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Sanders LM, Grigorev KA, Scott RT, Saravia-Butler AM, Polo SHL, Gilbert R, Overbey EG, Kim J, Mason CE, Costes SV. Inspiration4 data access through the NASA Open Science Data Repository. NPJ Microgravity 2024; 10:56. [PMID: 38744887 PMCID: PMC11094041 DOI: 10.1038/s41526-024-00393-5] [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: 12/14/2023] [Accepted: 04/03/2024] [Indexed: 05/16/2024] Open
Abstract
The increasing accessibility of commercial and private space travel necessitates a profound understanding of its impact on human health. The NASA Open Science Data Repository (OSDR) provides transparent and FAIR access to biological studies, notably the SpaceX Inspiration4 (I4) mission, which amassed extensive data from civilian astronauts. This dataset encompasses omics and clinical assays, facilitating comprehensive research on space-induced biological responses. These data allow for multi-modal, longitudinal assessments, bridging the gap between human and model organism studies. Crucially, community-driven data standards established by NASA's OSDR Analysis Working Groups empower artificial intelligence and machine learning to glean invaluable insights, guiding future mission planning and health risk mitigation. This article presents a concise guide to access and analyze I4 data in OSDR, including programmatic access through GLOpenAPI. This pioneering effort establishes a precedent for post-mission health monitoring programs within space agencies, propelling research in the burgeoning field of commercial space travel's impact on human physiology.
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Affiliation(s)
- Lauren M Sanders
- Space Biosciences Research Branch, NASA Ames Research Center, Moffett Field, CA, USA
- Blue Marble Space, Seattle, WA, USA
| | - Kirill A Grigorev
- Space Biosciences Research Branch, NASA Ames Research Center, Moffett Field, CA, USA
- Blue Marble Space, Seattle, WA, USA
| | - Ryan T Scott
- Space Biosciences Research Branch, NASA Ames Research Center, Moffett Field, CA, USA
- KBR, Houston, TX, USA
| | - Amanda M Saravia-Butler
- Space Biosciences Research Branch, NASA Ames Research Center, Moffett Field, CA, USA
- KBR, Houston, TX, USA
| | - San-Huei Lai Polo
- Space Biosciences Research Branch, NASA Ames Research Center, Moffett Field, CA, USA
- KBR, Houston, TX, USA
| | - Rachel Gilbert
- Space Biosciences Research Branch, NASA Ames Research Center, Moffett Field, CA, USA
- KBR, Houston, TX, USA
| | - Eliah G Overbey
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Center for STEM, University of Austin, Austin, TX, USA
| | - JangKeun Kim
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA
| | - Sylvain V Costes
- Space Biosciences Research Branch, NASA Ames Research Center, Moffett Field, CA, USA.
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Dumschott K, Dörpholz H, Laporte MA, Brilhaus D, Schrader A, Usadel B, Neumann S, Arnaud E, Kranz A. Ontologies for increasing the FAIRness of plant research data. FRONTIERS IN PLANT SCIENCE 2023; 14:1279694. [PMID: 38098789 PMCID: PMC10720748 DOI: 10.3389/fpls.2023.1279694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/15/2023] [Indexed: 12/17/2023]
Abstract
The importance of improving the FAIRness (findability, accessibility, interoperability, reusability) of research data is undeniable, especially in the face of large, complex datasets currently being produced by omics technologies. Facilitating the integration of a dataset with other types of data increases the likelihood of reuse, and the potential of answering novel research questions. Ontologies are a useful tool for semantically tagging datasets as adding relevant metadata increases the understanding of how data was produced and increases its interoperability. Ontologies provide concepts for a particular domain as well as the relationships between concepts. By tagging data with ontology terms, data becomes both human- and machine- interpretable, allowing for increased reuse and interoperability. However, the task of identifying ontologies relevant to a particular research domain or technology is challenging, especially within the diverse realm of fundamental plant research. In this review, we outline the ontologies most relevant to the fundamental plant sciences and how they can be used to annotate data related to plant-specific experiments within metadata frameworks, such as Investigation-Study-Assay (ISA). We also outline repositories and platforms most useful for identifying applicable ontologies or finding ontology terms.
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Affiliation(s)
- Kathryn Dumschott
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics) & Bioeconomy Science Center (BioSC), CEPLAS, Forschungszentrum Jülich, Jülich, Germany
| | - Hannah Dörpholz
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics) & Bioeconomy Science Center (BioSC), CEPLAS, Forschungszentrum Jülich, Jülich, Germany
| | - Marie-Angélique Laporte
- Digital Solutions Team, Digital Inclusion Lever, Bioversity International, Montpellier Office, Montpellier, France
| | - Dominik Brilhaus
- Data Science and Management & Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Andrea Schrader
- Data Science and Management & Cluster of Excellence on Plant Sciences (CEPLAS), University of Cologne, Cologne, Germany
| | - Björn Usadel
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics) & Bioeconomy Science Center (BioSC), CEPLAS, Forschungszentrum Jülich, Jülich, Germany
- Institute for Biological Data Science & Cluster of Excellence on Plant Sciences (CEPLAS), Faculty of Mathematics and Life Sciences, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Steffen Neumann
- Program Center MetaCom, Leibniz Institute of Plant Biochemistry, Halle, Germany
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Germany
| | - Elizabeth Arnaud
- Digital Solutions Team, Digital Inclusion Lever, Bioversity International, Montpellier Office, Montpellier, France
| | - Angela Kranz
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics) & Bioeconomy Science Center (BioSC), CEPLAS, Forschungszentrum Jülich, Jülich, Germany
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Nijsse B, Schaap PJ, Koehorst JJ. FAIR data station for lightweight metadata management and validation of omics studies. Gigascience 2022; 12:giad014. [PMID: 36879493 PMCID: PMC9989329 DOI: 10.1093/gigascience/giad014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 01/19/2023] [Accepted: 02/21/2023] [Indexed: 03/08/2023] Open
Abstract
BACKGROUND The life sciences are one of the biggest suppliers of scientific data. Reusing and connecting these data can uncover hidden insights and lead to new concepts. Efficient reuse of these datasets is strongly promoted when they are interlinked with a sufficient amount of machine-actionable metadata. While the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles have been accepted by all stakeholders, in practice, there are only a limited number of easy-to-adopt implementations available that fulfill the needs of data producers. FINDINGS We developed the FAIR Data Station, a lightweight application written in Java, that aims to support researchers in managing research metadata according to the FAIR principles. It implements the ISA metadata framework and uses minimal information metadata standards to capture experiment metadata. The FAIR Data Station consists of 3 modules. Based on the minimal information model(s) selected by the user, the "form generation module" creates a metadata template Excel workbook with a header row of machine-actionable attribute names. The Excel workbook is subsequently used by the data producer(s) as a familiar environment for sample metadata registration. At any point during this process, the format of the recorded values can be checked using the "validation module." Finally, the "resource module" can be used to convert the set of metadata recorded in the Excel workbook in RDF format, enabling (cross-project) (meta)data searches and, for publishing of sequence data, in an European Nucleotide Archive-compatible XML metadata file. CONCLUSIONS Turning FAIR into reality requires the availability of easy-to-adopt data FAIRification workflows that are also of direct use for data producers. As such, the FAIR Data Station provides, in addition to the means to correctly FAIRify (omics) data, the means to build searchable metadata databases of similar projects and can assist in ENA metadata submission of sequence data. The FAIR Data Station is available at https://fairbydesign.nl.
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Affiliation(s)
- Bart Nijsse
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
- UNLOCK Large Scale Infrastructure for Microbial Communities, Wageningen University & Research and Delft University of Technology, Stippeneng 4, 6708 WE Wageningen, The Netherlands
| | - Peter J Schaap
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
- UNLOCK Large Scale Infrastructure for Microbial Communities, Wageningen University & Research and Delft University of Technology, Stippeneng 4, 6708 WE Wageningen, The Netherlands
| | - Jasper J Koehorst
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
- UNLOCK Large Scale Infrastructure for Microbial Communities, Wageningen University & Research and Delft University of Technology, Stippeneng 4, 6708 WE Wageningen, The Netherlands
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Yu H, Li L, Huffman A, Beverley J, Hur J, Merrell E, Huang HH, Wang Y, Liu Y, Ong E, Cheng L, Zeng T, Zhang J, Li P, Liu Z, Wang Z, Zhang X, Ye X, Handelman SK, Sexton J, Eaton K, Higgins G, Omenn GS, Athey B, Smith B, Chen L, He Y. A new framework for host-pathogen interaction research. Front Immunol 2022; 13:1066733. [PMID: 36591248 PMCID: PMC9797517 DOI: 10.3389/fimmu.2022.1066733] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2022] Open
Abstract
COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed.
