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Network alignment aims to uncover topologically similar regions in theprotein-protein interaction (PPI) networks of two or more species under theassumption that topologically similar regions tend to perform similarfunctions. Although there exist a plethora of both network alignment algorithmsand measures of topological similarity, currently no gold standard exists forevaluating how well either is able to uncover functionally similar regions.Here we propose a formal, mathematically and statistically rigorous method forevaluating the statistical significance of shared GO terms in a global, 1-to-1alignment between two PPI networks. We use combinatorics to precisely count thenumber of possible network alignments in which $k$ proteins share a particularGO term. When divided by the number of all possible network alignments, thisprovides an explicit, exact $p$-value for a network alignment with respect to aparticular GO term. Just as with BLAST's p-values and bit-scores, this methodis designed not to guide the formation of any particular alignment, but insteadto provide an after-the-fact evaluation of a fixed, given alignment.

Exact $p$-values for global network alignments via combinatorial  analysis of shared GO terms (Subtitle: REFANGO: Rigorous Evaluation of  Functional Alignments of Networks using Gene Ontology)