Current efforts within the biomedical ontology community focus on achieving interoperability between various biomedical ontologies that cover a range of diverse domains. Achieving this interoperability will contribute to the creation of a rich knowledge base that can be used for querying, as well as generating and testing novel hypotheses. The OBO Foundry principles, as applied to a number of biomedical ontologies, are designed to facilitate this interoperability. However, semantic extensions are required to meet the OBO Foundry interoperability goals. Inconsistencies may arise when ontologies of properties – mostly phenotype ontologies – are combined with ontologies taking a canonical view of a domain – such as many anatomical ontologies. Currently, there is no support for a correct and consistent integration of such ontologies.
We have developed a methodology for accurately representing canonical domain ontologies within the OBO Foundry. This is achieved by adding an extension to the semantics for relationships in the biomedical ontologies that allows for treating canonical information as default. Conclusions drawn from default knowledge may be revoked when additional information becomes available. We show how this extension can be used to achieve interoperability between ontologies, and further allows for the inclusion of more knowledge within them. We apply the formalism to ontologies of mouse anatomy and mammalian phenotypes in order to demonstrate the approach.
Biomedical ontologies require a new class of relations that can be used in conjunction with default knowledge, thereby extending those currently in use. The inclusion of default knowledge is necessary in order to ensure interoperability between ontologies.
Ontologies have emerged as a fast growing research topic in the area of semantic web during last decade. Currently there are 204 ontologies that are available
through OBO Foundry and BioPortal. Several excellent tools for navigating the ontological structure are available, however most of them are dedicated to a
specific annotation data or integrated with specific analysis applications, and do not offer flexibility in terms of general-purpose usage for ontology exploration.
We developed OntoVisT, a web based ontological visualization tool. This application is designed for interactive visualization of any ontological hierarchy for a
specific node of interest, up to the chosen level of children and/or ancestor. It takes any ontology file in OBO format as input and generates output as DAG
hierarchical graph for the chosen query. To enhance the navigation capabilities of complex networks, we have embedded several features such as search criteria,
zoom in/out, center focus, nearest neighbor highlights and mouse hover events. The application has been tested on all 72 data sets available in OBO format through
OBO foundry. The results for few of them can be accessed through OntoVisT-Gallery.
The database is available for free at http://ccbb.jnu.ac.in/OntoVisT.html
ontology; visualization tool; directed acyclic graphs; web server; gene ontology
Ontologies have increasingly been used in the biomedical domain, which has prompted the emergence of different initiatives to facilitate their development and integration. The Open Biological and Biomedical Ontologies (OBO) Foundry consortium provides a repository of life-science ontologies, which are developed according to a set of shared principles. This consortium has developed an ontology called OBO Relation Ontology aiming at standardizing the different types of biological entity classes and associated relationships. Since ontologies are primarily intended to be used by humans, the use of graphical notations for ontology development facilitates the capture, comprehension and communication of knowledge between its users. However, OBO Foundry ontologies are captured and represented basically using text-based notations. The Unified Modeling Language (UML) provides a standard and widely-used graphical notation for modeling computer systems. UML provides a well-defined set of modeling elements, which can be extended using a built-in extension mechanism named Profile. Thus, this work aims at developing a UML profile for the OBO Relation Ontology to provide a domain-specific set of modeling elements that can be used to create standard UML-based ontologies in the biomedical domain.
We have studied the OBO Relation Ontology, the UML metamodel and the UML profiling mechanism. Based on these studies, we have proposed an extension to the UML metamodel in conformance with the OBO Relation Ontology and we have defined a profile that implements the extended metamodel. Finally, we have applied the proposed UML profile in the development of a number of fragments from different ontologies. Particularly, we have considered the Gene Ontology (GO), the PRotein Ontology (PRO) and the Xenopus Anatomy and Development Ontology (XAO).
The use of an established and well-known graphical language in the development of biomedical ontologies provides a more intuitive form of capturing and representing knowledge than using only text-based notations. The use of the profile requires the domain expert to reason about the underlying semantics of the concepts and relationships being modeled, which helps preventing the introduction of inconsistencies in an ontology under development and facilitates the identification and correction of errors in an already defined ontology.
The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or ‘ontologies’. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium is pursuing a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing coordinated reform, and new ontologies are being created on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality. We describe this OBO Foundry initiative and provide guidelines for those who might wish to become involved.
