Open Biomedical Ontologies Foundry Research Articles

MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology.

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.

The Infectious Disease Ontology in the age of COVID-19

BackgroundEffective response to public health emergencies, such as we are now experiencing with COVID-19, requires data sharing across multiple disciplines and data systems. Ontologies offer a powerful data sharing tool, and this holds especially for those ontologies built on the design principles of the Open Biomedical Ontologies Foundry. These principles are exemplified by the Infectious Disease Ontology (IDO), a suite of interoperable ontology modules aiming to provide coverage of all aspects of the infectious disease domain. At its center is IDO Core, a disease- and pathogen-neutral ontology covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is extended by disease and pathogen-specific ontology modules.ResultsTo assist the integration and analysis of COVID-19 data, and viral infectious disease data more generally, we have recently developed three new IDO extensions: IDO Virus (VIDO); the Coronavirus Infectious Disease Ontology (CIDO); and an extension of CIDO focusing on COVID-19 (IDO-COVID-19). Reflecting the fact that viruses lack cellular parts, we have introduced into IDO Core the term acellular structure to cover viruses and other acellular entities studied by virologists. We now distinguish between infectious agents – organisms with an infectious disposition – and infectious structures – acellular structures with an infectious disposition. This in turn has led to various updates and refinements of IDO Core’s content. We believe that our work on VIDO, CIDO, and IDO-COVID-19 can serve as a model for yielding greater conformance with ontology building best practices.ConclusionsIDO provides a simple recipe for building new pathogen-specific ontologies in a way that allows data about novel diseases to be easily compared, along multiple dimensions, with data represented by existing disease ontologies. The IDO strategy, moreover, supports ontology coordination, providing a powerful method of data integration and sharing that allows physicians, researchers, and public health organizations to respond rapidly and efficiently to current and future public health crises.

Coordinating dissent as an alternative to consensus classification: insights from systematics for bio-ontologies.

The collection and classification of data into meaningful categories is a key step in the process of knowledge making. In the life sciences, the design of data discovery and integration tools has relied on the premise that a formal classificatory system for expressing a body of data should be grounded in consensus definitions for classifications. On this approach, exemplified by the realist program of the Open Biomedical Ontologies Foundry, progress is maximized by grounding the representation and aggregation of data on settled knowledge. We argue that historical practices in systematic biology provide an important and overlooked alternative approach to classifying and disseminating data, based on a principle of coordinative rather than definitional consensus. Systematists have developed a robust system for referring to taxonomic entities that can deliver high quality data discovery and integration without invoking consensus about reality or "settled" science.

SNOMED CT standard ontology based on the ontology for general medical science

BackgroundSystematized Nomenclature of Medicine—Clinical Terms (SNOMED CT, hereafter abbreviated SCT) is a comprehensive medical terminology used for standardizing the storage, retrieval, and exchange of electronic health data. Some efforts have been made to capture the contents of SCT as Web Ontology Language (OWL), but these efforts have been hampered by the size and complexity of SCT.MethodOur proposal here is to develop an upper-level ontology and to use it as the basis for defining the terms in SCT in a way that will support quality assurance of SCT, for example, by allowing consistency checks of definitions and the identification and elimination of redundancies in the SCT vocabulary. Our proposed upper-level SCT ontology (SCTO) is based on the Ontology for General Medical Science (OGMS).ResultsThe SCTO is implemented in OWL 2, to support automatic inference and consistency checking. The approach will allow integration of SCT data with data annotated using Open Biomedical Ontologies (OBO) Foundry ontologies, since the use of OGMS will ensure consistency with the Basic Formal Ontology, which is the top-level ontology of the OBO Foundry. Currently, the SCTO contains 304 classes, 28 properties, 2400 axioms, and 1555 annotations. It is publicly available through the bioportal at http://bioportal.bioontology.org/ontologies/SCTO/.ConclusionThe resulting ontology can enhance the semantics of clinical decision support systems and semantic interoperability among distributed electronic health records. In addition, the populated ontology can be used for the automation of mobile health applications.

