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New Era of Developing and Using Ontologies for Microbiology Research and Diagnosis | OMICS International
ISSN: 2161-0703
Journal of Medical Microbiology & Diagnosis

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New Era of Developing and Using Ontologies for Microbiology Research and Diagnosis

Yu Lin1 and Yongqun He1*

1Unit for Lab Animal Medicine, Department of Microbiology and Immunology, and Center for Computational Medicine and Bioinformatics, University of Michigan Medical
School, Ann Arbor, MI 48109, USA

*Corresponding Author:
Yongqun He
Unit for Lab Animal Medicine
Department of Microbiology and Immunology
and Center for Computational Medicine and Bioinformatics
University of Michigan Medical School
Ann Arbor, MI 48109, USA
E-mail: [email protected]

Received Date: May 20, 2012; Accepted Date: May 22, 2012; Published Date: May 27, 2012

Citation: Lin Y, He Y (2012) New Era of Developing and Using Ontologies for Microbiology Research and Diagnosis. J Med Microb Diagn 1:e107. doi: 10.4172/2161-0703.1000e107

Copyright: © 2012 Lin Y, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Originated from philosophy, the term ontology is used in bioinformatics filed to refer to a representative vocabulary that describes the knowledge of some domain, typically a commonsense knowledge domain [1]. The term formal ontology used in biomedicine today typically means the definition of sets of terms and relations including logical assumptions and inference machinery [2]. Biomedical (formal) ontologies have been used intensively. For example, the Gene Ontology (GO) as a tool for unification of biology was first published in 2000 [3]. As of May 16, 2012, a search of "Gene Ontology" in PubMed retrieved more than 4000 papers. GO plays an important role in interpreting biological data to rational biological meanings. For instance, the GO annotations have served as a backbone for current OMICS data analysis. Currently over 300 biomedical ontologies have been stored in the Bioportal ontology repository of the National Center for Biological Ontology (NCBO):

To facilitate integrative research in infectious diseases, a community- based Infectious Disease Ontology (IDO) has been developed [4]. IDO is aimed to include a suite of interoperable ontologies that jointly cover the entire infectious disease domain, spanning infectious disease specialties and the clinical care, public health, and biomedical research. Under the framework of IDO, we have recently developed Brucellosis Ontology (IDOBRU), the first reported bacterial infectious disease ontology [5]. Brucellosis is a zoonotic infectious disease caused by intracellular facultative Gram-negative bacteria Brucella spp. As a brucellosis knowledgebase, IDOBRU represents different aspects of brucellosis, including host infection, zoonotic disease transmission, symptoms, virulence factors and pathogenesis, diagnosis, intentional release, vaccine prevention, and treatment [5]. As a formal ontology developed using the Web Ontology Language (OWL) [6], IDOBRU provides both machine-readable and human-readable vocabulary and supports computer-assisted automated reasoning. For example, based on biological knowledge captured within the ontology, simple scripts can be developed to query IDOBRU and identify 229 Brucella virulence factors, 29 protective antigens, and one protein that is both virulence factor and protective antigen [4]. IDOBRU also includes different Brucella diagnostic methods such as specimen culturing, immunoassays, and Polymerase Chain Reaction (PCR) assays. These methods are organized in logical and specifically defined hierarchies. As an example, a specific PCR assay in IDOBRU includes the assayed Brucella species, gene name and information for obtaining gene sequence, PCR primers and product details, the specimen used for diagnosis, and the assertion to make a diagnosis based on a PCR result. These details are represented logically and interlinked with computer-understandable relations. Such a meta data design can be used to represent various PCR assays. The IDOBRU knowledge base is also integrated with other biomedical ontologies such as GO, IDO-core, and other specific IDO-core extensions. Such an integrative approach supports complex data exchange, data integration, and automated reasoning.

The new era of developing and using biomedical ontologies in microbiology research and diagnosis has just started. The preliminary results out of IDOBRU and other biomedical ontologies have demonstrated their significant power and novelty of integrating data and creation of knowledge in the field of microbiology. More efforts are required for further development and applications of biomedical ontologies.


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