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Bartnik A, Serra LM, Smith M, Duncan WD, Wishnie L, Ruttenberg A, Dwyer MG, Diehl AD. MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology. Neuroinformatics 2024; 22:269-283. [PMID: 38763990 DOI: 10.1007/s12021-024-09664-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/22/2024] [Indexed: 05/21/2024]
Abstract
Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principles and has contributed several classes to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. MRIO addresses the need for a "common language" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.
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Affiliation(s)
- Alexander Bartnik
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Lucas M Serra
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Mackenzie Smith
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - William D Duncan
- College of Dentistry, University of Florida, Gainesville, FL, USA
| | - Lauren Wishnie
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Alan Ruttenberg
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Alexander D Diehl
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
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Zheng J, Li X, Masci AM, Kahn H, Huffman A, Asfaw E, Pan Y, Guo J, He V, Song J, Seleznev AI, Lin AY, He Y. Empowering standardization of cancer vaccines through ontology: enhanced modeling and data analysis. J Biomed Semantics 2024; 15:12. [PMID: 38890666 PMCID: PMC11186274 DOI: 10.1186/s13326-024-00312-3] [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: 04/02/2024] [Accepted: 05/21/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND The exploration of cancer vaccines has yielded a multitude of studies, resulting in a diverse collection of information. The heterogeneity of cancer vaccine data significantly impedes effective integration and analysis. While CanVaxKB serves as a pioneering database for over 670 manually annotated cancer vaccines, it is important to distinguish that a database, on its own, does not offer the structured relationships and standardized definitions found in an ontology. Recognizing this, we expanded the Vaccine Ontology (VO) to include those cancer vaccines present in CanVaxKB that were not initially covered, enhancing VO's capacity to systematically define and interrelate cancer vaccines. RESULTS An ontology design pattern (ODP) was first developed and applied to semantically represent various cancer vaccines, capturing their associated entities and relations. By applying the ODP, we generated a cancer vaccine template in a tabular format and converted it into the RDF/OWL format for generation of cancer vaccine terms in the VO. '12MP vaccine' was used as an example of cancer vaccines to demonstrate the application of the ODP. VO also reuses reference ontology terms to represent entities such as cancer diseases and vaccine hosts. Description Logic (DL) and SPARQL query scripts were developed and used to query for cancer vaccines based on different vaccine's features and to demonstrate the versatility of the VO representation. Additionally, ontological modeling was applied to illustrate cancer vaccine related concepts and studies for in-depth cancer vaccine analysis. A cancer vaccine-specific VO view, referred to as "CVO," was generated, and it contains 928 classes including 704 cancer vaccines. The CVO OWL file is publicly available on: http://purl.obolibrary.org/obo/vo/cvo.owl , for sharing and applications. CONCLUSION To facilitate the standardization, integration, and analysis of cancer vaccine data, we expanded the Vaccine Ontology (VO) to systematically model and represent cancer vaccines. We also developed a pipeline to automate the inclusion of cancer vaccines and associated terms in the VO. This not only enriches the data's standardization and integration, but also leverages ontological modeling to deepen the analysis of cancer vaccine information, maximizing benefits for researchers and clinicians. AVAILABILITY The VO-cancer GitHub website is: https://github.com/vaccineontology/VO/tree/master/CVO .
