Identification of Genetic Promoter through Stochastic Approach

Abstract

Analysis of a gene sequence, which is transcribed into RNA and then translated inti protein, is a difficult task. If this could be achieved, it would make possible better understand how the organisms are developed from DNA information. The behavior of gene is highly influenced by promoter sequences residing up stream or downstream of the Transcription Start Site (TSS). The promoter recognition pro, access is a part of the complex process where genes interact with each other over time and actually regulates the whole working process of a cell. This paper attempts to develop an efficient algorithm that can successfully distinguish promoters and non promoters by analyzing statistical data. A learning model is developed from the known dataset to predict the unknown ones. Results: We have developed an efficient algorithm that can successfully distinguish genes from non-gene sequences by analyzing statistical data. A learning model is initially developed to train the Support Vector Machine (SVM) to identify distinctive features between gene and non gene. Then this context was used to predict other foreign sequence by the SVM. Our system has been tested using standard plant prom data sequence from the EMBL and the performances are: 0.86 for the Sensitivity and 0.90 for the specificity. Identification