Bioinformatics Analysis Of Meiotic Recombination In Yeast Saccharomyces Cerevisiae Genome

Recombination is critical in the process of biological evolution. Without recombination, genes of each individual would be fixed unless mutations occur. Recombination rate along the genome is not stochastic. Recombination rates are relative high in some places. So there are recombination hotspots and coldspots. However, it's difficult to map the recombination hotspots and coldspots along genome. Fortunately, Gerton etc. mapped the meiotic recombination hotspots and coldspots in the yeast Saccharomyces cerevisiae with delicated experiments, which made it possible for us to apply bioinformatics in the research of meiotic recombination rate in the yeast. The key to analysis of the recombination data is the extraction of information along genome. Recombintaion hotspots and coldspots are retrieved according to the research by Gerton etc. We extract 350 yeast ORFs meating our criteria, among which 302 ORFs are hotspots and the rest 48 ORFs are coldspots. GC contents are analyzed. It is found that there is significant difference between GC contents of recombination hotspots and those of coldspots. Then we step into the details of GC content. GC3S has more significant difference compared with GC2S and GC1S. This result gives us a hint to analyze the codon usage of recombination hotspots and coldspots. So we calculate RSCU (Relative Synonymous Codon Usage) and cluster the 350 ORFs according to the RSCU. The 48 coldspots are clustered together and the 302 hotspots are around the coldspots. Obviously, codon usage of yeast genome is somewhat affected by recombination events. Interestingly, we find that if we exclude the six codons for Arg, we can get a better result for cluster analysis.To identify the characteristics that are affected by recombination events, we analyze the whole genome of yeast. 6150 ORFs are retrieved with our criteria. According to the analysis of codon usage, GC contents, DRA, amino acid usage and so on, we have the following results: (1) The relative recombination rate has significant correlation with codon usage. Factor analysis helps us to identify two major factors of codon usage, both of which have significant correlation with recombination rate of yeast genome. What's more, Factor 2 has more significant correlation with Pearson coefficient 0.456. (2) According to the correlation analysis between the two factors and other charactors of genes, we find Factor 1 related to gene expression, which is due to the significant negative correlation between Factor 1 and CAI (Pearson Coeffecient -0.956). Factor 2 is positively correlated with GC3S(Pearson Coeffecient 0.910). These correlations are all significant with P<0.0001. (3) There is no significant correlation between recombination rate and DRA of those basepairs with G or C. There is no significant correlation between recombination rate and amino acid usage either. Based on the above results, we find that the major codons in yeast genome are ended in A or T. However, those genes with relative high recombination rate prefer codons ended in G or C. It is concluded that codon usage of yeast genome is affected by recombination events. Nature selection is not considered the major pressure for the codon usage. Instead, biased gene conversion to GC is considered to be the most important explanation for what we have found. We apply statistic method to analyze the whole genome of yeast related to meiotic recombination rate, which gives us an overall understanding of meiotic recombination events among yeast genome.