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1.  On the Use of Gene Ontology Annotations to Assess Functional Similarity among Orthologs and Paralogs: A Short Report 
PLoS Computational Biology  2012;8(2):e1002386.
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.
Author Summary
Understanding gene function—how individual genes contribute to the biology of an organism at the molecular, cellular and organism levels—is one of the primary aims of biomedical research. It has been a longstanding tenet of model organism research that experimental knowledge obtained in one organism is often applicable to other organisms, particularly if the organisms share the relevant genes because they inherited them from their common ancestor. Nevertheless this tenet is, like any hypothesis, not beyond question. A recent paper has termed this hypothesis a “conjecture,” and performed a statistical analysis, the results of which were interpreted as evidence against the hypothesis. This statistical analysis relied on a computational representation of gene function, the Gene Ontology (GO). As representatives of the international consortium that produces the GO, we show how the apparent evidence against the “ortholog conjecture” can be better explained as an artifact of how molecular biology knowledge is accumulated. In short, a complementarity between knowledge obtained in mouse and human experimental systems was incorrectly interpreted as a disagreement. We discuss the proper interpretation of GO annotations and potential sources of bias, with an eye toward enhancing the informed use of the GO by the scientific community.
doi:10.1371/journal.pcbi.1002386
PMCID: PMC3280971  PMID: 22359495

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