Life scientists need help in coping with the plethora of fast growing and scattered knowledge resources. Ideally, this knowledge should be integrated in a form that allows them to pose complex questions that address the properties of biological systems, independently from the origin of the knowledge. Semantic Web technologies prove to be well suited for knowledge integration, knowledge production (hypothesis formulation), knowledge querying and knowledge maintenance.
We implemented a semantically integrated resource named BioGateway, comprising the entire set of the OBO foundry candidate ontologies, the GO annotation files, the SWISS-PROT protein set, the NCBI taxonomy and several in-house ontologies. BioGateway provides a single entry point to query these resources through SPARQL. It constitutes a key component for a Semantic Systems Biology approach to generate new hypotheses concerning systems properties. In the course of developing BioGateway, we faced challenges that are common to other projects that involve large datasets in diverse representations. We present a detailed analysis of the obstacles that had to be overcome in creating BioGateway. We demonstrate the potential of a comprehensive application of Semantic Web technologies to global biomedical data.
The time is ripe for launching a community effort aimed at a wider acceptance and application of Semantic Web technologies in the life sciences. We call for the creation of a forum that strives to implement a truly semantic life science foundation for Semantic Systems Biology.
Access to the system and supplementary information (such as a listing of the data sources in RDF, and sample queries) can be found at .
Ontologies are commonly used in biomedicine to organize concepts to describe domains such as anatomies, environments, experiment, taxonomies etc. NCBO BioPortal currently hosts about 180 different biomedical ontologies. These ontologies have been mainly expressed in either the Open Biomedical Ontology (OBO) format or the Web Ontology Language (OWL). OBO emerged from the Gene Ontology, and supports most of the biomedical ontology content. In comparison, OWL is a Semantic Web language, and is supported by the World Wide Web consortium together with integral query languages, rule languages and distributed infrastructure for information interchange. These features are highly desirable for the OBO content as well. A convenient method for leveraging these features for OBO ontologies is by transforming OBO ontologies to OWL.
We have developed a methodology for translating OBO ontologies to OWL using the organization of the Semantic Web itself to guide the work. The approach reveals that the constructs of OBO can be grouped together to form a similar layer cake. Thus we were able to decompose the problem into two parts. Most OBO constructs have easy and obvious equivalence to a construct in OWL. A small subset of OBO constructs requires deeper consideration. We have defined transformations for all constructs in an effort to foster a standard common mapping between OBO and OWL. Our mapping produces OWL-DL, a Description Logics based subset of OWL with desirable computational properties for efficiency and correctness. Our Java implementation of the mapping is part of the official Gene Ontology project source.
Our transformation system provides a lossless roundtrip mapping for OBO ontologies, i.e. an OBO ontology may be translated to OWL and back without loss of knowledge. In addition, it provides a roadmap for bridging the gap between the two ontology languages in order to enable the use of ontology content in a language independent manner.
The 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).
We 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.
The 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.
Ontologies in biomedicine facilitate information integration, data exchange, search and query of biomedical data, and other critical knowledge-intensive tasks. The OBO Foundry is a collaborative effort to establish a set of principles for ontology development with the eventual goal of creating a set of interoperable reference ontologies in the domain of biomedicine. One of the key requirements to achieve this goal is to ensure that ontology developers reuse term definitions that others have already created rather than create their own definitions, thereby making the ontologies orthogonal.
We used a simple lexical algorithm to analyze the extent to which the set of OBO Foundry candidate ontologies identified from September 2009 to September 2010 conforms to this vision. Specifically, we analyzed (1) the level of explicit term reuse in this set of ontologies, (2) the level of overlap, where two ontologies define similar terms independently, and (3) how the levels of reuse and overlap changed during the course of this year.
We found that 30% of the ontologies reuse terms from other Foundry candidates and 96% of the candidate ontologies contain terms that overlap with terms from the other ontologies. We found that while term reuse increased among the ontologies between September 2009 and September 2010, the level of overlap among the ontologies remained relatively constant. Additionally, we analyzed the six ontologies announced as OBO Foundry members on March 5, 2010, and identified that the level of overlap was extremely low, but, notably, so was the level of term reuse.
We have created a prototype web application that allows OBO Foundry ontology developers to see which classes from their ontologies overlap with classes from other ontologies in the OBO Foundry (http://obomap.bioontology.org). From our analysis, we conclude that while the OBO Foundry has made significant progress toward orthogonality during the period of this study through increased adoption of explicit term reuse, a large amount of overlap remains among these ontologies. Furthermore, the characteristics of the identified overlap, such as the terms it comprises and its distribution among the ontologies, indicate that the achieving orthogonality will be exceptionally difficult, if not impossible.