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Affiliation(s)
- Hong Yu
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
| | - Li Li
- Department of Genetics, Harvard Medical School, Boston, MA, United States
| | - Anthony Huffman
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - John Beverley
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States
- Asymmetric Operations Sector, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
| | - Junguk Hur
- Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, United States
| | - Eric Merrell
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States
| | - Hsin-hui Huang
- University of Michigan Medical School, Ann Arbor, MI, United States
- Department of Biotechnology and Laboratory Science in Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Yang Wang
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Yingtong Liu
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Edison Ong
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Liang Cheng
- Department of Bioinformatics, Harbin Medical University, Harbin, Helongjian, China
| | - Tao Zeng
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Jingsong Zhang
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Pengpai Li
- Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Zhiping Liu
- Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Zhigang Wang
- Department of Biomedical Engineering, Institute of Basic Medical Sciences and School of Basic Medicine, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Xiangyan Zhang
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
| | - Xianwei Ye
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
| | | | - Jonathan Sexton
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Kathryn Eaton
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Gerry Higgins
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Gilbert S. Omenn
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Brian Athey
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Barry Smith
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States
| | - Luonan Chen
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, MI, United States
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5
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pISA-tree - a data management framework for life science research projects using a standardised directory tree. Sci Data 2022; 9:685. [DOI: 10.1038/s41597-022-01805-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 10/24/2022] [Indexed: 11/12/2022] Open
Abstract
AbstractWe developed pISA-tree, a straightforward and flexible data management solution for organisation of life science project-associated research data and metadata. pISA-tree was initiated by end-user requirements thus its strong points are practicality and low maintenance cost. It enables on-the-fly creation of enriched directory tree structure (project/Investigation/Study/Assay) based on the ISA model, in a standardised manner via consecutive batch files. Templates-based metadata is generated in parallel at each level enabling guided submission of experiment metadata. pISA-tree is complemented by two R packages, pisar and seekr. pisar facilitates integration of pISA-tree datasets into bioinformatic pipelines and generation of ISA-Tab exports. seekr enables synchronisation with the FAIRDOMHub repository. Applicability of pISA-tree was demonstrated in several national and international multi-partner projects. The system thus supports findable, accessible, interoperable and reusable (FAIR) research and is in accordance with the Open Science initiative. Source code and documentation of pISA-tree are available at https://github.com/NIB-SI/pISA-tree.
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6
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He Y. Development and Applications of Interoperable Biomedical Ontologies for Integrative Data and Knowledge Representation and Multiscale Modeling in Systems Medicine. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2486:233-244. [PMID: 35437726 DOI: 10.1007/978-1-0716-2265-0_12] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The data FAIR Guiding Principles state that all data should be Findable, Accessible, Interoperable, and Reusable. Ontology is critical to data integration, sharing, and analysis. Given thousands of ontologies have been developed in the era of artificial intelligence, it is critical to have interoperable ontologies to support standardized data and knowledge presentation and reasoning. For interoperable ontology development, the eXtensible ontology development (XOD) strategy offers four principles including ontology term reuse, semantic alignment, ontology design pattern usage, and community extensibility. Many software programs are available to help implement these principles. As a demonstration, the XOD strategy is applied to developing the interoperable Coronavirus Infectious Disease Ontology (CIDO). Various applications of interoperable ontologies, such as COVID-19 and kidney precision medicine research, are also introduced in this chapter.
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Affiliation(s)
- Yongqun He
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
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7
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Raboudi A, Allanic M, Balvay D, Hervé PY, Viel T, Yoganathan T, Certain A, Hilbey J, Charlet J, Durupt A, Boutinaud P, Eynard B, Tavitian B. The BMS-LM ontology for biomedical data reporting throughout the lifecycle of a research study: From data model to ontology. J Biomed Inform 2022; 127:104007. [DOI: 10.1016/j.jbi.2022.104007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 12/24/2021] [Accepted: 01/28/2022] [Indexed: 11/16/2022]
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8
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A proteomics sample metadata representation for multiomics integration and big data analysis. Nat Commun 2021; 12:5854. [PMID: 34615866 PMCID: PMC8494749 DOI: 10.1038/s41467-021-26111-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 09/16/2021] [Indexed: 11/08/2022] Open
Abstract
The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.
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9
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Wang Z, He Y. Precision omics data integration and analysis with interoperable ontologies and their application for COVID-19 research. Brief Funct Genomics 2021; 20:235-248. [PMID: 34159360 PMCID: PMC8287950 DOI: 10.1093/bfgp/elab029] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 05/10/2021] [Accepted: 05/24/2021] [Indexed: 12/12/2022] Open
Abstract
Omics technologies are widely used in biomedical research. Precision medicine focuses on individual-level disease treatment and prevention. Here, we propose the usage of the term 'precision omics' to represent the combinatorial strategy that applies omics to translate large-scale molecular omics data for precision disease understanding and accurate disease diagnosis, treatment and prevention. Given the complexity of both omics and precision medicine, precision omics requires standardized representation and integration of heterogeneous data types. Ontology has emerged as an important artificial intelligence component to become critical for standard data and metadata representation, standardization and integration. To support precision omics, we propose a precision omics ontology hypothesis, which hypothesizes that the effectiveness of precision omics is positively correlated with the interoperability of ontologies used for data and knowledge integration. Therefore, to make effective precision omics studies, interoperable ontologies are required to standardize and incorporate heterogeneous data and knowledge in a human- and computer-interpretable manner. Methods for efficient development and application of interoperable ontologies are proposed and illustrated. With the interoperable omics data and knowledge, omics tools such as OmicsViz can also be evolved to process, integrate, visualize and analyze various omics data, leading to the identification of new knowledge and hypotheses of molecular mechanisms underlying the outcomes of diseases such as COVID-19. Given extensive COVID-19 omics research, we propose the strategy of precision omics supported by interoperable ontologies, accompanied with ontology-based semantic reasoning and machine learning, leading to systematic disease mechanism understanding and rational design of precision treatment and prevention. SHORT ABSTRACT Precision medicine focuses on individual-level disease treatment and prevention. Precision omics is a new strategy that applies omics for precision medicine research, which requires standardized representation and integration of individual genetics and phenotypes, experimental conditions, and data analysis settings. Ontology has emerged as an important artificial intelligence component to become critical for standard data and metadata representation, standardization and integration. To support precision omics, interoperable ontologies are required in order to standardize and incorporate heterogeneous data and knowledge in a human- and computer-interpretable manner. With the interoperable omics data and knowledge, omics tools such as OmicsViz can also be evolved to process, integrate, visualize and analyze various omics data, leading to the identification of new knowledge and hypotheses of molecular mechanisms underlying disease outcomes. The precision COVID-19 omics study is provided as the primary use case to illustrate the rationale and implementation of the precision omics strategy.
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Affiliation(s)
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, MI, USA
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10
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Wang H, Pujos-Guillot E, Comte B, de Miranda JL, Spiwok V, Chorbev I, Castiglione F, Tieri P, Watterson S, McAllister R, de Melo Malaquias T, Zanin M, Rai TS, Zheng H. Deep learning in systems medicine. Brief Bioinform 2021; 22:1543-1559. [PMID: 33197934 PMCID: PMC8382976 DOI: 10.1093/bib/bbaa237] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 08/25/2020] [Accepted: 08/26/2020] [Indexed: 12/11/2022] Open
Abstract
Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson's disease. The review offers valuable insights and informs the research in DL and SM.
