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Fouad K, Vavrek R, Surles-Zeigler MC, Huie JR, Radabaugh HL, Gurkoff GG, Visser U, Grethe JS, Martone ME, Ferguson AR, Gensel JC, Torres-Espin A. A practical guide to data management and sharing for biomedical laboratory researchers. Exp Neurol 2024; 378:114815. [PMID: 38762093 DOI: 10.1016/j.expneurol.2024.114815] [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: 11/04/2023] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 05/20/2024]
Abstract
Effective data management and sharing have become increasingly crucial in biomedical research; however, many laboratory researchers lack the necessary tools and knowledge to address this challenge. This article provides an introductory guide into research data management (RDM), and the importance of FAIR (Findable, Accessible, Interoperable, and Reusable) data-sharing principles for laboratory researchers produced by practicing scientists. We explore the advantages of implementing organized data management strategies and introduce key concepts such as data standards, data documentation, and the distinction between machine and human-readable data formats. Furthermore, we offer practical guidance for creating a data management plan and establishing efficient data workflows within the laboratory setting, suitable for labs of all sizes. This includes an examination of requirements analysis, the development of a data dictionary for routine data elements, the implementation of unique subject identifiers, and the formulation of standard operating procedures (SOPs) for seamless data flow. To aid researchers in implementing these practices, we present a simple organizational system as an illustrative example, which can be tailored to suit individual needs and research requirements. By presenting a user-friendly approach, this guide serves as an introduction to the field of RDM and offers practical tips to help researchers effortlessly meet the common data management and sharing mandates rapidly becoming prevalent in biomedical research.
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Affiliation(s)
- K Fouad
- Department of Physical Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada.
| | - R Vavrek
- Department of Physical Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada
| | - M C Surles-Zeigler
- Department of Neuroscience, University of California, San Diego, La Jolla, CA, United States
| | - J R Huie
- Department of Neurosurgery, Brain and Spinal Injury Center, Weill Institutes for Neurosciences, University of California, San Francisco, San Francisco, CA, United States; San Francisco Veterans Affairs Healthcare System, San Francisco, CA, United States
| | - H L Radabaugh
- Department of Neurosurgery, Brain and Spinal Injury Center, Weill Institutes for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - G G Gurkoff
- Center for Neuroscience, University of California Davis, Davis, CA, United States; Department of Neurological Surgery, University of California Davis, Davis, CA, United States; Northern California Veterans Affairs Healthcare System, Martinez, CA, United States
| | - U Visser
- Department of Computer Science, University of Miami, Coral Gables, FL, United States
| | - J S Grethe
- Department of Neuroscience, University of California, San Diego, La Jolla, CA, United States
| | - M E Martone
- Department of Neuroscience, University of California, San Diego, La Jolla, CA, United States; San Francisco Veterans Affairs Healthcare System, San Francisco, CA, United States
| | - A R Ferguson
- Department of Neurosurgery, Brain and Spinal Injury Center, Weill Institutes for Neurosciences, University of California, San Francisco, San Francisco, CA, United States; San Francisco Veterans Affairs Healthcare System, San Francisco, CA, United States
| | - J C Gensel
- Spinal Cord and Brain Injury Research Center and Department of Physiology, University of Kentucky College of Medicine, Lexington, KY, United States.
| | - A Torres-Espin
- Department of Physical Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada; Department of Neurosurgery, Brain and Spinal Injury Center, Weill Institutes for Neurosciences, University of California, San Francisco, San Francisco, CA, United States; School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.
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Cunha-Oliveira T, Ioannidis JPA, Oliveira PJ. Best practices for data management and sharing in experimental biomedical research. Physiol Rev 2024; 104:1387-1408. [PMID: 38451234 DOI: 10.1152/physrev.00043.2023] [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: 11/09/2023] [Revised: 02/07/2024] [Accepted: 02/29/2024] [Indexed: 03/08/2024] Open
Abstract
Effective data management is crucial for scientific integrity and reproducibility, a cornerstone of scientific progress. Well-organized and well-documented data enable validation and building on results. Data management encompasses activities including organization, documentation, storage, sharing, and preservation. Robust data management establishes credibility, fostering trust within the scientific community and benefiting researchers' careers. In experimental biomedicine, comprehensive data management is vital due to the typically intricate protocols, extensive metadata, and large datasets. Low-throughput experiments, in particular, require careful management to address variations and errors in protocols and raw data quality. Transparent and accountable research practices rely on accurate documentation of procedures, data collection, and analysis methods. Proper data management ensures long-term preservation and accessibility of valuable datasets. Well-managed data can be revisited, contributing to cumulative knowledge and potential new discoveries. Publicly funded research has an added responsibility for transparency, resource allocation, and avoiding redundancy. Meeting funding agency expectations increasingly requires rigorous methodologies, adherence to standards, comprehensive documentation, and widespread sharing of data, code, and other auxiliary resources. This review provides critical insights into raw and processed data, metadata, high-throughput versus low-throughput datasets, a common language for documentation, experimental and reporting guidelines, efficient data management systems, sharing practices, and relevant repositories. We systematically present available resources and optimal practices for wide use by experimental biomedical researchers.
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Affiliation(s)
- Teresa Cunha-Oliveira
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - John P A Ioannidis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford, California, United States
- Department of Statistics, Stanford University, Stanford, California, United States
| | - Paulo J Oliveira
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
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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. FAIR Header Reference genome: a TRUSTworthy standard. Brief Bioinform 2024; 25:bbae122. [PMID: 38555475 PMCID: PMC10981671 DOI: 10.1093/bib/bbae122] [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: 12/22/2023] [Revised: 02/16/2024] [Accepted: 02/22/2024] [Indexed: 04/02/2024] Open
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. 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 Biotecnolog’ıa-Biolog’ıa Vegetal, Escuela T’ecnica Superior de Ingenier’ıa Agron’omica, Alimentaria y de Biosistemas,Centro de Biotecnolog’ıa y Gen’omica de Plantas (CBGP, UPM-INIA/CSIC), Universidad Polit’ecnica de Madrid (UPM) - Instituto Nacional de Investigaci’on y Tecnolog’ıa Agraria y Alimentaria (INIA/CSIC), Pozuelo de Alarc’on, Madrid, ES, Spain
| | - Christopher 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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Brancato V, Esposito G, Coppola L, Cavaliere C, Mirabelli P, Scapicchio C, Borgheresi R, Neri E, Salvatore M, Aiello M. Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine. J Transl Med 2024; 22:136. [PMID: 38317237 PMCID: PMC10845786 DOI: 10.1186/s12967-024-04891-8] [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: 01/14/2024] [Indexed: 02/07/2024] Open
Abstract
Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.
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Affiliation(s)
| | - Giuseppina Esposito
- Bio Check Up S.R.L, 80121, Naples, Italy
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131, Naples, Italy
| | | | | | - Peppino Mirabelli
- UOS Laboratori di Ricerca e Biobanca, AORN Santobono-Pausilipon, Via Teresa Ravaschieri, 8, 80122, Naples, Italy
| | - Camilla Scapicchio
- Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy
| | - Rita Borgheresi
- Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy
| | - Emanuele Neri
- Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy
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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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6
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Emissah H, Ljungquist B, Ascoli GA. Bibliometric analysis of neuroscience publications quantifies the impact of data sharing. Bioinformatics 2023; 39:btad746. [PMID: 38070153 PMCID: PMC10733721 DOI: 10.1093/bioinformatics/btad746] [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: 09/13/2023] [Revised: 11/01/2023] [Accepted: 12/07/2023] [Indexed: 12/19/2023] Open
Abstract
SUMMARY Neural morphology, the branching geometry of brain cells, is an essential cellular substrate of nervous system function and pathology. Despite the accelerating production of digital reconstructions of neural morphology, the public accessibility of data remains a core issue in neuroscience. Deficiencies in the availability of existing data create redundancy of research efforts and limit synergy. We carried out a comprehensive bibliometric analysis of neural morphology publications to quantify the impact of data sharing in the neuroscience community. Our findings demonstrate that sharing digital reconstructions of neural morphology via NeuroMorpho.Org leads to a significant increase of citations to the original article, thus directly benefiting authors. The rate of data reusage remains constant for at least 16 years after sharing (the whole period analyzed), altogether nearly doubling the peer-reviewed discoveries in the field. Furthermore, the recent availability of larger and more numerous datasets fostered integrative applications, which accrue on average twice the citations of re-analyses of individual datasets. We also released an open-source citation tracking web-service allowing researchers to monitor reusage of their datasets in independent peer-reviewed reports. These results and tools can facilitate the recognition of shared data reuse for merit evaluations and funding decisions. AVAILABILITY AND IMPLEMENTATION The application is available at: http://cng-nmo-dev3.orc.gmu.edu:8181/. The source code at https://github.com/HerveEmissah/nmo-authors-app and https://github.com/HerveEmissah/nmo-bibliometric-analysis.
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Affiliation(s)
- Herve Emissah
- Bioinformatics Program, College of Science, George Mason University, Fairfax, VA 22030, United States
- Center for Neural Informatics, Structures, & Plasticity (CN3) and Bioengineering Department, College of Engineering & Computing, George Mason University, Fairfax, VA 22030, United States
| | - Bengt Ljungquist
- Center for Neural Informatics, Structures, & Plasticity (CN3) and Bioengineering Department, College of Engineering & Computing, George Mason University, Fairfax, VA 22030, United States
| | - Giorgio A Ascoli
- Bioinformatics Program, College of Science, George Mason University, Fairfax, VA 22030, United States
- Center for Neural Informatics, Structures, & Plasticity (CN3) and Bioengineering Department, College of Engineering & Computing, George Mason University, Fairfax, VA 22030, United States
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Jo T, Kim J, Bice P, Huynh K, Wang T, Arnold M, Meikle PJ, Giles C, Kaddurah-Daouk R, Saykin AJ, Nho K. Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data. EBioMedicine 2023; 97:104820. [PMID: 37806288 PMCID: PMC10579282 DOI: 10.1016/j.ebiom.2023.104820] [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: 07/06/2023] [Revised: 09/19/2023] [Accepted: 09/20/2023] [Indexed: 10/10/2023] Open
Abstract
BACKGROUND Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics. METHODS The c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification. FINDINGS The application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04 × 10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6). INTERPRETATION Our results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation. FUNDING The specific funding of this article is provided in the acknowledgements section.
