Evolutionary Bioinformatics

Co-evolutionary Rates of Functionally Related Yeast Genes

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Evolutionary Bioinformatics 2006:2 271-276

Published on 18 Feb 2007

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Leonardo Mariño-Ramírez1, Olivier Bodenreider2, Natalie Kantz1 and I. King Jordan3

1National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, U.S.A. 2National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, U.S.A. 3School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, U.S.A.

Abstract: Evolutionary knowledge is often used to facilitate computational attempts at gene function prediction. One rich source of evolutionary information is the relative rates of gene sequence divergence, and in this report we explore the connection between gene evolutionary rates and function. We performed a genome-scale evaluation of the relationship between evolutionary rates and functional annotations for the yeast Saccharomyces cerevisiae. Non-synonymous (dN) and synonymous (dS) substitution rates were calculated for 1,095 orthologous gene sets common to S. cerevisiae and six other closely related yeast species. Differences in evolutionary rates between pairs of genes (ΔdN & ΔdS) were then compared to their functional similarities (sGO), which were measured using Gene Ontology (GO) annotations. Substantial and statistically signifi cant correlations were found between ΔdN and sGO, whereas there is no apparent relationship between ΔdS and sGO. These results are consistent with a mode of action for natural selection that is based on similar rates of elimination of deleterious protein coding sequence variants for functionally related genes. The connection between gene evolutionary rates and function was stronger than seen for phylogenetic profiles, which have previously been employed to inform functional inference. The co-evolution of functionally related yeast genes points to the relevance of specific function for the efficacy of natural selection and underscores the utility of gene evolutionary rates for functional predictions.




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