Abstract
The Gene Ontology (GO) is a collaborative effort that provides structured vocabularies for annotating the molecular function, biological role, and cellular location of gene products in a highly systematic way and in a species-neutral manner with the aim of unifying the representation of gene function across different organisms. Each contributing member of the GO Consortium independently associates GO terms to gene products from the organism(s) they are annotating. Here we introduce the Reference Genome project, which brings together those independent efforts into a unified framework based on the evolutionary relationships between genes in these different organisms. The Reference Genome project has two primary goals: to increase the depth and breadth of annotations for genes in each of the organisms in the project, and to create data sets and tools that enable other genome annotation efforts to infer GO annotations for homologous genes in their organisms. In addition, the project has several important incidental benefits, such as increasing annotation consistency across genome databases, and providing important improvements to the GO's logical structure and biological content.
Highlights
The functional annotation of gene products, both proteins and RNAs, is a major endeavor that requires a judicious mix of manual analysis and computational tools
The Gene Ontology (GO) was developed within the community of the Model Organism Databases (MODs), whose goal is to annotate the genomes of organisms having important impact on biomedical research [3,4]
GO terms are manually associated with gene products by curators using two general methods: extracting annotations based on published experimental data; and inferring annotations based on homology with related gene products for which experimental data is available
Summary
Background The functional annotation of gene products, both proteins and RNAs, is a major endeavor that requires a judicious mix of manual analysis and computational tools. GO is one of the most widely used tools for functional annotation, in the analysis of data from high throughput experiments. Automated methods that are based on either sequence similarity or domain composition are used to make annotations without curator intervention. These different methods of assigning GO terms to gene products are distinguished by the use of different GO evidence codes [5]. The comprehensive annotation of a genome entails assigning functions to all gene products, including those that have not yet been experimentally characterized
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