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Affiliation(s)
- Taeho Jo
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Junpyo Kim
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Medical Research Institute, Sungkyunkwan University, School of Medicine, Seoul, South Korea
| | - Paula Bice
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, 3004, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, 3010, Victoria, Australia
| | - Tingting Wang
- Baker Heart and Diabetes Institute, Melbourne, 3004, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, 3010, Victoria, Australia
| | - Matthias Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, 27710, USA; Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, 3004, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, 3010, Victoria, Australia; Monash University, Melbourne, VIC 3800, Australia
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, 3004, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, 3010, Victoria, Australia
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, 27710, USA; Duke Institute of Brain Sciences, Duke University, Durham, NC, 27710, USA; Department of Medicine, Duke University, Durham, NC, 27710, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
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Kemmer I, Keppler A, Serrano-Solano B, Rybina A, Özdemir B, Bischof J, El Ghadraoui A, Eriksson JE, Mathur A. Building a FAIR image data ecosystem for microscopy communities. Histochem Cell Biol 2023; 160:199-209. [PMID: 37341795 PMCID: PMC10492678 DOI: 10.1007/s00418-023-02203-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/27/2023] [Indexed: 06/22/2023]
Abstract
Bioimaging has now entered the era of big data with faster-than-ever development of complex microscopy technologies leading to increasingly complex datasets. This enormous increase in data size and informational complexity within those datasets has brought with it several difficulties in terms of common and harmonized data handling, analysis, and management practices, which are currently hampering the full potential of image data being realized. Here, we outline a wide range of efforts and solutions currently being developed by the microscopy community to address these challenges on the path towards FAIR bioimaging data. We also highlight how different actors in the microscopy ecosystem are working together, creating synergies that develop new approaches, and how research infrastructures, such as Euro-BioImaging, are fostering these interactions to shape the field.
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Affiliation(s)
- Isabel Kemmer
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Antje Keppler
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Beatriz Serrano-Solano
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Arina Rybina
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Buğra Özdemir
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Johanna Bischof
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Ayoub El Ghadraoui
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - John E Eriksson
- Euro-BioImaging ERIC Statutory Seat, Tykistökatu 6, P.O. Box 123, 20521, Turku, Finland
| | - Aastha Mathur
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany.
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9
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Hegarty C, Neto N, Cahill P, Floudas A. Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions. Comput Struct Biotechnol J 2023; 21:4009-4020. [PMID: 37649712 PMCID: PMC10462794 DOI: 10.1016/j.csbj.2023.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 08/04/2023] [Accepted: 08/04/2023] [Indexed: 09/01/2023] Open
Abstract
Inflammatory arthritis, including rheumatoid (RA), and psoriatic (PsA) arthritis, are clinically and immunologically heterogeneous diseases with no identified cure. Chronic inflammation of the synovial tissue ushers loss of function of the joint that severely impacts the patient's quality of life, eventually leading to disability and life-threatening comorbidities. The pathogenesis of synovial inflammation is the consequence of compounded immune and stromal cell interactions influenced by genetic and environmental factors. Deciphering the complexity of the synovial cellular landscape has accelerated primarily due to the utilisation of bulk and single cell RNA sequencing. Particularly the capacity to generate cell-cell interaction networks could reveal evidence of previously unappreciated processes leading to disease. However, there is currently a lack of universal nomenclature as a result of varied experimental and technological approaches that discombobulates the study of synovial inflammation. While spatial transcriptomic analysis that combines anatomical information with transcriptomic data of synovial tissue biopsies promises to provide more insights into disease pathogenesis, in vitro functional assays with single-cell resolution will be required to validate current bioinformatic applications. In order to provide a comprehensive approach and translate experimental data to clinical practice, a combination of clinical and molecular data with machine learning has the potential to enhance patient stratification and identify individuals at risk of arthritis that would benefit from early therapeutic intervention. This review aims to provide a comprehensive understanding of the effect of computational approaches in deciphering synovial inflammation pathogenesis and discuss the impact that further experimental and novel computational tools may have on therapeutic target identification and drug development.
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Affiliation(s)
- Ciara Hegarty
- Translational Immunology lab, School of Biotechnology, Dublin City University, Dublin, Ireland
| | - Nuno Neto
- Trinity Centre for Biomedical Engineering, Trinity College Dublin, Ireland
| | - Paul Cahill
- Vascular Biology lab, School of Biotechnology, Dublin City University, Dublin, Ireland
| | - Achilleas Floudas
- Translational Immunology lab, School of Biotechnology, Dublin City University, Dublin, Ireland
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10
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Ahmed F, Samantasinghar A, Manzoor Soomro A, Kim S, Hyun Choi K. A systematic review of computational approaches to understand cancer biology for informed drug repurposing. J Biomed Inform 2023; 142:104373. [PMID: 37120047 DOI: 10.1016/j.jbi.2023.104373] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/25/2023] [Accepted: 04/23/2023] [Indexed: 05/01/2023]
Abstract
Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.
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Affiliation(s)
- Faheem Ahmed
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea
| | | | | | - Sejong Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
| | - Kyung Hyun Choi
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea.
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11
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Shea MM, Kuppermann J, Rogers MP, Smith DS, Edwards P, Boehm AB. Systematic review of marine environmental DNA metabarcoding studies: toward best practices for data usability and accessibility. PeerJ 2023; 11:e14993. [PMID: 36992947 PMCID: PMC10042160 DOI: 10.7717/peerj.14993] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 02/12/2023] [Indexed: 03/31/2023] Open
Abstract
The emerging field of environmental DNA (eDNA) research lacks universal guidelines for ensuring data produced are FAIR-findable, accessible, interoperable, and reusable-despite growing awareness of the importance of such practices. In order to better understand these data usability challenges, we systematically reviewed 60 peer reviewed articles conducting a specific subset of eDNA research: metabarcoding studies in marine environments. For each article, we characterized approximately 90 features across several categories: general article attributes and topics, methodological choices, types of metadata included, and availability and storage of sequence data. Analyzing these characteristics, we identified several barriers to data accessibility, including a lack of common context and vocabulary across the articles, missing metadata, supplementary information limitations, and a concentration of both sample collection and analysis in the United States. While some of these barriers require significant effort to address, we also found many instances where small choices made by authors and journals could have an outsized influence on the discoverability and reusability of data. Promisingly, articles also showed consistency and creativity in data storage choices as well as a strong trend toward open access publishing. Our analysis underscores the need to think critically about data accessibility and usability as marine eDNA metabarcoding studies, and eDNA projects more broadly, continue to proliferate.
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Affiliation(s)
- Meghan M. Shea
- Emmett Interdisciplinary Program in Environment & Resources (E-IPER), Stanford University, Stanford, CA, United States of America
| | - Jacob Kuppermann
- Earth Systems Program, Stanford University, Stanford, CA, United States of America
| | - Megan P. Rogers
- Program in Human Biology, Stanford University, Stanford, CA, United States of America
| | - Dustin Summer Smith
- Earth Systems Program, Stanford University, Stanford, CA, United States of America
| | - Paul Edwards
- Program in Science, Technology and Society, Stanford University, Stanford, CA, United States of America
| | - Alexandria B. Boehm
- Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, United States of America
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12
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Wishart DS, Girod S, Peters H, Oler E, Jovel J, Budinski Z, Milford R, Lui VW, Sayeeda Z, Mah R, Wei W, Badran H, Lo E, Yamamoto M, Djoumbou-Feunang Y, Karu N, Gautam V. ChemFOnt: the chemical functional ontology resource. Nucleic Acids Res 2022; 51:D1220-D1229. [PMID: 36305829 PMCID: PMC9825615 DOI: 10.1093/nar/gkac919] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/03/2022] [Accepted: 10/18/2022] [Indexed: 01/30/2023] Open
Abstract
The Chemical Functional Ontology (ChemFOnt), located at https://www.chemfont.ca, is a hierarchical, OWL-compatible ontology describing the functions and actions of >341 000 biologically important chemicals. These include primary metabolites, secondary metabolites, natural products, food chemicals, synthetic food additives, drugs, herbicides, pesticides and environmental chemicals. ChemFOnt is a FAIR-compliant resource intended to bring the same rigor, standardization and formal structure to the terms and terminology used in biochemistry, food chemistry and environmental chemistry as the gene ontology (GO) has brought to molecular biology. ChemFOnt is available as both a freely accessible, web-enabled database and a downloadable Web Ontology Language (OWL) file. Users may download and deploy ChemFOnt within their own chemical databases or integrate ChemFOnt into their own analytical software to generate machine readable relationships that can be used to make new inferences, enrich their omics data sets or make new, non-obvious connections between chemicals and their direct or indirect effects. The web version of the ChemFOnt database has been designed to be easy to search, browse and navigate. Currently ChemFOnt contains data on 341 627 chemicals, including 515 332 terms or definitions. The functional hierarchy for ChemFOnt consists of four functional 'aspects', 12 functional super-categories and a total of 173 705 functional terms. In addition, each of the chemicals are classified into 4825 structure-based chemical classes. ChemFOnt currently contains 3.9 million protein-chemical relationships and ∼10.3 million chemical-functional relationships. The long-term goal for ChemFOnt is for it to be adopted by databases and software tools used by the general chemistry community as well as the metabolomics, exposomics, metagenomics, genomics and proteomics communities.
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Affiliation(s)
- David S Wishart
- To whom correspondence should be addressed. Tel: +1 780 492 8574;
| | - Sagan Girod
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Harrison Peters
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Eponine Oler
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Juan Jovel
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Zachary Budinski
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Ralph Milford
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - Vicki W Lui
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Zinat Sayeeda
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Robert Mah
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - William Wei
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Hasan Badran
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Elvis Lo
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Mai Yamamoto
- Molecular You Corporation, 788 Beatty St., Suite 307, Vancouver, BC V6B 2M1, Canada
| | | | - Naama Karu
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2333 CC, The Netherlands
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
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13
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Proteomic profiling of concurrently isolated primary microvascular endothelial cells, pericytes, and vascular smooth muscle cells from adult mouse heart. Sci Rep 2022; 12:8835. [PMID: 35614104 PMCID: PMC9132906 DOI: 10.1038/s41598-022-12749-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 05/10/2022] [Indexed: 11/17/2022] Open
Abstract
The microcirculation serves crucial functions in adult heart, distinct from those carried out by epicardial vessels. Microvessels are governed by unique regulatory mechanisms, impairment of which leads to microvessel-specific pathology. There are few treatment options for patients with microvascular heart disease, primarily due to limited understanding of underlying pathology. High throughput mRNA sequencing and protein expression profiling in specific cells can improve our understanding of microvessel biology and disease at the molecular level. Understanding responses of individual microvascular cells to the same physiological or pathophysiological stimuli requires the ability to isolate the specific cell types that comprise the functional units of the microcirculation in the heart, preferably from the same heart, to ensure that different cells have been exposed to the same in-vivo conditions. We developed an integrated process for simultaneous isolation and culture of the main cell types comprising the microcirculation in adult mouse heart: endothelial cells, pericytes, and vascular smooth muscle cells. These cell types were characterized with isobaric labeling quantitative proteomics and mRNA sequencing. We defined microvascular cell proteomes, identified novel protein markers, and confirmed established cell-specific markers. Our results allow identification of unique markers and regulatory proteins that govern microvascular physiology and pathology.