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Affiliation(s)
- Jie Zheng
- Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Xingxian Li
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Anna Maria Masci
- Data Impact and Governance, Technology Data and Innovation, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Hayleigh Kahn
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Anthony Huffman
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Eliyas Asfaw
- University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Yuanyi Pan
- Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jinjing Guo
- Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Virginia He
- The College of Brown University, Brown University, Providence, RI, 02912, USA
| | - Justin Song
- College of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Andrey I Seleznev
- Dietrich School of Arts and Sciences, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Asiyah Yu Lin
- Axle Research and Technology, Rockville, MD, 20852, USA
| | - Yongqun He
- Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
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Mullen KR, Tammen I, Matentzoglu NA, Mather M, Mungall CJ, Haendel MA, Nicholas FW, Toro S. The Vertebrate Breed Ontology: Towards Effective Breed Data Standardization. ARXIV 2024:arXiv:2406.02623v1. [PMID: 38883236 PMCID: PMC11177956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Background – Limited universally adopted data standards in veterinary science hinders data interoperability and therefore integration and comparison; this ultimately impedes application of existing information-based tools to support advancement in veterinary diagnostics, treatments, and precision medicine. Hypothesis/Objectives – Creation of a Vertebrate Breed Ontology (VBO) as a single, coherent logic-based standard for documenting breed names in animal health, production and research-related records will improve data use capabilities in veterinary and comparative medicine. Animals – No live animals were used in this study. Methods – A list of breed names and related information was compiled from relevant sources, organizations, communities, and experts using manual and computational approaches to create VBO. Each breed is represented by a VBO term that includes all provenance and the breed's related information as metadata. VBO terms are classified using description logic to allow computational applications and Artificial Intelligence-readiness. Results – VBO is an open, community-driven ontology representing over 19,000 livestock and companion animal breeds covering 41 species. Breeds are classified based on community and expert conventions (e.g., horse breed, cattle breed). This classification is supported by relations to the breeds' genus and species indicated by NCBI Taxonomy terms. Relationships between VBO terms, e.g. relating breeds to their foundation stock, provide additional context to support advanced data analytics. VBO term metadata includes common names and synonyms, breed identifiers/codes, and attributed cross-references to other databases. Conclusion and clinical importance – Veterinary data interoperability and computability can be enhanced by the adoption of VBO as a source of standard breed names in databases and veterinary electronic health records.
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Schenk PM, Wright AJ, West R, Hastings J, Lorencatto F, Moore C, Hayes E, Schneider V, Howes E, Michie S. An ontology of mechanisms of action in behaviour change interventions. Wellcome Open Res 2024; 8:337. [PMID: 38481854 PMCID: PMC10933577 DOI: 10.12688/wellcomeopenres.19489.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/24/2024] [Indexed: 06/04/2024] Open
Abstract
Background Behaviour change interventions influence behaviour through causal processes called "mechanisms of action" (MoAs). Reports of such interventions and their evaluations often use inconsistent or ambiguous terminology, creating problems for searching, evidence synthesis and theory development. This inconsistency includes the reporting of MoAs. An ontology can help address these challenges by serving as a classification system that labels and defines MoAs and their relationships. The aim of this study was to develop an ontology of MoAs of behaviour change interventions. Methods To develop the MoA Ontology, we (1) defined the ontology's scope; (2) identified, labelled and defined the ontology's entities; (3) refined the ontology by annotating (i.e., coding) MoAs in intervention reports; (4) refined the ontology via stakeholder review of the ontology's comprehensiveness and clarity; (5) tested whether researchers could reliably apply the ontology to annotate MoAs in intervention evaluation reports; (6) refined the relationships between entities; (7) reviewed the alignment of the MoA Ontology with other relevant ontologies, (8) reviewed the ontology's alignment with the Theories and Techniques Tool; and (9) published a machine-readable version of the ontology. Results An MoA was defined as "a process that is causally active in the relationship between a behaviour change intervention scenario and its outcome behaviour". We created an initial MoA Ontology with 261 entities through Steps 2-5. Inter-rater reliability for annotating study reports using these entities was α=0.68 ("acceptable") for researchers familiar with the ontology and α=0.47 for researchers unfamiliar with it. As a result of additional revisions (Steps 6-8), 23 further entities were added to the ontology resulting in 284 entities organised in seven hierarchical levels. Conclusions The MoA Ontology extensively captures MoAs of behaviour change interventions. The ontology can serve as a controlled vocabulary for MoAs to consistently describe and synthesise evidence about MoAs across diverse sources.
