Specific loss of HSV1 LAT and neuronal transcripts during quality control filtering indicated widespread destruction of neurons, supporting the presence of contaminating cell-free RNA in other cells following tissue processing. In conclusion, the reported detection of latent HSV-1 in non-neuronal cells is best explained by inaccuracies in the data analyses.
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Affiliation(s)
- Werner J.D. Ouwendijk
- HerpesLabNL, Department of Viroscience, Erasmus Medical Center, Rotterdam, Netherlands
| | - Pavitra Roychoudhury
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, 98195, USA
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Anthony L. Cunningham
- Centre for Virus Research, The Westmead Institute for Medical Research, Sydney, NSW, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Keith R. Jerome
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, 98195, USA
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - David M. Koelle
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, 98195, USA
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Department of Medicine, University of Washington, Seattle, WA, 98195, USA
- Department of Global Health, University of Washington, Seattle, WA, 98195, USA
- Department of Translational Research, Benaroya Research Institute, Seattle, WA, 98101, USA
| | - Paul R. Kinchington
- Department of Ophthalmology and of Molecular Microbiology and Genetics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Ian Mohr
- Department of Microbiology, New York University School of Medicine, New York, NY 10016, USA
| | - Angus C. Wilson
- Department of Microbiology, New York University School of Medicine, New York, NY 10016, USA
| | | | - Daniel P. Depledge
- Department of Microbiology, New York University School of Medicine, New York, NY 10016, USA
- Institute of Virology, Hannover Medical School, Hannover, Germany
- German Center for Infection Research (DZIF), partner site Hannover-Braunschweig, Hannover, Germany
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Welter D, Juty N, Rocca-Serra P, Xu F, Henderson D, Gu W, Strubel J, Giessmann RT, Emam I, Gadiya Y, Abbassi-Daloii T, Alharbi E, Gray AJG, Courtot M, Gribbon P, Ioannidis V, Reilly DS, Lynch N, Boiten JW, Satagopam V, Goble C, Sansone SA, Burdett T. FAIR in action - a flexible framework to guide FAIRification. Sci Data 2023; 10:291. [PMID: 37208349 PMCID: PMC10199076 DOI: 10.1038/s41597-023-02167-2] [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: 10/14/2022] [Accepted: 03/28/2023] [Indexed: 05/21/2023] Open
Abstract
The COVID-19 pandemic has highlighted the need for FAIR (Findable, Accessible, Interoperable, and Reusable) data more than any other scientific challenge to date. We developed a flexible, multi-level, domain-agnostic FAIRification framework, providing practical guidance to improve the FAIRness for both existing and future clinical and molecular datasets. We validated the framework in collaboration with several major public-private partnership projects, demonstrating and delivering improvements across all aspects of FAIR and across a variety of datasets and their contexts. We therefore managed to establish the reproducibility and far-reaching applicability of our approach to FAIRification tasks.
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Affiliation(s)
- Danielle Welter
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367, Belval, Luxembourg
| | - Nick Juty
- University of Manchester, Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Philippe Rocca-Serra
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX13QG, Oxford, UK
| | - Fuqi Xu
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - David Henderson
- Bayer AG, Business Development & Licensing & OI, Muellerstrasse 178, 13353, Berlin, Germany
| | - Wei Gu
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367, Belval, Luxembourg
| | - Jolanda Strubel
- The Hyve BV, Arthur van Schendelstraat 650, 3511 MJ, Utrecht, The Netherlands
| | - Robert T Giessmann
- Bayer AG, Business Development & Licensing & OI, Muellerstrasse 178, 13353, Berlin, Germany
- Institute for Globally Distributed Open Research and Education (IGDORE), Gothenburg, Sweden
| | - Ibrahim Emam
- Data Science Institute, Imperial College, London, UK
| | - Yojana Gadiya
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP) and Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Schnackenburgallee 114, 22525 Hamburg, and Theodor Stern Kai 7, 60590, Frankfurt, Germany
| | - Tooba Abbassi-Daloii
- Department of Bioinformatics (BiGCaT), NUTRIM, FHML, Maastricht University, Maastricht, The Netherlands
| | - Ebtisam Alharbi
- College of Computer and Information Systems, Umm Al-Qura University, Mecca, Saudi Arabia
| | - Alasdair J G Gray
- Department of Computer Science, Heriot-Watt University, Edinburgh, EH14 4AS, Scotland, UK
| | - Melanie Courtot
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
- Ontario Institute for Cancer Research MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario, M5G 0A3, Canada
| | - Philip Gribbon
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP) and Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Schnackenburgallee 114, 22525 Hamburg, and Theodor Stern Kai 7, 60590, Frankfurt, Germany
| | - Vassilios Ioannidis
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - 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
| | - Carole Goble
- University of Manchester, Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Susanna-Assunta Sansone
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX13QG, Oxford, UK
| | - Tony Burdett
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK.
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