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Gene Ontology (GO) terms are frequently used to score alignments betweenprotein-protein interaction (PPI) networks. Methods exist to measure the GOsimilarity between two proteins in isolation, but pairs of proteins in anetwork alignment are not isolated: each pairing is implicitly dependent uponevery other pairing via the alignment itself. Current methods fail to take intoaccount the frequency of GO terms across the networks, and attempt to accountfor 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 usingreadily available frequency information in the PPI networks themselves. Here wedevelop a new measure, NetGO, that naturally weighs infrequent, informative GOterms more heavily than frequent, less informative GO terms, without requiringarbitrary cutoffs. In particular, NetGO down-weights the score of frequent GOterms according to their frequency in the networks being aligned. This is aglobal measure applicable only to alignments, independent of pairwise GOmeasures, in the same sense that the edge-based EC or S3 scores are globalmeasures of topological similarity independent of pairwise topologicalsimilarities. We demonstrate the superiority of NetGO by creating alignments ofpredetermined quality based on homologous pairs of nodes and show that NetGOcorrelates with alignment quality much better than any existing GO-basedalignment measures. We also demonstrate that NetGO provides a measure oftaxonomic similarity between species, consistent with existing taxonomicmeasures--a feature not shared with existing GO-based network alignmentmeasures. Finally, we re-score alignments produced by almost a dozen alignersfrom a previous study and show that NetGO does a better job than existingmeasures at separating good alignments from bad ones.

SANA NetGO: A combinatorial approach to using Gene Ontology (GO) terms to score network alignments