Late Breaking Abstracts.

Late Breaking Abstracts.

DMTO: a realistic ontology for standard diabetes mellitus treatment

BackgroundTreatment of type 2 diabetes mellitus (T2DM) is a complex problem. A clinical decision support system (CDSS) based on massive and distributed electronic health record data can facilitate the automation of this process and enhance its accuracy. The most important component of any CDSS is its knowledge base. This knowledge base can be formulated using ontologies. The formal description logic of ontology supports the inference of hidden knowledge. Building a complete, coherent, consistent, interoperable, and sharable ontology is a challenge.ResultsThis paper introduces the first version of the newly constructed Diabetes Mellitus Treatment Ontology (DMTO) as a basis for shared-semantics, domain-specific, standard, machine-readable, and interoperable knowledge relevant to T2DM treatment. It is a comprehensive ontology and provides the highest coverage and the most complete picture of coded knowledge about T2DM patients’ current conditions, previous profiles, and T2DM-related aspects, including complications, symptoms, lab tests, interactions, treatment plan (TP) frameworks, and glucose-related diseases and medications. It adheres to the design principles recommended by the Open Biomedical Ontologies Foundry and is based on ontological realism that follows the principles of the Basic Formal Ontology and the Ontology for General Medical Science. DMTO is implemented under Protégé 5.0 in Web Ontology Language (OWL) 2 format and is publicly available through the National Center for Biomedical Ontology’s BioPortal at http://bioportal.bioontology.org/ontologies/DMTO. The current version of DMTO includes more than 10,700 classes, 277 relations, 39,425 annotations, 214 semantic rules, and 62,974 axioms. We provide proof of concept for this approach to modeling TPs.ConclusionThe ontology is able to collect and analyze most features of T2DM as well as customize chronic TPs with the most appropriate drugs, foods, and physical exercises. DMTO is ready to be used as a knowledge base for semantically intelligent and distributed CDSS systems.

DDO: a diabetes mellitus diagnosis ontology

Diabetes mellitus is a major cause of morbidity and mortality in humans. Early diagnosis is the first step toward the management of this condition. However, a diagnosis involves several variables, which makes it difficult to arrive at an accurate and timely diagnosis and to construct accurate personalized treatment plans. An electronic health record system requires an integrated decision support capability, and ontologies are rapidly becoming necessary for the design of efficient, reliable, extendable, reusable, and semantically intelligent knowledge bases. In this study, we take the first step in this direction, by designing an OWL2 diabetes diagnosis ontology (DDO). Protege 5 software was used for the construction of the ontology. DDO is developed within the framework of the basic formal ontology and the ontology for general medical science to represent entities in the domain of diabetes, and it follows the design principles recommended by the Open Biomedical Ontology Foundry. Currently, DDO contains 6444 concepts, 48 properties, 13,551 annotations, and 27,127 axioms. DDO can serve as a diabetes knowledge base and supports automatic reasoning. It represents a major step toward the development of a new generation of patient-centric decision support tools. DDO is available through BioPortal at: http://www.bioportal.bioontology.org/ontologies/DDO .

The ontology of medically related social entities: recent developments.

BackgroundThe Ontology of Medically Related Social Entities (OMRSE) was initially developed in 2011 to provide a framework for modeling demographic data in Resource Description Framework/Web Ontology Language. It is built upon the Basic Formal Ontology and conforms to Open Biomedical Ontologies Foundry’s best practices.DescriptionWe report recent development of OMRSE which includes representations of organizations, roles, facilities, demographic data, enrollment in insurance plans, and data about socio-economic indicators.ConclusionsOMRSE’s coverage has been expanding in recent years to include a wide variety of classes and has been useful in several biomedical applications.

Ontology for the asexual development and anatomy of the colonial chordate Botryllus schlosseri.