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14
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Post JN, Loerakker S, Merks R, Carlier A. Implementing computational modeling in tissue engineering: where disciplines meet. Tissue Eng Part A 2022; 28:542-554. [PMID: 35345902 DOI: 10.1089/ten.tea.2021.0215] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
In recent years, the mathematical and computational sciences have developed novel methodologies and insights that can aid in designing advanced bioreactors, microfluidic set-ups or organ-on-chip devices, in optimizing culture conditions, or predicting long-term behavior of engineered tissues in vivo. In this review, we introduce the concept of computational models and how they can be integrated in an interdisciplinary workflow for Tissue Engineering and Regenerative Medicine (TERM). We specifically aim this review of general concepts and examples at experimental scientists with little or no computational modeling experience. We also describe the contribution of computational models in understanding TERM processes and in advancing the TERM field by providing novel insights.
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Affiliation(s)
- Janine Nicole Post
- University of Twente, 3230, Tissue Regeneration, Enschede, Overijssel, Netherlands;
| | - Sandra Loerakker
- Eindhoven University of Technology, 3169, Department of Biomedical Engineering, Eindhoven, Noord-Brabant, Netherlands.,Eindhoven University of Technology, 3169, Institute for Complex Molecular Systems, Eindhoven, Noord-Brabant, Netherlands;
| | - Roeland Merks
- Leiden University, 4496, Institute for Biology Leiden and Mathematical Institute, Leiden, Zuid-Holland, Netherlands;
| | - Aurélie Carlier
- Maastricht University, 5211, MERLN Institute for Technology-Inspired Regenerative Medicine, Universiteitssingel 40, 6229 ER Maastricht, Maastricht, Netherlands, 6200 MD;
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15
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AIM in Medical Informatics. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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16
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Torres-Espín A, Almeida CA, Chou A, Huie JR, Chiu M, Vavrek R, Sacramento J, Orr MB, Gensel JC, Grethe JS, Martone ME, Fouad K, Ferguson AR. Promoting FAIR Data Through Community-driven Agile Design: the Open Data Commons for Spinal Cord Injury (odc-sci.org). Neuroinformatics 2022; 20:203-219. [PMID: 34347243 PMCID: PMC9537193 DOI: 10.1007/s12021-021-09533-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/16/2021] [Indexed: 01/07/2023]
Abstract
The past decade has seen accelerating movement from data protectionism in publishing toward open data sharing to improve reproducibility and translation of biomedical research. Developing data sharing infrastructures to meet these new demands remains a challenge. One model for data sharing involves simply attaching data, irrespective of its type, to publisher websites or general use repositories. However, some argue this creates a 'data dump' that does not promote the goals of making data Findable, Accessible, Interoperable and Reusable (FAIR). Specialized data sharing communities offer an alternative model where data are curated by domain experts to make it both open and FAIR. We report on our experiences developing one such data-sharing ecosystem focusing on 'long-tail' preclinical data, the Open Data Commons for Spinal Cord Injury (odc-sci.org). ODC-SCI was developed with community-based agile design requirements directly pulled from a series of workshops with multiple stakeholders (researchers, consumers, non-profit funders, governmental agencies, journals, and industry members). ODC-SCI focuses on heterogeneous tabular data collected by preclinical researchers including bio-behaviour, histopathology findings and molecular endpoints. This has led to an example of a specialized neurocommons that is well-embraced by the community it aims to serve. In the present paper, we provide a review of the community-based design template and describe the adoption by the community including a high-level review of current data assets, publicly released datasets, and web analytics. Although odc-sci.org is in its late beta stage of development, it represents a successful example of a specialized data commons that may serve as a model for other fields.
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Affiliation(s)
- Abel Torres-Espín
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, Department of Neurological Surgery, University of California San Francisco, San Francisco, CA USA
| | - Carlos A. Almeida
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, Department of Neurological Surgery, University of California San Francisco, San Francisco, CA USA
| | - Austin Chou
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, Department of Neurological Surgery, University of California San Francisco, San Francisco, CA USA
| | - J. Russell Huie
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, Department of Neurological Surgery, University of California San Francisco, San Francisco, CA USA
| | - Michael Chiu
- Department of Neuroscience, University of California, San Diego, San Diego, CA USA
| | - Romana Vavrek
- Faculty of Rehabilitation Medicine and the Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB Canada
| | - Jeff Sacramento
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, Department of Neurological Surgery, University of California San Francisco, San Francisco, CA USA
| | - Michael B. Orr
- Spinal Cord and Brain Injury Research Center, Department of Physiology, University of Kentucky College of Medicine, Lexington, KY USA
| | - John C. Gensel
- Spinal Cord and Brain Injury Research Center, Department of Physiology, University of Kentucky College of Medicine, Lexington, KY USA
| | - Jeffery S. Grethe
- Department of Neuroscience, University of California, San Diego, San Diego, CA USA
| | - Maryann E. Martone
- Department of Neuroscience, University of California, San Diego, San Diego, CA USA
| | - Karim Fouad
- Faculty of Rehabilitation Medicine and the Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB Canada
| | - Adam R. Ferguson
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, Department of Neurological Surgery, University of California San Francisco, San Francisco, CA USA ,San Francisco Veterans Affairs Health Care System, San Francisco, CA USA
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17
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Grafström R, Haase A, Kohonen P, Jeliazkova N, Nymark P. Reply to: Prospects and challenges for FAIR toxicogenomics data. NATURE NANOTECHNOLOGY 2022; 17:19-20. [PMID: 34949776 DOI: 10.1038/s41565-021-01050-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 11/11/2021] [Indexed: 06/14/2023]
Affiliation(s)
- Roland Grafström
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
- Division of Toxicology, Misvik Biology, Turku, Finland
| | - Andrea Haase
- German Federal Institute for Risk Assessment, Berlin, Germany
| | - Pekka Kohonen
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
- Division of Toxicology, Misvik Biology, Turku, Finland
| | | | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden.
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18
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Zhang X, Saltman R. Impact of Electronic Health Records Interoperability on Telehealth Service Outcomes. JMIR Med Inform 2021; 10:e31837. [PMID: 34890347 PMCID: PMC8790688 DOI: 10.2196/31837] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 10/20/2021] [Accepted: 11/14/2021] [Indexed: 12/21/2022] Open
Abstract
This paper aims to develop a telehealth success model and discusses three critical components: (1) health information quality, (2) electronic health record system quality, and (3) telehealth service quality to ensure effective telehealth service delivery, reduce professional burnout, and enhance access to care. The paper applied a policy analysis method and discussed telehealth applications in rural health, mental health, and veterans health services. The results pointed out the fact that, although telehealth paired with semantic/organizational interoperability facilitates value-based and team-based care, challenges remain to enhance user (both patients and clinicians) experience and satisfaction. The conclusion indicates that approaches at systemic and physician levels are needed to reduce disparities in health technology adoption and improve access to telehealth care.
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19
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Abstract
Wearable technology has a history in sleep research dating back to the 1970s. Because modern wearable technology is relatively cheap and widely used by the general population, this represents an opportunity to leverage wearable devices to advance sleep medicine and research. However, there is a lack of published validation studies designed to quantify device performance against accepted gold standards, especially across different populations. Recommendations for conducting performance assessments and using wearable devices are now published with the goal of standardizing wearable device implementation and advancing the field.
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20
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Tarazona S, Arzalluz-Luque A, Conesa A. Undisclosed, unmet and neglected challenges in multi-omics studies. NATURE COMPUTATIONAL SCIENCE 2021; 1:395-402. [PMID: 38217236 DOI: 10.1038/s43588-021-00086-z] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 05/17/2021] [Indexed: 01/15/2024]
Abstract
Multi-omics approaches have become a reality in both large genomics projects and small laboratories. However, the multi-omics research community still faces a number of issues that have either not been sufficiently discussed or for which current solutions are still limited. In this Perspective, we elaborate on these limitations and suggest points of attention for future research. We finally discuss new opportunities and challenges brought to the field by the rapid development of single-cell high-throughput molecular technologies.
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Affiliation(s)
- Sonia Tarazona
- Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Valencia, Spain
| | - Angeles Arzalluz-Luque
- Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Valencia, Spain
| | - Ana Conesa
- Microbiology and Cell Science Department, Institute for Food and Agricultural Research, University of Florida, Gainesville, FL, USA.
- Genetics Institute, University of Florida, Gainesville, FL, USA.
- Institute for Integrative Systems Biology, Spanish National Research Council, Valencia, Spain.
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21
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Chiara M, D’Erchia AM, Gissi C, Manzari C, Parisi A, Resta N, Zambelli F, Picardi E, Pavesi G, Horner DS, Pesole G. Next generation sequencing of SARS-CoV-2 genomes: challenges, applications and opportunities. Brief Bioinform 2021; 22:616-630. [PMID: 33279989 PMCID: PMC7799330 DOI: 10.1093/bib/bbaa297] [Citation(s) in RCA: 118] [Impact Index Per Article: 39.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 09/27/2020] [Accepted: 10/07/2020] [Indexed: 12/31/2022] Open
Abstract
Various next generation sequencing (NGS) based strategies have been successfully used in the recent past for tracing origins and understanding the evolution of infectious agents, investigating the spread and transmission chains of outbreaks, as well as facilitating the development of effective and rapid molecular diagnostic tests and contributing to the hunt for treatments and vaccines. The ongoing COVID-19 pandemic poses one of the greatest global threats in modern history and has already caused severe social and economic costs. The development of efficient and rapid sequencing methods to reconstruct the genomic sequence of SARS-CoV-2, the etiological agent of COVID-19, has been fundamental for the design of diagnostic molecular tests and to devise effective measures and strategies to mitigate the diffusion of the pandemic. Diverse approaches and sequencing methods can, as testified by the number of available sequences, be applied to SARS-CoV-2 genomes. However, each technology and sequencing approach has its own advantages and limitations. In the current review, we will provide a brief, but hopefully comprehensive, account of currently available platforms and methodological approaches for the sequencing of SARS-CoV-2 genomes. We also present an outline of current repositories and databases that provide access to SARS-CoV-2 genomic data and associated metadata. Finally, we offer general advice and guidelines for the appropriate sharing and deposition of SARS-CoV-2 data and metadata, and suggest that more efficient and standardized integration of current and future SARS-CoV-2-related data would greatly facilitate the struggle against this new pathogen. We hope that our 'vademecum' for the production and handling of SARS-CoV-2-related sequencing data, will contribute to this objective.
