Predicting motifs in human and mouse genes by using Probabilistic Suffix Trees

The identification of regulatory elements (motifs) is a challenging task in molecular biology. An important challenge in this study is to identify regulatory elements (motifs), notably the binding sites in Deocsiribonucleic Acid (DNA) for transcription factors. Based on this motivation we propose a method for motif prediction of mouse and human genes by using Probabilistic Suffix Tree (PST). Experimental results are evaluated comparatively by thirteen distinct motif prediction tools. Our results show that, the proposed method gives a better recognition rate than the compared motif prediction tools, where the recognition rate is nucleotide level sensitivity (nSn).