Abstract

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.

Highlights

  • The 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

  • It is being used in the Rat Genome Database and Animal quantitative trait loci (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

  • A search of ontologies to address the trait domain shows that while there are several ontologies that represent entities and processes in phenotypes, diseases, and clinical measurements, there has not been one for vertebrate traits; the Vertebrate Trait Ontology (VT) was developed to fill this void. Impetus for this project came from multiple groups including the Rat Genome Database (RGD; [13]), Mouse Genome Informatics (MGI; [14]), and the Animal QTL Database (QTLdb; [15]), and it began as a way to standardize descriptions and definitions of quantitative trait loci (QTL) for cross-species comparisons and other analyses

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Summary

Conclusions

The annotation of genomic data with ontology terms provides unique opportunities for data mining and analysis. This provides a starting point for annotating other species with the VT and visualizing all the data at a glance Availability and requirements This ontology is free and open to all users. Additional file 2: Mockup of hypothetical cross-species data portal We provide an example of a putative “trait portal,” which would allow users to view large amounts of related data via a single entry point. Authors’ contributions CP drafted the manuscript, carried out evaluation of VT structure, crafted term definitions, and utilized the VT for data annotation. CS carried out evaluation of structure, crafted definitions, and helped to draft the manuscript. JS utilized the VT for data annotation, crafted definitions, and helped to draft the manuscript.

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