Although policy providers have outlined minimal metadata guidelines and naming conventions, ontologies of today still display inter- and intra-ontology heterogeneities in class labelling schemes and metadata completeness. This fact is at least partially due to missing or inappropriate tools. Software support can ease this situation and contribute to overall ontology consistency and quality by helping to enforce such conventions.
We provide a plugin for the Protégé Ontology editor to allow for easy checks on compliance towards ontology naming conventions and metadata completeness, as well as curation in case of found violations.
In a requirement analysis, derived from a prior standardization approach carried out within the OBO Foundry, we investigate the needed capabilities for software tools to check, curate and maintain class naming conventions. A Protégé tab plugin was implemented accordingly using the Protégé 4.1 libraries. The plugin was tested on six different ontologies. Based on these test results, the plugin could be refined, also by the integration of new functionalities.
The new Protégé plugin, OntoCheck, allows for ontology tests to be carried out on OWL ontologies. In particular the OntoCheck plugin helps to clean up an ontology with regard to lexical heterogeneity, i.e. enforcing naming conventions and metadata completeness, meeting most of the requirements outlined for such a tool. Found test violations can be corrected to foster consistency in entity naming and meta-annotation within an artefact. Once specified, check constraints like name patterns can be stored and exchanged for later re-use. Here we describe a first version of the software, illustrate its capabilities and use within running ontology development efforts and briefly outline improvements resulting from its application. Further, we discuss OntoChecks capabilities in the context of related tools and highlight potential future expansions.
The OntoCheck plugin facilitates labelling error detection and curation, contributing to lexical quality assurance in OWL ontologies. Ultimately, we hope this Protégé extension will ease ontology alignments as well as lexical post-processing of annotated data and hence can increase overall secondary data usage by humans and computers.
The Open Biomedical Ontologies (OBO) Foundry is a coordinated community-wide effort to develop ontologies that support the annotation and integration of scientific data. In work supported by the National Database of Autism Research (NDAR), we are developing an ontology of autism that extends the ontologies available in the OBO Foundry. We undertook a systematic literature review to identify domain terms and relationships relevant to autism phenotypes. To enable user queries and inferences about such phenotypes using data in the NDAR repository, we augmented the domain ontology with an information model. In this paper, we show how our approach, using a combination of description logic and rule-based reasoning, enables high-level phenotypic abstractions to be inferred from subject-specific data. Our integrated domain ontology–information model approach allows scientific data repositories to be augmented with rule-based abstractions that facilitate the ability of researchers to undertake data analysis.
The Gene Ontology (GO) consists of nearly 30,000 classes for describing the activities and locations of gene products. Manual maintenance of an ontology of this size is a considerable effort, and errors and inconsistencies inevitably arise. Reasoners can be used to assist with ontology development, automatically placing classes in a subsumption hierarchy based on their properties. However, the historic lack of computable definitions within the GO has prevented the user of these tools.
In this paper we present preliminary results of an ongoing effort to normalize the GO by explicitly stating the definitions of compositional classes in a form that can be used by reasoners. These definitions are partitioned into mutually exclusive cross-product sets, many of which reference other OBO Foundry candidate ontologies for chemical entities, proteins, biological qualities and anatomical entities. Using these logical definitions we are gradually beginning to automate many aspects of ontology development, detecting errors and filling in missing relationships. These definitions also enhance the GO by weaving it into the fabric of a wider collection of interoperating ontologies, increasing opportunities for data integration and enhancing genomic analyses.
Biological ontologies are now being widely used for annotation, sharing and retrieval of the biological data. Many of these ontologies are hosted under the umbrella of the Open Biological Ontologies Foundry. In order to support interterminology mapping, composite terms in these ontologies need to be translated into atomic or primitive terms in other, orthogonal ontologies, for example, gluconeogenesis (biological process term) to glucose (chemical ontology term). Identifying such decompositional ontology translations is a challenging problem. In this paper, we propose a network-theoretic approach based on the structure of the integrated OBO relationship graph. We use a network-theoretic measure, called the clustering coefficient, to find relevant atomic terms in the neighborhood of a composite term. By eliminating the existing GO to ChEBI Ontology mappings from OBO, we evaluate whether the proposed approach can re-identify the corresponding relationships. The results indicate that the network structure provides strong cues for decompositional ontology translation and the existing relationships can be used to identify new translations.
