Research On Prediction And The Analysis Of Recombination Hotspots And Coldspots In Yeast Based On Sequence Information

With the rapid development of biological technology, especially since the Human Genome Project (HGP) was completed. Through gene sequencing method get large amounts data of biological information, and how to interpret and explore the life information is particularly important. Therefore, a series of methods was invented which based on statistics and calculation to predict the functions and properties of gene and protein. Genetic recombination is important process for the whole life, it can exchange genetic information and promote the evolution of life. It occurs in different region of the gene, so there is difference in the cold and hot region of gene recombination. In this paper mainly studies on prediction and the analysis of recombination hotspots and coldspots in yeast based on sequence information. The use of statistical methods such as the content of GC, Dinucleotide Relative Abundance, Base-Base Correlation,Codon Bias to analyze the gene recombination relationship.The conclusion is the GC content close relationship between cold and hot spots and recombination hot spots more like use the codons ending with GC. Because of the cold and hot spots DNA sequence is high similarity, so the predict effect with alignment and base composition is no well. In order to predict recombination cold and hot spots sequence in yeast, we propose pseudo nucleotide feature method base on the combination of GM theory, complexity and dinucleotide relative abundance. Finally, use the K-Nearest Neighbor classification Method and Jackknife test. The result show that we proposed model has better prediction rate. The prediction method can find the recombinant information in sequence for biologists, reveal the genetic recombination mechanism offer for help.