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Affiliation(s)
- Paulina M Schenk
- Centre for Behaviour Change, University College London, London, England, UK
| | - Alison J Wright
- Centre for Behaviour Change, University College London, London, England, UK
- Institute of Pharmaceutical Science, King's College London, London, England, UK
| | - Robert West
- Department of Behavioural Science and Health, University College London, London, England, UK
| | - Janna Hastings
- Institute for Implementation Science in Health Care, Universitat Zurich, Zürich, Zurich, Switzerland
- School of Medicine, University of St Gallen, St. Gallen, St. Gallen, Switzerland
| | - Fabiana Lorencatto
- Centre for Behaviour Change, University College London, London, England, UK
| | - Candice Moore
- Centre for Behaviour Change, University College London, London, England, UK
| | - Emily Hayes
- Centre for Behaviour Change, University College London, London, England, UK
| | - Verena Schneider
- Research Department of Epidemiology and Public Health, University College London, London, England, UK
| | - Ella Howes
- Leeds Unit for Complex Intervention Development, University of Leeds, Leeds, England, UK
| | - Susan Michie
- Centre for Behaviour Change, University College London, London, England, UK
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5
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Hoyt CT, Gyori BM. The O3 guidelines: open data, open code, and open infrastructure for sustainable curated scientific resources. Sci Data 2024; 11:547. [PMID: 38811583 PMCID: PMC11136952 DOI: 10.1038/s41597-024-03406-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 05/22/2024] [Indexed: 05/31/2024] Open
Affiliation(s)
- Charles Tapley Hoyt
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Benjamin M Gyori
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.
- Department of Bioengineering, College of Engineering, Northeastern University, Boston, MA, USA.
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6
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Rutherford KM, Lera-Ramírez M, Wood V. PomBase: a Global Core Biodata Resource-growth, collaboration, and sustainability. Genetics 2024; 227:iyae007. [PMID: 38376816 PMCID: PMC11075564 DOI: 10.1093/genetics/iyae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/13/2024] [Indexed: 02/21/2024] Open
Abstract
PomBase (https://www.pombase.org), the model organism database (MOD) for fission yeast, was recently awarded Global Core Biodata Resource (GCBR) status by the Global Biodata Coalition (GBC; https://globalbiodata.org/) after a rigorous selection process. In this MOD review, we present PomBase's continuing growth and improvement over the last 2 years. We describe these improvements in the context of the qualitative GCBR indicators related to scientific quality, comprehensivity, accelerating science, user stories, and collaborations with other biodata resources. This review also showcases the depth of existing connections both within the biocuration ecosystem and between PomBase and its user community.
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Affiliation(s)
- Kim M Rutherford
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
| | - Manuel Lera-Ramírez
- Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK
| | - Valerie Wood
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
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Azzi R, Bordea G, Griffier R, Nikiema JN, Mougin F. Enriching the FIDEO ontology with food-drug interactions from online knowledge sources. J Biomed Semantics 2024; 15:1. [PMID: 38438913 PMCID: PMC10913206 DOI: 10.1186/s13326-024-00302-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 02/05/2024] [Indexed: 03/06/2024] Open
Abstract
The increasing number of articles on adverse interactions that may occur when specific foods are consumed with certain drugs makes it difficult to keep up with the latest findings. Conflicting information is available in the scientific literature and specialized knowledge bases because interactions are described in an unstructured or semi-structured format. The FIDEO ontology aims to integrate and represent information about food-drug interactions in a structured way. This article reports on the new version of this ontology in which more than 1700 interactions are integrated from two online resources: DrugBank and Hedrine. These food-drug interactions have been represented in FIDEO in the form of precompiled concepts, each of which specifies both the food and the drug involved. Additionally, competency questions that can be answered are reviewed, and avenues for further enrichment are discussed.
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Affiliation(s)
- Rabia Azzi
- Univ. Bordeaux, Inserm, BPH, U1219, F-33000, Bordeaux, France
- CHU de Bordeaux, Service d'information médicale, F-33000, Bordeaux, France
| | - Georgeta Bordea
- Univ. Bordeaux, Inserm, BPH, U1219, F-33000, Bordeaux, France
- Univ. La Rochelle, L3i, F-17000, La Rochelle, France
| | - Romain Griffier
- Univ. Bordeaux, Inserm, BPH, U1219, F-33000, Bordeaux, France
- CHU de Bordeaux, Service d'information médicale, F-33000, Bordeaux, France
| | - Jean Noël Nikiema
- Department of Management, Evaluation and Health Policy, School of Public Health, Université de Montréal, Québec, Canada
| | - Fleur Mougin
- Univ. Bordeaux, Inserm, BPH, U1219, F-33000, Bordeaux, France.
