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Martorelli I, Pooryousefi A, van Thiel H, Sicking FJ, Ramackers GJ, Merckx V, Verbeek FJ. Multiple graphical views for automatically generating SQL for the MycoDiversity DB; making fungal biodiversity studies accessible. Biodivers Data J 2024; 12:e119660. [PMID: 38933486 PMCID: PMC11199959 DOI: 10.3897/bdj.12.e119660] [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: 01/27/2024] [Accepted: 06/06/2024] [Indexed: 06/28/2024] Open
Abstract
Fungi is a highly diverse group of eukaryotic organisms that live under an extremely wide range of environmental conditions. Nowadays, there is a fundamental focus on observing how biodiversity varies on different spatial scales, in addition to understanding the environmental factors which drive fungal biodiversity. Metabarcoding is a high-throughput DNA sequencing technology that has positively contributed to observing fungal communities in environments. While the DNA sequencing data generated from metabarcoding studies are available in public archives, this valuable data resource is not directly usable for fungal biodiversity investigation. Additionally, due to its fragmented storage and distributed nature, it is not immediately accessible through a single user interface. We developed the MycoDiversity DataBase User Interface (https://mycodiversity.liacs.nl) to provide direct access and retrieval of fungal data that was previously inaccessible in the public domain. The user interface provides multiple graphical views of the data components used to reveal fungal biodiversity. These components include reliable geo-location terms, the reference taxonomic scientific names associated with fungal species and the standard features describing the environment where they occur. Direct observation of the public DNA sequencing data in association with fungi is accessible through SQL search queries created by interactively manipulating topological maps and dynamic hierarchical tree views. The search results are presented in configurable data table views that can be downloaded for further use. With the MycoDiversity DataBase User Interface, we make fungal biodiversity data accessible, assisting researchers and other stakeholders in using metabarcoding studies for assessing fungal biodiversity.
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Affiliation(s)
- Irene Martorelli
- Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, NetherlandsLeiden Institute of Advanced Computer Science (LIACS), Leiden UniversityLeidenNetherlands
- Naturalis Biodiversity Center, Leiden, NetherlandsNaturalis Biodiversity CenterLeidenNetherlands
| | - Aram Pooryousefi
- Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, NetherlandsLeiden Institute of Advanced Computer Science (LIACS), Leiden UniversityLeidenNetherlands
| | - Haike van Thiel
- Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, NetherlandsLeiden Institute of Advanced Computer Science (LIACS), Leiden UniversityLeidenNetherlands
| | - Floris J Sicking
- Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, NetherlandsLeiden Institute of Advanced Computer Science (LIACS), Leiden UniversityLeidenNetherlands
| | - Guus J Ramackers
- Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, NetherlandsLeiden Institute of Advanced Computer Science (LIACS), Leiden UniversityLeidenNetherlands
| | - Vincent Merckx
- Naturalis Biodiversity Center, Leiden, NetherlandsNaturalis Biodiversity CenterLeidenNetherlands
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, NetherlandsInstitute for Biodiversity and Ecosystem Dynamics, University of AmsterdamAmsterdamNetherlands
| | - Fons J Verbeek
- Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, NetherlandsLeiden Institute of Advanced Computer Science (LIACS), Leiden UniversityLeidenNetherlands
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Leigh S, Baines R, Stevens S, Garba-Sani Z, Austin D, Chatterjee A. Walk a mile in my shoes: perspectives towards sharing of health and experience data among individuals living with sickle cell disorder. Mhealth 2024; 10:4. [PMID: 38323148 PMCID: PMC10839506 DOI: 10.21037/mhealth-23-18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 11/29/2023] [Indexed: 02/08/2024] Open
Abstract
Background Advancements in digital health technologies (DHTs) mean people are increasingly recording and managing personal health data. As observed during the COVID-19 pandemic, sharing of such data may provide unrivalled opportunities in advancing our understanding of conditions otherwise poorly understood, including rare conditions. Methods A semi-structured focus group (n=25) explored perspectives and experiences of sharing health data among those with a group of rare haematological conditions, sickle cell disorder (SCD). The focus group explored (I) what 'feeling well' looks like; (II) how this could be monitored using DHTs; (III) which data healthcare professionals (HCPs) should pay greater attention to and; (IV) types of data willing to be shared, with whom, and under which conditions. Key themes were further assessed via an online survey (n=50). Results Patient-relevant measures of condition-management focused on "everything else that comes with" SCD, suggesting HCPs did not pay sufficient attention to day-to-day symptom variability. This was juxtaposed against the "fixed and one-off" electronic health record (EHR), collecting pre-specified data at pre-determined snapshots of time, not considered reflective of outcomes associated with "feeling well" day-to-day. Forty-four-point-seven percent of respondents had previously shared health data. Most were willing to share data concerning symptoms and health service utilisation, but were less willing to share genomic and EHR data. Sixty-one-point-seven percent believed HCPs did not pay enough attention to daily fluctuations in mental and physical health. Financial benefits (74.5%), trust in organisations seeking data (72.3%), and knowing how data will be used (61.7%) were key facilitators of data sharing. Seventy-one percent, 70% and 65.2% had not previously shared health data with the pharmaceutical industry, charitable organisations and digital health interventions respectively, but were open to doing so in the future. Conclusions Those living with the rare condition SCD were supportive of collecting and sharing data to foster research and improve understanding and outcomes. However, specific requirements were identified to respect privacy and informational needs regarding future use of data. DHTs can be a valuable tool in improving understanding of the day-to-day impact of health conditions, but understanding patient needs is critical in ensuring involvement in the process, as not all data types are considered of equal value, benefit, or risk.
