title: On the Use of Gene Ontology Annotations to Assess Functional Similarity among Orthologs and Paralogs: A Short Report.
abstract: A recent paper (Nehrt et al., PLoS Comput. Biol. 7:e1002073, 2011) has proposed a metric for the "functional similarity" between two genes that uses only the Gene Ontology (GO) annotations directly derived from published experimental results. Applying this metric, the authors concluded that paralogous genes within the mouse genome or the human genome are more functionally similar on average than orthologous genes between these genomes, an unexpected result with broad implications if true. We suggest, based on both theoretical and empirical considerations, that this proposed metric should not be interpreted as a functional similarity, and therefore cannot be used to support any conclusions about the "ortholog conjecture" (or, more properly, the "ortholog functional conservation hypothesis"). First, we reexamine the case studies presented by Nehrt et al. as examples of orthologs with divergent functions, and come to a very different conclusion: they actually exemplify how GO annotations for orthologous genes provide complementary information about conserved biological functions. We then show that there is a global ascertainment bias in the experiment-based GO annotations for human and mouse genes: particular types of experiments tend to be performed in different model organisms. We conclude that the reported statistical differences in annotations between pairs of orthologous genes do not reflect differences in biological function, but rather complementarity in experimental approaches. Our results underscore two general considerations for researchers proposing novel types of analysis based on the GO: 1) that GO annotations are often incomplete, potentially in a biased manner, and subject to an "open world assumption" (absence of an annotation does not imply absence of a function), and 2) that conclusions drawn from a novel, large-scale GO analysis should whenever possible be supported by careful, in-depth examination of examples, to help ensure the conclusions have a justifiable biological basis.
authors.affiliation: Division of Bioinformatics, Department of Preventive Medicine, University of Southern California, Los Angeles, California, United States of America. go-discuss@lists.stanford.edu
authors.forename: Paul D
authors.initials: PD
authors.lastname: Thomas
authors.forename: Valerie
authors.initials: V
authors.lastname: Wood
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Mungall
authors.forename: Suzanna E
authors.initials: SE
authors.lastname: Lewis
authors.forename: Judith A
authors.initials: JA
authors.lastname: Blake
mesh_terms: D000465:Algorithms; D000818:Animals; D002467:Cell Nucleus / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D016678:Genome; D015894:Genome, Human; D023281:Genomics / Q000379:methods; D006801:Humans; D051379:Mice; D008957:Models, Genetic; D015233:Models, Statistical; D008967:Molecular Biology / Q000379:methods; D058977:Molecular Sequence Annotation / Q000379:methods; D010766:Phosphorylation; D011336:Probability; D013045:Species Specificity