Dataset Information

Animal trait ontology: The importance and usefulness of a unified trait vocabulary for animal species.

ABSTRACT: Ontologies help to identify and formally define the entities and relationships in specific domains of interest. Bio-ontologies, in particular, play a central role in the annotation, integration, analysis, and interpretation of biological data. Missing from the number of bio-ontologies is one that includes phenotypic trait information found in livestock species. As a result, the Animal Trait Ontology (ATO) project being carried out under the auspices of the USDA-National Animal Genome Research Program is aimed at the development of a standardized trait ontology for farm animals and software tools to assist the research community in collaborative creation, editing, maintenance, and use of such an ontology. The ATO is currently inclusive of cattle, pig, and chicken species, and will include other livestock species in the future. The ATO will eventually be linked to other species (e.g., human, rat, mouse) so that comparative analysis can be efficiently performed between species.

SUBMITTER: Hughes LM

PROVIDER: S-EPMC2569847 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature

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Animal trait ontology: The importance and usefulness of a unified trait vocabulary for animal species.

Hughes L M LM Bao J J Hu Z-L ZL Honavar V V Reecy J M JM

Journal of animal science 20080213 6

Ontologies help to identify and formally define the entities and relationships in specific domains of interest. Bio-ontologies, in particular, play a central role in the annotation, integration, analysis, and interpretation of biological data. Missing from the number of bio-ontologies is one that includes phenotypic trait information found in livestock species. As a result, the Animal Trait Ontology (ATO) project being carried out under the auspices of the USDA-National Animal Genome Research Pr ...[more]

PMID: 18272850

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Project description:BackgroundThe use of ontologies to standardize biological data and facilitate comparisons among datasets has steadily grown as the complexity and amount of available data have increased. Despite the numerous ontologies available, one area currently lacking a robust ontology is the description of vertebrate traits. A trait is defined as any measurable or observable characteristic pertaining to an organism or any of its substructures. While there are several ontologies to describe entities and processes in phenotypes, diseases, and clinical measurements, one has not been developed for vertebrate traits; the Vertebrate Trait Ontology (VT) was created to fill this void.DescriptionSignificant inconsistencies in trait nomenclature exist in the literature, and additional difficulties arise when trait data are compared across species. The VT is a unified trait vocabulary created to aid in the transfer of data within and between species and to facilitate investigation of the genetic basis of traits. Trait information provides a valuable link between the measurements that are used to assess the trait, the phenotypes related to the traits, and the diseases associated with one or more phenotypes. Because multiple clinical and morphological measurements are often used to assess a single trait, and a single measurement can be used to assess multiple physiological processes, providing investigators with standardized annotations for trait data will allow them to investigate connections among these data types.ConclusionsThe annotation of genomic data with ontology terms provides unique opportunities for data mining and analysis. Links between data in disparate databases can be identified and explored, a strategy that is particularly useful for cross-species comparisons or in situations involving inconsistent terminology. The VT provides a common basis for the description of traits in multiple vertebrate species. It is being used in the Rat Genome Database and Animal QTL Database for annotation of QTL data for rat, cattle, chicken, swine, sheep, and rainbow trout, and in the Mouse Phenome Database to annotate strain characterization data. In these databases, data are also cross-referenced to applicable terms from other ontologies, providing additional avenues for data mining and analysis. The ontology is available at http://bioportal.bioontology.org/ontologies/50138.

Project description:Scientists and clinicians who study genetic alterations and disease have traditionally described phenotypes in natural language. The considerable variation in these free-text descriptions has posed a hindrance to the important task of identifying candidate genes and models for human diseases and indicates the need for a computationally tractable method to mine data resources for mutant phenotypes. In this study, we tested the hypothesis that ontological annotation of disease phenotypes will facilitate the discovery of new genotype-phenotype relationships within and across species. To describe phenotypes using ontologies, we used an Entity-Quality (EQ) methodology, wherein the affected entity (E) and how it is affected (Q) are recorded using terms from a variety of ontologies. Using this EQ method, we annotated the phenotypes of 11 gene-linked human diseases described in Online Mendelian Inheritance in Man (OMIM). These human annotations were loaded into our Ontology-Based Database (OBD) along with other ontology-based phenotype descriptions of mutants from various model organism databases. Phenotypes recorded with this EQ method can be computationally compared based on the hierarchy of terms in the ontologies and the frequency of annotation. We utilized four similarity metrics to compare phenotypes and developed an ontology of homologous and analogous anatomical structures to compare phenotypes between species. Using these tools, we demonstrate that we can identify, through the similarity of the recorded phenotypes, other alleles of the same gene, other members of a signaling pathway, and orthologous genes and pathway members across species. We conclude that EQ-based annotation of phenotypes, in conjunction with a cross-species ontology, and a variety of similarity metrics can identify biologically meaningful similarities between genes by comparing phenotypes alone. This annotation and search method provides a novel and efficient means to identify gene candidates and animal models of human disease, which may shorten the lengthy path to identification and understanding of the genetic basis of human disease.

Dataset Information

Animal trait ontology: The importance and usefulness of a unified trait vocabulary for animal species.

Publications

Animal trait ontology: The importance and usefulness of a unified trait vocabulary for animal species.

Similar Datasets

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