A hybrid promoter analysis methodology for prokaryotic genomes. One of the big challenges of the post-genomic era is identifying regulatory systems and integrating them into genetic networks. Gene expression is determined by protein-protein interactions among regulatory proteins and with RNA polymerase(s), and protein-DNA interactions of these trans-acting factors with cis-acting DNA sequences in the promoter regions of those regulated genes. Therefore, identifying these protein-DNA interactions, by means of the DNA motifs that characterize the regulatory factors operating in the transcription of a gene, becomes crucial for determining which genes participate in a regulation process, how they behave and how they are connected to build genetic networks. We propose a hybrid promoter analysis methodology (HPAM) to discover complex promoter motifs that combines the neural network efficiency and ability of representing imprecise and incomplete patterns; the flexibility and interpretability of fuzzy models; and the multi-objective evolutionary algorithms capability to identify optimal instances of a model by searching according to multiple criteria. We test our methodology by learning and predicting the RNA polymerase motif in prokaryotic genomes. This constitutes a special challenge due to the multiplicity of the RNA polymerase targets and its connectivity with other transcription factors, which sometimes require multiple functional binding sites even in close located regulatory regions; and the uncertainty of its motif, which allows sites with low specificity (i.e., differing from the best alignment or consensus) to still be functional. HPAM is available for public use in { t http://soar-tools.wustl.edu.}

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