An improved hidden markov model methodology to discover prokaryotic promoters

Gene expression on prokaryotes initiates when the RNA-polymerase enzyme interacts with DNA regions called promoters, where are located the main regulatory elements of the transcription process. Despite the improvement of in vitro techniques for molecular biology analysis, characterizing and identifying promoters is a complex task. In silico approaches are used to recognize theses regions. Nevertheless, they confront the absence of a large set of promoters to identify conserved patterns among the species. Hence, a methodology able to predict them on any genome is a challenge. This work proposes a methodology based on Hidden Markov Models (HMMs), Decision Threshold Estimation and Discrimination Analysis. For three investigated prokaryotic species, the mainly results are: a reduction in 44.96% of recognition error rate compared with previous works on Escherichia coli, an accuracy of 95% on recognition and 78% on prediction for Bacillus subtilis. However, it was found a large number of false positives on Helicobacter pylori.