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8
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Carmody LC, Gargano MA, Toro S, Vasilevsky NA, Adam MP, Blau H, Chan LE, Gomez-Andres D, Horvath R, Kraus ML, Ladewig MS, Lewis-Smith D, Lochmüller H, Matentzoglu NA, Munoz-Torres MC, Schuetz C, Seitz B, Similuk MN, Sparks TN, Strauss T, Swietlik EM, Thompson R, Zhang XA, Mungall CJ, Haendel MA, Robinson PN. The Medical Action Ontology: A tool for annotating and analyzing treatments and clinical management of human disease. MED 2023; 4:913-927.e3. [PMID: 37963467 PMCID: PMC10842845 DOI: 10.1016/j.medj.2023.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/31/2023] [Accepted: 10/14/2023] [Indexed: 11/16/2023]
Abstract
BACKGROUND Navigating the clinical literature to determine the optimal clinical management for rare diseases presents significant challenges. We introduce the Medical Action Ontology (MAxO), an ontology specifically designed to organize medical procedures, therapies, and interventions. METHODS MAxO incorporates logical structures that link MAxO terms to numerous other ontologies within the OBO Foundry. Term development involves a blend of manual and semi-automated processes. Additionally, we have generated annotations detailing diagnostic modalities for specific phenotypic abnormalities defined by the Human Phenotype Ontology (HPO). We introduce a web application, POET, that facilitates MAxO annotations for specific medical actions for diseases using the Mondo Disease Ontology. FINDINGS MAxO encompasses 1,757 terms spanning a wide range of biomedical domains, from human anatomy and investigations to the chemical and protein entities involved in biological processes. These terms annotate phenotypic features associated with specific disease (using HPO and Mondo). Presently, there are over 16,000 MAxO diagnostic annotations that target HPO terms. Through POET, we have created 413 MAxO annotations specifying treatments for 189 rare diseases. CONCLUSIONS MAxO offers a computational representation of treatments and other actions taken for the clinical management of patients. Its development is closely coupled to Mondo and HPO, broadening the scope of our computational modeling of diseases and phenotypic features. We invite the community to contribute disease annotations using POET (https://poet.jax.org/). MAxO is available under the open-source CC-BY 4.0 license (https://github.com/monarch-initiative/MAxO). FUNDING NHGRI 1U24HG011449-01A1 and NHGRI 5RM1HG010860-04.
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Affiliation(s)
- Leigh C Carmody
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | | | - Sabrina Toro
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Margaret P Adam
- University of Washington School of Medicine, Seattle, WA, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | | | - David Gomez-Andres
- Pediatric Neurology, Vall d'Hebron Institut de Recerca (VHIR), Hospital Universitari Vall d'Hebron, Vall d'Hebron Barcelona Hospital Campus, Passeig Vall d'Hebron 119-129, 08035 Barcelona, Spain
| | - Rita Horvath
- Department of Clinical Neurosciences, University of Cambridge, Robinson Way, Cambridge CB2 0PY, UK
| | - Megan L Kraus
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Markus S Ladewig
- Department of Ophthalmology, Klinikum Saarbrücken, Saarbrücken, Germany
| | - David Lewis-Smith
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Hanns Lochmüller
- Children's Hospital of Eastern Ontario Research Institute, Ottowa, Canada; Division of Neurology, Department of Medicine, The Ottawa Hospital, Ottawa, Canada; Brain and Mind Research Institute, University of Ottawa, Ottawa, Canada; Department of Neuropediatrics and Muscle Disorders, Medical Center - University of Freiburg, Faculty of Medicine, Freiburg, Germany; Centro Nacional de Análisis Genómico, Barcelona, Spain
| | | | | | - Catharina Schuetz
- Department of Pediatrics, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
| | - Berthold Seitz
- Department of Ophthalmology, Saarland University Medical Center UKS, Homburg, Saar, Germany
| | - Morgan N Similuk
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Teresa N Sparks
- Department of Obstetrics, Gynecology, & Reproductive Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Timmy Strauss
- Department of Pediatrics, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
| | - Emilia M Swietlik
- Department of Medicine, University of Cambridge, Heart and Lung Research Institute, Cambridge CB2 0BB, UK
| | - Rachel Thompson
- Children's Hospital of Eastern Ontario Research Institute, Ottowa, Canada
| | | | | | | | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
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9
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Girón JC, Tarasov S, González Montaña LA, Matentzoglu N, Smith AD, Koch M, Boudinot BE, Bouchard P, Burks R, Vogt L, Yoder M, Osumi-Sutherland D, Friedrich F, Beutel RG, Mikó I. Formalizing Invertebrate Morphological Data: A Descriptive Model for Cuticle-Based Skeleto-Muscular Systems, an Ontology for Insect Anatomy, and their Potential Applications in Biodiversity Research and Informatics. Syst Biol 2023; 72:1084-1100. [PMID: 37094905 DOI: 10.1093/sysbio/syad025] [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: 02/16/2022] [Revised: 04/17/2023] [Accepted: 04/21/2023] [Indexed: 04/26/2023] Open
