The Mammalian Phenotype Ontology as a unifying standard for experimental and high-throughput phenotyping data

The Jackson Laboratory, Bar Harbor, ME, 04609, USA.
Mammalian Genome (Impact Factor: 3.07). 09/2012; 23(9-10):653-68. DOI: 10.1007/s00335-012-9421-3
Source: PubMed


The Mammalian Phenotype Ontology (MP) is a structured vocabulary for describing mammalian phenotypes and serves as a critical tool for efficient annotation and comprehensive retrieval of phenotype data. Importantly, the ontology contains broad and specific terms, facilitating annotation of data from initial observations or screens and detailed data from subsequent experimental research. Using the ontology structure, data are retrieved inclusively, i.e., data annotated to chosen terms and to terms subordinate in the hierarchy. Thus, searching for "abnormal craniofacial morphology" also returns annotations to "megacephaly" and "microcephaly," more specific terms in the hierarchy path. The development and refinement of the MP is ongoing, with new terms and modifications to its organization undergoing continuous assessment as users and expert reviewers propose expansions and revisions. A wealth of phenotype data on mouse mutations and variants annotated to the MP already exists in the Mouse Genome Informatics database. These data, along with data curated to the MP by many mouse mutagenesis programs and mouse repositories, provide a platform for comparative analyses and correlative discoveries. The MP provides a standard underpinning to mouse phenotype descriptions for existing and future experimental and large-scale phenotyping projects. In this review we describe the MP as it presently exists, its application to phenotype annotations, the relationship of the MP to other ontologies, and the integration of the MP within large-scale phenotyping projects. Finally we discuss future application of the MP in providing standard descriptors of the phenotype pipeline test results from the International Mouse Phenotype Consortium projects.

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Available from: Cynthia L Smith, Sep 17, 2014
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    • "Finally, the Neuro Behaviour Ontology (NBO) (Gkoutos et al. 2012) was developed in order to capture and analyse phenotypes and processes relating to normal and abnormal behaviours and their underlying neurological basis, across multiple species including humans for the purpose of producing a comprehensive representation of the whole of the domain of neurobiology and behaviour, including those areas relevant to human psychiatric disease. NBO was developed as an extension of the GO behavioural processes domain, and extends and subsumes behavioural and neurological phenotype classes present in various ontologies, for example, the mammalian and human phenotype ontologies (Kohler et al. 2013; Smith and Eppig 2012, 2015). Furthermore, classes from the NBO representing behavioural processes have been annotated with mouse gene products and can be applied, for example, in ontology-based enrichment analysis for the interpretation of gene expression data (Hoehndorf et al. 2014). "
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    ABSTRACT: The development of ontologies for describing animal behaviour has proved to be one of the most difficult of all scientific knowledge domains. Ranging from neurological processes to human emotions, the range and scope needed for such ontologies is highly challenging, but if data integration and computational tools such as automated reasoning are to be fully applied in this important area the underlying principles of these ontologies need to be better established and development needs detailed coordination. Whilst the state of scientific knowledge is always paramount in ontology and formal description framework design, this is a particular problem with neurobehavioural ontologies where our understanding of the relationship between behaviour and its underlying biophysical basis is currently in its infancy. In this commentary, we discuss some of the fundamental problems in designing and using behaviour ontologies, and present some of the best developed tools in this domain.
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    • "MouseMine is based on the InterMine framework. InterMine has been described previously (Smith et al. 2012) as well as elsewhere in this volume. "
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    ABSTRACT: The Gene Expression Database (GXD) is an extensive and freely available community resource of mouse developmental expression data. GXD curates and integrates expression data from the literature, via electronic data submissions, and by collaborations with large-scale projects. As an integral component of the Mouse Genome Informatics (MGI) Resource, GXD combines expression data with genetic, functional, phenotypic and disease-related data, and provides tools for the research community to search for and analyze expression data in this larger context. Recent enhancements include: an interactive browser to navigate the mouse developmental anatomy and find expression data for specific anatomical structures; the capability to search for expression data of genes located in specific genomic regions, supporting the identification of disease candidate genes; a summary displaying all the expression images that meet specified search criteria; interactive matrix views that provide overviews of spatio-temporal expression patterns (Tissue x Stage Matrix) and enable the comparison of expression patterns between genes (Tissue x Gene Matrix); data zoom and filter utilities to iteratively refine summary displays and data sets; and gene-based links to expression data from other model organisms, such as chicken, Xenopus and zebrafish, fostering comparative expression analysis for species that are highly relevant for developmental research. This article is protected by copyright. All rights reserved. © 2015 Wiley Periodicals, Inc.
    genesis 06/2015; 53(8). DOI:10.1002/dvg.22864 · 2.02 Impact Factor
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    • "Different anatomy ontologies specify the organismal components for multiple species, and – on a smaller scale of granularity – the developmental relations and features of cell types are characterized by the Celltype Ontology [18]. Phenotype ontologies are also available for multiple species and are widely used for the annotation of the abnormalities observed in mutagenesis experiments [19-21] as well as for the characterization of diseases and drug effects [22]. "
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