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Affiliation(s)
| | - Estelle Pujos-Guillot
- metabolomic platform dedicated to metabolism studies in nutrition and health in the French National Research Institute for Agriculture, Food and Environment
| | - Blandine Comte
- French National Research Institute for Agriculture, Food and Environment
| | - Joao Luis de Miranda
- (ESTG/IPP) and a Researcher (CERENA/IST) in optimization methods and process systems engineering
| | - Vojtech Spiwok
- Molecular Modelling Researcher applying machine learning to accelerate molecular simulations
| | - Ivan Chorbev
- Faculty for Computer Science and Engineering, University Ss Cyril and Methodius in Skopje, North Macedonia working in the area of eHealth and assistive technologies
| | | | - Paolo Tieri
- National Research Council of Italy (CNR) and a lecturer at Sapienza University in Rome, working in the field of network medicine and computational biology
| | | | - Roisin McAllister
- Research Associate working in CTRIC, University of Ulster, Derry, and has worked in clinical and academic roles in the fields of molecular diagnostics and biomarker discovery
| | | | - Massimiliano Zanin
- Researcher working in the Institute for Cross-Disciplinary Physics and Complex Systems, Spain, with an interest on data analysis and integration using statistical physics techniques
| | - Taranjit Singh Rai
- Lecturer in cellular ageing at the Centre for Stratified Medicine. Dr Rai’s research interests are in cellular senescence, which is thought to promote cellular and tissue ageing in disease, and the development of senolytic compounds to restrict this process
| | - Huiru Zheng
- Professor of computer sciences at Ulster University
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11
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Berrios DC, Galazka J, Grigorev K, Gebre S, Costes SV. NASA GeneLab: interfaces for the exploration of space omics data. Nucleic Acids Res 2021; 49:D1515-D1522. [PMID: 33080015 PMCID: PMC7778922 DOI: 10.1093/nar/gkaa887] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/16/2020] [Accepted: 10/06/2020] [Indexed: 12/14/2022] Open
Abstract
The mission of NASA's GeneLab database (https://genelab.nasa.gov/) is to collect, curate, and provide access to the genomic, transcriptomic, proteomic and metabolomic (so-called 'omics') data from biospecimens flown in space or exposed to simulated space stressors, maximizing their utilization. This large collection of data enables the exploration of molecular network responses to space environments using a systems biology approach. We review here the various components of the GeneLab platform, including the new data repository web interface, and the GeneLab Online Data Entry (GEODE) web portal, which will support the expansion of the database in the future to include companion non-omics assay data. We discuss our design for GEODE, particularly how it promotes investigators providing more accurate metadata, reducing the curation effort required of GeneLab staff. We also introduce here a new GeneLab Application Programming Interface (API) specifically designed to support tools for the visualization of processed omics data. We review the outreach efforts by GeneLab to utilize the spaceflight data in the repository to generate novel discoveries and develop new hypotheses, including spearheading data analysis working groups, and a high school student training program. All these efforts are aimed ultimately at supporting precision risk management for human space exploration.
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Affiliation(s)
| | | | | | - Samrawit Gebre
- KBR/NASA Ames Research Center, Moffett Field, CA 94035, USA
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12
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Vendelin M, Laasmaa M, Kalda M, Branovets J, Karro N, Barsunova K, Birkedal R. IOCBIO Kinetics: An open-source software solution for analysis of data traces. PLoS Comput Biol 2020; 16:e1008475. [PMID: 33351800 PMCID: PMC7787677 DOI: 10.1371/journal.pcbi.1008475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 01/06/2021] [Accepted: 10/28/2020] [Indexed: 11/19/2022] Open
Abstract
Biological measurements frequently involve measuring parameters as a function of time, space, or frequency. Later, during the analysis phase of the study, the researcher splits the recorded data trace into smaller sections, analyzes each section separately by finding a mean or fitting against a specified function, and uses the analysis results in the study. Here, we present the software that allows to analyze these data traces in a manner that ensures repeatability of the analysis and simplifies the application of FAIR (findability, accessibility, interoperability, and reusability) principles in such studies. At the same time, it simplifies the routine data analysis pipeline and gives access to a fast overview of the analysis results. For that, the software supports reading the raw data, processing the data as specified in the protocol, and storing all intermediate results in the laboratory database. The software can be extended by study- or hardware-specific modules to provide the required data import and analysis facilities. To simplify the development of the data entry web interfaces, that can be used to enter data describing the experiments, we released a web framework with an example implementation of such a site. The software is covered by open-source license and is available through several online channels. In biological and other types of experiments, we frequently record changes of some parameters in time or space. It is common to analyze the data by splitting the recording into smaller sections and relating it to some changes induced by the researchers. The steps involved in the analysis are: splitting of the data, fitting them to some function, relating the fit result to the change in the environment, and normalization. These steps are frequently done through several software packages, spreedsheets, and manual copy and paste between the programs. The software presented in this work allows to make all these analysis steps in one database in a manner that is easy, can be reproduced by others, and clearly tracks the history of all the analysis steps. In addition, it allows to link the experimental data with the description of the experiment, making it simple to perform tasks such as normalization of the recorded values, relating experimental recordings to the sample or animal, as well as extracting data from the laboratory database for publishing. The software is written to be easily extendable by user-defined modules to fit the analysis pipelines and is expected to improve the data analysis practices in research.
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Affiliation(s)
- Marko Vendelin
- Laboratory of Systems Biology, Department of Cybernetics, School of Science, Tallinn University of Technology, Tallinn, Estonia
- * E-mail:
| | - Martin Laasmaa
- Laboratory of Systems Biology, Department of Cybernetics, School of Science, Tallinn University of Technology, Tallinn, Estonia
| | - Mari Kalda
- Laboratory of Systems Biology, Department of Cybernetics, School of Science, Tallinn University of Technology, Tallinn, Estonia
| | - Jelena Branovets
- Laboratory of Systems Biology, Department of Cybernetics, School of Science, Tallinn University of Technology, Tallinn, Estonia
| | - Niina Karro
- Laboratory of Systems Biology, Department of Cybernetics, School of Science, Tallinn University of Technology, Tallinn, Estonia
| | - Karina Barsunova
- Laboratory of Systems Biology, Department of Cybernetics, School of Science, Tallinn University of Technology, Tallinn, Estonia
| | - Rikke Birkedal
- Laboratory of Systems Biology, Department of Cybernetics, School of Science, Tallinn University of Technology, Tallinn, Estonia
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13
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Thessen AE, Walls RL, Vogt L, Singer J, Warren R, Buttigieg PL, Balhoff JP, Mungall CJ, McGuinness DL, Stucky BJ, Yoder MJ, Haendel MA. Transforming the study of organisms: Phenomic data models and knowledge bases. PLoS Comput Biol 2020; 16:e1008376. [PMID: 33232313 PMCID: PMC7685442 DOI: 10.1371/journal.pcbi.1008376] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanied by computable phenomic data. The majority of phenomic data are contained in countless small, heterogeneous phenotypic data sets that are very difficult or impossible to integrate at scale because of variable formats, lack of digitization, and linguistic problems. One powerful solution is to represent phenotypic data using data models with precise, computable semantics, but adoption of semantic standards for representing phenotypic data has been slow, especially in biodiversity and ecology. Some phenotypic and trait data are available in a semantic language from knowledge bases, but these are often not interoperable. In this review, we will compare and contrast existing ontology and data models, focusing on nonhuman phenotypes and traits. We discuss barriers to integration of phenotypic data and make recommendations for developing an operationally useful, semantically interoperable phenotypic data ecosystem.
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Affiliation(s)
- Anne E. Thessen
- Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon, United States of America
- Ronin Institute for Independent Scholarship, Monclair, New Jersey, United States of America
| | - Ramona L. Walls
- Bio5 Institute, University of Arizona, Tucson, Arizona, United States of America
| | - Lars Vogt
- TIB Leibniz Information Centre for Science and Technology, Hannover, Germany
| | | | | | - Pier Luigi Buttigieg
- Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
| | - James P. Balhoff
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Christopher J. Mungall
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | | | - Brian J. Stucky
- Florida Museum of Natural History, University of Florida, Gainesville, Florida, United States of America
| | - Matthew J. Yoder
- Illinois Natural History Survey, Champaign, Illinois, United States of America
| | - Melissa A. Haendel
- Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon, United States of America
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14
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Abstract
Metadata is essential in proteomics data repositories and is crucial to interpret and reanalyze the deposited data sets. For every proteomics data set, we should capture at least three levels of metadata: (i) data set description, (ii) the sample to data files related information, and (iii) standard data file formats (e.g., mzIdentML, mzML, or mzTab). While the data set description and standard data file formats are supported by all ProteomeXchange partners, the information regarding the sample to data files is mostly missing. Recently, members of the European Bioinformatics Community for Mass Spectrometry (EuBIC) have created an open-source project called Sample to Data file format for Proteomics (https://github.com/bigbio/proteomics-metadata-standard/) to enable the standardization of sample metadata of public proteomics data sets. Here, the project is presented to the proteomics community, and we call for contributors, including researchers, journals, and consortiums to provide feedback about the format. We believe this work will improve reproducibility and facilitate the development of new tools dedicated to proteomics data analysis.