Abstract
The spectacular radiation of insects has produced a stunning diversity of phenotypes. During the past 250 years, research on insect systematics has generated hundreds of terms for naming and comparing them. In its current form, this terminological diversity is presented in natural language and lacks formalization, which prohibits computer-assisted comparison using semantic web technologies. Here we propose a Model for Describing Cuticular Anatomical Structures (MoDCAS) which incorporates structural properties and positional relationships for standardized, consistent, and reproducible descriptions of arthropod phenotypes. We applied the MoDCAS framework in creating the ontology for the Anatomy of the Insect Skeleto-Muscular system (AISM). The AISM is the first general insect ontology that aims to cover all taxa by providing generalized, fully logical, and queryable, definitions for each term. It was built using the Ontology Development Kit (ODK), which maximizes interoperability with Uberon (Uberon multispecies anatomy ontology) and other basic ontologies, enhancing the integration of insect anatomy into the broader biological sciences. A template system for adding new terms, extending, and linking the AISM to additional anatomical, phenotypic, genetic, and chemical ontologies is also introduced. The AISM is proposed as the backbone for taxon-specific insect ontologies and has potential applications spanning systematic biology and biodiversity informatics, allowing users to: 1) use controlled vocabularies and create semiautomated computer-parsable insect morphological descriptions; 2) integrate insect morphology into broader fields of research, including ontology-informed phylogenetic methods, logical homology hypothesis testing, evo-devo studies, and genotype to phenotype mapping; and 3) automate the extraction of morphological data from the literature, enabling the generation of large-scale phenomic data, by facilitating the production and testing of informatic tools able to extract, link, annotate, and process morphological data. This descriptive model and its ontological applications will allow for clear and semantically interoperable integration of arthropod phenotypes in biodiversity studies.
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Affiliation(s)
- Jennifer C Girón
- Department of Entomology, Purdue University, West Lafayette, IN, USA
- Natural Science Research Laboratory, Museum of Texas Tech University, Lubbock, TX, USA
| | - Sergei Tarasov
- Finnish Museum of Natural History, University of Helsinki, Pohjoinen Rautatiekatu 13, FI-00014 Helsinki, Finland
| | | | | | - Aaron D Smith
- Department of Entomology, Purdue University, West Lafayette, IN, USA
| | - Markus Koch
- Institute of Evolutionary Biology and Ecology, University of Bonn, An der Immenburg 1, 53121 Bonn, Germany
| | - Brendon E Boudinot
- Department of Entomology & Nematology, University of California, Davis, One Shields Ave, CA, USA
- Institut für Zoologie und Evolutionsforschung, Friedrich-Schiller-Universität Jena, Erbertstraße 1, 07743 Jena, Germany
- Department of Entomology, National Museum of Natural History, Smithsonian Institution, Washington DC, USA
| | - Patrice Bouchard
- Biodiversity and Bioresources, Canadian National Collection of Insects, Arachnids and Nematodes, Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, Ontario, K1A 0C6, Canada
| | - Roger Burks
- Entomology Department, University of California, Riverside, 900 University Ave. Riverside, CA, USA
| | - Lars Vogt
- TIB Leibniz Information Centre for Science and Technology, Welfengarten 1B, 30167 Hannover, Germany
| | - Matthew Yoder
- Illinois Natural History Survey, University of Illinois, Champaign, IL, USA
| | | | - Frank Friedrich
- Institut für Zell- und Systembiologie der Tiere, Universität Hamburg, Martin-Luther-King-Platz 3, 20146, Hamburg, Germany
| | - Rolf G Beutel
- Institut für Zoologie und Evolutionsforschung, Friedrich-Schiller-Universität Jena, Erbertstraße 1, 07743 Jena, Germany
| | - István Mikó
- Department of Biological Sciences, University of New Hampshire, Durham, NH, USA
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10
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Stefancsik R, Balhoff JP, Balk MA, Ball RL, Bello SM, Caron AR, Chesler EJ, de Souza V, Gehrke S, Haendel M, Harris LW, Harris NL, Ibrahim A, Koehler S, Matentzoglu N, McMurry JA, Mungall CJ, Munoz-Torres MC, Putman T, Robinson P, Smedley D, Sollis E, Thessen AE, Vasilevsky N, Walton DO, Osumi-Sutherland D. The Ontology of Biological Attributes (OBA)-computational traits for the life sciences. Mamm Genome 2023; 34:364-378. [PMID: 37076585 PMCID: PMC10382347 DOI: 10.1007/s00335-023-09992-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/06/2023] [Indexed: 04/21/2023]
Abstract
Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.