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Affiliation(s)
- Matteo Chiara
- molecular biology and bioinformatics at the University of Milan
| | - Anna Maria D’Erchia
- molecular biology at the University of Bari and research associate at the Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies of the National Research Council in Bari
| | - Carmela Gissi
- molecular biology at the University of Bari and research associate at the Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies of the National Research Council in Bari
| | - Caterina Manzari
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies of the National Research Council in Bari
| | - Antonio Parisi
- Genetic and Molecular Epidemiology Laboratory at the Experimental Zooprophylactic Institute of Apulia and Basilicata
| | - Nicoletta Resta
- Medical Genetics at the University of Bari. She heads the Laboratory Unit of Medical Genetics and the School of Specialization in Medical Genetics
| | | | - Ernesto Picardi
- molecular biology and bioinformatics at the University of Bari and research associate at the Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies of the National Research Council in Bari
| | - Giulio Pavesi
- Associate Professor of bioinformatics at the University of Milan (Italy)
| | - David S Horner
- molecular biology and bioinformatics at the University of Milan
| | - Graziano Pesole
- molecular biology at the University of Bari and Research Associate at the Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies of the National Research Council in Bari
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22
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Minimum Information Required to Annotate Food Safety Risk Assessment Models (MIRARAM). Food Res Int 2021; 139:109952. [PMID: 33509505 DOI: 10.1016/j.foodres.2020.109952] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 11/20/2020] [Accepted: 11/27/2020] [Indexed: 11/21/2022]
Abstract
In the last decades, mathematical models and model-based simulations became important elements not only in the area of risk assessment concerning microbiological and chemical hazards but also in modelling biological phenomena in general. Unfortunately, many of the developed models are published in non-standardized ways, which hinders efficient exchange, re-use and continuous improvement of models within the risk assessment domain. The establishment of guidelines for model annotation is an important pre-condition to overcome these obstacles. Additionally, implementation of annotation guidelines can improve transparency, quality control and even aid the clarification of intellectual property rights. Here, we address the question of "What is the minimum set of metadata that should be provided for a model in the risk assessment domain?". The proposed guideline focuses on food safety risk assessment models and is called "Minimum Information Required to Annotate food safety Risk Assessment Models (MIRARAM)". MIRARAM supports the model creator during the model documentation step and could also be used as a checklist by scientific journal editors or database curators. Software developers could take up MIRARAM and develop easy-to-use software tools or new features in existing programs that can help model creators to provide proposed model annotations in harmonized file formats. Based on experiences from similar guidelines in related scientific disciplines (like systems biology), it is expected that MIRARAM could contribute to the promotion of application and re-use of models as well as to implementing more standardized quality control in the food safety modelling domain.
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23
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Bruno P, Calimeri F, Greco G. AIM in Medical Informatics. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_32-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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24
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Roosan D, Hwang A, Law AV, Chok J, Roosan MR. The inclusion of health data standards in the implementation of pharmacogenomics systems: a scoping review. Pharmacogenomics 2020; 21:1191-1202. [PMID: 33124487 DOI: 10.2217/pgs-2020-0066] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Background: Despite potential benefits, the practice of incorporating pharmacogenomics (PGx) results in clinical decisions has yet to diffuse widely. In this study, we conducted a review of recent discussions on data standards and interoperability with a focus on sharing PGx test results among health systems. Materials & methods: We conducted a literature search for PGx clinical decision support systems between 1 January 2012 and 31 January 2020. Thirty-two out of 727 articles were included for the final review. Results: Nine of the 32 articles mentioned data standards and only four of the 32 articles provided solutions for the lack of interoperability. Discussions: Although PGx interoperability is essential for widespread implementation, a lack of focus on standardized data creates a formidable challenge for health information exchange. Conclusion: Standardization of PGx data is essential to improve health information exchange and the sharing of PGx results between disparate systems. However, PGx data standards and interoperability are often not addressed in the system-level implementation.
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Affiliation(s)
- Don Roosan
- Assistant Professor, Department of Pharmacy Practice & Administration, College of Pharmacy, Western University of Health Sciences, 309 E 2nd street, Pomona, CA 91766, USA
| | - Angela Hwang
- Research Assistant, Department of Pharmacy Practice & Administration, College of Pharmacy, Western University of Health Sciences, Pomona, CA 91766, USA
| | - Anandi V Law
- Professor, Department of Pharmacy Practice & Administration, College of Pharmacy, Western University of Health Sciences, Pomona, CA 91766, USA
| | - Jay Chok
- Associate Professor, School of Applied Life Sciences, Keck Graduate Institute, Claremont Colleges, Pomona, CA 91711, USA
| | - Moom R Roosan
- Assistant Professor, School of Pharmacy, Department of Pharmacy Practice, Chapman University, Irvine, CA 92618, USA
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25
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Raghunath A, Nagarajan R, Perumal E. ZFARED: A Database of the Antioxidant Response Elements in Zebrafish. Curr Bioinform 2020. [DOI: 10.2174/1574893614666191018172213] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Antioxidant Response Elements (ARE) play a key role in the expression
of Nrf2 target genes by regulating the Keap1-Nrf2-ARE pathway, which offers protection against
toxic agents and oxidative stress-induced diseases.
Objective:
To develop a database of putative AREs for all the genes in the zebrafish genome. This
database will be helpful for researchers to investigate Nrf2 regulatory mechanisms in detail.
Methods:
To facilitate researchers functionally characterize zebrafish AREs, we have developed a
database of AREs, Zebrafish Antioxidant Response Element Database (ZFARED), for all the
protein-coding genes including antioxidant and mitochondrial genes in the zebrafish genome. The
front end of the database was developed using HTML, JavaScript, and CSS and tested in different
browsers. The back end of the database was developed using Perl scripts and Perl-CGI and Perl-
DBI modules.
Results:
ZFARED is the first database on the AREs in zebrafish, which facilitates fast and
efficient searching of AREs. AREs were identified using the in-house developed Perl algorithms
and the database was developed using HTML, JavaScript, and Perl-CGI scripts. From this
database, researchers can access the AREs based on chromosome number (1 to 25 and M for
mitochondria), strand (positive or negative), ARE pattern and keywords. Users can also specify the
size of the upstream/promoter regions (5 to 30 kb) from transcription start site to access the AREs
located in those specific regions.
Conclusion:
ZFARED will be useful in the investigation of the Keap1-Nrf2-ARE pathway and its
gene regulation. ZFARED is freely available at http://zfared.buc.edu.in/.
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Affiliation(s)
- Azhwar Raghunath
- Molecular Toxicology Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore 641 046, Tamilnadu, India
| | - Raju Nagarajan
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India
| | - Ekambaram Perumal
- Molecular Toxicology Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore 641 046, Tamilnadu, India
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26
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Gonzalez-Beltran AN, Masuzzo P, Ampe C, Bakker GJ, Besson S, Eibl RH, Friedl P, Gunzer M, Kittisopikul M, Dévédec SEL, Leo S, Moore J, Paran Y, Prilusky J, Rocca-Serra P, Roudot P, Schuster M, Sergeant G, Strömblad S, Swedlow JR, van Erp M, Van Troys M, Zaritsky A, Sansone SA, Martens L. Community standards for open cell migration data. Gigascience 2020; 9:giaa041. [PMID: 32396199 PMCID: PMC7317087 DOI: 10.1093/gigascience/giaa041] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Revised: 04/02/2020] [Accepted: 04/02/2020] [Indexed: 01/08/2023] Open
Abstract
Cell migration research has become a high-content field. However, the quantitative information encapsulated in these complex and high-dimensional datasets is not fully exploited owing to the diversity of experimental protocols and non-standardized output formats. In addition, typically the datasets are not open for reuse. Making the data open and Findable, Accessible, Interoperable, and Reusable (FAIR) will enable meta-analysis, data integration, and data mining. Standardized data formats and controlled vocabularies are essential for building a suitable infrastructure for that purpose but are not available in the cell migration domain. We here present standardization efforts by the Cell Migration Standardisation Organisation (CMSO), an open community-driven organization to facilitate the development of standards for cell migration data. This work will foster the development of improved algorithms and tools and enable secondary analysis of public datasets, ultimately unlocking new knowledge of the complex biological process of cell migration.
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Affiliation(s)
- Alejandra N Gonzalez-Beltran
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, Oxford OX1 3QG, Oxford, UK
| | - Paola Masuzzo
- VIB-UGent Center for Medical Biotechnology, VIB, A. Baertsoenkaai 3, B-9000, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, A. Baertsoenkaai 3, B-9000, Ghent, Belgium
- Institute for Globally Distributed Open Research and Education (IGDORE), Kabupaten Gianyar, Bali 80571, Indonesia
| | - Christophe Ampe
- Department of Biomolecular Medicine, Ghent University, A. Baertsoenkaai 3, B-9000, Ghent, Belgium
| | - Gert-Jan Bakker
- Department of Cell Biology, Radboud Institute for Molecular Life Sciences, Geert Grooteplein 28 6525 GA Nijmegen, The Netherlands
| | - Sébastien Besson
- Centre for Gene Regulation & Expression & Division of Computational Biology, University of Dundee, School of Life Sciences, Dow St Dundee DD1 5EH, Scotland, UK
| | - Robert H Eibl
- German Cancer Research Center, DKFZ Alumni Association, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Peter Friedl
- Department of Cell Biology, Radboud Institute for Molecular Life Sciences, Geert Grooteplein 28 6525 GA Nijmegen, The Netherlands
- David H. Koch Center for Applied Genitourinary Medicine, UT MD Anderson Cancer Center, 6767 Bertner Ave, Mitchell Basic Science Research Building, 77030 Houston, TX, USA
- Cancer Genomics Center, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands
| | - Matthias Gunzer
- Institute for Experimental Immunology and Imaging, University Hospital, University Duisburg-Essen, Universitätsstr. 2, 45141 Essen, Germany
- Leibniz Institute for Analytical Sciences, ISAS, Bunsen-Kirchhoff-Straße 11, 44139 Dortmund, Germany
| | - Mark Kittisopikul
- Department of Biophysics, UT Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390, USA
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, 303 E. Chicago Ave, Chicago, IL 60611, USA
| | - Sylvia E Le Dévédec
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, PO box 9502 2300 RA Leiden, The Netherlands
| | - Simone Leo
- Centre for Gene Regulation & Expression & Division of Computational Biology, University of Dundee, School of Life Sciences, Dow St Dundee DD1 5EH, Scotland, UK
- Center for Advanced Studies, Research, and Development in Sardinia (CRS4), Loc. Piscina Manna, Edificio 1, 09050 Pula (CA) , Italy
| | - Josh Moore
- Centre for Gene Regulation & Expression & Division of Computational Biology, University of Dundee, School of Life Sciences, Dow St Dundee DD1 5EH, Scotland, UK
| | - Yael Paran
- IDEA Bio-Medical Ltd, 2 Prof. Bergman St., Rehovot 76705, Israel
| | - Jaime Prilusky
- Life Science Core Facilities, Weizmann Institute of Science, P.O. Box 26 Rehovot 76100, Israel
| | - Philippe Rocca-Serra
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, Oxford OX1 3QG, Oxford, UK
| | - Philippe Roudot
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390, USA
| | - Marc Schuster
- Institute for Experimental Immunology and Imaging, University Hospital, University Duisburg-Essen, Universitätsstr. 2, 45141 Essen, Germany
| | - Gwendolien Sergeant
- Department of Biomolecular Medicine, Ghent University, A. Baertsoenkaai 3, B-9000, Ghent, Belgium
| | - Staffan Strömblad
- Department of Biosciences and Nutrition, Karolinska Institutet, Neo, SE-141 83 Huddinge, Sweden
| | - Jason R Swedlow
- Centre for Gene Regulation & Expression & Division of Computational Biology, University of Dundee, School of Life Sciences, Dow St Dundee DD1 5EH, Scotland, UK
| | - Merijn van Erp
- Department of Cell Biology, Radboud Institute for Molecular Life Sciences, Geert Grooteplein 28 6525 GA Nijmegen, The Netherlands
| | - Marleen Van Troys
- Department of Biomolecular Medicine, Ghent University, A. Baertsoenkaai 3, B-9000, Ghent, Belgium
| | - Assaf Zaritsky
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, P.O.B. 653, 8410501 Beer-Sheva, Israel
| | - Susanna-Assunta Sansone
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, Oxford OX1 3QG, Oxford, UK
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, A. Baertsoenkaai 3, B-9000, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, A. Baertsoenkaai 3, B-9000, Ghent, Belgium
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27
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Picache J, May JC, McLean JA. Crowd-Sourced Chemistry: Considerations for Building a Standardized Database to Improve Omic Analyses. ACS OMEGA 2020; 5:980-985. [PMID: 31984253 PMCID: PMC6977078 DOI: 10.1021/acsomega.9b03708] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 12/24/2019] [Indexed: 05/09/2023]
Abstract
Mass spectrometry (MS) is used in multiple omics disciplines to generate large collections of data. This data enables advancements in biomedical research by providing global profiles of a given system. One of the main barriers to generating these profiles is the inability to accurately annotate omics data, especially small molecules. To complement pre-existing large databases that are not quite complete, research groups devote efforts to generating personal libraries to annotate their data. Scientific progress is impeded during the generation of these personal libraries because the data contained within them is often redundant and/or incompatible with other databases. To overcome these redundancies and incompatibilities, we propose that communal, crowd-sourced databases be curated in a standardized fashion. A small number of groups have shown this model is feasible and successful. While the needs of a specific field will dictate the functionality of a communal database, we discuss some features to consider during database development. Special emphasis is made on standardization of terminology, documentation, format, reference materials, and quality assurance practices. These standardization procedures enable a field to have higher confidence in the quality of the data within a given database. We also discuss the three conceptual pillars of database design as well as how crowd-sourcing is practiced. Generating open-source databases requires front-end effort, but the result is a well curated, high quality data set that all can use. Having a resource such as this fosters collaboration and scientific advancement.
