Reconhecimento e predição de promotores procarióticos: investigação de uma metodologia in silico baseada em HMMs

AUTOR(ES)
DATA DE PUBLICAÇÃO

2005

RESUMO

Gene expression on prokaryotes initiates when the RNA-polymerase enzyme interacts with DNA regions called promoters. In these regions are located the main regulatory elements of the transcription process. Despite the improvement of in vitro techniques for molecular biology analysis, characterizing and identifying a great number of promoters on a genome is a complex task. In silico approaches are usually employed to recognize theses regions on prokaryotes. Nevertheless, the main drawback is the absence of a large set of promoters to identify conserved patterns among the species. Hence, a in silico method to predict them on any species is a challenge. This work proposes a protocol to use hidden Markov models (HMMs) methodology with Decision Threshold Estimation and Discrimination Analysis on this problem. Four prokaryotic species are investigated (Escherichia coli, Bacillus subtilis, Helicobacter pylori e Helicobacter hepaticus). The influence of different aspects in the recognition and prediction are examined: the size of promoter datasets, structural and functional available information, and A+T content of each genome. The results show that the proposed protocol increases the recognition ability of the HMM, obtaining a reduction in 44.96% of error rate compared with previous works on Escherichia coli promoters. For Bacillus subtilis, the accuracy is 95% on recognition and 78% on prediction. However, the protocol presents a high error rate on promoter prediction of Helicobacter species, since it generates a large number of false positives.

ASSUNTO(S)

promoters ciencia da computacao prokaryotes reconhecimento de padrões procariotos hmms pattern recognition hmms promotores

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