Transcription Factor Binding Site Detection Algorithm Using Distance Metrics Based on a Position Frequency Matrix Concept

Regulatory sequence detection is a fundamental challenge in computational biology. The transcription process in protein synthesis starts with the binding of the transcription factor (TF) to its binding site. These binding sites are short DNA segments that are called motifs. Different sites can bind to the same factor. This variability in binding sequences besides their low information content and low specificity increases the difficulty of their detection using computational algorithms. This paper proposes a novel algorithm for transcription factor binding sites (TFBSs) detection in the entire genomic structure and allow discovery of new motif sequences. This is achieved by using distance metrics based on a position frequency matrix (PFM) concept that quantify the similitude between the set of conserved sequences belonging to a particular TF and the entire DNA sequence under study. Hence, the PFM in this context can be thought of as a consensus sequence as it provides a representative measure of the said set of binding sites belonging to a particular TF. The algorithm then quantifies the correlation between the PFM and each binding site belonging to a given TF. Same scenario is then applied to the genome sequence under study. The obtained distance metrics are then utilized to discover new potential TFBSs based on their similitude of the set of binding sites investigated. Analysis is applied to Escherichia coli (E. coli) bacterial genomes. Simulation results verify the correctness and the biological relevance of the proposed algorithm.