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Affiliation(s)
- Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, U.K
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15
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Shaw F, Etuk A, Minotto A, Gonzalez-Beltran A, Johnson D, Rocca-Serra P, Laporte MA, Arnaud E, Devare M, Kersey P, Sansone SA, Davey RP. COPO: a metadata platform for brokering FAIR data in the life sciences. F1000Res 2020. [DOI: 10.12688/f1000research.23889.1] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Scientific innovation is increasingly reliant on data and computational resources. Much of today’s life science research involves generating, processing, and reusing heterogeneous datasets that are growing exponentially in size. Demand for technical experts (data scientists and bioinformaticians) to process these data is at an all-time high, but these are not typically trained in good data management practices. That said, we have come a long way in the last decade, with funders, publishers, and researchers themselves making the case for open, interoperable data as a key component of an open science philosophy. In response, recognition of the FAIR Principles (that data should be Findable, Accessible, Interoperable and Reusable) has become commonplace. However, both technical and cultural challenges for the implementation of these principles still exist when storing, managing, analysing and disseminating both legacy and new data. COPO is a computational system that attempts to address some of these challenges by enabling scientists to describe their research objects (raw or processed data, publications, samples, images, etc.) using community-sanctioned metadata sets and vocabularies, and then use public or institutional repositories to share them with the wider scientific community. COPO encourages data generators to adhere to appropriate metadata standards when publishing research objects, using semantic terms to add meaning to them and specify relationships between them. This allows data consumers, be they people or machines, to find, aggregate, and analyse data which would otherwise be private or invisible, building upon existing standards to push the state of the art in scientific data dissemination whilst minimising the burden of data publication and sharing.
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16
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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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17
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Yu H, Nysak S, Garg N, Ong E, Ye X, Zhang X, He Y. ODAE: Ontology-based systematic representation and analysis of drug adverse events and its usage in study of adverse events given different patient age and disease conditions. BMC Bioinformatics 2019; 20:199. [PMID: 31074377 PMCID: PMC6509876 DOI: 10.1186/s12859-019-2729-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Background Drug adverse events (AEs), or called adverse drug events (ADEs), are ranked one of the leading causes of mortality. The Ontology of Adverse Events (OAE) has been widely used for adverse event AE representation, standardization, and analysis. OAE-based ADE-specific ontologies, including ODNAE for drug-associated neuropathy-inducing AEs and OCVDAE for cardiovascular drug AEs, have also been developed and used. However, these ADE-specific ontologies do not consider the effects of other factors (e.g., age and drug-treated disease) on the outcomes of ADEs. With more ontological studies of ADEs, it is also critical to develop a general purpose ontology for representing ADEs for various types of drugs. Results Our survey of FDA drug package insert documents and other resources for 224 neuropathy-inducing drugs discovered that many drugs (e.g., sirolimus and linezolid) cause different AEs given patients’ age or the diseases treated by the drugs. To logically represent the complex relations among drug, drug ingredient and mechanism of action, AE, age, disease, and other related factors, an ontology design pattern was developed and applied to generate a community-driven open-source Ontology of Drug Adverse Events (ODAE). The ODAE development follows the OBO Foundry ontology development principles (e.g., openness and collaboration). Built on a generalizable ODAE design pattern and extending the OAE and NDF-RT ontology, ODAE has represented various AEs associated with the over 200 neuropathy-inducing drugs given different age and disease conditions. ODAE is now deposited in the Ontobee for browsing and queries. As a demonstration of usage, a SPARQL query of the ODAE knowledge base was developed to identify all the drugs having the mechanisms of ion channel interactions, the diseases treated with the drugs, and AEs after the treatment in adult patients. AE-specific drug class effects were also explored using ODAE and SPARQL. Conclusion ODAE provides a general representation of ADEs given different conditions and can be used for querying scientific questions. ODAE is also a robust knowledge base and platform for semantic and logic representation and study of ADEs of more drugs in the future. Electronic supplementary material The online version of this article (10.1186/s12859-019-2729-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hong Yu
- Department of Pulmonary and Critical Care Medicine, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou, China. .,Guizhou University Medical College, Guiyang, 550025, Guizhou, China.
| | - Solomiya Nysak
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Noemi Garg
- College of Pharmacy, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Edison Ong
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Xianwei Ye
- Department of Pulmonary and Critical Care Medicine, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou, China.,Guizhou University Medical College, Guiyang, 550025, Guizhou, China
| | - Xiangyan Zhang
- Department of Pulmonary and Critical Care Medicine, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou, China.,Guizhou University Medical College, Guiyang, 550025, Guizhou, China
| | - Yongqun He
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, and Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
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18
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Brandizi M, Singh A, Rawlings C, Hassani-Pak K. Towards FAIRer Biological Knowledge Networks Using a Hybrid Linked Data and Graph Database Approach. J Integr Bioinform 2018; 15:/j/jib.ahead-of-print/jib-2018-0023/jib-2018-0023.xml. [PMID: 30085931 PMCID: PMC6340125 DOI: 10.1515/jib-2018-0023] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 06/07/2018] [Indexed: 01/01/2023] Open
Abstract
The speed and accuracy of new scientific discoveries – be it by humans or artificial intelligence – depends on the quality of the underlying data and on the technology to connect, search and share the data efficiently. In recent years, we have seen the rise of graph databases and semi-formal data models such as knowledge graphs to facilitate software approaches to scientific discovery. These approaches extend work based on formalised models, such as the Semantic Web. In this paper, we present our developments to connect, search and share data about genome-scale knowledge networks (GSKN). We have developed a simple application ontology based on OWL/RDF with mappings to standard schemas. We are employing the ontology to power data access services like resolvable URIs, SPARQL endpoints, JSON-LD web APIs and Neo4j-based knowledge graphs. We demonstrate how the proposed ontology and graph databases considerably improve search and access to interoperable and reusable biological knowledge (i.e. the FAIRness data principles).
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Affiliation(s)
- Marco Brandizi
- Rothamsted Research, Computational and Analytical Sciences Department, Harpenden, AL5 2JQ, UK
| | - Ajit Singh
- Rothamsted Research, Computational and Analytical Sciences Department, Harpenden, AL5 2JQ, UK
| | - Christopher Rawlings
- Rothamsted Research, Computational and Analytical Sciences Department, Harpenden, AL5 2JQ, UK
| | - Keywan Hassani-Pak
- Rothamsted Research, Computational and Analytical Sciences Department, Harpenden, AL5 2JQ, UK
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19
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Pinart M, Nimptsch K, Bouwman J, Dragsted LO, Yang C, De Cock N, Lachat C, Perozzi G, Canali R, Lombardo R, D'Archivio M, Guillaume M, Donneau AF, Jeran S, Linseisen J, Kleiser C, Nöthlings U, Barbaresko J, Boeing H, Stelmach-Mardas M, Heuer T, Laird E, Walton J, Gasparini P, Robino A, Castaño L, Rojo-Martínez G, Merino J, Masana L, Standl M, Schulz H, Biagi E, Nurk E, Matthys C, Gobbetti M, de Angelis M, Windler E, Zyriax BC, Tafforeau J, Pischon T. Joint Data Analysis in Nutritional Epidemiology: Identification of Observational Studies and Minimal Requirements. J Nutr 2018; 148:285-297. [PMID: 29490094 DOI: 10.1093/jn/nxx037] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Accepted: 11/06/2017] [Indexed: 02/06/2023] Open
Abstract
Background Joint data analysis from multiple nutrition studies may improve the ability to answer complex questions regarding the role of nutritional status and diet in health and disease. Objective The objective was to identify nutritional observational studies from partners participating in the European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI) Consortium, as well as minimal requirements for joint data analysis. Methods A predefined template containing information on study design, exposure measurements (dietary intake, alcohol and tobacco consumption, physical activity, sedentary behavior, anthropometric measures, and sociodemographic and health status), main health-related outcomes, and laboratory measurements (traditional and omics biomarkers) was developed and circulated to those European research groups participating in the ENPADASI under the strategic research area of "diet-related chronic diseases." Information about raw data disposition and metadata sharing was requested. A set of minimal requirements was abstracted from the gathered information. Results Studies (12 cohort, 12 cross-sectional, and 2 case-control) were identified. Two studies recruited children only and the rest recruited adults. All studies included dietary intake data. Twenty studies collected blood samples. Data on traditional biomarkers were available for 20 studies, of which 17 measured lipoproteins, glucose, and insulin and 13 measured inflammatory biomarkers. Metabolomics, proteomics, and genomics or transcriptomics data were available in 5, 3, and 12 studies, respectively. Although the study authors were willing to share metadata, most refused, were hesitant, or had legal or ethical issues related to sharing raw data. Forty-one descriptors of minimal requirements for the study data were identified to facilitate data integration. Conclusions Combining study data sets will enable sufficiently powered, refined investigations to increase the knowledge and understanding of the relation between food, nutrition, and human health. Furthermore, the minimal requirements for study data may encourage more efficient secondary usage of existing data and provide sufficient information for researchers to draft future multicenter research proposals in nutrition.
