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      Modifier Ontologies for frequency, certainty, degree, and coverage phenotype modifier

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          Abstract

          Abstract

          Background: When phenotypic characters are described in the literature, they may be constrained or clarified with additional information such as the location or degree of expression, these terms are called “modifiers”. With effort underway to convert narrative character descriptions to computable data, ontologies for such modifiers are needed. Such ontologies can also be used to guide term usage in future publications. Spatial and method modifiers are the subjects of ontologies that already have been developed or are under development. In this work, frequency (e.g., rarely, usually), certainty (e.g., probably, definitely), degree (e.g., slightly, extremely), and coverage modifiers (e.g., sparsely, entirely) are collected, reviewed, and used to create two modifier ontologies with different design considerations. The basic goal is to express the sequential relationships within a type of modifiers, for example, usually is more frequent than rarely, in order to allow data annotated with ontology terms to be classified accordingly.

          Method: Two designs are proposed for the ontology, both using the list pattern: a closed ordered list (i.e., five-bin design) and an open ordered list design. The five-bin design puts the modifier terms into a set of 5 fixed bins with interval object properties, for example, one_level_more/less_frequently_than, where new terms can only be added as synonyms to existing classes. The open list approach starts with 5 bins, but supports the extensibility of the list via ordinal properties, for example, more/less_frequently_than, allowing new terms to be inserted as a new class anywhere in the list. The consequences of the different design decisions are discussed in the paper. CharaParser was used to extract modifiers from plant, ant, and other taxonomic descriptions. After a manual screening, 130 modifier words were selected as the candidate terms for the modifier ontologies. Four curators/experts (three biologists and one information scientist specialized in biosemantics) reviewed and categorized the terms into 20 bins using the Ontology Term Organizer (OTO) ( http://biosemantics.arizona.edu/OTO). Inter-curator variations were reviewed and expressed in the final ontologies.

          Results: Frequency, certainty, degree, and coverage terms with complete agreement among all curators were used as class labels or exact synonyms. Terms with different interpretations were either excluded or included using “broader synonym” or “not recommended” annotation properties. These annotations explicitly allow for the user to be aware of the semantic ambiguity associated with the terms and whether they should be used with caution or avoided. Expert categorization results showed that 16 out of 20 bins contained terms with full agreements, suggesting differentiating the modifiers into 5 levels/bins balances the need to differentiate modifiers and the need for the ontology to reflect user consensus. Two ontologies, developed using the Protege ontology editor, are made available as OWL files and can be downloaded from https://github.com/biosemantics/ontologies.

          Contribution: We built the first two modifier ontologies following a consensus-based approach with terms commonly used in taxonomic literature. The five-bin ontology has been used in the Explorer of Taxon Concepts web toolkit to compute the similarity between characters extracted from literature to facilitate taxon concepts alignments. The two ontologies will also be used in an ontology-informed authoring tool for taxonomists to facilitate consistency in modifier term usage.

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          Most cited references 28

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          The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration.

          The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or 'ontologies'. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium is pursuing a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing coordinated reform, and new ontologies are being created on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality. We describe this OBO Foundry initiative and provide guidelines for those who might wish to become involved.
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            The Human Phenotype Ontology in 2017

            Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.
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              NEXUS: an extensible file format for systematic information.

              NEXUS is a file format designed to contain systematic data for use by computer programs. The goals of the format are to allow future expansion, to include diverse kinds of information, to be independent of particular computer operating systems, and to be easily processed by a program. To this end, the format is modular, with a file consisting of separate blocks, each containing one particular kind of information, and consisting of standardized commands. Public blocks (those containing information utilized by several programs) house information about taxa, morphological and molecular characters, distances, genetic codes, assumptions, sets, trees, etc.; private blocks contain information of relevance to single programs. A detailed description of commands in public blocks is given. Guidelines are provided for reading and writing NEXUS files and for extending the format.
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                Author and article information

                Contributors
                Journal
                Biodivers Data J
                Biodivers Data J
                Biodiversity Data Journal
                Biodiversity Data Journal
                Biodiversity Data Journal
                Pensoft Publishers
                1314-2828
                2018
                28 November 2018
                : 6
                Affiliations
                [1 ] University of Florida, Gainesville, United States of America University of Florida Gainesville United States of America
                [2 ] The Ronin Institute for Independent Scholarship, Monclair, NJ, United States of America The Ronin Institute for Independent Scholarship Monclair, NJ United States of America
                [3 ] Science and Technology Branch, Agriculture and Agri-Food Canada, Government of Canada, Ottawa, Canada Science and Technology Branch, Agriculture and Agri-Food Canada, Government of Canada Ottawa Canada
                [4 ] CyVerse, Tucson, United States of America CyVerse Tucson United States of America
                [5 ] College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, United Kingdom College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham Birmingham United Kingdom
                [6 ] Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, B15 2TT, Birmingham, United Kingdom Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, B15 2TT Birmingham United Kingdom
                [7 ] Center for Studies of Information Resources, Wuhan Universtity, Wuhan, China Center for Studies of Information Resources, Wuhan Universtity Wuhan China
                [8 ] National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara, Santa Barbara, United States of America National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara Santa Barbara United States of America
                [9 ] University of Arizona, Tucson, United States of America University of Arizona Tucson United States of America
                Author notes
                Corresponding authors: Lorena Endara ( clendara@ 123456gmail.com ), Hong Cui ( hongcui@ 123456email.arizona.edu ).

                Academic editor: Ross Mounce

                Article
                Biodiversity Data Journal 9817
                10.3897/BDJ.6.e29232
                6281706
                Lorena Endara, Anne Thessen, Heather Cole, Ramona Walls, Georgios Gkoutos, Yujie Cao, Steven Chong, Hong Cui

                This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 4, Tables: 4, References: 34
                Categories
                Research Article
                Animalia
                Archaea
                Bacteria
                Chromista
                Fungi
                Plantae
                Protozoa
                Bioinformatics
                Cenozoic
                Mesozoic
                Paleozoic
                Precambrian
                World

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