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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Available from: Melinda R Dwinell, Oct 05, 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. "
    Frontiers in Genetics 02/2014; 5:18. DOI:10.3389/fgene.2014.00018
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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 (
    Journal of Biomedical Semantics 11/2013; 4(1):36. DOI:10.1186/2041-1480-4-36 · 2.26 Impact Factor
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    • "This is not the case for the (extrinsic) boundary conditions. The Experimental Conditions Ontology (XCO) [21] provides a rich vocabulary of experimental conditions for phenotype experiments. The Measurement Method Ontology (MMO) [21] can be used for specifying the measurement method in descriptions of extrinsic experimental settings. "
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    ABSTRACT: Background Dynamic models in Systems Biology are used in computational simulation experiments for addressing biological questions. The complexity of the modelled biological systems and the growing number and size of the models calls for computer support for modelling and simulation in Systems Biology. This computer support has to be based on formal representations of relevant knowledge fragments. Results In this paper we describe different functional aspects of dynamic models. This description is conceptually embedded in our "meaning facets" framework which systematises the interpretation of dynamic models in structural, functional and behavioural facets. Here we focus on how function links the structure and the behaviour of a model. Models play a specific role (teleological function) in the scientific process of finding explanations for dynamic phenomena. In order to fulfil this role a model has to be used in simulation experiments (pragmatical function). A simulation experiment always refers to a specific situation and a state of the model and the modelled system (conditional function). We claim that the function of dynamic models refers to both the simulation experiment executed by software (intrinsic function) and the biological experiment which produces the phenomena under investigation (extrinsic function). We use the presented conceptual framework for the function of dynamic models to review formal accounts for functional aspects of models in Systems Biology, such as checklists, ontologies, and formal languages. Furthermore, we identify missing formal accounts for some of the functional aspects. In order to fill one of these gaps we propose an ontology for the teleological function of models. Conclusion We have thoroughly analysed the role and use of models in Systems Biology. The resulting conceptual framework for the function of models is an important first step towards a comprehensive formal representation of the functional knowledge involved in the modelling and simulation process. Any progress in this area will in turn improve computer-supported modelling and simulation in Systems Biology.
    Journal of Biomedical Semantics 10/2013; 4(1):24. DOI:10.1186/2041-1480-4-24 · 2.26 Impact Factor
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