The conserved sequences in gene regulatory regions dominate gene regulation. Discovering these sequences and their functions is important in post genome era. A novel model is constructed to represent conserved motifs of DNA sequences. This model is a combination of PWM and WAM models. The advantage is the new model not only can comprise individual base frequencies in the motifs, but also can embody relationship of neighbourhood bases. In addition, a varied Gibbs sampling algorithm is applied with consideration of the different motif occurrences in each sequence. This variation is more accordant with the true situation of gene transcription controlling mechanism. By combining the model and the discovery algorithm, a program is constructed. After analysed a set of DNA sequences of upstream regions of genes using this program, putative motifs are discovered and are compared to experimental verified regulatory sequences. Results showed that this combination is ideal for motif discovery and the practice is meaningful for gene regulation research..

i Now a days, large quantities of gene sequences of related species of plants, animals and microorganisms show complex patterns of similarity to one another and many molecular biologists are convinced that an understanding of sequence evolution is the first step towards understanding the evolution itself. In fact this is one of the most fascinating aspects of the study of evolution. Thus the comparison of gene sequences or biological sequence analysis is one of the processes used to understand sequence evolution. Just as the ancient Greeks used comparative anatomy to understand the human body and linguists used the Rosetta stone to decipher Egyptian hieroglyphs, today we can use comparative sequence analysis to understand genomes. There is variety of different tools available to perform sequence analysis. Various DNA sequences alignment tools have been developed. Various software packages of automated tools have been developed that had improved the efficiency of much biological research. Fast, economical, flexible, and extensible computing power is