A new modeling strategy for eukaryotic promoter recognition and prediction

In this paper, we present a new modeling strategy for the recognition and prediction of promoter region. In our model, we base on following considerations: (1) promoter region comprises a number of binding sites (consensus sequences) that RNA polymerase II can bind to and start the transcription of gene, different promoter can be determined by a combination of different binding sites; (2) the spacing of these binding sites is not always consistent and there is some nucleotide variation in some position in different genes and species. Based on above considerations, we first split promoter region into equal intervals and calculate the occurring probability for each words that is assumed to be the sequences of binding sites in each interval by training sets respectively. Here we combined those interval probabilities into one matrix and refer it to as Interval Position Weight Matrix (IPWM); then a new promoter modeling strategy and feature abstracting method are introduced based on maximal probability model and IPWM. The results of testing on large genomic sequences and comparisons with several currently famous algorithms show that our algorithm is efficient with higher sensitivity and specificity.