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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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Bonnardel F, Mariethoz J, Pérez S, Imberty A, Lisacek F. LectomeXplore, an update of UniLectin for the discovery of carbohydrate-binding proteins based on a new lectin classification. Nucleic Acids Res 2021; 49:D1548-D1554. [PMID: 33174598 PMCID: PMC7778903 DOI: 10.1093/nar/gkaa1019] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/13/2020] [Accepted: 10/16/2020] [Indexed: 12/22/2022] Open
Abstract
Lectins are non-covalent glycan-binding proteins mediating cellular interactions but their annotation in newly sequenced organisms is lacking. The limited size of functional domains and the low level of sequence similarity challenge usual bioinformatics tools. The identification of lectin domains in proteomes requires the manual curation of sequence alignments based on structural folds. A new lectin classification is proposed. It is built on three levels: (i) 35 lectin domain folds, (ii) 109 classes of lectins sharing at least 20% sequence similarity and (iii) 350 families of lectins sharing at least 70% sequence similarity. This information is compiled in the UniLectin platform that includes the previously described UniLectin3D database of curated lectin 3D structures. Since its first release, UniLectin3D has been updated with 485 additional 3D structures. The database is now complemented by two additional modules: PropLec containing predicted β-propeller lectins and LectomeXplore including predicted lectins from sequences of the NBCI-nr and UniProt for every curated lectin class. UniLectin is accessible at https://www.unilectin.eu/.
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Affiliation(s)
- François Bonnardel
- Univ. Grenoble Alpes, CNRS, CERMAV, 38000 Grenoble, France
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, CH-1227 Geneva, Switzerland
- Computer Science Department, University of Geneva, CH-1227 Geneva, Switzerland
| | - Julien Mariethoz
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, CH-1227 Geneva, Switzerland
- Computer Science Department, University of Geneva, CH-1227 Geneva, Switzerland
- Section of Biology, University of Geneva, CH-1205 Geneva, Switzerland
| | - Serge Pérez
- Univ. Grenoble Alpes, CNRS, CERMAV, 38000 Grenoble, France
| | - Anne Imberty
- Univ. Grenoble Alpes, CNRS, CERMAV, 38000 Grenoble, France
| | - Frédérique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, CH-1227 Geneva, Switzerland
- Computer Science Department, University of Geneva, CH-1227 Geneva, Switzerland
- Section of Biology, University of Geneva, CH-1205 Geneva, Switzerland
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Köhler S, Gargano M, Matentzoglu N, Carmody LC, Lewis-Smith D, Vasilevsky NA, Danis D, Balagura G, Baynam G, Brower AM, Callahan TJ, Chute CG, Est JL, Galer PD, Ganesan S, Griese M, Haimel M, Pazmandi J, Hanauer M, Harris NL, Hartnett M, Hastreiter M, Hauck F, He Y, Jeske T, Kearney H, Kindle G, Klein C, Knoflach K, Krause R, Lagorce D, McMurry JA, Miller JA, Munoz-Torres M, Peters RL, Rapp CK, Rath AM, Rind SA, Rosenberg A, Segal MM, Seidel MG, Smedley D, Talmy T, Thomas Y, Wiafe SA, Xian J, Yüksel Z, Helbig I, Mungall CJ, Haendel MA, Robinson PN. The Human Phenotype Ontology in 2021. Nucleic Acids Res 2021; 49:D1207-D1217. [PMID: 33264411 PMCID: PMC7778952 DOI: 10.1093/nar/gkaa1043] [Citation(s) in RCA: 547] [Impact Index Per Article: 182.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 10/11/2020] [Accepted: 11/16/2020] [Indexed: 12/21/2022] Open
Abstract
The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail.We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.
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Affiliation(s)
| | - Michael Gargano
- Monarch Initiative
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Nicolas Matentzoglu
- Monarch Initiative
- Semanticly Ltd, London, UK
- European Bioinformatics Institute (EMBL-EBI)
| | - Leigh C Carmody
- Monarch Initiative
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - David Lewis-Smith
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Clinical Neurosciences, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Nicole A Vasilevsky
- Monarch Initiative
- Oregon Clinical & Translational Research Institute, Oregon Health & Science University
| | | | - Ganna Balagura
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, and Maternal and Child Health, University of Genoa, Genoa, Italy
- Pediatric Neurology and Muscular Diseases Unit, IRCCS ‘G. Gaslini’ Institute, Genoa, Italy
| | - Gareth Baynam
- Western Australian Register of Developmental Anomalies, King Edward memorial Hospital, Perth, Australia
- Telethon Kids Institute and the Division of Paediatrics, Faculty of Helath and Medical Sciences, University of Western Australia, Perth, Australia
| | - Amy M Brower
- American College of Medical Genetics and Genomics (ACMG), Bethesda, MD, USA
| | - Tiffany J Callahan
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Colorado, USA
| | | | - Johanna L Est
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Peter D Galer
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Shiva Ganesan
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Matthias Griese
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- Ludwig-Maximilians University, German Center for Lung Research (DZL), Munich, Germany
| | - Matthias Haimel
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Julia Pazmandi
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA
| | - Marc Hanauer
- INSERM, US14––Orphanet, Plateforme Maladies Rares, Paris, France
| | - Nomi L Harris
- Monarch Initiative
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley CA, USA
| | - Michael J Hartnett
- American College of Medical Genetics and Genomics (ACMG), Bethesda, MD, USA
| | - Maximilian Hastreiter
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Fabian Hauck
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- German Centre for Infection Research (DZIF), Munich, Germany
| | - 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
| | - Tim Jeske
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Hugh Kearney
- FutureNeuro, SFI Research Centre for Chronic and Rare Neurological Diseases, Ireland
| | - Gerhard Kindle
