Gene Ontology (GO) terms are frequently used to score alignments between
protein-protein interaction (PPI) networks. Methods exist to measure the GO
similarity between two proteins in isolation, but pairs of proteins in a
network alignment are not isolated: each pairing is implicitly dependent upon
every other pairing via the alignment itself. Current methods fail to take into
account the frequency of GO terms across the networks, and attempt to account
for common GO terms in an ad hoc fashion by imposing arbitrary rules on when to
"allow" GO terms based on their location in the GO hierarchy, rather than using
readily available frequency information in the PPI networks themselves. Here we
develop a new measure, NetGO, that naturally weighs infrequent, informative GO
terms more heavily than frequent, less informative GO terms, without requiring
arbitrary cutoffs. In particular, NetGO down-weights the score of frequent GO
terms according to their frequency in the networks being aligned. This is a
global measure applicable only to alignments, independent of pairwise GO
measures, in the same sense that the edge-based EC or S3 scores are global
measures of topological similarity independent of pairwise topological
similarities. We demonstrate the superiority of NetGO by creating alignments of
predetermined quality based on homologous pairs of nodes and show that NetGO
correlates with alignment quality much better than any existing GO-based
alignment measures. We also demonstrate that NetGO provides a measure of
taxonomic similarity between species, consistent with existing taxonomic
measures--a feature not shared with existing GO-based network alignment
measures. Finally, we re-score alignments produced by almost a dozen aligners
from a previous study and show that NetGO does a better job than existing
measures at separating good alignments from bad ones