Ontologies provide an important resource to integrate information. For developmental biology and comparative anatomy studies, ontologies of a species are used to formalize and annotate data that are related to anatomical structures, their lineage and timing of development. Here, we have constructed the first ontology for anatomy and asexual development (blastogenesis) of a bilaterian, the colonial tunicate Botryllus schlosseri. Tunicates, like Botryllus schlosseri, are non-vertebrates and the only chordate taxon species that reproduce both sexually and asexually. Their tadpole larval stage possesses structures characteristic of all chordates, i.e. a notochord, a dorsal neural tube, and gill slits. Larvae settle and metamorphose into individuals that are either solitary or colonial. The latter reproduce both sexually and asexually and these two reproductive modes lead to essentially the same adult body plan. The Botryllus schlosseri Ontology of Development and Anatomy (BODA) will facilitate the comparison between both types of development. BODA uses the rules defined by the Open Biomedical Ontologies Foundry. It is based on studies that investigate the anatomy, blastogenesis and regeneration of this organism. BODA features allow the users to easily search and identify anatomical structures in the colony, to define the developmental stage, and to follow the morphogenetic events of a tissue and/or organ of interest throughout asexual development. We invite the scientific community to use this resource as a reference for the anatomy and developmental ontology of B. schlosseri and encourage recommendations for updates and improvements.

The neurological disease ontology.

BackgroundWe are developing the Neurological Disease Ontology (ND) to provide a framework to enable representation of aspects of neurological diseases that are relevant to their treatment and study. ND is a representational tool that addresses the need for unambiguous annotation, storage, and retrieval of data associated with the treatment and study of neurological diseases. ND is being developed in compliance with the Open Biomedical Ontology Foundry principles and builds upon the paradigm established by the Ontology for General Medical Science (OGMS) for the representation of entities in the domain of disease and medical practice. Initial applications of ND will include the annotation and analysis of large data sets and patient records for Alzheimer’s disease, multiple sclerosis, and stroke.DescriptionND is implemented in OWL 2 and currently has more than 450 terms that refer to and describe various aspects of neurological diseases. ND directly imports the development version of OGMS, which uses BFO 2. Term development in ND has primarily extended the OGMS terms ‘disease’, ‘diagnosis’, ‘disease course’, and ‘disorder’. We have imported and utilize over 700 classes from related ontology efforts including the Foundational Model of Anatomy, Ontology for Biomedical Investigations, and Protein Ontology. ND terms are annotated with ontology metadata such as a label (term name), term editors, textual definition, definition source, curation status, and alternative terms (synonyms). Many terms have logical definitions in addition to these annotations. Current development has focused on the establishment of the upper-level structure of the ND hierarchy, as well as on the representation of Alzheimer’s disease, multiple sclerosis, and stroke. The ontology is available as a version-controlled file at http://code.google.com/p/neurological-disease-ontology along with a discussion list and an issue tracker.ConclusionND seeks to provide a formal foundation for the representation of clinical and research data pertaining to neurological diseases. ND will enable its users to connect data in a robust way with related data that is annotated using other terminologies and ontologies in the biomedical domain.

Ontology for vector surveillance and management.