- Institute for Immunodeficiency, Center for Chronic Immunodeficiency (CCI). Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany
- Centre for Biobanking FREEZE, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany
| | - Christoph Klein
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Katrin Knoflach
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- Ludwig-Maximilians University, German Center for Lung Research (DZL), Munich, Germany
| | - Roland Krause
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - David Lagorce
- INSERM, US14––Orphanet, Plateforme Maladies Rares, Paris, France
| | - Julie A McMurry
- Monarch Initiative
- Translational and Integrative Sciences Center, Department of Environmental and Molecular Toxicology, Oregon State University, OR, USA
| | - Jillian A Miller
- American College of Medical Genetics and Genomics (ACMG), Bethesda, MD, USA
| | - Monica C Munoz-Torres
- Monarch Initiative
- Translational and Integrative Sciences Center, Department of Environmental and Molecular Toxicology, Oregon State University, OR, USA
| | - Rebecca L Peters
- American College of Medical Genetics and Genomics (ACMG), Bethesda, MD, USA
| | - Christina K Rapp
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- Ludwig-Maximilians University, German Center for Lung Research (DZL), Munich, Germany
| | - Ana M Rath
- INSERM, US14––Orphanet, Plateforme Maladies Rares, Paris, France
| | - Shahmir A Rind
- WA Register of Developmental Anomalies
- Curtin University, Western Australia, Australia
| | - Avi Z Rosenberg
- Division of Kidney-Urologic Pathology, Johns Hopkins University, Baltimore, MD 21205, USA
| | | | - Markus G Seidel
- Research Unit for Pediatric Hematology and Immunology, Division of Pediatric Hemato-Oncology, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria
| | - Damian Smedley
- The William Harvey Research Institute, Charterhouse Square Barts and the London School of Medicine and Dentistry Queen Mary University of London, London EC1M 6BQ, UK
| | - Tomer Talmy
- Genomic Research Department, Emedgene Technologies, Tel Aviv, Israel
- Faculty of Medicine, Hebrew University Hadassah Medical School, Jerusalem, Israel
| | - Yarlalu Thomas
- West Australian Register of Developmental Anomalies, East Perth, WA, Australia
| | | | - Julie Xian
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, PA, USA
| | - Zafer Yüksel
- Human Genetics, Bioscientia GmbH, Ingelheim, Germany
| | - Ingo Helbig
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Christopher J Mungall
- Monarch Initiative
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley CA, USA
| | - Melissa A Haendel
- Monarch Initiative
- Oregon Clinical & Translational Research Institute, Oregon Health & Science University
- Translational and Integrative Sciences Center, Department of Environmental and Molecular Toxicology, Oregon State University, OR, USA
| | - Peter N Robinson
- Monarch Initiative
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA
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Zheng L, Min H, Chen Y, Keloth V, Geller J, Perl Y, Hripcsak G. Outlier concepts auditing methodology for a large family of biomedical ontologies. BMC Med Inform Decis Mak 2020; 20:296. [PMID: 33319713 PMCID: PMC7737254 DOI: 10.1186/s12911-020-01311-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 10/28/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Summarization networks are compact summaries of ontologies. The "Big Picture" view offered by summarization networks enables to identify sets of concepts that are more likely to have errors than control concepts. For ontologies that have outgoing lateral relationships, we have developed the "partial-area taxonomy" summarization network. Prior research has identified one kind of outlier concepts, concepts of small partials-areas within partial-area taxonomies. Previously we have shown that the small partial-area technique works successfully for four ontologies (or their hierarchies). METHODS To improve the Quality Assurance (QA) scalability, a family-based QA framework, where one QA technique is potentially applicable to a whole family of ontologies with similar structural features, was developed. The 373 ontologies hosted at the NCBO BioPortal in 2015 were classified into a collection of families based on structural features. A meta-ontology represents this family collection, including one family of ontologies having outgoing lateral relationships. The process of updating the current meta-ontology is described. To conclude that one QA technique is applicable for at least half of the members for a family F, this technique should be demonstrated as successful for six out of six ontologies in F. We describe a hypothesis setting the condition required for a technique to be successful for a given ontology. The process of a study to demonstrate such success is described. This paper intends to prove the scalability of the small partial-area technique. RESULTS We first updated the meta-ontology classifying 566 BioPortal ontologies. There were 371 ontologies in the family with outgoing lateral relationships. We demonstrated the success of the small partial-area technique for two ontology hierarchies which belong to this family, SNOMED CT's Specimen hierarchy and NCIt's Gene hierarchy. Together with the four previous ontologies from the same family, we fulfilled the "six out of six" condition required to show the scalability for the whole family. CONCLUSIONS We have shown that the small partial-area technique can be potentially successful for the family of ontologies with outgoing lateral relationships in BioPortal, thus improve the scalability of this QA technique.
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Affiliation(s)
- Ling Zheng
- Computer Science and Software Engineering Department, Monmouth University, West Long Branch, NJ, 07764, USA.
| | - Hua Min
- Department of Health Administration and Policy, George Mason University, Fairfax, VA, 22030, USA
| | - Yan Chen
- CIS Department, Borough of Manhattan Community College, CUNY, New York, NY, 10007, USA
| | - Vipina Keloth
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, 07102, USA
| | - James Geller
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, 07102, USA
| | - Yehoshua Perl
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, 07102, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, 10032, USA
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