功能相关酵母基因的共同进化速率

L. Mariño-Ramírez, O. Bodenreider, N. Kantz, I. Jordan
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引用次数: 24

摘要

进化知识经常被用来促进基因功能预测的计算尝试。基因序列分化的相对速率是进化信息的一个丰富来源,在本报告中,我们探讨了基因进化速率和功能之间的联系。我们对酿酒酵母的进化速率和功能注释之间的关系进行了基因组规模的评估。计算了酿酒酵母和其他6种近缘酵母共有的1095个同源基因集的非同义(dN)和同义(dS)替代率。然后将基因对之间的进化率差异(ΔdN & ΔdS)与它们的功能相似性(sGO)进行比较,后者使用基因本体(GO)注释进行测量。在ΔdN和sGO之间发现了实质性的和统计上显著的相关性,而ΔdS和sGO之间没有明显的关系。这些结果与自然选择的作用模式是一致的,这种模式是基于对功能相关基因的有害蛋白质编码序列变异的相似消除率。基因进化速率和功能之间的联系比以前用于功能推断的系统发育谱更强。功能相关酵母基因的共同进化指出了自然选择功效的特定功能的相关性,并强调了基因进化率对功能预测的效用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Co-evolutionary Rates of Functionally Related Yeast Genes
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 significant 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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