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Affiliation(s)
- Jaqueline
A. Picache
- Department of Chemistry,
Center for Innovative Technology, Vanderbilt Institute of Chemical
Biology, Vanderbilt Institute for Integrative Biosystems Research
and Education, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Jody C. May
- Department of Chemistry,
Center for Innovative Technology, Vanderbilt Institute of Chemical
Biology, Vanderbilt Institute for Integrative Biosystems Research
and Education, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - John A. McLean
- Department of Chemistry,
Center for Innovative Technology, Vanderbilt Institute of Chemical
Biology, Vanderbilt Institute for Integrative Biosystems Research
and Education, Vanderbilt University, Nashville, Tennessee 37235, United States
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28
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Kumuthini J, van Woerden C, Mallett A, Zass L, Chaouch M, Thompson M, Johnston K, Mbiyavanga M, Baichoo S, Mungloo-Dilmohamud Z, Patel C, Mulder N. Proposed minimum information guideline for kidney disease-research and clinical data reporting: a cross-sectional study. BMJ Open 2019; 9:e029539. [PMID: 31772086 PMCID: PMC6887010 DOI: 10.1136/bmjopen-2019-029539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVE This project aimed to develop and propose a standardised reporting guideline for kidney disease research and clinical data reporting, in order to improve kidney disease data quality and integrity, and combat challenges associated with the management and challenges of 'Big Data'. METHODS A list of recommendations was proposed for the reporting guideline based on the systematic review and consolidation of previously published data collection and reporting standards, including PhenX measures and Minimal Information about a Proteomics Experiment (MIAPE). Thereafter, these recommendations were reviewed by domain-specialists using an online survey, developed in Research Electronic Data Capture (REDCap). Following interpretation and consolidation of the survey results, the recommendations were mapped to existing ontologies using Zooma, Ontology Lookup Service and the Bioportal search engine. Additionally, an associated eXtensible Markup Language schema was created for the REDCap implementation to increase user friendliness and adoption. RESULTS The online survey was completed by 53 respondents; the majority of respondents were dual clinician-researchers (57%), based in Australia (35%), Africa (33%) and North America (22%). Data elements within the reporting standard were identified as participant-level, study-level and experiment-level information, further subdivided into essential or optional information. CONCLUSION The reporting guideline is readily employable for kidney disease research projects, and also adaptable for clinical utility. The adoption of the reporting guideline in kidney disease research can increase data quality and the value for long-term preservation, ensuring researchers gain the maximum benefit from their collected and generated data.
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Affiliation(s)
- Judit Kumuthini
- H3ABioNet, Centre for Proteomic & Genomic Research, Cape Town, South Africa
| | - Christiaan van Woerden
- Department of Surgery, Division of Child Urology, University of Cape Town, Cape Town, South Africa
- Global Child Health Group, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Andrew Mallett
- KidGen Collaborative and AGHA Renal Genetics Flagships, Parkville, Victoria, Australia
- Institute of Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
| | - Lyndon Zass
- H3ABioNet, Centre for Proteomic & Genomic Research, Cape Town, South Africa
| | - Melek Chaouch
- Department of Parasitology, Institut Pasteur de Tunis, Tunis, Tunisia
| | - Michael Thompson
- National Institute of Mathematical Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Katherine Johnston
- Department of Surgery, Division of Child Urology, University of Cape Town, Cape Town, South Africa
| | - Mamana Mbiyavanga
- Department of Surgery, Division of Child Urology, University of Cape Town, Cape Town, South Africa
| | - Shakuntala Baichoo
- Department of Digital Technologies, University of Mauritius, Reduit, Mauritius
| | | | - Chirag Patel
- KidGen Collaborative and AGHA Renal Genetics Flagships, Parkville, Victoria, Australia
- Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
| | - Nicola Mulder
- Department of Integrative Biomedical Sciences, Division of Computational Biology, University of Cape Town, Cape Town, South Africa
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29
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Santiago CRDN, Assis RDAB, Moreira LM, Digiampietri LA. Gene Tags Assessment by Comparative Genomics (GTACG): A User-Friendly Framework for Bacterial Comparative Genomics. Front Genet 2019; 10:725. [PMID: 31507629 PMCID: PMC6718126 DOI: 10.3389/fgene.2019.00725] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 07/10/2019] [Indexed: 12/04/2022] Open
Abstract
Genomics research has produced an exponential amount of data. However, the genetic knowledge pertaining to certain phenotypic characteristics is lacking. Also, a considerable part of these genomes have coding sequences (CDSs) with unknown functions, posing additional challenges to researchers. Phylogenetically close microorganisms share much of their CDSs, and certain phenotypes unique to a set of microorganisms may be the result of the genes found exclusively in those microorganisms. This study presents the GTACG framework, an easy-to-use tool for identifying in the subgroups of bacterial genomes whose microorganisms have common phenotypic characteristics, to find data that differentiates them from other associated genomes in a simple and fast way. The GTACG analysis is based on the formation of homologous CDS clusters from local alignments. The front-end is easy to use, and the installation packages have been developed to enable users lacking knowledge of programming languages or bioinformatics analyze high-throughput data using the tool. The validation of the GTACG framework has been carried out based on a case report involving a set of 161 genomes from the Xanthomonadaceae family, in which 19 families of orthologous proteins were found in 90% of the plant-associated genomes, allowing the identification of the proteins potentially associated with adaptation and virulence in plant tissue. The results show the potential use of GTACG in the search for new targets for molecular studies, and GTACG can be used as a research tool by biologists who lack advanced knowledge in the use of computational tools for bacterial comparative genomics.
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Affiliation(s)
| | - Renata de Almeida Barbosa Assis
- Biotecnology Graduate Program, Núcleo de Pesquisas em Ciências Biológicas, Federal University of Ouro Preto, Ouro Preto, Brazil
| | - Leandro Marcio Moreira
- Biotecnology Graduate Program, Núcleo de Pesquisas em Ciências Biológicas, Federal University of Ouro Preto, Ouro Preto, Brazil
- Department of Biological Sciences, Federal University of Ouro Preto, Ouro Preto, Brazil
| | - Luciano Antonio Digiampietri
- Bioinformatics Graduate Program, University of Sao Paulo, Sao Paulo, Brazil
- School of Arts, Science, and Humanities, University of Sao Paulo, Sao Paulo, Brazil
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30
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Cazaly E, Saad J, Wang W, Heckman C, Ollikainen M, Tang J. Making Sense of the Epigenome Using Data Integration Approaches. Front Pharmacol 2019; 10:126. [PMID: 30837884 PMCID: PMC6390500 DOI: 10.3389/fphar.2019.00126] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 01/31/2019] [Indexed: 12/19/2022] Open
Abstract
Epigenetic research involves examining the mitotically heritable processes that regulate gene expression, independent of changes in the DNA sequence. Recent technical advances such as whole-genome bisulfite sequencing and affordable epigenomic array-based technologies, allow researchers to measure epigenetic profiles of large cohorts at a genome-wide level, generating comprehensive high-dimensional datasets that may contain important information for disease development and treatment opportunities. The epigenomic profile for a certain disease is often a result of the complex interplay between multiple genetic and environmental factors, which poses an enormous challenge to visualize and interpret these data. Furthermore, due to the dynamic nature of the epigenome, it is critical to determine causal relationships from the many correlated associations. In this review we provide an overview of recent data analysis approaches to integrate various omics layers to understand epigenetic mechanisms of complex diseases, such as obesity and cancer. We discuss the following topics: (i) advantages and limitations of major epigenetic profiling techniques, (ii) resources for standardization, annotation and harmonization of epigenetic data, and (iii) statistical methods and machine learning methods for establishing data-driven hypotheses of key regulatory mechanisms. Finally, we discuss the future directions for data integration that shall facilitate the discovery of epigenetic-based biomarkers and therapies.