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Affiliation(s)
- Mariona Pinart
- Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Katharina Nimptsch
- Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Jildau Bouwman
- TNO, Microbiology and Systems Biology Group, Zeist, Netherlands
| | - Lars O Dragsted
- Department of Nutrition, Exercise, and Sports, University of Copenhagen, Copenhagen, Denmark
| | - Chen Yang
- Department of Food Safety and Food Quality, Ghent University, Ghent, Belgium
| | - Nathalie De Cock
- Department of Food Safety and Food Quality, Ghent University, Ghent, Belgium
| | - Carl Lachat
- Department of Food Safety and Food Quality, Ghent University, Ghent, Belgium
| | | | | | - Rosario Lombardo
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Trentino, Italy
| | - Massimo D'Archivio
- Center for Gender-Specific Medicine, Istituto Superiore di Sanità, Rome, Italy
| | | | | | - Stephanie Jeran
- Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Jakob Linseisen
- Helmholtz Zentrum München, Institute of Epidemiology, Neuherberg, Germany.,Ludwig-Maximilians-Universität (LMU) München, Medical Faculty, Institute of Medical Information Processing, Biometry, and Epidemiology (IBE), Chair of Epidemiology at University Centre for Health Care Sciences at the Augsburg Clinic (UNIKA-T Augsburg), Ausburg, Germany
| | - Christina Kleiser
- Helmholtz Zentrum München, Institute of Epidemiology, Neuherberg, Germany
| | - Ute Nöthlings
- Department of Nutrition and Food Sciences, University of Bonn, Bonn, Germany
| | - Janett Barbaresko
- Department of Nutrition and Food Sciences, University of Bonn, Bonn, Germany
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany
| | - Marta Stelmach-Mardas
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany.,Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, Poznan, Poland
| | - Thorsten Heuer
- Department of Nutritional Behavior, Max Rubner-Institut, Federal Research Institute of Nutrition and Food, Karlsruhe, Germany
| | - Eamon Laird
- Centre for Medical Gerontology, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Janette Walton
- School of Food and Nutritional Sciences, University College Cork, Cork, Ireland
| | - Paolo Gasparini
- Department of Medical Sciences, University of Trieste, Trieste, Italy.,Institute for Maternal and Child Health-IRCCS "Burlo Garofolo", Trieste, Italy
| | - Antonietta Robino
- Institute for Maternal and Child Health-IRCCS "Burlo Garofolo", Trieste, Italy
| | - Luis Castaño
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Rare Diseases Networking Biomedical Research Centre (CIBERER), BioCruces-Hospital Universitario Cruces-The University of the Basque Country (Basque: Euskal Herriko Unibertsitatea/Spanish: Universidad del País Vasco (UPV/EHU)), Barakaldo, Spain
| | - Gemma Rojo-Martínez
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain.,Unidad de Gestión Clínica (UGC) Endocrinology and Nutrition. Hospital Regional Universitario de Málaga, Institute of Biomedical Research in Malaga (IBIMA), Málaga, Spain
| | - Jordi Merino
- Research Unit on Lipids and Atherosclerosis, Universitat Rovira i Virgili, Reus, Spain.,Diabetes Unit and Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Luis Masana
- Research Unit on Lipids and Atherosclerosis, Universitat Rovira i Virgili, Reus, Spain
| | - Marie Standl
- Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Epidemiology I, Neuherberg, Germany
| | - Holger Schulz
- Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Epidemiology I, Neuherberg, Germany
| | - Elena Biagi
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum-University of Bologna, Bologna, Italy
| | - Eha Nurk
- National Institute for Health Development, Tallinn, Estonia
| | - Christophe Matthys
- Department of Clinical and Experimental Medicine, The Katholieke Universiteit Leuven (KU Leuven) and Department of Endocrinology, University Hospitals Leuven, Leuven, Belgium
| | - Marco Gobbetti
- Faculty of Science and Technology, Free University of Bozen, Bolzano, Italy
| | - Maria de Angelis
- Department of Soil, Plant and Food Sciences, University of Bari Aldo Moro, Bari, Italy
| | - Eberhard Windler
- Preventive Medicine, University Heart Center, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Birgit-Christiane Zyriax
- Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jean Tafforeau
- Scientific Institute of Public Health, Brussels, Belgium
| | - Tobias Pischon
- Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany.,Charité Universitätsmedizin Berlin, Berlin, Germany.,MDC/BIH Biobank, Max Delbrück Center for Molecular Medicine and Berlin Institute of Health, Berlin, Germany.,German Centre for Cardiovascular Research (DZHK), Berlin, Germany
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He Y, Xiang Z, Zheng J, Lin Y, Overton JA, Ong E. The eXtensible ontology development (XOD) principles and tool implementation to support ontology interoperability. J Biomed Semantics 2018; 9:3. [PMID: 29329592 PMCID: PMC5765662 DOI: 10.1186/s13326-017-0169-2] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Accepted: 12/07/2017] [Indexed: 11/13/2022] Open
Abstract
Ontologies are critical to data/metadata and knowledge standardization, sharing, and analysis. With hundreds of biological and biomedical ontologies developed, it has become critical to ensure ontology interoperability and the usage of interoperable ontologies for standardized data representation and integration. The suite of web-based Ontoanimal tools (e.g., Ontofox, Ontorat, and Ontobee) support different aspects of extensible ontology development. By summarizing the common features of Ontoanimal and other similar tools, we identified and proposed an “eXtensible Ontology Development” (XOD) strategy and its associated four principles. These XOD principles reuse existing terms and semantic relations from reliable ontologies, develop and apply well-established ontology design patterns (ODPs), and involve community efforts to support new ontology development, promoting standardized and interoperable data and knowledge representation and integration. The adoption of the XOD strategy, together with robust XOD tool development, will greatly support ontology interoperability and robust ontology applications to support data to be Findable, Accessible, Interoperable and Reusable (i.e., FAIR).
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Affiliation(s)
- Yongqun He
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
| | - Zuoshuang Xiang
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Jie Zheng
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Yu Lin
- Center for Computational Science, University of Miami, Coral Gables, FL, USA
| | | | - Edison Ong
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
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21
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Ontology-based systematical representation and drug class effect analysis of package insert-reported adverse events associated with cardiovascular drugs used in China. Sci Rep 2017; 7:13819. [PMID: 29061976 PMCID: PMC5653862 DOI: 10.1038/s41598-017-12580-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Accepted: 09/07/2017] [Indexed: 01/31/2023] Open
Abstract
With increased usage of cardiovascular drugs (CVDs) for treating cardiovascular diseases, it is important to analyze CVD-associated adverse events (AEs). In this study, we systematically collected package insert-reported AEs associated with CVDs used in China, and developed and analyzed an Ontology of Cardiovascular Drug AEs (OCVDAE). Extending the Ontology of AEs (OAE) and NDF-RT, OCVDAE includes 194 CVDs, CVD ingredients, mechanisms of actions (MoAs), and CVD-associated 736 AEs. An AE-specific drug class effect is defined to exist when all the drugs (drug chemical ingredients or drug products) in a drug class are associated with an AE, which is formulated as a new proportional class level ratio (“PCR”) = 1. Our PCR-based heatmap analysis identified many class level drug effects on different AE classes such as behavioral and neurological AE and digestive system AE. Additional drug-AE correlation tests (i.e., class-level PRR, Chi-squared, and minimal case reports) were also modified and applied to further detect statistically significant drug class effects. Two drug ingredient classes and three CVD MoA classes were found to have statistically significant class effects on 13 AEs. For example, the CVD Active Transporter Interactions class (including reserpine, indapamide, digoxin, and deslanoside) has statistically significant class effect on anorexia and diarrhea AEs.
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The CEDAR Workbench: An Ontology-Assisted Environment for Authoring Metadata that Describe Scientific Experiments. THE SEMANTIC WEB--ISWC ... : ... INTERNATIONAL SEMANTIC WEB CONFERENCE ... PROCEEDINGS. INTERNATIONAL SEMANTIC WEB CONFERENCE 2017; 10588:103-110. [PMID: 32219223 DOI: 10.1007/978-3-319-68204-4_10] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The Center for Expanded Data Annotation and Retrieval (CEDAR) aims to revolutionize the way that metadata describing scientific experiments are authored. The software we have developed-the CEDAR Workbench-is a suite of Web-based tools and REST APIs that allows users to construct metadata templates, to fill in templates to generate high-quality metadata, and to share and manage these resources. The CEDAR Workbench provides a versatile, REST-based environment for authoring metadata that are enriched with terms from ontologies. The metadata are available as JSON, JSON-LD, or RDF for easy integration in scientific applications and reusability on the Web. Users can leverage our APIs for validating and submitting metadata to external repositories. The CEDAR Workbench is freely available and open-source.
