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      The anatomy of phenotype ontologies: principles, properties and applications

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          Abstract

          The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large but also highly multidimensional. Computational analysis of phenotypes has therefore become critical for our ability to understand the biological meaning of genomic data in the biological sciences. At the heart of computational phenotype analysis are the phenotype ontologies. A large number of these ontologies have been developed across many domains, and we are now at a point where the knowledge captured in the structure of these ontologies can be used for the integration and analysis of large interrelated data sets. The Phenotype And Trait Ontology framework provides a method for formal definitions of phenotypes and associated data sets and has proved to be key to our ability to develop methods for the integration and analysis of phenotype data. Here, we describe the development and products of the ontological approach to phenotype capture, the formal content of phenotype ontologies and how their content can be used computationally.

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          Most cited references91

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Uberon, an integrative multi-species anatomy ontology

            We present Uberon, an integrated cross-species ontology consisting of over 6,500 classes representing a variety of anatomical entities, organized according to traditional anatomical classification criteria. The ontology represents structures in a species-neutral way and includes extensive associations to existing species-centric anatomical ontologies, allowing integration of model organism and human data. Uberon provides a necessary bridge between anatomical structures in different taxa for cross-species inference. It uses novel methods for representing taxonomic variation, and has proved to be essential for translational phenotype analyses. Uberon is available at http://uberon.org
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              SGD: Saccharomyces Genome Database.

              J. Cherry (1998)
              The Saccharomyces Genome Database (SGD) provides Internet access to the complete Saccharomyces cerevisiae genomic sequence, its genes and their products, the phenotypes of its mutants, and the literature supporting these data. The amount of information and the number of features provided by SGD have increased greatly following the release of the S.cerevisiae genomic sequence, which is currently the only complete sequence of a eukaryotic genome. SGD aids researchers by providing not only basic information, but also tools such as sequence similarity searching that lead to detailed information about features of the genome and relationships between genes. SGD presents information using a variety of user-friendly, dynamically created graphical displays illustrating physical, genetic and sequence feature maps. SGD can be accessed via the World Wide Web at http://genome-www.stanford.edu/Saccharomyces/
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                Author and article information

                Journal
                Brief Bioinform
                Brief. Bioinformatics
                bib
                Briefings in Bioinformatics
                Oxford University Press
                1467-5463
                1477-4054
                September 2018
                06 April 2017
                06 April 2017
                : 19
                : 5
                : 1008-1021
                Affiliations
                [1 ]University of Birmingham Medical School
                [2 ]University of Cambridge
                [3 ]Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, King Abdullah University of Science and Technology, Thuwal
                Author notes
                Corresponding author. Robert Hoehndorf, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, 4700 King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia. Tel.: +966-54-0523450; E-mail: robert.hoehndorf@ 123456kaust.edu.sa
                Article
                bbx035
                10.1093/bib/bbx035
                6169674
                28387809
                6297db33-8781-4b75-a879-586f8e8923ec
                © The Author 2017. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 01 January 2017
                : 05 February 2017
                Page count
                Pages: 14
                Funding
                Funded by: National Science Foundation 10.13039/100000001
                Award ID: IOS:1340112
                Funded by: European Commission 10.13039/501100000780
                Award ID: 731075
                Categories
                Paper

                Bioinformatics & Computational biology
                phenotype,ontology,pato,data integration,semantic web
                Bioinformatics & Computational biology
                phenotype, ontology, pato, data integration, semantic web

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