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Affiliation(s)
- Emma Cazaly
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Joseph Saad
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Wenyu Wang
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Caroline Heckman
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Miina Ollikainen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.,Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Jing Tang
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.,Department of Mathematics and Statistics, University of Turku, Turku, Finland.,Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
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31
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Vander Heiden JA, Marquez S, Marthandan N, Bukhari SAC, Busse CE, Corrie B, Hershberg U, Kleinstein SH, Matsen IV FA, Ralph DK, Rosenfeld AM, Schramm CA, Christley S, Laserson U. AIRR Community Standardized Representations for Annotated Immune Repertoires. Front Immunol 2018; 9:2206. [PMID: 30323809 PMCID: PMC6173121 DOI: 10.3389/fimmu.2018.02206] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 09/05/2018] [Indexed: 01/21/2023] Open
Abstract
Increased interest in the immune system's involvement in pathophysiological phenomena coupled with decreased DNA sequencing costs have led to an explosion of antibody and T cell receptor sequencing data collectively termed "adaptive immune receptor repertoire sequencing" (AIRR-seq or Rep-Seq). The AIRR Community has been actively working to standardize protocols, metadata, formats, APIs, and other guidelines to promote open and reproducible studies of the immune repertoire. In this paper, we describe the work of the AIRR Community's Data Representation Working Group to develop standardized data representations for storing and sharing annotated antibody and T cell receptor data. Our file format emphasizes ease-of-use, accessibility, scalability to large data sets, and a commitment to open and transparent science. It is composed of a tab-delimited format with a specific schema. Several popular repertoire analysis tools and data repositories already utilize this AIRR-seq data format. We hope that others will follow suit in the interest of promoting interoperable standards.
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Affiliation(s)
| | - Susanna Marquez
- Department of Pathology, Yale School of Medicine, New Haven, CT, United States
| | - Nishanth Marthandan
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | | | - Christian E. Busse
- Division of B Cell Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Brian Corrie
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Uri Hershberg
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
- Department of Microbiology and Immunology, College of Medicine, Drexel University, Philadelphia, PA, United States
- Department of Human Biology, Faculty of Sciences, University of Haifa, Haifa, Israel
| | - Steven H. Kleinstein
- Department of Pathology, Yale School of Medicine, New Haven, CT, United States
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States
| | | | - Duncan K. Ralph
- Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Aaron M. Rosenfeld
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Chaim A. Schramm
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States
| | | | - Scott Christley
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, United States
| | - Uri Laserson
- Department of Genetics and Genomic Sciences and Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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32
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McKee L. Meeting the imperative to accelerate environmental bioelectromagnetics research. ENVIRONMENTAL RESEARCH 2018; 164:100-108. [PMID: 29482182 DOI: 10.1016/j.envres.2018.01.036] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 01/17/2018] [Accepted: 01/23/2018] [Indexed: 06/08/2023]
Abstract
In this article, the author draws on his experience in the world of geospatial information technology standards to suggest a path toward acceleration of bioelectromagnetics science. Many studies show biological effects of extremely low frequency (ELF) and radiofrequency (RF) radiation despite that fact that the radiation is too weak to cause temperature changes in biological features. Considered together in worst case scenarios, such effects, many of which appear to have long latencies, could have potentially disastrous consequences for the health and safety of humans and wildlife. Other studies show no such effects, and in both cases, often there are significant research quality deficits that make it difficult to draw firm conclusions from the data. The progress of bioelectromagnetics science is retarded by a lack of standard data models and experimental protocols that could improve the overall quality of research and make it easier for researchers to benefit from omics-related bioinformatics resources. "Certainty of safety" of wireless devices used in digital communications and remote sensing (radar) is impossible without dosimetry standards that reflect the effects of non-thermal exposures. Electrical signaling in biological systems, a poorly funded research domain, is as biologically important as chemical signaling, a richly funded research domain, and these two types of signaling are inextricably connected. Entreprenuerial scientists pursuing bioelectronic innovations have begun to attract new funding. With appropriate institutional coordination, this new funding could equally benefit those investigating environmental effects of ELF and RF radiation. The author proposes a concerted effort among both bioelectronics technology stakeholders and environmental bioelectromagnetics science researchers to collaborate in developing institutional arrangements and standard data models that would give the science a stronger bioinformatics platform and give researchers better access to omics data. What is proposed here is essentially a bioelectromagnetics omics initiative.
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Affiliation(s)
- Lance McKee
- 10 Circuit Avenue East, Worcester, MA 01603, USA.
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33
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Dickie IA, Boyer S, Buckley HL, Duncan RP, Gardner PP, Hogg ID, Holdaway RJ, Lear G, Makiola A, Morales SE, Powell JR, Weaver L. Towards robust and repeatable sampling methods in eDNA-based studies. Mol Ecol Resour 2018; 18:940-952. [PMID: 29802793 DOI: 10.1111/1755-0998.12907] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 05/10/2018] [Accepted: 05/14/2018] [Indexed: 01/28/2023]
Abstract
DNA-based techniques are increasingly used for measuring the biodiversity (species presence, identity, abundance and community composition) of terrestrial and aquatic ecosystems. While there are numerous reviews of molecular methods and bioinformatic steps, there has been little consideration of the methods used to collect samples upon which these later steps are based. This represents a critical knowledge gap, as methodologically sound field sampling is the foundation for subsequent analyses. We reviewed field sampling methods used for metabarcoding studies of both terrestrial and freshwater ecosystem biodiversity over a nearly three-year period (n = 75). We found that 95% (n = 71) of these studies used subjective sampling methods and inappropriate field methods and/or failed to provide critical methodological information. It would be possible for researchers to replicate only 5% of the metabarcoding studies in our sample, a poorer level of reproducibility than for ecological studies in general. Our findings suggest greater attention to field sampling methods, and reporting is necessary in eDNA-based studies of biodiversity to ensure robust outcomes and future reproducibility. Methods must be fully and accurately reported, and protocols developed that minimize subjectivity. Standardization of sampling protocols would be one way to help to improve reproducibility and have additional benefits in allowing compilation and comparison of data from across studies.
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Affiliation(s)
- Ian A Dickie
- Bio-Protection Research Centre, Lincoln University, Lincoln, New Zealand
- School of Biological Sciences, University of Canterbury, Christchurch, New Zealand
| | - Stephane Boyer
- Institut de Recherche sur la Biologie de l'Insecte - UMR 7261 CNRS, Université de Tours, Tours, France
- Applied Molecular Solutions Research Group, Environmental and Animal Sciences, Unitec Institute of Technology, Auckland, New Zealand
| | - Hannah L Buckley
- School of Science, Auckland University of Technology, Auckland, New Zealand
| | - Richard P Duncan
- Institute for Applied Ecology, University of Canberra, Bruce, ACT, Australia
| | - Paul P Gardner
- School of Biological Sciences, University of Canterbury, Christchurch, New Zealand
| | - Ian D Hogg
- School of Science, University of Waikato, Hamilton, New Zealand
- Polar Knowledge Canada, CHARS Campus, Cambridge Bay, NU, Canada
| | | | - Gavin Lear
- School of Biological Sciences, The University of Auckland, Auckland, New Zealand
| | - Andreas Makiola
- Bio-Protection Research Centre, Lincoln University, Lincoln, New Zealand
| | - Sergio E Morales
- Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand
| | - Jeff R Powell
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia
| | - Louise Weaver
- Institute of Environmental Science and Research Ltd., Christchurch, New Zealand
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34
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Adeola HA, Van Wyk JC, Arowolo A, Ngwanya RM, Mkentane K, Khumalo NP. Emerging Diagnostic and Therapeutic Potentials of Human Hair Proteomics. Proteomics Clin Appl 2017; 12. [PMID: 28960873 DOI: 10.1002/prca.201700048] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 06/09/2017] [Indexed: 01/22/2023]
Abstract
The use of noninvasive human substrates to interrogate pathophysiological conditions has become essential in the post- Human Genome Project era. Due to its high turnover rate, and its long term capability to incorporate exogenous and endogenous substances from the circulation, hair testing is emerging as a key player in monitoring long term drug compliance, chronic alcohol abuse, forensic toxicology, and biomarker discovery, among other things. Novel high-throughput 'omics based approaches like proteomics have been underutilized globally in comprehending human hair morphology and its evolving use as a diagnostic testing substrate in the era of precision medicine. There is paucity of scientific evidence that evaluates the difference in drug incorporation into hair based on lipid content, and very few studies have addressed hair growth rates, hair forms, and the biological consequences of hair grooming or bleaching. It is apparent that protein-based identification using the human hair proteome would play a major role in understanding these parameters akin to DNA single nucleotide polymorphism profiling, up to single amino acid polymorphism resolution. Hence, this work seeks to identify and discuss the progress made thus far in the field of molecular hair testing using proteomic approaches, and identify ways in which proteomics would improve the field of hair research, considering that the human hair is mostly composed of proteins. Gaps in hair proteomics research are identified and the potential of hair proteomics in establishing a historic medical repository of normal and disease-specific proteome is also discussed.
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Affiliation(s)
- Henry A Adeola
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
| | - Jennifer C Van Wyk
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
| | - Afolake Arowolo
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
| | - Reginald M Ngwanya
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
| | - Khwezikazi Mkentane
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
| | - Nonhlanhla P Khumalo
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
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Khalsa J, Duffy LC, Riscuta G, Starke-Reed P, Hubbard VS. Omics for Understanding the Gut-Liver-Microbiome Axis and Precision Medicine. Clin Pharmacol Drug Dev 2017; 6:176-185. [DOI: 10.1002/cpdd.310] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Accepted: 09/15/2016] [Indexed: 12/14/2022]
Affiliation(s)
- Jag Khalsa
- National Institute on Drug Abuse; National Institutes of Health; Bethesda MD USA
| | - Linda C. Duffy
- National Center for Complementary and Integrative Health; National Institutes of Health; Bethesda MD USA
| | - Gabriela Riscuta
- National Cancer Institute; National Institutes of Health; Bethesda MD USA
| | - Pamela Starke-Reed
- Agricultural Research Service; United States Department of Agriculture; Washington DC USA
| | - Van S. Hubbard
- Formerly National Institute of Diabetes and Digestive and Kidney Diseases; National Institutes of Health; Bethesda MD
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Domínguez C, Heras J, Mata E, Pascual V, Vázquez-Garcidueñas MS, Vázquez-Marrufo G. Extending GelJ for interoperability: Filling the gap in the bioinformatics resources for population genetics analysis with dominant markers. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 140:69-76. [PMID: 28254092 DOI: 10.1016/j.cmpb.2016.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Revised: 10/14/2016] [Accepted: 12/05/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE The manual transformation of DNA fingerprints of dominant markers into the input of tools for population genetics analysis is a time-consuming and error-prone task; especially when the researcher deals with a large number of samples. In addition, when the researcher needs to use several tools for population genetics analysis, the situation worsens due to the incompatibility of data-formats across tools. The goal of this work consists in automating, from banding patterns of gel images, the input-generation for the great diversity of tools devoted to population genetics analysis. METHODS After a thorough analysis of tools for population genetics analysis with dominant markers, and tools for working with phylogenetic trees; we have detected the input requirements of those systems. In the case of programs devoted to phylogenetic trees, the Newick and Nexus formats are widely employed; whereas, each population genetics analysis tool uses its own specific format. In order to handle such a diversity of formats in the latter case, we have developed a new XML format, called PopXML, that takes into account the variety of information required by each population genetics analysis tool. Moreover, the acquired knowledge has been incorporated into the pipeline of the GelJ system - a tool for analysing DNA fingerprint gel images - to reach our automatisation goal. RESULTS We have implemented, in the GelJ system, a pipeline that automatically generates, from gel banding patterns, the input of tools for population genetics analysis and phylogenetic trees. Such a pipeline has been employed to successfully generate, from thousands of banding patterns, the input of 29 population genetics analysis tools and 32 tools for managing phylogenetic trees. CONCLUSIONS GelJ has become the first tool that fills the gap between gel image processing software and population genetics analysis with dominant markers, phylogenetic reconstruction, and tree editing software. This has been achieved by automating the process of generating the input for the latter software from gel banding patterns processed by GelJ.
