As the number of high-throughput methodologies has increased, so has the number of ways in which GO annotation data has been exploited to link experimental results to current functional knowledge.
Proteomes and differentially regulated mRNAs can be analysed with GO data to provide an overview of the predominant activities the constituent proteins are involved in or where they are normally located. For example Ashley et al. 
used GO to compare the genes up-regulated in de novo
atherosclerosis with those associated with in-stent restenosis. They found a significant proportion of genes up-regulated following de novo
atherosclerosis were associated with inflammatory processes, whereas a high proportion of in-stent restenosis up-regulated genes had GO terms indicating an involvement with cell growth and association with the extracellular matrix 
Often the generation of hypotheses to explain proteome-wide alterations in response to certain diseases, such as cardiac hypertrophy 
, or stress states, such as hypoxia 
, rely on the use of GO annotation data. In such studies an indication of underlying cellular mechanisms that may account for an observed phenotype can be obtained using GO to cluster subsets of proteins that share related GO annotation, and found to be similarly over- or under-expressed in the disease or stress state. For example, Pan et al. 
found over-expressed cardiac microsomal membrane proteins in mouse hyper- or hypocontractile hearts were enriched with GO terms describing fat and carbohydrate metabolism and G-protein-dependent signalling pathways 
. Enrichment of these GO terms validated the investigators proteomic method and was consistent with the suggestion that the deregulation of calcium-dependent cardiac contractility resulted in compensatory growth activities.
The ability to review experimental results with respect to known functional information has also proved useful when investigators need to select a subset of proteins to analyse in greater depth in order to identify new sets of biomarkers for a certain disease. This approach has enabled investigators of buccal carcinoma 
, Parkinson disease 
and chronic kidney disease 
to identify new biomarkers for these diseases. Furthermore, in all of these reports the enriched GO categories indicated disease-associated deregulated processes.
GO can also be used to provide a link between the protein binding network and the activities/locations of the participant proteins. Use of cellular component GO annotations can aid data visualisation or confirm whether a particular set of interactions is likely to occur in vivo
. Dyer et al. 
used GO data to investigate interactions of human proteins with viral pathogens and found that many different pathogens target the same processes in the human cell, such as regulation of apoptosis, even though they may interact with different proteins. Similarly, many studies have focused on a ‘guilt-by-association’ hypothesis, where the involvement of proteins in a particular pathway can be hypothesised in relation to the processes their interacting proteins carry out. To this end GO annotations are integrated in the GEOMI 
and Cytoscape 
network visualization tools.
A number of proteomic investigations have found that GO data provides an indispensable resource to indicate the success of a particular subcellular enrichment strategy or large scale confocal microscopy analyses [15–18]
. Kislinger et al. 
used GO data to verify that their subcellular fractionation protocol efficiently isolated subcellular compartments. For example, of the nearly 600 proteins detected exclusively in the nuclear fractions, nearly half were either annotated solely to the nucleus or had a function known to be localized within the nucleus (e.g. transcription factors 
). Barbe et al. 
applied GO annotation to validate the protein subcellular locations identified using protein-specific antibodies and large scale confocal microscopy analysis, and in this case 80% of the subcellular locations identified in human cell lines were supported by existing GO annotation data 
Despite the wide variety of applications that GO is used for, there are many aspects to biological processes that are not addressed by this database. In particular, GO only describes the normal, physiological function of a protein, rather than the pathological function of a protein in a diseased situation. Furthermore, the dynamic relationships between protein function, its cellular location (including its intracellular location, cell specificity and developmental specific expression) and how this relationship influences the biological processes a protein is involved in are not currently represented by GO. Protein interaction databases such as BioGrid 
, Biomolecular Interaction Network Database 
, Human Protein Reference Database 
and IntAct 
enable complex protein interaction networks to be investigated. However, at present there is no single database that enables complex biological relationships to be investigated. The content of GOC database is influenced by the curation groups who are submitting data, therefore some groups of organisms, such as viruses, are not well represented by this database. Details about some of the other available ontologies, such as cell type or human disease ontologies, are available at the Open Biomedical Ontologies web site (www.obofoundry.org