network theory; biomedical ontologies; ontology translation; open biomedical ontologies
The goal of the OBO (Open Biomedical Ontologies) Foundry initiative is to create and maintain an evolving collection of non-overlapping interoperable ontologies that will offer unambiguous representations of the types of entities in biological and biomedical reality. These ontologies are designed to serve non-redundant annotation of data and scientific text. To achieve these ends, the Foundry imposes strict requirements upon the ontologies eligible for inclusion. While these requirements are not met by most existing biomedical terminologies, the latter may nonetheless support the Foundry’s goal of consistent and non-redundant annotation if appropriate mappings of data annotated with their aid can be achieved. To construct such mappings in reliable fashion, however, it is necessary to analyze terminological resources from an ontologically realistic perspective in such a way as to identify the exact import of the ‘concepts’ and associated terms which they contain. We propose a framework for such analysis that is designed to maximize the degree to which legacy terminologies and the data coded with their aid can be successfully used for information-driven clinical and translational research.
Ontology; terminology; mapping
Ontologies are intended to capture and formalize a domain of knowledge. The
ontologies comprising the Open Biological Ontologies (OBO) project, which includes
the Gene Ontology (GO), are formalizations of various domains of biological
knowledge. Ontologies within OBO typically lack computable definitions that serve to
differentiate a term from other similar terms. The computer is unable to determine the
meaning of a term, which presents problems for tools such as automated reasoners.
Reasoners can be of enormous benefit in managing a complex ontology. OBO term
names frequently implicitly encode the kind of definitions that can be used by
computational tools, such as automated reasoners. The definitions encoded in the
names are not easily amenable to computation, because the names are ostensibly
natural language phrases designed for human users. These names are highly regular
in their grammar, and can thus be treated as valid sentences in some formal or
computable language.With a description of the rules underlying this formal language,
term names can be parsed to derive computable definitions, which can then be
reasoned over. This paper describes the effort to elucidate that language, called Obol,
and the attempts to reason over the resulting definitions. The current implementation
finds unique non-trivial definitions for around half of the terms in the GO, and
has been used to find 223 missing relationships, which have since been added to
the ontology. Obol has utility as an ontology maintenance tool, and as a means of
generating computable definitions for a whole ontology.
The software is available under an open-source license from: http://www.fruitfly.
org/~cjm/obol. Supplementary material for this article can be found at: http://www.
The Basic Formal Ontology (BFO) is a top-level formal foundational ontology for the biomedical domain. It has been developed with the purpose to serve as an ontologically consistent template for top-level categories of application oriented and domain reference ontologies within the Open Biological and Biomedical Ontologies Foundry (OBO). BFO is important for enabling OBO ontologies to facilitate in reliably communicating and managing data and metadata within and across biomedical databases. Following its intended single inheritance policy, BFO's three top-level categories of material entity (i.e. ‘object’, ‘fiat object part’, ‘object aggregate’) must be exhaustive and mutually disjoint. We have shown elsewhere that for accommodating all types of constitutively organized material entities, BFO must be extended by additional categories of material entity.
Unfortunately, most biomedical material entities are cumulative-constitutively organized. We show that even the extended BFO does not exhaustively cover cumulative-constitutively organized material entities. We provide examples from biology and everyday life that demonstrate the necessity for ‘portion of matter’ as another material building block. This implies the necessity for further extending BFO by ‘portion of matter’ as well as three additional categories that possess portions of matter as aggregate components. These extensions are necessary if the basic assumption that all parts that share the same granularity level exhaustively sum to the whole should also apply to cumulative-constitutively organized material entities. By suggesting a notion of granular representation we provide a way to maintain the single inheritance principle when dealing with cumulative-constitutively organized material entities.
We suggest to extend BFO to incorporate additional categories of material entity and to rearrange its top-level material entity taxonomy. With these additions and the notion of granular representation, BFO would exhaustively cover all top-level types of material entities that application oriented ontologies may use as templates, while still maintaining the single inheritance principle.
The 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.
We 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.
The 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.
Motivation: Ontologies and taxonomies have proven highly beneficial for biocuration. The Open Biomedical Ontology (OBO) Foundry alone lists over 90 ontologies mainly built with OBO-Edit. Creating and maintaining such ontologies is a labour-intensive, difficult, manual process. Automating parts of it is of great importance for the further development of ontologies and for biocuration.
