Obol: integrating language and meaning in bio‐ontologies

Mungall, Christopher J.

doi:10.1002/cfg.435

Cited by 84 publications

(58 citation statements)

References 15 publications

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“…However, we expect that approaches to finding associations between categories using lexical and statistical analysis like (Bodenreider et al, 2005;Burgun et al, 2004) can be exploited and combined with the OF, in order to add categories and relations between them automatically. These could further be verified by existing natural language processing techniques (Mungall, 2004).…”

Section: Adding Information Systematicallymentioning

confidence: 80%

A top-level ontology of functions and its application in the Open Biomedical Ontologies

et al. 2006

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Motivation: A clear understanding of functions in biology is a key component in accurate modelling of molecular, cellular and organismal biology. Using the existing biomedical ontologies it has been impossible to capture the complexity of the community's knowledge about biological functions. Results: We present here a top-level ontological framework for representing knowledge about biological functions. This framework lends greater accuracy, power and expressiveness to biomedical ontologies by providing a means to capture existing functional knowledge in a more formal manner. An initial major application of the ontology of functions is the provision of a principled way in which to curate functional knowledge and annotations in biomedical ontologies. Further potential applications include the facilitation of ontology interoperability and automated reasoning. A major advantage of the proposed implementation is that it is an extension to existing biomedical ontologies, and can be applied without substantial changes to these domain ontologies. Availability: The Ontology of Functions (OF) can be downloaded in OWL format from http://onto.eva.mpg.de/. Additionally, a UML profile and supplementary information and guides for using the OF can be accessed from the same website.

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Section: Adding Information Systematicallymentioning

confidence: 80%

A top-level ontology of functions and its application in the Open Biomedical Ontologies

et al. 2006

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“…But the approach will work only if the resultant terms are unambiguous, and here the Foundry helps provide the necessary rigor. The orthogonality principle helps to reduce the need for arbitrary decisions between equivalent-seeming terms drawn from different ontologies, the PATO phenotypic-quality ontology provides templates for term formation, and the RO provides formally coherent glue for combination 33 .…”

Section: Models Of Good Practicementioning

confidence: 99%

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

et al. 2007

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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.In the search for what is biologically and clinically significant in the swarms of data being generated by today's high-throughput technologies, a common strategy involves the creation and analysis of 'annotations' linking primary data to expressions in controlled, structured vocabularies, thereby making the data available to search and to algorithmic processing 1 . The most successful such endeavor, measured both by numbers of users and by reach across species and granularities, is the Gene Ontology (GO) 2 . There exist over 11 million annotations relating gene products described in the UniProt, Ensembl and other databases to terms in the GO3, of which half a million have been manually verified by specialist curators in different modelorganism communities on the basis of the analysis of experimental results reported in 52,000 scientific journal articles (http://www.ebi.ac.uk/GOA/). Data related to some 180,000 genes have been manually annotated in this way, an endeavor now being refined and systematized within the Reference Genome Project (US National Institutes of Health National Human Genome Research Institute grant 2P41HG002273-07), which will provide comprehensive GO annotations for both the human genome and a representative set of model-organism genomes in support of research on the primary molecular systems affecting human health. From retrospective mapping to prospective standardizationThe domain of molecular biology is marked by the availability of large amounts of well defined data that can be used without restriction as inputs to algorithmic processing. In the clinical domain, by contrast, only limited amounts of data are available for research purposes, and these still consist overwhelmingly of natural language text. Even where more systematic clinical data are available, the use of local coding schemes means that these data do not cumulate in ways useful to research 4 . One approach to solving this problem is the Unified Medical Language System (UMLS) 5 , a compendium of some 100 source vocabularies combined through a process of...

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“…These details can be formalized by referencing structural qualities specified in the Phenotype Attribute and Trait Ontology. 9 Structural classification is limited in its ability to capture some of the dynamic structural changes which are important to developmental biologists. Specifically, they are interested in defining and classifying portions of developing tissue.…”

Section: Developing Structure Typesmentioning

confidence: 99%

“…GO will retain types such as neural tube closure, but the corresponding definitions can refer to definitions taken from CARO or from one of the multi-species or single-species anatomy ontologies created in a way which will allow the ontologies to be kept synchronized [9].…”

Section: Long Term Caro Goalsmentioning

confidence: 99%

CARO – The Common Anatomy Reference Ontology

Haendel¹,

Neuhaus²,

Osumi-Sutherland³

et al. 2008

Computational Biology

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Summary. The Common Anatomy Reference Ontology (CARO) is being developed to facilitate interoperability between existing anatomy ontologies for different species, and will provide a template for building new anatomy ontologies. CARO has a structural axis of classification based on the top-level nodes of the Foundational Model of Anatomy. CARO will complement the developmental process sub-ontology of the GO Biological Process ontology, using the latter to ensure the coherent treatment of developmental stages, and to provide a common framework for the model organism communities to classify developmental structures. Definitions for the types and relationships are being generated by a consortium of investigators from diverse backgrounds to ensure applicability to all organisms. CARO will support the coordination of cross-species ontologies at all levels of anatomical granularity by crossreferencing types within the cell type ontology (CL) and the Gene Ontology (GO) Cellular Component ontology. A complete cross-species CARO could be utilized by other ontologies for cross-product generation. Necessity of a Common Anatomy Reference OntologyGenomes are modified over evolutionary time to produce a diversity of anatomical forms. Understanding the relationship between a genome and its phenotypic outcome requires an integrative approach that synthesizes knowledge derived from the study of biological entities at various levels of granularity, encompassing gene structure and function, development, phylogenetic relationships, and ecology.Many model organism databases collect large amounts of data on the relationship between genetic/genomic variation and morphological phenotypes in databases. Model organism databases standardize the description of morphological phenotypes and gene expression patterns by using types from anatomy ontologies that are specific to their focus species of interest. These ontologies have allowed the model organism databases to group phenotypic and gene expression data pertaining to partic- * Melissa Haendel, Fabian Neuhaus, and David Osumi-Sutherland contributed equally to this chapter.

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Obol: integrating language and meaning in bio‐ontologies

Cited by 84 publications

References 15 publications

A top-level ontology of functions and its application in the Open Biomedical Ontologies

A top-level ontology of functions and its application in the Open Biomedical Ontologies

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

CARO – The Common Anatomy Reference Ontology

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