Analysis of the nucleotide content of Escherichia coli promoter sequences related to the alternative sigma factors.

Promoters are DNA sequences located upstream of the transcription start site of genes. In bacteria, the RNA polymerase enzyme requires additional subunits, called sigma factors (σ) to begin specific gene transcription in distinct environmental conditions. Currently, promoter prediction still poses many challenges due to the characteristics of these sequences. In this paper, the nucleotide content of Escherichia coli promoter sequences, related to five alternative σ factors, was analyzed by a machine learning technique in order to provide profiles according to the σ factor which recognizes them. For this, the clustering technique was applied since it is a viable method for finding hidden patterns on a data set. As a result, 20 groups of sequences were formed, and, aided by the Weblogo tool, it was possible to determine sequence profiles. These found patterns should be considered for implementing computational prediction tools. In addition, evidence was found of an overlap between the functions of the genes regulated by different σ factors, suggesting that DNA structural properties are also essential parameters for further studies. HighlightsCurrently, promoter prediction still poses many challenges due to the characteristics of promoter sequences.A clustering technique was applied on promoter sequences related to the alternative sigma factors of Escherichia coli. Creating 20 distinct profiles.The results exhibited evidence of an overlap in gene functions between the sigma factors. These found patterns should be considered for implementing computational prediction tools. [ABSTRACT FROM AUTHOR]