Ontologies, which are made up by standardized and defined controlled vocabulary terms and their interrelationships, are comprehensive and readily searchable repositories for knowledge in a given domain. The Open Biomedical Ontologies (OBO) Foundry was initiated in 2001 with the aims of becoming an "umbrella" for life-science ontologies and promoting the use of ontology development best practices. A software application (OBO-Edit; *.obo file format) was developed to facilitate ontology development and editing. The OBO Foundry now comprises over 100 ontologies and candidate ontologies, including the NCBI organismal classification ontology (NCBITaxon), the Mosquito Insecticide Resistance Ontology (MIRO), the Infectious Disease Ontology (IDO), the IDOMAL malaria ontology, and ontologies for mosquito gross anatomy and tick gross anatomy. We previously developed a disease data management system for dengue and malaria control programs, which incorporated a set of information trees built upon ontological principles, including a "term tree" to promote the use of standardized terms. In the course of doing so, we realized that there were substantial gaps in existing ontologies with regards to concepts, processes, and, especially, physical entities (e.g., vector species, pathogen species, and vector surveillance and management equipment) in the domain of surveillance and management of vectors and vector-borne pathogens. We therefore produced an ontology for vector surveillance and management, focusing on arthropod vectors and vector-borne pathogens with relevance to humans or domestic animals, and with special emphasis on content to support operational activities through inclusion in databases, data management systems, or decision support systems. The Vector Surveillance and Management Ontology (VSMO) includes >2,200 unique terms, of which the vast majority (>80%) were newly generated during the development of this ontology. One core feature of the VSMO is the linkage, through the has vector relation, of arthropod species to the pathogenic microorganisms for which they serve as biological vectors. We also recognized and addressed a potential roadblock for use of the VSMO by the vector-borne disease community: the difficulty in extracting information from OBO-Edit ontology files (*.obo files) and exporting the information to other file formats. A novel ontology explorer tool was developed to facilitate extraction and export of information from the VSMO*.obo file into lists of terms and their associated unique IDs in *.txt or *.csv file formats. These lists can then be imported into a database or data management system for use as select lists with predefined terms. This is an important step to ensure that the knowledge contained in our ontology can be put into practical use.

Unintended consequences of existential quantifications in biomedical ontologies.

BackgroundThe Open Biomedical Ontologies (OBO) Foundry is a collection of freely available ontologically structured controlled vocabularies in the biomedical domain. Most of them are disseminated via both the OBO Flatfile Format and the semantic web format Web Ontology Language (OWL), which draws upon formal logic. Based on the interpretations underlying OWL description logics (OWL-DL) semantics, we scrutinize the OWL-DL releases of OBO ontologies to assess whether their logical axioms correspond to the meaning intended by their authors.ResultsWe analyzed ontologies and ontology cross products available via the OBO Foundry site http://www.obofoundry.org for existential restrictions (someValuesFrom), from which we examined a random sample of 2,836 clauses.According to a rating done by four experts, 23% of all existential restrictions in OBO Foundry candidate ontologies are suspicious (Cohens' κ = 0.78). We found a smaller proportion of existential restrictions in OBO Foundry cross products are suspicious, but in this case an accurate quantitative judgment is not possible due to a low inter-rater agreement (κ = 0.07). We identified several typical modeling problems, for which satisfactory ontology design patterns based on OWL-DL were proposed. We further describe several usability issues with OBO ontologies, including the lack of ontological commitment for several common terms, and the proliferation of domain-specific relations.ConclusionsThe current OWL releases of OBO Foundry (and Foundry candidate) ontologies contain numerous assertions which do not properly describe the underlying biological reality, or are ambiguous and difficult to interpret. The solution is a better anchoring in upper ontologies and a restriction to relatively few, well defined relation types with given domain and range constraints.

An Evolutionary Approach to Realism-based Adverse Event Representations

Part of the ReMINE project involved the creation of an ontology enabling computer-assisted decision support for optimal adverse event management. The ontology was required to satisfy the following requirements: 1) to be able to account for the distinct and context-dependent ways in which authoritative sources define the term 'adverse event', 2) to allow the identification of relevant risks against patient safety (RAPS) on the basis of the disease history of a patient as documented in electronic health records, and 3) to be compatible with present and future ontologies developed under the Open Biomedical Ontology (OBO) Foundry framework. We used as feeder ontologies the Basic Formal Ontology, the Foundational Model of Anatomy, the Ontology for General Medical Science, the Information Artifact Ontology and the Ontology of Mental Health. We further used relations defined according to the pattern set forth in the OBO Relation Ontology. In light of the intended use of the ontology for the representation of adverse events that have actually occurred and therefore are registered in a database, we also applied the principles of referent tracking. We merged the upper portions of the mentioned feeder ontologies and introduced 22 additional representational units of which 13 are generally applicable in biomedicine and nine in the adverse event context. We provided for each representational unit a textual definition that can be translated into equivalent formal definitions. The resulting ontology satisfies all of the requirements set forth. Merging the feeder ontologies, although all designed under the OBO Foundry principles, brought new insight into what the representational units of such ontologies actually denote.

