Computer Analysis and Recognition of Drosophila melanogasterGene Promoters

A new method for recognizing eukaryotic gene promoters was based on their partition and on analysis of correlations of dinucleotide frequencies for each individual fragment. The method was used to recognize the TATA-containing and TATA-less promoters of Drosophila melanogastergenes. The dinucleotide context was correlated with conformational and physicochemical DNA properties in promoter fragments. Mean values of several parameters proved to dramatically change on transition from the TATA box to its GC-rich flanks. In TATA-less promoters, specific properties were revealed in the DPE region. The method was employed in a promoter recognition program, which is available through Internet.     

Finding Signals for Plant Promoters

The strongest signal of plant promoter is searched with the model of single motif with two types. It turns out that the dominant type is the TATA-box. The other type may be called TATA-less signal, and may be used in gene finders for promoter recognition. While the TATA signals are very close for the monocot and the dicot, their TATA-less signals are significantly different. A general and flexible multi-motif model is also proposed for promoter analysis based on dynamic programming. By extending the Gibbs sampler to the dynamic programming and introducing temperature, an efficient algorithm is developed for searching signals in plant promoters.     

Finding signals for plant promoters

The strongest signal of plant promoter is searched with the model of single motif with two types. It turns out that the dominant type is the TATA-box. The other type may be called TATA-less signal, and may be used in gene finders for promoter recognition. While the TATA signals are very close for the monocot and the dicot, their TATA-less signals are significantly different. A general and flexible multi-motif model is also proposed for promoter analysis based on dynamic programming. By extending the Gibbs sampler to the dynamic programming and introducing temperature, an efficient algorithm is developed for searching signals in plant promoters.     

Characterizing exons 11 and 1 promoters of the mu opioid receptor (Oprm) gene in transgenic mice

Background  

The complexity of the mouse mu opioid receptor (Oprm) gene was demonstrated by the identification of multiple alternatively spliced variants and promoters. Our previous studies have identified a novel promoter, exon 11 (E11) promoter, in the mouse Oprm gene. The E11 promoter is located ~10 kb upstream of the exon 1 (E1) promoter. The E11 promoter controls the expression of nine splice variants in the mouse Oprm gene. Distinguished from the TATA-less E1 promoter, the E11 promoter resembles a typical TATA-containing eukaryote class II promoter. The aim of this study is to further characterize the E11 and E1 promoters in vivo using a transgenic mouse model.