Protein ontology development using OWL

To efficiently represent the protein annotation framework and to integrate all the existing data representations into a standardized protein data specification for the bioinformatics community, the protein ontology need to be represented in a format that not enforce semantic constraints on protein data, but can also facilitate reasoning tasks on protein data using semantic query algebra. This motivates the representation of Protein Ontology (PO) Model in Web Ontology Language (OWL). In this paper we briefly discuss the usage of OWL in achieving the objectives of Protein Ontology Project. We provide a brief overview of Protein Ontology (PO) to start with. In the later sections discuss why OWL was an ideal choice for PO Development

VO: Vaccine Ontology

Vaccine research, as well as the development, testing, clinical trials, and commercial uses of vaccines involve complex processes with various biological data that include gene and protein expression, analysis of molecular and cellular interactions, study of tissue and whole body responses, and extensive epidemiological modeling. Although many data resources are available to meet different aspects of vaccine needs, it remains a challenge how we are to standardize vaccine annotation, integrate data about varied vaccine types and resources, and support advanced vaccine data analysis and inference. To address these problems, the community-based Vaccine Ontology (VO, "http://www.violinet.org/vaccineontology":http://www.violinet.org/vaccineontology) has been developed through collaboration with vaccine researchers and many national and international centers and programs, including the National Center for Biomedical Ontology (NCBO), the Infectious Disease Ontology (IDO) Initiative, and the Ontology for Biomedical Investigations (OBI). VO utilizes the Basic Formal Ontology (BFO) as the top ontology and the Relation Ontology (RO) for definition of term relationships. VO is represented in the Web Ontology Language (OWL) and edited using the Protégé-OWL. Currently VO contains more than 2000 terms and relationships. VO emphasizes on classification of vaccines and vaccine components, vaccine quality and phenotypes, and host immune response to vaccines. These reflect different aspects of vaccine composition and biology and can thus be used to model individual vaccines. More than 200 licensed vaccines and many vaccine candidates in research or clinical trials have been modeled in VO. VO is being used for vaccine literature mining through collaboration with the National Center for Integrative Biomedical Informatics (NCIBI). Multiple VO applications will be presented.
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Transforming the Axiomisation of Ontologies: The Ontology Pre-Processor Language

As ontologies are developed there is a common need to transform them, especially from those that are axiomatically lean to those that are axiomatically rich. Such transformations often require large numbers of axioms to be generated that affect many different parts of the ontology. This paper describes the Ontology Pre-Processor Language (OPPL), a domain-specific macro language, based in the Manchester OWL Syntax, for manipulating ontologies written in OWL. OPPL instructions can add/remove entities, and add/remove axioms (semantics or annotations) to/from entities in an OWL ontology. OPPL is suitable for applying the same change to different ontologies or at different development stages, and for keeping track of the changes made (e.g. in pipelines). It is also suitable for defining independent modelling macros (e.g. Ontology Design Patterns) that can be applied at will and systematically across an ontology. The presented OPPL Instruction Manager is a Java library that processes OPPL instructions making the changes to an OWL ontology. A reference implementation that uses the OPPL Instruction Manager is also presented. The use of OPPL has been demonstrated in the Cell Cycle Ontolog

Annotation of SBML Models Through Rule-Based Semantic Integration

*Motivation:* The creation of accurate quantitative Systems Biology Markup Language (SBML) models is a time-intensive, manual process often complicated by the many data sources and formats required to annotate even a small and well-scoped model. Ideally, the retrieval and integration of biological knowledge for model annotation should be performed quickly, precisely, and with a minimum of manual effort. Here, we present a method using off-the-shelf semantic web technology which enables this process: the heterogeneous data sources are first syntactically converted into ontologies; these are then aligned to a small domain ontology by applying a rule base. Integrating resources in this way can accommodate multiple formats with different semantics; it provides richly modelled biological knowledge suitable for annotation of SBML models.
*Results:* We demonstrate proof-of-principle for this rule-based mediation with two use cases for SBML model annotation. This was implemented with existing tools, decreasing development time and increasing reusability. This initial work establishes the feasibility of this approach as part of an automated SBML model annotation system.
*Availability:* Detailed information including download and mapping of the ontologies as well as integration results is available from "http://www.cisban.ac.uk/RBM":http://www.cisban.ac.uk/RB

User and Developer Interaction with Editable and Readable Ontologies

The process of building ontologies is a difficult task that involves collaboration between ontology developers and domain experts and requires an ongoing interaction between them. This collaboration is made more difficult, because they tend to use different tool sets, which can hamper this interaction. In this paper, we propose to decrease this distance between domain experts and ontology developers by creating more readable forms of ontologies, and further to enable editing in normal office environments. Building on a programmatic ontology development environment, such as Tawny-OWL, we are now able to generate these readable/editable from the raw ontological source and its embedded comments. We have this translation to HTML for reading; this environment provides rich hyperlinking as well as active features such as hiding the source code in favour of comments. We are now working on translation to a Word document that also enables editing. Taken together this should provide a significant new route for collaboration between the ontologist and domain specialist.Comment: 5 pages, 5 figures, accepted at ICBO 2017, License update

The OBO Foundry: Coordinated Evolution of Ontologies to Support Biomedical Data Integration

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 has set in train a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing a process of coordinated reform, and new ontologies 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, logically well-formed, and to incorporate accurate representations of biological reality. We describe the OBO Foundry initiative, and provide guidelines for those who might wish to become involved in the future