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Affiliation(s)
- Ray Stefancsik
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK.
| | - James P Balhoff
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC, 27517, USA
| | - Meghan A Balk
- Natural History Museum, University of Oslo, Oslo, Norway
| | - Robyn L Ball
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA
| | | | - Anita R Caron
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | - Vinicius de Souza
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Sarah Gehrke
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Melissa Haendel
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Laura W Harris
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Nomi L Harris
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Arwa Ibrahim
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | | | - Julie A McMurry
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Tim Putman
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | | | - Damian Smedley
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Elliot Sollis
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Anne E Thessen
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Nicole Vasilevsky
- Data Collaboration Center, Critical Path Institute, Tucson, AZ, 85718, USA
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11
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Bartnik A, Serra LM, Smith M, Duncan WD, Wishnie L, Ruttenberg A, Dwyer MG, Diehl AD. MRIO: The Magnetic Resonance Imaging Acquisition and Analysis Ontology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.04.552020. [PMID: 37609265 PMCID: PMC10441376 DOI: 10.1101/2023.08.04.552020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Objective Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. Materials and Methods To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. Results MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. Discussion MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principals and has contributed several terms to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. Conclusion MRIO addresses the need for a "common language" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.
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Affiliation(s)
- Alexander Bartnik
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Lucas M. Serra
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Mackenzie Smith
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | | | - Lauren Wishnie
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Alan Ruttenberg
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Michael G. Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Alexander D. Diehl
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
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12
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Charlet J, Cui L. Knowledge Representation and Management 2022: Findings in Ontology Development and Applications. Yearb Med Inform 2023; 32:225-229. [PMID: 38147864 PMCID: PMC10751114 DOI: 10.1055/s-0043-1768747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023] Open
Abstract
OBJECTIVES To select, present, and summarize the best papers in 2022 for the Knowledge Representation and Management (KRM) section of the International Medical Informatics Association (IMIA) Yearbook. METHODS We conducted PubMed queries and followed the IMIA Yearbook guidelines for performing biomedical informatics literature review to select the best papers in KRM published in 2022. RESULTS We retrieved 1,847 publications from PubMed. We nominated 15 candidate best papers, and two of them were finally selected as the best papers in the KRM section. The topics covered by the candidate papers include ontology and knowledge graph creation, ontology applications, ontology quality assurance, ontology mapping standard, and conceptual model. CONCLUSIONS In the KRM best paper selection for 2022, the candidate best papers encompassed a broad range of topics, with ontology and knowledge graph creation remaining a considerable research focus.
