Improvements to cardiovascular gene ontology.
Gene Ontology (GO) provides a controlled vocabulary to describe the attributes of genes and gene products in any organism. Although one might initially wonder what relevance a 'controlled vocabulary' might have for cardiovascular science, such a resource is proving highly useful for researchers investigating complex cardiovascular disease phenotypes as well as those interpreting results from high-throughput methodologies. GO enables the current functional knowledge of individual genes to be used to annotate genomic or proteomic datasets. In this way, the GO data provides a very effective way of linking biological knowledge with the analysis of the large datasets of post-genomics research. Consequently, users of high-throughput methodologies such as expression arrays or proteomics will be the main beneficiaries of such annotation sets. However, as GO annotations increase in quality and quantity, groups using small-scale approaches will gradually begin to benefit too. For example, genome wide association scans for coronary heart disease are identifying novel genes, with previously unknown connections to cardiovascular processes, and the comprehensive annotation of these novel genes might provide clues to their cardiovascular link. At least 4000 genes, to date, have been implicated in cardiovascular processes and an initiative is underway to focus on annotating these genes for the benefit of the cardiovascular community. In this article we review the current uses of Gene Ontology annotation to highlight why Gene Ontology should be of interest to all those involved in cardiovascular research.
Lovering
Ruth C
RC
Department of Medicine, University College London, Rayne Institute, London, UK. r.lovering@ucl.ac.uk
Dimmer
Emily C
EC
Talmud
Philippa J
PJ
D002318:Cardiovascular Diseases / Q000145:classification* / Q000175:diagnosis / Q000235:genetics*; D002899:Chromosomes, Human, Pair 9 / Q000648:ultrastructure; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D030562:Databases, Protein; D005796:Genes; D016678:Genome; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods; D006801:Humans; D040901:Proteomics / Q000379:methods; D009626:Terminology as Topic; D018875:Vocabulary, Controlled