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Adam-Blondon AF, Alaux M, Pommier C, Cantu D, Cheng ZM, Cramer GR, Davies C, Delrot S, Deluc L, Di Gaspero G, Grimplet J, Fennell A, Londo JP, Kersey P, Mattivi F, Naithani S, Neveu P, Nikolski M, Pezzotti M, Reisch BI, Töpfer R, Vivier MA, Ware D, Quesneville H. Towards an open grapevine information system. HORTICULTURE RESEARCH 2016; 3:16056. [PMID: 27917288 PMCID: PMC5120350 DOI: 10.1038/hortres.2016.56] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Revised: 10/10/2016] [Accepted: 10/21/2016] [Indexed: 05/26/2023]
Abstract
Viticulture, like other fields of agriculture, is currently facing important challenges that will be addressed only through sustained, dedicated and coordinated research. Although the methods used in biology have evolved tremendously in recent years and now involve the routine production of large data sets of varied nature, in many domains of study, including grapevine research, there is a need to improve the findability, accessibility, interoperability and reusability (FAIR-ness) of these data. Considering the heterogeneous nature of the data produced, the transnational nature of the scientific community and the experience gained elsewhere, we have formed an open working group, in the framework of the International Grapevine Genome Program (www.vitaceae.org), to construct a coordinated federation of information systems holding grapevine data distributed around the world, providing an integrated set of interfaces supporting advanced data modeling, rich semantic integration and the next generation of data mining tools. To achieve this goal, it will be critical to develop, implement and adopt appropriate standards for data annotation and formatting. The development of this system, the GrapeIS, linking genotypes to phenotypes, and scientific research to agronomical and oeneological data, should provide new insights into grape biology, and allow the development of new varieties to meet the challenges of biotic and abiotic stress, environmental change, and consumer demand.
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Affiliation(s)
- A-F Adam-Blondon
- URGI, UR1164 INRA, Université Paris-Saclay, Versailles 78026, France
| | - M Alaux
- URGI, UR1164 INRA, Université Paris-Saclay, Versailles 78026, France
| | - C Pommier
- URGI, UR1164 INRA, Université Paris-Saclay, Versailles 78026, France
| | - D Cantu
- Department of Viticulture and Enology, University of California, Davis, CA 95616, USA
| | - Z-M Cheng
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA
| | - GR Cramer
- Department of Biochemistry and Molecular Biology, University of Nevada, Reno, NV 89557, USA
| | - C Davies
- CSIRO Agriculture and Food, Waite Campus, WIC West Building, PMB2, Glen Osmond, South Australia 5064, Australia
| | - S Delrot
- Université de Bordeaux, ISVV, EGFV, UMR 1287, F-33140 Villenave d’Ornon, France
| | - L Deluc
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
| | - G Di Gaspero
- Istituto di Genomica Applicata, Udine 33100, Italy
| | - J Grimplet
- Instituto de Ciencias de la Vid y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja), Logroño 26006, Spain
| | - A Fennell
- Plant Science Department, South Dakota State University, BioSNTR, Brookings, SD 57007, USA
| | - JP Londo
- United States Department of Agriculture-Agricultural Research Service-Grape Genetics Research Unit, Geneva, NY 14456, USA
| | - P Kersey
- European Molecular Biology Laboratory, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - F Mattivi
- Dipartimento Qualità Alimentare e Nutrizione, Centro Ricerca ed Innovazione Fondazione Edmund Mach, Via E. Mach 1, 38010 San Michele all'Adige, Italia
| | - S Naithani
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
| | - P Neveu
- UMR Mistea, INRA, Montpellier 34060, France
| | - M Nikolski
- University of Bordeaux, CBiB, Bordeaux 33000, France
- University of Bordeaux, CNRS/LaBRI, Talence 33405, France
| | - M Pezzotti
- Department of Biotechnology, Università degli Studi di Verona, Verona 37134, Italy
| | - BI Reisch
- Horticulture Section, School of Integrative Plant Science, Cornell University, Geneva, NY 14456, USA
| | - R Töpfer
- JKI Institute for Grapevine Breeding Geilweilerhof, Siebeldingen 76833, Germany
| | - MA Vivier
- Department of Viticulture and Oenology, Institute for Wine Biotechnology, Stellenbosch University, Stellenbosch, Matieland 7602, South Africa
| | - D Ware
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
- US Department of Agriculture-Agricultural Research Service, NEA Robert W. Holley Center for Agriculture and Health, Cornell University, Ithaca, NY 14853, USA
| | - H Quesneville
- URGI, UR1164 INRA, Université Paris-Saclay, Versailles 78026, France
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Bandrowski A, Brinkman R, Brochhausen M, Brush MH, Bug B, Chibucos MC, Clancy K, Courtot M, Derom D, Dumontier M, Fan L, Fostel J, Fragoso G, Gibson F, Gonzalez-Beltran A, Haendel MA, He Y, Heiskanen M, Hernandez-Boussard T, Jensen M, Lin Y, Lister AL, Lord P, Malone J, Manduchi E, McGee M, Morrison N, Overton JA, Parkinson H, Peters B, Rocca-Serra P, Ruttenberg A, Sansone SA, Scheuermann RH, Schober D, Smith B, Soldatova LN, Stoeckert CJ, Taylor CF, Torniai C, Turner JA, Vita R, Whetzel PL, Zheng J. The Ontology for Biomedical Investigations. PLoS One 2016; 11:e0154556. [PMID: 27128319 PMCID: PMC4851331 DOI: 10.1371/journal.pone.0154556] [Citation(s) in RCA: 142] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2016] [Accepted: 04/17/2016] [Indexed: 12/18/2022] Open
Abstract
The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl.
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Affiliation(s)
- Anita Bandrowski
- University of California San Diego, La Jolla, California, United States of America
| | - Ryan Brinkman
- British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Mathias Brochhausen
- University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Matthew H. Brush
- Oregon Health and Science University, Portland, Oregon, United States of America
| | - Bill Bug
- Drexel University College of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Marcus C. Chibucos
- University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Kevin Clancy
- Thermo Fisher Scientific, Carlsbad, California, United States of America
| | | | - Dirk Derom
- The Vrije Universiteit Brussel, Ixelles, Brussels, Belgium
| | - Michel Dumontier
- Stanford University, Stanford, California, United States of America
| | - Liju Fan
- Ontology Workshop, LLC, Columbia, Maryland, United States of America
| | - Jennifer Fostel
- National Toxicology Program, NIEHS, National Institutes of Health, Research Triangle Park, North Carolina, United States of America
| | - Gilberto Fragoso
- Center for Biomedical Informatics and Information Technology, National Institutes of Health, Rockville, Maryland, United States of America
| | - Frank Gibson
- Royal Society of Chemistry, Cambridge, Cambridgeshire, United Kingdom
| | | | - Melissa A. Haendel
- Oregon Health and Science University, Portland, Oregon, United States of America
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Mervi Heiskanen
- National Cancer Institute, Rockville, Maryland, United States of America
| | | | - Mark Jensen
- University at Buffalo, Buffalo, New York, United States of America
| | - Yu Lin
- University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | | | - Phillip Lord
- Newcastle University, Newcastle-upon-Tyne, Tyne and Wear, United Kingdom
| | - James Malone
- European Molecular Biology Laboratory- European Bioinformatics Institute, Hinxton, Cambridgeshire, United Kingdom
| | - Elisabetta Manduchi
- University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Monnie McGee
- Southern Methodist University, Dallas, Texas, United States of America
| | - Norman Morrison
- The University of Manchester, Manchester, Greater Manchester, United Kingdom
| | - James A. Overton
- La Jolla Institute for Allergy and Immunology, La Jolla, California, United States of America
| | - Helen Parkinson
- European Molecular Biology Laboratory- European Bioinformatics Institute, Hinxton, Cambridgeshire, United Kingdom
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, La Jolla, California, United States of America
| | | | - Alan Ruttenberg
- University at Buffalo, Buffalo, New York, United States of America
| | | | | | - Daniel Schober
- Leibniz Institute of Plant Biochemistry, Halle, Saxony-Anhalt, Germany
| | - Barry Smith
- University at Buffalo, Buffalo, New York, United States of America
| | | | | | - Chris F. Taylor
- European Molecular Biology Laboratory- European Bioinformatics Institute, Hinxton, Cambridgeshire, United Kingdom
| | - Carlo Torniai
- Oregon Health and Science University, Portland, Oregon, United States of America
| | - Jessica A. Turner
- Georgia State University, Atlanta, Georgia, United States of America
| | - Randi Vita
- La Jolla Institute for Allergy and Immunology, La Jolla, California, United States of America
| | - Patricia L. Whetzel
- University of California San Diego, La Jolla, California, United States of America
| | - Jie Zheng
- University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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Nickerson D, Atalag K, de Bono B, Geiger J, Goble C, Hollmann S, Lonien J, Müller W, Regierer B, Stanford NJ, Golebiewski M, Hunter P. The Human Physiome: how standards, software and innovative service infrastructures are providing the building blocks to make it achievable. Interface Focus 2016; 6:20150103. [PMID: 27051515 PMCID: PMC4759754 DOI: 10.1098/rsfs.2015.0103] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Reconstructing and understanding the Human Physiome virtually is a complex mathematical problem, and a highly demanding computational challenge. Mathematical models spanning from the molecular level through to whole populations of individuals must be integrated, then personalized. This requires interoperability with multiple disparate and geographically separated data sources, and myriad computational software tools. Extracting and producing knowledge from such sources, even when the databases and software are readily available, is a challenging task. Despite the difficulties, researchers must frequently perform these tasks so that available knowledge can be continually integrated into the common framework required to realize the Human Physiome. Software and infrastructures that support the communities that generate these, together with their underlying standards to format, describe and interlink the corresponding data and computer models, are pivotal to the Human Physiome being realized. They provide the foundations for integrating, exchanging and re-using data and models efficiently, and correctly, while also supporting the dissemination of growing knowledge in these forms. In this paper, we explore the standards, software tooling, repositories and infrastructures that support this work, and detail what makes them vital to realizing the Human Physiome.
