School of Agriculture, Food and Ecosystem Sciences - Research Publications
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ItemNo Preview AvailableTesting the neutral theory of molecular evolution using genomic data: a comparison of the human and bovine transcriptome(EDP SCIENCES S A, 2006)Despite growing evidence of rapid evolution in protein coding genes, the contribution of positive selection to intra- and interspecific differences in protein coding regions of the genome is unclear. We attempted to see if genes coding for secreted proteins and genes with narrow expression, specifically those preferentially expressed in the mammary gland, have diverged at a faster rate between domestic cattle (Bos taurus) and humans (Homo sapiens) than other genes and whether positive selection is responsible. Using a large data set, we identified groups of genes based on secretion and expression patterns and compared them for the rate of nonsynonymous (dN) and synonymous (dS) substitutions per site and the number of radical (Dr) and conservative (Dc) amino acid substitutions. We found evidence of rapid evolution in genes with narrow expression, especially for those expressed in the liver and mammary gland and for genes coding for secreted proteins. We compared common human polymorphism data with human-cattle divergence and found that genes with high evolutionary rates in human-cattle divergence also had a large number of common human polymorphisms. This argues against positive selection causing rapid divergence in these groups of genes. In most cases dN/dS ratios were lower in human-cattle divergence than in common human polymorphism presumably due to differences in the effectiveness of purifying selection between long-term divergence and short-term polymorphism.
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ItemGene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks(BMC, 2006-03-03)BACKGROUND: Genes and proteins are organized into functional modular networks in which the network context of a gene or protein has implications for cellular function. Highly connected hub proteins, largely responsible for maintaining network connectivity, have been found to be much more likely to be essential for yeast survival. RESULTS: Here we investigate the properties of weighted gene co-expression networks formed from multiple microarray datasets. The constructed networks approximate scale-free topology, but this is not universal across all datasets. We show strong positive correlations between gene connectivity within the whole network and gene essentiality as well as gene sequence conservation. We demonstrate the preservation of a modular structure of the networks formed, and demonstrate that, within some of these modules, it is possible to observe a strong correlation between connectivity and essentiality or between connectivity and conservation within the modules particularly within modules containing larger numbers of essential genes. CONCLUSION: Application of these techniques can allow a finer scale prediction of relative gene importance for a particular process within a group of similarly expressed genes.
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