As the number of genomes being sequenced, and gene products being characterized increases, the GO annotations made from this data concomitantly increases (the GOA database contains over 45 million GO annotations as of July 2009) so there is a need for researchers to be able to sort and view these annotations and quickly retrieve relevant information to direct their research. All GO annotations from the GO Consortium member groups are available as Gene Association Files which are downloadable from the GO Consortium website (http://www.geneontology.org/
) and the individual database websites. Such files have a very simple tab-delimited format; however, these files are large and somewhat cryptic to a biologist, requiring some computational knowledge in order to obtain from them subsets of information they are interested in. QuickGO was developed by the GOA group in August 2001 as a fast, web-based browser of GO term information and all GO annotations assigned to UniProt Knowledgebase (UniProtKB) accessions. In 2007, the GOA group was awarded a grant from the BBSRC Tools and Resources Development Fund to redevelop QuickGO by adding extensive new features. In March 2008, following this redevelopment, the new version of QuickGO was released. The GO annotations contained within the GOA database are now at the centre of QuickGO, users are able to customize annotation sets by using the extensive filtering options provided, these include being able to filter on protein accession, evidence code, taxonomic identifier and GO term. The latter functionality also means that users can create GO slims, subsets of GO terms used to simplify the view of annotations to a set of gene products (Binns,D. et al.
, submitted for publication).
A number of different web-based GO browsers are publicly available (see the GO Consortium Tools website: http://www.geneontology.org/GO.tools.browsers.shtml
), and the vast majority provide equivalent detail on the terms and structure of the GO, it is in the display and manipulation of associated annotations where the main difference between browsers can be seen. A number of GO browsers are provided by model organism groups, which display the full set of electronic and manual GO annotations for individual species, such as the MGI GO browser (http://www.informatics.jax.org/searches/GO_form.shtml
), whereas the GO Consortium browser, AmiGO (http://amigo.geneontology.org/cgi-bin/amigo/go.cgi
), provides a comprehensive display of manual annotations provided by the groups in the GO Consortium. AmiGO is the most comparable GO browser to QuickGO in that the ontology can be searched and browsed, terms and their relationships can be viewed in context with the GO hierarchy, GO annotations can be viewed and downloaded for multiple species and it is updated frequently. Similar to QuickGO, AmiGO also has a GO slim facility used to map-up annotations to more general GO terms to give a simplified overview of the attributes of a list of gene products.
QuickGO is unique among these other browsers in that it is the only web-based browser to display annotation to almost 190 000 species, including both manually and electronically assigned annotations, as well as the facility to extensively filter on a number of annotation attributes and map between 17 different identifier types. This facility is of particular interest for researchers requiring functional predictions for genes or proteins originating from non-model organism species.
QuickGO is updated weekly with GO annotations and nightly with GO term information making it one of the most up-to-date GO browsers available, this is a critical feature of GO browsers, and GO analysis tools in general, due to the constant growth and updating of both the ontology and annotations. Unfortunately, there are some GO browsers where there is a long lag between updates [e.g. Gofetcher (http://mcbc.usm.edu/gofetcher/home.php
), GenNav (http://mor.nlm.nih.gov/perl/gennav.pl
)] requiring users, sometimes unwittingly, to use old data.
This article hopes to provide users with some examples of the more complex and novel functions that QuickGO can perform, in an easy to follow guide. The researcher will then be able to apply this knowledge to their own data set enabling them to draw conclusions more easily about their chosen area of research. Some of the examples cited within are taken from real-life tasks that are commonly requested by our users through the GO (gohelp/at/genome.stanford.edu) and GOA (goa/at/ebi.ac.uk) helpdesks.