Application Of Improved K-means Clustering Phylogenetic Profile In Gene Functional Annotation

With the post-genome era, the research focus of Bioinformatics has been transferred from the sequencing to the annotation. With the rapid development of large-scale sequencing tools, large amount of the whole genome has been sequencing. Rely solely on traditional experimental approach to analyze the function of these new sequencing data has been far from meeting the current requirements. Therefore, how to design the functional annotation method that based on calculation to predict the hidden biological function of these massive data becomes an important research topic of current bioinformatics.At present, the computational method of gene functional annotation can be divided into two broad categories: the homology based method and the non- homology based method. Phylogenetic profile method is a common method that based on non-homology. This paper analyzes the choice of reference genomes of existing phylogenetic profiles methods and constructs the profiles that based on weight. Thus it not only reflected the evolution information of genes effectively, but avoids the tremendous workload which bring by traditional method. In addition, this paper improves the classical K-meanss clustering algorithm, and applies the improved clustering algorithm to analyze the similarity of phylogenetic profiles. The experimental results show that: the building of phylogenetic profiles that based on weight and the application of improved clustering algorithms can effectively improve the performance of algorithm. Finally according to the KEGG database to further verify the improvement is effective.