Results: We have developed the Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG), a system which supports the creation and extension of OBO ontologies by semi-automatically generating terms, definitions and parent–child relations from text in PubMed, the web and PDF repositories. DOG4DAG is seamlessly integrated into OBO-Edit. It generates terms by identifying statistically significant noun phrases in text. For definitions and parent–child relations it employs pattern-based web searches. We systematically evaluate each generation step using manually validated benchmarks. The term generation leads to high-quality terms also found in manually created ontologies. Up to 78% of definitions are valid and up to 54% of child–ancestor relations can be retrieved. There is no other validated system that achieves comparable results.
By combining the prediction of high-quality terms, definitions and parent–child relations with the ontology editor OBO-Edit we contribute a thoroughly validated tool for all OBO ontology engineers.
Availability: DOG4DAG is available within OBO-Edit 2.1 at http://www.oboedit.org
Supplementary Information: Supplementary data are available at Bioinformatics online.
ChEBI (http://www.ebi.ac.uk/chebi) is a database and ontology of chemical entities of biological interest. Over the past few years, ChEBI has continued to grow steadily in content, and has added several new features. In addition to incorporating all user-requested compounds, our annotation efforts have emphasized immunology, natural products and metabolites in many species. All database entries are now ‘is_a’ classified within the ontology, meaning that all of the chemicals are available to semantic reasoning tools that harness the classification hierarchy. We have completely aligned the ontology with the Open Biomedical Ontologies (OBO) Foundry-recommended upper level Basic Formal Ontology. Furthermore, we have aligned our chemical classification with the classification of chemical-involving processes in the Gene Ontology (GO), and as a result of this effort, the majority of chemical-involving processes in GO are now defined in terms of the ChEBI entities that participate in them. This effort necessitated incorporating many additional biologically relevant compounds. We have incorporated additional data types including reference citations, and the species and component for metabolites. Finally, our website and web services have had several enhancements, most notably the provision of a dynamic new interactive graph-based ontology visualization.
Effective Medline database exploration is critical for the understanding of high throughput experimental results and the development of novel hypotheses about the mechanisms underlying the targeted biological processes. While existing solutions enhance Medline exploration through different approaches such as document clustering, network presentations of underlying conceptual relationships and the mapping of search results to MeSH and Gene Ontology trees, we believe the use of multiple ontologies from the Open Biomedical Ontology can greatly help researchers to explore literature from different perspectives as well as to quickly locate the most relevant Medline records for further investigation.
We developed an ontology-based interactive Medline exploration solution called PubOnto to enable the interactive exploration and filtering of search results through the use of multiple ontologies from the OBO foundry. The PubOnto program is a rich internet application based on the FLEX platform. It contains a number of interactive tools, visualization capabilities, an open service architecture, and a customizable user interface. It is freely accessible at: .
Biomedical ontologies exist to serve integration of clinical and experimental data, and it is critical to their success that they be put to widespread use in the annotation of data. How, then, can ontologies achieve the sort of user-friendliness, reliability, cost-effectiveness, and breadth of coverage that is necessary to ensure extensive usage?
Our focus here is on two different sets of answers to these questions that have been proposed, on the one hand in medicine, by the SNOMED CT community, and on the other hand in biology, by the OBO Foundry. We address more specifically the issue as to how adherence to certain development principles can advance the usability and effectiveness of an ontology or terminology resource, for example by allowing more accurate maintenance, more reliable application, and more efficient interoperation with other ontologies and information resources.
SNOMED CT and the OBO Foundry differ considerably in their general approach. Nevertheless, a general trend towards more formal rigor and cross-domain interoperability can be seen in both and we argue that this trend should be accepted by all similar initiatives in the future.
Future efforts in ontology development have to address the need for harmonization and integration of ontologies across disciplinary borders, and for this, coherent formalization of ontologies is a prerequisite.