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Affiliation(s)
- Jean Charlet
- Sorbonne Université, INSERM, Univ Sorbonne Paris Nord, LIMICS, Paris, France
- AP-HP, DRCI, Paris, France
| | - Licong Cui
- McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
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13
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Carmody LC, Gargano MA, Toro S, Vasilevsky NA, Adam MP, Blau H, Chan LE, Gomez-Andres D, Horvath R, Kraus ML, Ladewig MS, Lewis-Smith D, Lochmüller H, Matentzoglu NA, Munoz-Torres MC, Schuetz C, Seitz B, Similuk MN, Sparks TN, Strauss T, Swietlik EM, Thompson R, Zhang XA, Mungall CJ, Haendel MA, Robinson PN. The Medical Action Ontology: A Tool for Annotating and Analyzing Treatments and Clinical Management of Human Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.13.23292612. [PMID: 37503136 PMCID: PMC10370244 DOI: 10.1101/2023.07.13.23292612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Navigating the vast landscape of clinical literature to find optimal treatments and management strategies can be a challenging task, especially for rare diseases. To address this task, we introduce the Medical Action Ontology (MAxO), the first ontology specifically designed to organize medical procedures, therapies, and interventions in a structured way. Currently, MAxO contains 1757 medical action terms added through a combination of manual and semi-automated processes. MAxO was developed with logical structures that make it compatible with several other ontologies within the Open Biological and Biomedical Ontologies (OBO) Foundry. These cover a wide range of biomedical domains, from human anatomy and investigations to the chemical and protein entities involved in biological processes. We have created a database of over 16000 annotations that describe diagnostic modalities for specific phenotypic abnormalities as defined by the Human Phenotype Ontology (HPO). Additionally, 413 annotations are provided for medical actions for 189 rare diseases. We have developed a web application called POET (https://poet.jax.org/) for the community to use to contribute MAxO annotations. MAxO provides a computational representation of treatments and other actions taken for the clinical management of patients. The development of MAxO is closely coupled to the Mondo Disease Ontology (Mondo) and the Human Phenotype Ontology (HPO) and expands the scope of our computational modeling of diseases and phenotypic features to include diagnostics and therapeutic actions. MAxO is available under the open-source CC-BY 4.0 license (https://github.com/monarch-initiative/MAxO).
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Affiliation(s)
- Leigh C Carmody
- The Jackson Laboratory for Genomic Medicine,Farmington,CT,United States
| | - Michael A Gargano
- The Jackson Laboratory for Genomic Medicine,Farmington,CT,United States
| | - Sabrina Toro
- University of Colorado Anschutz Medical Campus,Aurora,CO,United States
| | | | - Margaret P Adam
- University of Washington School of Medicine, Seattle, WA, United States
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine,Farmington,CT,United States
| | | | - David Gomez-Andres
- Pediatric Neurology, Vall d'Hebron Institut de Recerca (VHIR), Hospital Universitari Vall d'Hebron, Vall d'Hebron Barcelona Hospital Campus., Passeig Vall d'Hebron 119-129, 08035 Barcelona, Spain
| | - Rita Horvath
- Department of Clinical Neurosciences, University of Cambridge, Robinson Way CB2 0PY, Cambridge UK
| | - Megan L Kraus
- University of Colorado Anschutz Medical Campus,Aurora,CO,United States
| | - Markus S Ladewig
- Department of Ophthalmology,Klinikum Saarbrücken,Saarbrücken,Germany
| | - David Lewis-Smith
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | | | | | | | - Catharina Schuetz
- Department of Pediatrics, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
| | - Berthold Seitz
- Department of Ophthalmology,Saarland University Hospital UKS,Homburg/Saar Germany
| | - Morgan N Similuk
- National Institute of Allergy and Infectious Diseases,National Institutes of Health,Bethesda,MD,United States
| | - Teresa N Sparks
- Department of Obstetrics, Gynecology, & Reproductive Sciences, University of California, San Francisco, San Francisco, CA 94143
| | - Timmy Strauss
- Department of Pediatrics, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
| | - Emilia M Swietlik
- Department of Medicine, University of Cambridge, Heart and Lung Research Institute, CB2 0BB, Cambridge, UK
| | | | | | | | - Melissa A Haendel
- University of Colorado Anschutz Medical Campus,Aurora,CO,United States
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine,Farmington,CT,United States
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14
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Callahan TJ, Stefanski AL, Wyrwa JM, Zeng C, Ostropolets A, Banda JM, Baumgartner WA, Boyce RD, Casiraghi E, Coleman BD, Collins JH, Deakyne Davies SJ, Feinstein JA, Lin AY, Martin B, Matentzoglu NA, Meeker D, Reese J, Sinclair J, Taneja SB, Trinkley KE, Vasilevsky NA, Williams AE, Zhang XA, Denny JC, Ryan PB, Hripcsak G, Bennett TD, Haendel MA, Robinson PN, Hunter LE, Kahn MG. Ontologizing health systems data at scale: making translational discovery a reality. NPJ Digit Med 2023; 6:89. [PMID: 37208468 PMCID: PMC10196319 DOI: 10.1038/s41746-023-00830-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 04/28/2023] [Indexed: 05/21/2023] Open
Abstract
Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.