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Affiliation(s)
- César Domínguez
- Department of Mathematics and Computer Science, University of La Rioja, Logroño, Spain.
| | - Jónathan Heras
- Department of Mathematics and Computer Science, University of La Rioja, Logroño, Spain.
| | - Eloy Mata
- Department of Mathematics and Computer Science, University of La Rioja, Logroño, Spain
| | - Vico Pascual
- Department of Mathematics and Computer Science, University of La Rioja, Logroño, Spain.
| | - Maria Soledad Vázquez-Garcidueñas
- Division of Postgraduate Studies, Faculty of Medical and Biological Sciences "Dr. Ignacio Chávez", Universidad Michoacana de San Nicolás de Hidalgo, Mexico.
| | - Gerardo Vázquez-Marrufo
- Multidisciplinary Center of Biotechnology Studies (CMEB), Faculty of Veterinary Medicine, Universidad Michoacana de San Nicolás de Hidalgo, Mexico.
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Abstract
Advances in the development of delivery, repair, and specificity strategies for the CRISPR-Cas9 genome engineering toolbox are helping researchers understand gene function with unprecedented precision and sensitivity. CRISPR-Cas9 also holds enormous therapeutic potential for the treatment of genetic disorders by directly correcting disease-causing mutations. Although the Cas9 protein has been shown to bind and cleave DNA at off-target sites, the field of Cas9 specificity is rapidly progressing, with marked improvements in guide RNA selection, protein and guide engineering, novel enzymes, and off-target detection methods. We review important challenges and breakthroughs in the field as a comprehensive practical guide to interested users of genome editing technologies, highlighting key tools and strategies for optimizing specificity. The genome editing community should now strive to standardize such methods for measuring and reporting off-target activity, while keeping in mind that the goal for specificity should be continued improvement and vigilance.
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Affiliation(s)
- Josh Tycko
- Editas Medicine, 300 Third Street, Cambridge, MA 02142, USA
| | - Vic E Myer
- Editas Medicine, 300 Third Street, Cambridge, MA 02142, USA
| | - Patrick D Hsu
- Editas Medicine, 300 Third Street, Cambridge, MA 02142, USA; Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, CA 92037, USA.
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Yohe SL, Carter AB, Pfeifer JD, Crawford JM, Cushman-Vokoun A, Caughron S, Leonard DGB. Standards for Clinical Grade Genomic Databases. Arch Pathol Lab Med 2016; 139:1400-12. [PMID: 26516938 DOI: 10.5858/arpa.2014-0568-cp] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
CONTEXT Next-generation sequencing performed in a clinical environment must meet clinical standards, which requires reproducibility of all aspects of the testing. Clinical-grade genomic databases (CGGDs) are required to classify a variant and to assist in the professional interpretation of clinical next-generation sequencing. Applying quality laboratory standards to the reference databases used for sequence-variant interpretation presents a new challenge for validation and curation. OBJECTIVES To define CGGD and the categories of information contained in CGGDs and to frame recommendations for the structure and use of these databases in clinical patient care. DESIGN Members of the College of American Pathologists Personalized Health Care Committee reviewed the literature and existing state of genomic databases and developed a framework for guiding CGGD development in the future. RESULTS Clinical-grade genomic databases may provide different types of information. This work group defined 3 layers of information in CGGDs: clinical genomic variant repositories, genomic medical data repositories, and genomic medicine evidence databases. The layers are differentiated by the types of genomic and medical information contained and the utility in assisting with clinical interpretation of genomic variants. Clinical-grade genomic databases must meet specific standards regarding submission, curation, and retrieval of data, as well as the maintenance of privacy and security. CONCLUSION These organizing principles for CGGDs should serve as a foundation for future development of specific standards that support the use of such databases for patient care.
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Affiliation(s)
| | | | | | | | | | | | - Debra G B Leonard
- From the Department of Laboratory Medicine and Pathology, University of Minnesota Medical Center, Minneapolis (Dr Yohe); the Department of Pathology and Laboratory Medicine and the Department of Biomedical Informatics, Emory University, Atlanta, Georgia (Dr Carter); the Department of Pathology, Washington University School of Medicine, St. Louis, Missouri (Dr Pfeifer); the Department of Pathology and Laboratory Medicine, Hofstra North Shore-Long Island Jewish School of Medicine, Hempstead, New York (Dr Crawford); the Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha (Dr Cushman-Vokoun); the MAWD Pathology Group, North Kansas City, Missouri (Dr Caughron); and the Department of Pathology and Laboratory Medicine, University of Vermont College of Medicine, Burlington (Dr Leonard)
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Abstract
Systems medicine promotes a range of approaches and strategies to study human health and disease at a systems level with the aim of improving the overall well-being of (healthy) individuals, and preventing, diagnosing, or curing disease. In this chapter we discuss how bioinformatics critically contributes to systems medicine. First, we explain the role of bioinformatics in the management and analysis of data. In particular we show the importance of publicly available biological and clinical repositories to support systems medicine studies. Second, we discuss how the integration and analysis of multiple types of omics data through integrative bioinformatics may facilitate the determination of more predictive and robust disease signatures, lead to a better understanding of (patho)physiological molecular mechanisms, and facilitate personalized medicine. Third, we focus on network analysis and discuss how gene networks can be constructed from omics data and how these networks can be decomposed into smaller modules. We discuss how the resulting modules can be used to generate experimentally testable hypotheses, provide insight into disease mechanisms, and lead to predictive models. Throughout, we provide several examples demonstrating how bioinformatics contributes to systems medicine and discuss future challenges in bioinformatics that need to be addressed to enable the advancement of systems medicine.
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Affiliation(s)
- Ulf Schmitz
- Dept of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany
| | - Olaf Wolkenhauer
- Dept of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany
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Lapatas V, Stefanidakis M, Jimenez RC, Via A, Schneider MV. Data integration in biological research: an overview. JOURNAL OF BIOLOGICAL RESEARCH (THESSALONIKE, GREECE) 2015; 22:9. [PMID: 26336651 PMCID: PMC4557916 DOI: 10.1186/s40709-015-0032-5] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Accepted: 08/10/2015] [Indexed: 11/16/2022]
Abstract
Data sharing, integration and annotation are essential to ensure the reproducibility of the analysis and interpretation of the experimental findings. Often these activities are perceived as a role that bioinformaticians and computer scientists have to take with no or little input from the experimental biologist. On the contrary, biological researchers, being the producers and often the end users of such data, have a big role in enabling biological data integration. The quality and usefulness of data integration depend on the existence and adoption of standards, shared formats, and mechanisms that are suitable for biological researchers to submit and annotate the data, so it can be easily searchable, conveniently linked and consequently used for further biological analysis and discovery. Here, we provide background on what is data integration from a computational science point of view, how it has been applied to biological research, which key aspects contributed to its success and future directions.
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Affiliation(s)
- Vasileios Lapatas
- />Department of Informatics, Ionian University, 7 Tsirigoti Square, Corfu, 49100 Greece
| | - Michalis Stefanidakis
- />Department of Informatics, Ionian University, 7 Tsirigoti Square, Corfu, 49100 Greece
| | | | - Allegra Via
- />Biocomputing Group, Sapienza University, Piazzale Aldo Moro 5, Rome, 00185 Italy
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Sustaining large-scale infrastructure to promote pre-competitive biomedical research: lessons from mouse genomics. N Biotechnol 2015; 33:280-94. [PMID: 26563511 DOI: 10.1016/j.nbt.2015.10.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2015] [Revised: 08/07/2015] [Accepted: 10/12/2015] [Indexed: 01/25/2023]
Abstract
Bio-repositories and databases for biomedical research enable the efficient community-wide sharing of reagents and data. These archives play an increasingly prominent role in the generation and dissemination of bioresources and data essential for fundamental and translational research. Evidence suggests, however, that current funding and governance models, generally short-term and nationally focused, do not adequately support the role of archives in long-term, transnational endeavours to make and share high-impact resources. Our qualitative case study of the International Knockout Mouse Consortium and the International Mouse Phenotyping Consortium examines new governance mechanisms for archive sustainability. Funders and archive managers highlight in interviews that archives need stable public funding and new revenue-generation models to be sustainable. Sustainability also requires archives, journal publishers, and funders to implement appropriate incentives, associated metrics, and enforcement mechanisms to ensure that researchers use archives to deposit reagents and data to make them publicly accessible for academia and industry alike.
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Landry BD, Clarke DC, Lee MJ. Studying Cellular Signal Transduction with OMIC Technologies. J Mol Biol 2015; 427:3416-40. [PMID: 26244521 PMCID: PMC4818567 DOI: 10.1016/j.jmb.2015.07.021] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2015] [Revised: 07/25/2015] [Accepted: 07/27/2015] [Indexed: 11/24/2022]
Abstract
In the gulf between genotype and phenotype exists proteins and, in particular, protein signal transduction systems. These systems use a relatively limited parts list to respond to a much longer list of extracellular, environmental, and/or mechanical cues with rapidity and specificity. Most signaling networks function in a highly non-linear and often contextual manner. Furthermore, these processes occur dynamically across space and time. Because of these complexities, systems and "OMIC" approaches are essential for the study of signal transduction. One challenge in using OMIC-scale approaches to study signaling is that the "signal" can take different forms in different situations. Signals are encoded in diverse ways such as protein-protein interactions, enzyme activities, localizations, or post-translational modifications to proteins. Furthermore, in some cases, signals may be encoded only in the dynamics, duration, or rates of change of these features. Accordingly, systems-level analyses of signaling may need to integrate multiple experimental and/or computational approaches. As the field has progressed, the non-triviality of integrating experimental and computational analyses has become apparent. Successful use of OMIC methods to study signaling will require the "right" experiments and the "right" modeling approaches, and it is critical to consider both in the design phase of the project. In this review, we discuss common OMIC and modeling approaches for studying signaling, emphasizing the philosophical and practical considerations for effectively merging these two types of approaches to maximize the probability of obtaining reliable and novel insights into signaling biology.