Biomedical Ontologies; Ontology Harmonization; Quality Assurance; SNOMED CT
Biomedical ontologies provide essential domain knowledge to drive data integration, information retrieval, data annotation, natural-language processing and decision support. BioPortal (http://bioportal.bioontology.org) is an open repository of biomedical ontologies that provides access via Web services and Web browsers to ontologies developed in OWL, RDF, OBO format and Protégé frames. BioPortal functionality includes the ability to browse, search and visualize ontologies. The Web interface also facilitates community-based participation in the evaluation and evolution of ontology content by providing features to add notes to ontology terms, mappings between terms and ontology reviews based on criteria such as usability, domain coverage, quality of content, and documentation and support. BioPortal also enables integrated search of biomedical data resources such as the Gene Expression Omnibus (GEO), ClinicalTrials.gov, and ArrayExpress, through the annotation and indexing of these resources with ontologies in BioPortal. Thus, BioPortal not only provides investigators, clinicians, and developers ‘one-stop shopping’ to programmatically access biomedical ontologies, but also provides support to integrate data from a variety of biomedical resources.
The Cell Ontology (CL) aims for the representation of in vivo and in vitro cell types from all of biology. The CL is a candidate reference ontology of the OBO Foundry and requires extensive revision to bring it up to current standards for biomedical ontologies, both in its structure and its coverage of various subfields of biology. We have now addressed the specific content of one area of the CL, the section of the ontology dealing with hematopoietic cells. This section has been extensively revised to improve its content and eliminate multiple inheritance in the asserted hierarchy, and the groundwork was laid for structuring the hematopoietic cell type terms as cross-products incorporating logical definitions built from relationships to external ontologies, such as the Protein Ontology and the Gene Ontology. The methods and improvements to the CL in this area represent a paradigm for improvement of the entire ontology over time.
ontology; hematopoietic cells; immunology
The Sequence Ontology is an established ontology, with a large user community, for the purpose of genomic annotation. We are reforming the ontology to provide better terms and relationships to describe the features of biological sequence, for both genomic and derived sequence. The SO is working within the guidelines of the OBO Foundry to provide interoperability between SO and the other related OBO ontologies. Here we report changes and improvements made to SO including new relationships to better define the mereological, spatial and temporal aspects of biological sequence.
Sequence Ontology; biomedical ontology; genome annotation
Caused by intracellular Gram-negative bacteria Brucella spp., brucellosis is the most common bacterial zoonotic disease. Extensive studies in brucellosis have yielded a large number of publications and data covering various topics ranging from basic Brucella genetic study to vaccine clinical trials. To support data interoperability and reasoning, a community-based brucellosis-specific biomedical ontology is needed.
The Brucellosis Ontology (IDOBRU: http://sourceforge.net/projects/idobru), a biomedical ontology in the brucellosis domain, is an extension ontology of the core Infectious Disease Ontology (IDO-core) and follows OBO Foundry principles. Currently IDOBRU contains 1503 ontology terms, which includes 739 Brucella-specific terms, 414 IDO-core terms, and 350 terms imported from 10 existing ontologies. IDOBRU has been used to model different aspects of brucellosis, including host infection, zoonotic disease transmission, symptoms, virulence factors and pathogenesis, diagnosis, intentional release, vaccine prevention, and treatment. Case studies are typically used in our IDOBRU modeling. For example, diurnal temperature variation in Brucella patients, a Brucella-specific PCR method, and a WHO-recommended brucellosis treatment were selected as use cases to model brucellosis symptom, diagnosis, and treatment, respectively. Developed using OWL, IDOBRU supports OWL-based ontological reasoning. For example, by performing a Description Logic (DL) query in the OWL editor Protégé 4 or a SPARQL query in an IDOBRU SPARQL server, a check of Brucella virulence factors showed that eight of them are known protective antigens based on the biological knowledge captured within the ontology.
IDOBRU is the first reported bacterial infectious disease ontology developed to represent different disease aspects in a formal logical format. It serves as a brucellosis knowledgebase and supports brucellosis data integration and automated reasoning.
Biomedical ontologies are emerging as critical tools in genomic and proteomic research, where complex data in disparate resources need to be integrated. A number of ontologies describe properties that can be attributed to proteins. For example, protein functions are described by the Gene Ontology (GO) and human diseases by SNOMED CT or ICD10. There is, however, a gap in the current set of ontologies – one that describes the protein entities themselves and their relationships. We have designed the PRotein Ontology (PRO) to facilitate protein annotation and to guide new experiments. The components of PRO extend from the classification of proteins on the basis of evolutionary relationships to the representation of the multiple protein forms of a gene (products generated by genetic variation, alternative splicing, proteolytic cleavage, and other post-translational modifications). PRO will allow the specification of relationships between PRO, GO and other ontologies in the OBO Foundry. Here we describe the initial development of PRO, illustrated using human and mouse proteins involved in the transforming growth factor-beta and bone morphogenetic protein signaling pathways.