The Design And Realization Of Promoter Recognition System Based On HMM

Promoter is a very important regular element in gene expression, it determines the position of transcriptional start point and the frequency with which the gene is transcribed.the recognition of promoter will play an important rule in understanding the biological mechanism. As the primary key in the promoter research, the promoter recognition is becoming a hotspot nowadays.Traditional methods for promoter recognition is biology experiments,such as immunoprecipitation and mutation analysis.Because theses methods are with high cost and low efficacy,they cramped the development of the promoter research. So using data mining technology to develop some software for promoter recognition is becoming a very important research direction in bioinformatics.In this article, we designed a promoter recognition system based on hidden markov models,it can find Statistics law out deeply inside the promoter sequeces and use it for promoter recognition. The main work and results are as follows:1,Designed a promoter recognition system based on hidden markov models, including sequence inputment, preprocessing, learning, promoter prediction and output.2,using human promoters and Arabidopsis thaliana promoters to test,the result shows the systems sensitivity for human promoter prediction is 84.5% and 83.5% for Arabidopsis thaliana prediction.The system is of generality and high efficiency,it can greatly promote the efficiency of promoter recognition and provide strong support for promoter research. Besides,it offers current solution and can accelerate the process of the other elements'recognition,finally improve the construction of regulatory networks. Key words:promoter prediction, hidden markov models (HMM), JAVA, mathematical modeling, database design...