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  • Review Article
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Ontologies in biology: design, applications and future challenges

Nature Reviews Genetics volume 5, pages 213–222 (2004)Cite this article

Key Points

  • Bio-ontologies provide a means of formalizing biological knowledge — for example, about genes, anatomy and phenotypes — in complex hierarchies that are composed of terms and rules.

  • Most bio-ontologies are stored at http://obo.sourceforge.net and are accepted by the community as authoritative.

  • All bio-ontologies assign a unique identifier (ID) for each term and these allow the archiving, storing and accessing of data in databases.

  • Ontology IDs provide a means of querying between databases (a function known as 'interoperability').

  • Complicated knowledge (such as that describing mutant phenotypes) can most easily be handled by composite annotations to multiple ontologies (anatomy, cell biology, pathology, traits, and so on).

  • The review concludes by discussing some of the problems that the field is now facing.

Abstract

Biological knowledge is inherently complex and so cannot readily be integrated into existing databases of molecular (for example, sequence) data. An ontology is a formal way of representing knowledge in which concepts are described both by their meaning and their relationship to each other. Unique identifiers that are associated with each concept in biological ontologies (bio-ontologies) can be used for linking to and querying molecular databases. This article reviews the principal bio-ontologies and the current issues in their design and development: these include the ability to query across databases and the problems of constructing ontologies that describe complex knowledge, such as phenotypes.

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Figure 1: The GO process ontology visualized with the DAG-edit program (version 4).
Figure 2: Coding phenotype.
Figure 3: Interoperability between mouse anatomy from digital sections and a gene-expression database.
Figure 4: Diversity of floral morphology in angiosperms.

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Acknowledgements

We thank the curators of the various animal, plant and prokaryote databases who participated in the mutant phenotype ontology meetings (see list of URLs in online links box for groups that participated). We are grateful to S. Aitkin for commenting on the material in box 1 and to M. Buzgo for providing the photographs in figure 4 and for helpful comments on the manuscript. S.Y.R. is supported in part by the National Science Foundation (NSF), and J.B.L.B. thanks the Biotechology and Biological Sciences Research Council (BBSRC) for funding. This is Carnegie publication 1680.

We dedicate this paper to the late Robin Winter who articulated much of our knowledge about human congenital dysmorphologies and who is sorely missed.

Author information

Authors and Affiliations

  1. Bioinformatics, Biomedical Sciences University of Edinburgh, Edinburgh, EH8 9XD, UK

    Jonathan B. L. Bard

  2. Plant Biology, Carnegie Institution of Washington, Stanford, 94305, California, USA

    Seung Y. Rhee

Authors
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The authors declare no competing financial interests.

Related links

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FURTHER INFORMATION

Discussion paper by Michael Ashburner on phenotype and trait ontology

Minutes from phenotype meetings

Database groups that participated in phenotype meetings

The Arabidopsis Information Resource

Berkeley Drosophila Genome Project

DictyBase

Flybase

Gramene

International Crop Information System

The Institute for Genome Resources — microbial systems

The London Dysmorphology Database

MaizeGDB

Mouse Anatomy

Mouse Genome Informatics

Mouse mutagenesis centres

Nugene

OMIM

Rat Genome Database

Saccharomyces Genome Database

Glossary

PHARMACOGENETICS

The study of drug responses related to inherited genetic differences.

QUANTITATIVE TRAIT LOCUS

Genetic locus or chromosomal region that contributes to the phenotypic variation in continuously varying traits, such as weight.

SEMANTICS

The meaning of a string in some language; this is distinct from syntax, which describes how symbols can be combined independently of their meaning.

NATURAL LANGUAGE PROCESSING

Computer understanding, analysis, manipulation and/or generation of natural (human) language.

VOXEL

The three-dimensional, or volume, equivalent of a pixel (two-dimensional picture unit).

GRAPH THEORETICAL APPROACH

An approach to extracting meaning from ontologies that depends on using the intrinsic properties of graphs.

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Bard, J., Rhee, S. Ontologies in biology: design, applications and future challenges. Nat Rev Genet 5, 213–222 (2004). https://doi.org/10.1038/nrg1295

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