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Affiliation(s)
- Tiffany J Callahan
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA.
| | - Adrianne L Stefanski
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Jordan M Wyrwa
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Chenjie Zeng
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Juan M Banda
- Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA
| | - William A Baumgartner
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Richard D Boyce
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15260, USA
| | - Elena Casiraghi
- Computer Science, Università degli Studi di Milano, Milan, Italy
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Ben D Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Janine H Collins
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Sara J Deakyne Davies
- Department of Research Informatics & Data Science, Analytics Resource Center, Children's Hospital Colorado, Aurora, CO, 80045, USA
| | - James A Feinstein
- Adult and Child Center for Health Outcomes Research and Delivery Science (ACCORDS), University of Colorado Anschutz School of Medicine, Aurora, CO, 80045, USA
| | - Asiyah Y Lin
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Blake Martin
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | | | | | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Sanya B Taneja
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Katy E Trinkley
- Department of Family Medicine, University of Colorado Anschutz School of Medicine, Aurora, CO, 80045, USA
| | - Nicole A Vasilevsky
- Translational and Integrative Sciences Lab, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Andrew E Williams
- Tufts Institute for Clinical Research and Health Policy Studies, Tufts University, Boston, MA, 02155, USA
| | - Xingmin A Zhang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Joshua C Denny
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Patrick B Ryan
- Janssen Research and Development, Raritan, NJ, 08869, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Tellen D Bennett
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Melissa A Haendel
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Lawrence E Hunter
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Michael G Kahn
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
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15
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Stefancsik R, Balhoff JP, Balk MA, Ball R, Bello SM, Caron AR, Chessler E, de Souza V, Gehrke S, Haendel M, Harris LW, Harris NL, Ibrahim A, Koehler S, Matentzoglu N, McMurry JA, Mungall CJ, Munoz-Torres MC, Putman T, Robinson P, Smedley D, Sollis E, Thessen AE, Vasilevsky N, Walton DO, Osumi-Sutherland D. The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.26.525742. [PMID: 36747660 PMCID: PMC9900877 DOI: 10.1101/2023.01.26.525742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.
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Affiliation(s)
- Ray Stefancsik
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - James P. Balhoff
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27517, USA
| | - Meghan A. Balk
- National Ecological Observatory Network, Battelle, Boulder, CO 80301, USA
| | - Robyn Ball
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | | | - Anita R. Caron
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | - Vinicius de Souza
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Sarah Gehrke
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Melissa Haendel
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Laura W. Harris
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Nomi L. Harris
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Arwa Ibrahim
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | | | - Julie A. McMurry
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Christopher J. Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Tim Putman
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | | | - Damian Smedley
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Elliot Sollis
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Anne E Thessen
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Nicole Vasilevsky
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
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16
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Brain Data Standards - A method for building data-driven cell-type ontologies. Sci Data 2023; 10:50. [PMID: 36693887 PMCID: PMC9873614 DOI: 10.1038/s41597-022-01886-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 12/06/2022] [Indexed: 01/25/2023] Open
Abstract
Large-scale single-cell 'omics profiling is being used to define a complete catalogue of brain cell types, something that traditional methods struggle with due to the diversity and complexity of the brain. But this poses a problem: How do we organise such a catalogue - providing a standard way to refer to the cell types discovered, linking their classification and properties to supporting data? Cell ontologies provide a partial solution to these problems, but no existing ontology schemas support the definition of cell types by direct reference to supporting data, classification of cell types using classifications derived directly from data, or links from cell types to marker sets along with confidence scores. Here we describe a generally applicable schema that solves these problems and its application in a semi-automated pipeline to build a data-linked extension to the Cell Ontology representing cell types in the Primary Motor Cortex of humans, mice and marmosets. The methods and resulting ontology are designed to be scalable and applicable to similar whole-brain atlases currently in preparation.
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