A gross anatomy ontology for hymenoptera.

Hymenoptera is an extraordinarily diverse lineage, both in terms of species numbers and morphotypes, that includes sawflies, bees, wasps, and ants. These organisms serve critical roles as herbivores, predators, parasitoids, and pollinators, with several species functioning as models for agricultural, behavioral, and genomic research. The collective anatomical knowledge of these insects, however, has been described or referred to by labels derived from numerous, partially overlapping lexicons. The resulting corpus of information—millions of statements about hymenopteran phenotypes—remains inaccessible due to language discrepancies. The Hymenoptera Anatomy Ontology (HAO) was developed to surmount this challenge and to aid future communication related to hymenopteran anatomy. The HAO was built using newly developed interfaces within mx, a Web-based, open source software package, that enables collaborators to simultaneously contribute to an ontology. Over twenty people contributed to the development of this ontology by adding terms, genus differentia, references, images, relationships, and annotations. The database interface returns an Open Biomedical Ontology (OBO) formatted version of the ontology and includes mechanisms for extracting candidate data and for publishing a searchable ontology to the Web. The application tools are subject-agnostic and may be used by others initiating and developing ontologies. The present core HAO data constitute 2,111 concepts, 6,977 terms (labels for concepts), 3,152 relations, 4,361 sensus (links between terms, concepts, and references) and over 6,000 text and graphical annotations. The HAO is rooted with the Common Anatomy Reference Ontology (CARO), in order to facilitate interoperability with and future alignment to other anatomy ontologies, and is available through the OBO Foundry ontology repository and BioPortal. The HAO provides a foundation through which connections between genomic, evolutionary developmental biology, phylogenetic, taxonomic, and morphological research can be actualized. Inherent mechanisms for feedback and content delivery demonstrate the effectiveness of remote, collaborative ontology development and facilitate future refinement of the HAO.

The Protein Ontology: a structured representation of protein forms and complexes

The Protein Ontology (PRO) provides a formal, logically-based classification of specific protein classes including structured representations of protein isoforms, variants and modified forms. Initially focused on proteins found in human, mouse and Escherichia coli, PRO now includes representations of protein complexes. The PRO Consortium works in concert with the developers of other biomedical ontologies and protein knowledge bases to provide the ability to formally organize and integrate representations of precise protein forms so as to enhance accessibility to results of protein research. PRO (http://pir.georgetown.edu/pro) is part of the Open Biomedical Ontology Foundry.

Saliva Ontology: An ontology-based framework for a Salivaomics Knowledge Base

BackgroundThe Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO).ResultsWe define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated to the salivaomics domain and to saliva-related diagnostics following the principles of the OBO (Open Biomedical Ontologies) Foundry.ConclusionsThe Saliva Ontology is an ongoing exploratory initiative. The ontology will be used to facilitate salivaomics data retrieval and integration across multiple fields of research together with data analysis and data mining. The ontology will be tested through its ability to serve the annotation ('tagging') of a representative corpus of salivaomics research literature that is to be incorporated into the SKB.

Ontological realism: A methodology for coordinated evolution of scientific ontologies.

Since 2002 we have been testing and refining a methodology for ontology development that is now being used by multiple groups of researchers in different life science domains. Gary Merrill, in a recent paper in this journal, describes some of the reasons why this methodology has been found attractive by researchers in the biological and biomedical sciences. At the same time he assails the methodology on philosophical grounds, focusing specifically on our recommendation that ontologies developed for scientific purposes should be constructed in such a way that their terms are seen as referring to what we call universals or types in reality. As we show, Merrill's critique is of little relevance to the success of our realist project, since it not only reveals no actual errors in our work but also criticizes views on universals that we do not in fact hold. However, it nonetheless provides us with a valuable opportunity to clarify the realist methodology, and to show how some of its principles are being applied, especially within the framework of the OBO (Open Biomedical Ontologies) Foundry initiative.