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Affiliation(s)
- Simon Leigh
- Prometheus Health Technologies, Mor Workspace, Newquay, UK
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Rebecca Baines
- Centre for Health Technology, University of Plymouth, Plymouth, UK
| | - Sebastian Stevens
- Prometheus Health Technologies, Mor Workspace, Newquay, UK
- Centre for Health Technology, University of Plymouth, Plymouth, UK
| | | | - Daniella Austin
- School of Psychology, Faculty of Health, University of Plymouth, Plymouth, UK
| | - Arunangsu Chatterjee
- Centre for Health Technology, University of Plymouth, Plymouth, UK
- School of Medicine, University of Leeds, Leeds, UK
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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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Wright A, Wilkinson MD, Mungall C, Cain S, Richards S, Sternberg P, Provin E, Jacobs JL, Geib S, Raciti D, Yook K, Stein L, Molik DC. DATA RESOURCES AND ANALYSES FAIR Header Reference genome: A TRUSTworthy standard. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.29.569306. [PMID: 38076838 PMCID: PMC10705436 DOI: 10.1101/2023.11.29.569306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
The lack of interoperable data standards among reference genome data-sharing platforms inhibits cross-platform analysis while increasing the risk of data provenance loss. Here, we describe the FAIR-bioHeaders Reference genome (FHR), a metadata standard guided by the principles of Findability, Accessibility, Interoperability, and Reuse (FAIR) in addition to the principles of Transparency, Responsibility, User focus, Sustainability, and Technology (TRUST). The objective of FHR is to provide an extensive set of data serialisation methods and minimum data field requirements while still maintaining extensibility, flexibility, and expressivity in an increasingly decentralised genomic data ecosystem. The effort needed to implement FHR is low; FHR's design philosophy ensures easy implementation while retaining the benefits gained from recording both machine and human-readable provenance.
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Affiliation(s)
- Adam Wright
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Mark D Wilkinson
- Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas,Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA/CSIC), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA/CSIC), Pozuelo de Alarcón, Madrid, ES, Spain
| | - Chris Mungall
- Biosystems Data Science, Lawrence Berkeley National Laboratory, Building: 977, 1 Cyclotron Rd, Berkeley, CA 94720 USA
| | - Scott Cain
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Stephen Richards
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, MS: BCM226, Houston, TX 77030, USA
| | - Paul Sternberg
- Division of Biology and Biological Engineering 140-18, California Institute of Technology, Pasadena, CA 91125, USA
| | - Ellen Provin
- Department of Horticultural Studies, Texas A&M University, HFSB 204, TAMU 2133, College Station, TX 77848, USA
| | - Jonathan L Jacobs
- American Type Culture Collection, 10801 University Blvd, Manassas, VA 20110, USA
| | - Scott Geib
- Tropical Pest Genetics and Molecular Biology Research Unit, Daniel K. Inouye U.S. Pacific Basin Agricultural Research Center, United States Department of Agriculture, Agricultural Research Service, 64 Nowelo St, Hilo HI 96720 USA
| | - Daniela Raciti
- Division of Biology and Biological Engineering 140-18, California Institute of Technology, Pasadena, CA 91125, USA
| | - Karen Yook
- Division of Biology and Biological Engineering 140-18, California Institute of Technology, Pasadena, CA 91125, USA
| | - Lincoln Stein
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - David C Molik
- Arthropod-borne Animal Diseases Research Unit, Center for Grain and Animal Health Research United States Department of Agriculture, Agricultural Research Service, 1515 College Ave, Manhattan, KS 66502 USA
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Sheffield NC, LeRoy NJ, Khoroshevskyi O. Challenges to sharing sample metadata in computational genomics. Front Genet 2023; 14:1154198. [PMID: 37287537 PMCID: PMC10243526 DOI: 10.3389/fgene.2023.1154198] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 05/09/2023] [Indexed: 06/09/2023] Open
Affiliation(s)
- Nathan C. Sheffield
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA, United States
- School of Data Science, University of Virginia, Charlottesville, VA, United States
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Nathan J. LeRoy
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Oleksandr Khoroshevskyi
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA, United States
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