In silico modelling of hormone response elements

BACKGROUND: An important step in understanding the conditions that specify gene expression is the recognition of gene regulatory elements. Due to high diversity of different types of transcription factors and their DNA binding preferences, it is a challenging problem to establish an accurate model for recognition of functional regulatory elements in promoters of eukaryotic genes. RESULTS: We present a method for precise prediction of a large group of transcription factor binding sites – steroid hormone response elements. We use a large training set of experimentally confirmed steroid hormone response elements, and adapt a sequence-based statistic method of position weight matrix, for identification of the binding sites in the query sequences. To estimate the accuracy level, a table of correspondence of sensitivity vs. specificity values is constructed from a number of independent tests. Furthermore, feed-forward neural network is used for cross-verification of the predicted response elements on genomic sequences. CONCLUSION: The proposed method demonstrates high accuracy level, and therefore can be used for prediction of hormone response elements de novo. Experimental results support our analysis by showing significant improvement of the proposed method over previous HRE recognition methods