Parallel Clustering Algorithm Of Phantom And Applied Research On Core Promoter Prediction Of Brachypodium Serrata

The rapid development of new technologies have led to declining costs of genome sequencing,and as a result,thousands of genomes are being sequenced in response.Furthermore,numerous comparative genomics-based algorithms have been developed in order to decipher the biological functions of various sequenced genomes;this can be computed because these biological functions are encoded and relatively conserved in a group of closely related genomes.Transcription regulation is one of the key steps of gene expression,which is usually triggered by the binding of proteins— called transcription factors(TFs)— to specific DNA segments known as TF binding sites(TFBSs).A set of TFBSs recognized by the same TF are called a motif,which summarizes the commonalities among the binding sites of a TF.Frequently,after a certain amount of new putative motifs are obtained by motif finding,the next step becomes clustering them into groups so that grouped motifs belong to the same TF,and accordingly,different groups correspond to different TFs.Therefore,a novel clustering algorithm is desired for merging motifs of the same TF family.In this paper,a new clustering algorithm,CliP,is proposed and sped up by parallelizing its program for motif clustering.Then we compare the performance of CliP to the performances of the other two outstanding algorithms.The data demonstrates that Cli P outperforms the other algorithms for motif clustering.Finally,the algorithm is used to study on the Brachypodium distachyon core promoter prediction,and obtained ideal results.