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Affiliation(s)
- David Nickerson
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Koray Atalag
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- National Institute for Health Innovation (NIHI), The University of Auckland, Auckland, New Zealand
| | - Bernard de Bono
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Jörg Geiger
- Interdisciplinary Bank of Biomaterials and Data, University Hospital Würzburg, Würzburg, Germany
| | - Carole Goble
- School of Computer Science, University of Manchester, Manchester, UK
| | - Susanne Hollmann
- Research Center Plant Genomics and Systems Biology, Universitat Potsdam, Potsdam, Germany
| | | | - Wolfgang Müller
- Heidelberg Institute for Theoretical Studies (HITS gGmbH), Heidelberg, Germany
| | | | | | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies (HITS gGmbH), Heidelberg, Germany
| | - Peter Hunter
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
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The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016; 3:160018. [PMID: 26978244 PMCID: PMC4792175 DOI: 10.1038/sdata.2016.18] [Citation(s) in RCA: 5078] [Impact Index Per Article: 634.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Accepted: 02/12/2016] [Indexed: 11/08/2022] Open
Abstract
There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders-representing academia, industry, funding agencies, and scholarly publishers-have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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27
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Misra BB, van der Hooft JJJ. Updates in metabolomics tools and resources: 2014-2015. Electrophoresis 2015; 37:86-110. [DOI: 10.1002/elps.201500417] [Citation(s) in RCA: 100] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2015] [Revised: 10/04/2015] [Accepted: 10/05/2015] [Indexed: 12/12/2022]
Affiliation(s)
- Biswapriya B. Misra
- Department of Biology, Genetics Institute; University of Florida; Gainesville FL USA
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28
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Wolstencroft K, Owen S, Krebs O, Nguyen Q, Stanford NJ, Golebiewski M, Weidemann A, Bittkowski M, An L, Shockley D, Snoep JL, Mueller W, Goble C. SEEK: a systems biology data and model management platform. BMC SYSTEMS BIOLOGY 2015; 9:33. [PMID: 26160520 PMCID: PMC4702362 DOI: 10.1186/s12918-015-0174-y] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Accepted: 06/01/2015] [Indexed: 12/19/2022]
Abstract
Background Systems biology research typically involves the integration and analysis of heterogeneous data types in order to model and predict biological processes. Researchers therefore require tools and resources to facilitate the sharing and integration of data, and for linking of data to systems biology models. There are a large number of public repositories for storing biological data of a particular type, for example transcriptomics or proteomics, and there are several model repositories. However, this silo-type storage of data and models is not conducive to systems biology investigations. Interdependencies between multiple omics datasets and between datasets and models are essential. Researchers require an environment that will allow the management and sharing of heterogeneous data and models in the context of the experiments which created them. Results The SEEK is a suite of tools to support the management, sharing and exploration of data and models in systems biology. The SEEK platform provides an access-controlled, web-based environment for scientists to share and exchange data and models for day-to-day collaboration and for public dissemination. A plug-in architecture allows the linking of experiments, their protocols, data, models and results in a configurable system that is available 'off the shelf'. Tools to run model simulations, plot experimental data and assist with data annotation and standardisation combine to produce a collection of resources that support analysis as well as sharing. Underlying semantic web resources additionally extract and serve SEEK metadata in RDF (Resource Description Format). SEEK RDF enables rich semantic queries, both within SEEK and between related resources in the web of Linked Open Data. Conclusion The SEEK platform has been adopted by many systems biology consortia across Europe. It is a data management environment that has a low barrier of uptake and provides rich resources for collaboration. This paper provides an update on the functions and features of the SEEK software, and describes the use of the SEEK in the SysMO consortium (Systems biology for Micro-organisms), and the VLN (virtual Liver Network), two large systems biology initiatives with different research aims and different scientific communities.
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Affiliation(s)
- Katherine Wolstencroft
- Leiden Institute of Advanced Computer Science Leiden Institute of Advanced Computer Science, Leiden University, 111 Snellius, Niels Bohrweg 1, Leiden, CA, 2333, Netherlands.
| | - Stuart Owen
- School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester, M13 9PL, UK
| | - Olga Krebs
- Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg, Heidelberg, 35 69118, Germany
| | - Quyen Nguyen
- Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg, Heidelberg, 35 69118, Germany
| | - Natalie J Stanford
- School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester, M13 9PL, UK.
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg, Heidelberg, 35 69118, Germany
| | - Andreas Weidemann
- Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg, Heidelberg, 35 69118, Germany
| | - Meik Bittkowski
- Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg, Heidelberg, 35 69118, Germany
| | - Lihua An
- Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg, Heidelberg, 35 69118, Germany
| | - David Shockley
- Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg, Heidelberg, 35 69118, Germany
| | - Jacky L Snoep
- School of Chemical Engineering & Analytical Science, The University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom.,Department of Biochemistry, University of Stellenbosch, Private Bag X1, 7602, Matieland, South Africa
| | - Wolfgang Mueller
- Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg, Heidelberg, 35 69118, Germany
| | - Carole Goble
- School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester, M13 9PL, UK
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29
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González-Beltrán A, Li P, Zhao J, Avila-Garcia MS, Roos M, Thompson M, van der Horst E, Kaliyaperumal R, Luo R, Lee TL, Lam TW, Edmunds SC, Sansone SA, Rocca-Serra P. From Peer-Reviewed to Peer-Reproduced in Scholarly Publishing: The Complementary Roles of Data Models and Workflows in Bioinformatics. PLoS One 2015; 10:e0127612. [PMID: 26154165 PMCID: PMC4495984 DOI: 10.1371/journal.pone.0127612] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2014] [Accepted: 04/16/2015] [Indexed: 12/20/2022] Open
Abstract
MOTIVATION Reproducing the results from a scientific paper can be challenging due to the absence of data and the computational tools required for their analysis. In addition, details relating to the procedures used to obtain the published results can be difficult to discern due to the use of natural language when reporting how experiments have been performed. The Investigation/Study/Assay (ISA), Nanopublications (NP), and Research Objects (RO) models are conceptual data modelling frameworks that can structure such information from scientific papers. Computational workflow platforms can also be used to reproduce analyses of data in a principled manner. We assessed the extent by which ISA, NP, and RO models, together with the Galaxy workflow system, can capture the experimental processes and reproduce the findings of a previously published paper reporting on the development of SOAPdenovo2, a de novo genome assembler. RESULTS Executable workflows were developed using Galaxy, which reproduced results that were consistent with the published findings. A structured representation of the information in the SOAPdenovo2 paper was produced by combining the use of ISA, NP, and RO models. By structuring the information in the published paper using these data and scientific workflow modelling frameworks, it was possible to explicitly declare elements of experimental design, variables, and findings. The models served as guides in the curation of scientific information and this led to the identification of inconsistencies in the original published paper, thereby allowing its authors to publish corrections in the form of an errata. AVAILABILITY SOAPdenovo2 scripts, data, and results are available through the GigaScience Database: http://dx.doi.org/10.5524/100044; the workflows are available from GigaGalaxy: http://galaxy.cbiit.cuhk.edu.hk; and the representations using the ISA, NP, and RO models are available through the SOAPdenovo2 case study website http://isa-tools.github.io/soapdenovo2/. CONTACT philippe.rocca-serra@oerc.ox.ac.uk and susanna-assunta.sansone@oerc.ox.ac.uk.
