First, we compared predicted annotations to annotations drawn from the same ontologies that were previously added to Gemma by curators.
Further, because these text annotations are visually separated from the phenotypic annotations for the same model in the web display, connections between phenotype and disease may be difficult for users to identify.
Indeed, a cursory view of the reference genome sequence and the annotation of genes in this region shows multiple sequence gaps, many of which are in ORFs causing partial annotations, fragments from the same gene annotated as independent genes, etc.
In addition to these three methods, we generated another set of 'predictions' by collecting experimental annotations for the same set of proteins from a database generated by the GO Consortium released at about the same time as our version of Swiss-Prot.
To perform the evaluation, we compared the annotations derived from the same publication by different databases.
The clustering function measures the co‐association among the enriched annotation terms to group the similar, redundant, and heterogeneous annotation contents from the same or different resources into annotation groups.
DAVID software uses a novel algorithm to measure the relationships among the annotation terms based on the degrees of their coassociation genes to group the similar, redundant, and heterogeneous annotation contents from the same or different resources into annotation groups.
DAVID Functional Clustering brings the similar, redundant, and heterogeneous annotation contents from the same or different resources into annotation groups.
The similar, redundant, and heterogeneous annotation contents from the same or different resources were clustered into annotation groups due to their similar biological meaning.
This means that the GO terms assigned to the least annotated gene in a clique occur in all the other annotations of genes from the same clique, which validate the pairwise orthology predictions for that clique.