MIRO and IRbase: IT Tools for the Epidemiological Monitoring of Insecticide Resistance in Mosquito Disease Vectors

BackgroundMonitoring of insect vector populations with respect to their susceptibility to one or more insecticides is a crucial element of the strategies used for the control of arthropod-borne diseases. This management task can nowadays be achieved more efficiently when assisted by IT (Information Technology) tools, ranging from modern integrated databases to GIS (Geographic Information System). Here we describe an application ontology that we developed de novo, and a specially designed database that, based on this ontology, can be used for the purpose of controlling mosquitoes and, thus, the diseases that they transmit.Methodology/Principal FindingsThe ontology, named MIRO for Mosquito Insecticide Resistance Ontology, developed using the OBO-Edit software, describes all pertinent aspects of insecticide resistance, including specific methodology and mode of action. MIRO, then, forms the basis for the design and development of a dedicated database, IRbase, constructed using open source software, which can be used to retrieve data on mosquito populations in a temporally and spatially separate way, as well as to map the output using a Google Earth interface. The dependency of the database on the MIRO allows for a rational and efficient hierarchical search possibility.Conclusions/SignificanceThe fact that the MIRO complies with the rules set forward by the OBO (Open Biomedical Ontologies) Foundry introduces cross-referencing with other biomedical ontologies and, thus, both MIRO and IRbase are suitable as parts of future comprehensive surveillance tools and decision support systems that will be used for the control of vector-borne diseases. MIRO is downloadable from and IRbase is accessible at VectorBase, the NIAID-sponsored open access database for arthropod vectors of disease.

TGF-beta signaling proteins and the Protein Ontology

BackgroundThe Protein Ontology (PRO) is designed as a formal and principled Open Biomedical Ontologies (OBO) Foundry ontology for proteins. The components of PRO extend from a classification of proteins on the basis of evolutionary relationships at the homeomorphic level to the representation of the multiple protein forms of a gene, including those resulting from alternative splicing, cleavage and/or post-translational modifications. Focusing specifically on the TGF-beta signaling proteins, we describe the building, curation, usage and dissemination of PRO.ResultsPRO is manually curated on the basis of PrePRO, an automatically generated file with content derived from standard protein data sources. Manual curation ensures that the treatment of the protein classes and the internal and external relationships conform to the PRO framework. The current release of PRO is based upon experimental data from mouse and human proteins wherein equivalent protein forms are represented by single terms. In addition to the PRO ontology, the annotation of PRO terms is released as a separate PRO association file, which contains, for each given PRO term, an annotation from the experimentally characterized sub-types as well as the corresponding database identifiers and sequence coordinates. The annotations are added in the form of relationship to other ontologies. Whenever possible, equivalent forms in other species are listed to facilitate cross-species comparison. Splice and allelic variants, gene fusion products and modified protein forms are all represented as entities in the ontology. Therefore, PRO provides for the representation of protein entities and a resource for describing the associated data. This makes PRO useful both for proteomics studies where isoforms and modified forms must be differentiated, and for studies of biological pathways, where representations need to take account of the different ways in which the cascade of events may depend on specific protein modifications.ConclusionPRO provides a framework for the formal representation of protein classes and protein forms in the OBO Foundry. It is designed to enable data retrieval and integration and machine reasoning at the molecular level of proteins, thereby facilitating cross-species comparisons, pathway analysis, disease modeling and the generation of new hypotheses.

On the Eve of Personalized Medicine in Oncology

The future of cancer care and treatment lies in the concept of “personalized medicine,” a model that focuses on the individual, not just the disease. Personalized medicine in cancer care will need to use molecular signatures to match the right patients to the right drugs, first in clinical