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Affiliation(s)
| | - Peter Li
- GigaScience, BGI HK Research Institute, 16 Dai Fu Street, Tai Po Industrial Estate, Hong Kong, People’s Republic of China
| | - Jun Zhao
- InfoLab21, Lancaster University, Bailrigg, Lancaster, LA1 4WA, United Kingdom
| | - Maria Susana Avila-Garcia
- Nuffield Department of Medicine, Experimental Medicine Division, John Radcliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU, United Kingdom
| | - Marco Roos
- Department of Human Genetics, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Mark Thompson
- Department of Human Genetics, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Eelke van der Horst
- Department of Human Genetics, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Rajaram Kaliyaperumal
- Department of Human Genetics, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Ruibang Luo
- HKU-BGI Bioinformatics Algorithms and Core Technology Research Laboratory & Department of Computer Science, University of Hong Kong, Pokfulam, Hong Kong, People’s Republic of China
| | - Tin-Lap Lee
- School of Biomedical Sciences and CUHK-BGI Innovation Institute of Trans-omics, The Chinese University of Hong Kong, Shatin, Hong Kong, People’s Republic of China
| | - Tak-wah Lam
- HKU-BGI Bioinformatics Algorithms and Core Technology Research Laboratory & Department of Computer Science, University of Hong Kong, Pokfulam, Hong Kong, People’s Republic of China
| | - Scott C. Edmunds
- GigaScience, BGI HK Research Institute, 16 Dai Fu Street, Tai Po Industrial Estate, Hong Kong, People’s Republic of China
| | | | - Philippe Rocca-Serra
- Oxford e-Research Centre, University of Oxford, 7 Keble Road, OX1 3QG, United Kingdom
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Marchese Robinson RL, Cronin MTD, Richarz AN, Rallo R. An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2015; 6:1978-99. [PMID: 26665069 PMCID: PMC4660926 DOI: 10.3762/bjnano.6.202] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 08/27/2015] [Indexed: 05/20/2023]
Abstract
Analysis of trends in nanotoxicology data and the development of data driven models for nanotoxicity is facilitated by the reporting of data using a standardised electronic format. ISA-TAB-Nano has been proposed as such a format. However, in order to build useful datasets according to this format, a variety of issues has to be addressed. These issues include questions regarding exactly which (meta)data to report and how to report them. The current article discusses some of the challenges associated with the use of ISA-TAB-Nano and presents a set of resources designed to facilitate the manual creation of ISA-TAB-Nano datasets from the nanotoxicology literature. These resources were developed within the context of the NanoPUZZLES EU project and include data collection templates, corresponding business rules that extend the generic ISA-TAB-Nano specification as well as Python code to facilitate parsing and integration of these datasets within other nanoinformatics resources. The use of these resources is illustrated by a "Toy Dataset" presented in the Supporting Information. The strengths and weaknesses of the resources are discussed along with possible future developments.
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Affiliation(s)
- Richard L Marchese Robinson
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool, L3 3AF, United Kingdom
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool, L3 3AF, United Kingdom
| | - Andrea-Nicole Richarz
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool, L3 3AF, United Kingdom
| | - Robert Rallo
- Departament d'Enginyeria Informatica i Matematiques, Universitat Rovira i Virgili, Av. Paisos Catalans 26, 43007 Tarragona, Catalunya, Spain
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Salek RM, Neumann S, Schober D, Hummel J, Billiau K, Kopka J, Correa E, Reijmers T, Rosato A, Tenori L, Turano P, Marin S, Deborde C, Jacob D, Rolin D, Dartigues B, Conesa P, Haug K, Rocca-Serra P, O’Hagan S, Hao J, van Vliet M, Sysi-Aho M, Ludwig C, Bouwman J, Cascante M, Ebbels T, Griffin JL, Moing A, Nikolski M, Oresic M, Sansone SA, Viant MR, Goodacre R, Günther UL, Hankemeier T, Luchinat C, Walther D, Steinbeck C. COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access. Metabolomics 2015; 11:1587-1597. [PMID: 26491418 PMCID: PMC4605977 DOI: 10.1007/s11306-015-0810-y] [Citation(s) in RCA: 97] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2014] [Accepted: 05/14/2015] [Indexed: 01/04/2023]
Abstract
Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative 'coordination of standards in metabolomics' (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities' participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.
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Affiliation(s)
- Reza M. Salek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD UK
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA UK
| | - Steffen Neumann
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany
| | - Daniel Schober
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany
| | - Jan Hummel
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany
| | - Kenny Billiau
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany
| | - Joachim Kopka
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany
| | - Elon Correa
- School of Chemistry & Manchester Institute of Biotechnology, University of Manchester, 131 Princess St., Manchester, M1 7DN UK
| | - Theo Reijmers
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands
| | - Antonio Rosato
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, FI Italy
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, FI Italy
- FiorGen Foundation, 50019 Sesto Fiorentino, FI Italy
| | - Paola Turano
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, FI Italy
| | - Silvia Marin
- Department of Biochemistry and Molecular Biology, Faculty of Biology, IBUB, Universitat de Barcelona, Diagonal 643, 08028 Barcelona, Spain
| | - Catherine Deborde
- INRA, Univ. Bordeaux, UMR1332 Fruit Biology and Pathology, Metabolome Facility of Bordeaux - MetaboHUB, Functional Genomics Center, IBVM, Centre INRA Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
| | - Daniel Jacob
- INRA, Univ. Bordeaux, UMR1332 Fruit Biology and Pathology, Metabolome Facility of Bordeaux - MetaboHUB, Functional Genomics Center, IBVM, Centre INRA Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
| | - Dominique Rolin
- INRA, Univ. Bordeaux, UMR1332 Fruit Biology and Pathology, Metabolome Facility of Bordeaux - MetaboHUB, Functional Genomics Center, IBVM, Centre INRA Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
| | - Benjamin Dartigues
- Centre of bioinformatics of Bordeaux (CBiB), University of Bordeaux, 33000 Bordeaux, France
| | - Pablo Conesa
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD UK
| | - Kenneth Haug
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD UK
| | | | - Steve O’Hagan
- School of Chemistry & Manchester Institute of Biotechnology, University of Manchester, 131 Princess St., Manchester, M1 7DN UK
| | - Jie Hao
- Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington, London, SW7 2AZ UK
| | - Michael van Vliet
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands
| | | | - Christian Ludwig
- School of Cancer Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK
| | | | - Marta Cascante
- Department of Biochemistry and Molecular Biology, Faculty of Biology, IBUB, Universitat de Barcelona, Diagonal 643, 08028 Barcelona, Spain
| | - Timothy Ebbels
- Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington, London, SW7 2AZ UK
| | - Julian L. Griffin
- Medical Research Council Human Nutrition Research, Fulbour Road, Cambridge, CB1 9NL UK
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA UK
| | - Annick Moing
- INRA, Univ. Bordeaux, UMR1332 Fruit Biology and Pathology, Metabolome Facility of Bordeaux - MetaboHUB, Functional Genomics Center, IBVM, Centre INRA Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
| | - Macha Nikolski
- University of Bordeaux, CBiB/LaBRI, 33000 Bordeaux, France
| | | | | | - Mark R. Viant
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK
| | - Royston Goodacre
- School of Chemistry & Manchester Institute of Biotechnology, University of Manchester, 131 Princess St., Manchester, M1 7DN UK
| | - Ulrich L. Günther
- School of Cancer Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK
| | - Thomas Hankemeier
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, FI Italy
| | - Dirk Walther
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany
| | - Christoph Steinbeck
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD UK
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Romano P, Cannata N. NETTAB 2013: Semantic, social, and mobile applications for bioinformatics and biomedical laboratories. BMC Bioinformatics 2014; 15 Suppl 14:S1. [PMID: 25471662 PMCID: PMC4255736 DOI: 10.1186/1471-2105-15-s14-s1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
The thirteenth NETTAB workshop, NETTAB 2013, was devoted to semantic, social, and mobile applications for bioinformatics and biomedical laboratories. Topics included issues, methods, algorithms, and technologies for the design and development of tools and platforms able to provide semantic, social, and mobile applications supporting bioinformatics and the activities carried out in a biomedical laboratory. About 30 scientific contributions were presentedat NETTAB 2013, including keynote and tutorial talks, oral communications, and posters. Best contributions presented at the workshop were later submitted to a special Call for this Supplement. Here, we provide an overview of the workshop and introduce manuscripts that have been accepted for publication in this Supplement.
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