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Affiliation(s)
- Benjamin D Landry
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - David C Clarke
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, V5A 1S6 Canada
| | - Michael J Lee
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA; Program in Molecular Medicine, Department of Molecular, Cell, and Cancer Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
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Marx-Stoelting P, Braeuning A, Buhrke T, Lampen A, Niemann L, Oelgeschlaeger M, Rieke S, Schmidt F, Heise T, Pfeil R, Solecki R. Application of omics data in regulatory toxicology: report of an international BfR expert workshop. Arch Toxicol 2015; 89:2177-84. [PMID: 26486796 DOI: 10.1007/s00204-015-1602-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2015] [Accepted: 09/15/2015] [Indexed: 02/07/2023]
Abstract
Advances in omics techniques and molecular toxicology are necessary to provide new perspectives for regulatory toxicology. By the application of modern molecular techniques, more mechanistic information should be gained to support standard toxicity studies and to contribute to a reduction and refinement of animal experiments required for certain regulatory purposes. The relevance and applicability of data obtained by omics methods to regulatory purposes such as grouping of chemicals, mode of action analysis or classification and labelling needs further improvement, defined validation and cautious expert judgment. Based on the results of an international expert workshop organized 2014 by the Federal Institute for Risk Assessment in Berlin, this paper is aimed to provide a critical overview of the regulatory relevance and reliability of omics methods, basic requirements on data quality and validation, as well as regulatory criteria to decide which effects observed by omics methods should be considered adverse or non-adverse. As a way forward, it was concluded that the inclusion of omics data can facilitate a more flexible approach for regulatory risk assessment and may help to reduce or refine animal testing.
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Affiliation(s)
- P Marx-Stoelting
- Federal Institute for Risk Assessment, Max-Dohrn-Str 8-10, 10589, Berlin, Germany.
| | - A Braeuning
- Federal Institute for Risk Assessment, Max-Dohrn-Str 8-10, 10589, Berlin, Germany
| | - T Buhrke
- Federal Institute for Risk Assessment, Max-Dohrn-Str 8-10, 10589, Berlin, Germany
| | - A Lampen
- Federal Institute for Risk Assessment, Max-Dohrn-Str 8-10, 10589, Berlin, Germany
| | - L Niemann
- Federal Institute for Risk Assessment, Max-Dohrn-Str 8-10, 10589, Berlin, Germany
| | - M Oelgeschlaeger
- Federal Institute for Risk Assessment, Max-Dohrn-Str 8-10, 10589, Berlin, Germany
| | - S Rieke
- Federal Institute for Risk Assessment, Max-Dohrn-Str 8-10, 10589, Berlin, Germany
| | - F Schmidt
- Federal Institute for Risk Assessment, Max-Dohrn-Str 8-10, 10589, Berlin, Germany
| | - T Heise
- Federal Institute for Risk Assessment, Max-Dohrn-Str 8-10, 10589, Berlin, Germany
| | - R Pfeil
- Federal Institute for Risk Assessment, Max-Dohrn-Str 8-10, 10589, Berlin, Germany
| | - R Solecki
- Federal Institute for Risk Assessment, Max-Dohrn-Str 8-10, 10589, Berlin, Germany
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Benkhalifa M, Madkour A, Louanjli N, Bouamoud N, Saadani B, Kaarouch I, Chahine H, Sefrioui O, Merviel P, Copin H. From global proteome profiling to single targeted molecules of follicular fluid and oocyte: contribution to embryo development and IVF outcome. Expert Rev Proteomics 2015; 12:407-23. [DOI: 10.1586/14789450.2015.1056782] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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45
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Altmäe S, Esteban FJ, Stavreus-Evers A, Simón C, Giudice L, Lessey BA, Horcajadas JA, Macklon NS, D'Hooghe T, Campoy C, Fauser BC, Salamonsen LA, Salumets A. Guidelines for the design, analysis and interpretation of 'omics' data: focus on human endometrium. Hum Reprod Update 2014; 20:12-28. [PMID: 24082038 PMCID: PMC3845681 DOI: 10.1093/humupd/dmt048] [Citation(s) in RCA: 101] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2013] [Revised: 08/04/2013] [Accepted: 08/19/2013] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND 'Omics' high-throughput analyses, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, are widely applied in human endometrial studies. Analysis of endometrial transcriptome patterns in physiological and pathophysiological conditions has been to date the most commonly applied 'omics' technique in human endometrium. As the technologies improve, proteomics holds the next big promise for this field. The 'omics' technologies have undoubtedly advanced our knowledge of human endometrium in relation to fertility and different diseases. Nevertheless, the challenges arising from the vast amount of data generated and the broad variation of 'omics' profiling according to different environments and stimuli make it difficult to assess the validity, reproducibility and interpretation of such 'omics' data. With the expansion of 'omics' analyses in the study of the endometrium, there is a growing need to develop guidelines for the design of studies, and the analysis and interpretation of 'omics' data. METHODS Systematic review of the literature in PubMed, and references from relevant articles were investigated up to March 2013. RESULTS The current review aims to provide guidelines for future 'omics' studies on human endometrium, together with a summary of the status and trends, promise and shortcomings in the high-throughput technologies. In addition, the approaches presented here can be adapted to other areas of high-throughput 'omics' studies. CONCLUSION A highly rigorous approach to future studies, based on the guidelines provided here, is a prerequisite for obtaining data on biological systems which can be shared among researchers worldwide and will ultimately be of clinical benefit.
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Affiliation(s)
- Signe Altmäe
- Competence Centre on Reproductive Medicine and Biology, Tartu, Estonia
- School of Medicine, Department of Paediatrics, University of Granada, 18012 Granada, Spain
| | | | - Anneli Stavreus-Evers
- Department of Women's and Children's Health, Uppsala University, Akademiska Sjukhuset, 75185 Uppsala, Sweden
| | - Carlos Simón
- Fundación Instituto Valenciano de Infertilidad (FIVI) and Instituto Universitario IVI/INCLIVA, Valencia University, 46021 Valencia, Spain
| | - Linda Giudice
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, San Francisco, CA 94143-0132, USA
| | - Bruce A. Lessey
- Division of Reproductive Endocrinology, Department of Obstetrics and Gynecology, University Medical Group, Greenville Hospital System, Greenville, South Carolina, SC 29605, USA
| | - Jose A. Horcajadas
- Araid-Hospital Miguel Servet, 50004 Zaragoza, Spain
- Department of Genetics, Universidad Pablo de Olavide, 41013 Sevilla, Spain
| | - Nick S. Macklon
- Department of Obstetrics and Gynaecology, Division of Developmental Origins of Adult Disease, University of Southampton, Princess Anne Hospital, SO16 5YA Southampton, UK
- Department of Reproductive Medicine and Gynaecology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Thomas D'Hooghe
- Leuven University Fertility Center, Department of Obstetrics and Gynecology, University Hospital Leuven, Leuven, Belgium
- Department of Development and Regeneration, KU Leuven (Leuven University), 3000 Leuven, Belgium
| | - Cristina Campoy
- School of Medicine, Department of Paediatrics, University of Granada, 18012 Granada, Spain
| | - Bart C. Fauser
- Department of Reproductive Medicine and Gynaecology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Lois A. Salamonsen
- Prince Henry's Institute of Medical Research, Melbourne, Victoria 3168, Australia
| | - Andres Salumets
- Competence Centre on Reproductive Medicine and Biology, Tartu, Estonia
- Department of Obstetrics and Gynaecology, University of Tartu, 51014 Tartu, Estonia
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Vogt L. eScience and the need for data standards in the life sciences: in pursuit of objectivity rather than truth. SYST BIODIVERS 2013. [DOI: 10.1080/14772000.2013.818588] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Andersson MG, Tomuzia K, Löfström C, Appel B, Bano L, Keremidis H, Knutsson R, Leijon M, Lövgren SE, De Medici D, Menrath A, van Rotterdam BJ, Wisselink HJ, Barker GC. Separated by a common language: awareness of term usage differences between languages and disciplines in biopreparedness. Biosecur Bioterror 2013; 11 Suppl 1:S276-85. [PMID: 23971818 PMCID: PMC3752503 DOI: 10.1089/bsp.2012.0083] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2012] [Accepted: 06/07/2013] [Indexed: 10/26/2022]
Abstract
Preparedness for bioterrorism is based on communication between people in organizations who are educated and trained in several disciplines, including law enforcement, health, and science. Various backgrounds, cultures, and vocabularies generate difficulties in understanding and interpretating terms and concepts, which may impair communication. This is especially true in emergency situations, in which the need for clarity and consistency is vital. The EU project AniBioThreat initiated methods and made a rough estimate of the terms and concepts that are crucial for an incident, and a pilot database with key terms and definitions has been constructed. Analysis of collected terms and sources has shown that many of the participating organizations use various international standards in their area of expertise. The same term often represents different concepts in the standards from different sectors, or, alternatively, different terms were used to represent the same or similar concepts. The use of conflicting terminology can be problematic for decision makers and communicators in planning and prevention or when handling an incident. Since the CBRN area has roots in multiple disciplines, each with its own evolving terminology, it may not be realistic to achieve unequivocal communication through a standardized vocabulary and joint definitions for words from common language. We suggest that a communication strategy should include awareness of alternative definitions and ontologies and the ability to talk and write without relying on the implicit knowledge underlying specialized jargon. Consequently, cross-disciplinary communication skills should be part of training of personnel in the CBRN field. In addition, a searchable repository of terms and definitions from relevant organizations and authorities would be a valuable addition to existing glossaries for improving awareness concerning bioterrorism prevention planning.
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A Generic, Service-based Data Integration Framework Applied to Linking Drugs & Clinical Trials. ACTA ACUST UNITED AC 2013. [DOI: 10.1016/j.procs.2013.10.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Kyzar E, Zapolsky I, Green J, Gaikwad S, Pham M, Collins C, Roth A, Stewart AM, St-Pierre P, Hirons B, Kalueff AV. The Zebrafish Neurophenome Database (ZND): a dynamic open-access resource for zebrafish neurophenotypic data. Zebrafish 2011; 9:8-14. [PMID: 22171801 DOI: 10.1089/zeb.2011.0725] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Zebrafish (Danio rerio) are widely used in neuroscience research, where their utility as a model organism is rapidly expanding. Low cost, ease of experimental manipulations, and sufficient behavioral complexity make zebrafish a valuable tool for high-throughput studies in biomedicine. To complement the available repositories for zebrafish genetic information, there is a growing need for the collection of zebrafish neurobehavioral and neurological phenotypes. For this, we are establishing the Zebrafish Neurophenome Database (ZND; www.tulane.edu/∼znpindex/search ) as a new dynamic online open-access data repository for behavioral and related physiological data. ZND, currently focusing on adult zebrafish, combines zebrafish neurophenotypic data with a simple, easily searchable user interface, which allow scientists to view and compare results obtained by other laboratories using various treatments in different testing paradigms. As a developing community effort, ZND is expected to foster innovative research using zebrafish by federating the growing body of zebrafish neurophenotypic data.
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Affiliation(s)
- Evan Kyzar
- Department of Pharmacology and Neuroscience Program, Zebrafish Neuroscience Research Consortium, Tulane University Medical School, New Orleans, Louisiana, USA
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