Three Ontologies to Define Phenotype Measurement Data

Human and Molecular Genetics Center, Medical College of Wisconsin Milwaukee, WI, USA.
Frontiers in Genetics 05/2012; 3:87. DOI: 10.3389/fgene.2012.00087
Source: PubMed


There is an increasing need to integrate phenotype measurement data across studies for both human studies and those involving model organisms. Current practices allow researchers to access only those data involved in a single experiment or multiple experiments utilizing the same protocol.
Three ontologies were created: Clinical Measurement Ontology, Measurement Method Ontology and Experimental Condition Ontology. These ontologies provided the framework for integration of rat phenotype data from multiple studies into a single resource as well as facilitated data integration from multiple human epidemiological studies into a centralized repository.
An ontology based framework for phenotype measurement data affords the ability to successfully integrate vital phenotype data into critical resources, regardless of underlying technological structures allowing the user to easily query and retrieve data from multiple studies.

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    • "In fact, although there is no specific ontology for urinalysis, a substantial part of its concepts is already represented in other biomedical ontologies[7][12]. They model urinalysis as a procedure, with concepts for its procedural steps (both in physicochemical analysis[8]and microscopy[7]) as well as concepts for all types of urinary contents (e.g. cells, chemical substances[7][9], including notions of abnormal values[12]) and tools for the test[10]. "
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    ABSTRACT: Urinalysis is the test of urine, which, identifying presence of particles and substances in the sample can provide valuable information about patient's urinary and renal condition as well as about major metabolic functions. Even though being a quick, accurate and cost-effective test, it has not received proper attention, preventing it to achieve its whole power. Capability to identify the main urinary particles and arrange urinary findings in a clinical context are among the requirements to change this scenario. Since these are chiefly informational tasks, they seem to be liable to computational representation. This way, this work presents an ontological model to represent urinary profiles in order to allow such arrange of findings and help to predict contents to be searched in the sample. It is based on an ontological analysis of the problem and formalized according to the Unified Foundational Ontology (UFO). The paper also brings an implementation of such model to deal with the case of nephritic profile. It is implemented using OWL 2 and allows important inference through simple DL queries. Besides dealing with urinary profiles, the proposed model may also give insight on how to deal with correlate domains (e.g. blood or liquor tests) and may be generalized to similar situations in different scopes (e.g. employee profiling).
    Full-text · Conference Paper · Nov 2015
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    • "This has been an active area of research in recent years (Hancock et al., 2009; Schofield et al., 2010). Shimoyama et al. (2012) make an important contribution to this area by describing a set of ontologies used to describe clinical measurements, measurement methods and experimental conditions for traits common to rat and man (and, by extension, in other mammalian model systems such as mouse and, potentially, more distantly related species). These measurements are similar to those used in large-scale phenotyping experiments (Hancock and Gates, 2011) so that this ontology system provides a potentially valuable mechanism for the study of genotype-phenotype relations in mammals. "

    Full-text · Article · Feb 2014 · Frontiers in Genetics
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    • "PhenoMiner has 18580 records with quantified phenotype values attached to consomic strains, 11524 values attached to inbred strains, 2870 to congenic strains, 2204 to all mutant (ZFN) strains and 2063 to all mutant (ENU) strains as of September 2013 [20]. These are entered into PhenoMiner by using the RS Ontology and three other ontologies, namely, clinical measurement (CMO), measurement method (MMO), and experimental condition ontologies (XCO) [21]. All rat QTLs are annotated to the RS Ontology to facilitate querying, retrieval and filtering of QTL data [22]. "
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    ABSTRACT: The Rat Genome Database (RGD) ( is the premier site for comprehensive data on the different strains of the laboratory rat (Rattus norvegicus). The strain data are collected from various publications, direct submissions from individual researchers, and rat providers worldwide. Rat strain, substrain designation and nomenclature follow the Guidelines for Nomenclature of Mouse and Rat Strains, instituted by the International Committee on Standardized Genetic Nomenclature for Mice. While symbols and names aid in identifying strains correctly, the flat nature of this information prohibits easy search and retrieval, as well as other data mining functions. In order to improve these functionalities, particularly in ontology-based tools, the Rat Strain Ontology (RS) was developed. The Rat Strain Ontology (RS) reflects the breeding history, parental background, and genetic manipulation of rat strains. This controlled vocabulary organizes strains by type: inbred, outbred, chromosome altered, congenic, mutant and so on. In addition, under the chromosome altered category, strains are organized by chromosome, and further by type of manipulations, such as mutant or congenic. This allows users to easily retrieve strains of interest with modifications in specific genomic regions. The ontology was developed using the Open Biological and Biomedical Ontology (OBO) file format, and is organized on the Directed Acyclic Graph (DAG) structure. Rat Strain Ontology IDs are included as part of the strain report (RS: ######). As rat researchers are often unaware of the number of substrains or altered strains within a breeding line, this vocabulary now provides an easy way to retrieve all substrains and accompanying information. Its usefulness is particularly evident in tools such as the PhenoMiner at RGD, where users can now easily retrieve phenotype measurement data for related strains, strains with similar backgrounds or those with similar introgressed regions. This controlled vocabulary also allows better retrieval and filtering for QTLs and in genomic tools such as the GViewer.The Rat Strain Ontology has been incorporated into the RGD Ontology Browser ( and is available through the National Center for Biomedical Ontology ( or the RGD ftp site (
    Full-text · Article · Nov 2013 · Journal of Biomedical Semantics
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