Promoter addresses: revelations from oligonucleotide profiling applied to the Escherichia coli genome

BACKGROUND: Transcription is the first step in cellular information processing. It is regulated by cis-acting elements such as promoters and operators in the DNA, and trans-acting elements such as transcription factors and sigma factors. Identification of cis-acting regulatory elements on a genomic scale requires computational analysis. RESULTS: We have used oligonucleotide profiling to predict regulatory regions in a bacterial genome. The method has been applied to the Escherichia coli K12 genome and the results analyzed. The information content of the putative regulatory oligonucleotides so predicted is validated through intra-genomic analyses, correlations with experimental data and inter-genome comparisons. Based on the results we have proposed a model for the bacterial promoter. The results show that the method is capable of identifying, in the E.coli genome, cis-acting elements such as TATAAT (sigma70 binding site), CCCTAT (1 base relative of sigma32 binding site), CTATNN (LexA binding site), AGGA-containing hexanucleotides (Shine Dalgarno consensus) and CTAG-containing hexanucleotides (core binding sites for Trp and Met repressors). CONCLUSION: The method adopted is simple yet effective in predicting upstream regulatory elements in bacteria. It does not need any prior experimental data except the sequence itself. This method should be applicable to most known genomes. Profiling, as applied to the E.coli genome, picks up known cis-acting and regulatory elements. Based on the profile results, we propose a model for the bacterial promoter that is extensible even to eukaryotes. The model is that the core promoter lies within a plateau of bent AT-rich DNA. This bent DNA acts as a homing segment for the sigma factor to recognize the promoter. The model thus suggests an important role for local landscapes in prokaryotic and eukaryotic gene regulation

Complete Ranking of Intuitionistic Fuzzy Numbers

AbstractFuzzy number was introduced by Dubois and Prade [10] to handle imprecise numerical quantities. Later it was generalized to intuitionistic fuzzy number by Burillo et al. [5]. Ranking intuitionistic fuzzy numbers plays an important role in decision making and information systems. All over the world many researchers have proposed different score functions for ranking intuitionistic fuzzy numbers but unfortunately every method produces some anti-intuitive results in certain places. A complete ranking on the entire class of fuzzy numbers have been achieved by W. Wang, Z. Wang [22] using upper dense sequence defined in (0,1]. But a complete ranking on the set of all intuitionistic fuzzy number remains an open problem till today. Complete ranking on the class of intuitionistic fuzzy interval number was done by Geetha et al. [13]. In this paper, total ordering on the entire class of intuitionistic fuzzy number (IFN) using upper lower dense sequence is proposed and compared with existing techniques using illustrative examples. This new total ordering on intuitionistic fuzzy numbers (IFNs) generalizes the total ordering defined in W. Wang, Z. Wang [22] for fuzzy numbers (FNs)