Employing data mining techniques on biological sequences for transcription factor binding site identification

Identification of transcription factor binding sites (TFBSs) is a significant task in contemporary biology towards deciphering the genome functions and understanding gene regulatory networks. One way to identify them is via laboratory experiments which are laborious, time consuming and costly. Alternatively, computational methods based on pattern recognition techniques are proposed in the literature for automatic extraction of TFBS instances from given DNA sequences.In this study, three different computational methods are proposed. First approach is based on clustering all w-mers and attempting to find a statistically interesting local alignment via z-score testing. Four clustering methods, Self-organizing map, Fuzzy C-Means, K-means and Expectation Maximization with Gaussian Mixture Models, are considered in this context. The second technique is similar to the first one except that it has a Bayesian post-optimization procedure to fine-tune local alignments composed of Position weight matrices. The third computational technique developed in the thesis adopts a different approach by utilizing a stochastic search procedure, namely Particle swarm optimization. The developed methods each of which offers novel contributions to the relevant literature are evaluated against several types of datasets including low and high organism DNA. Moreover, they are also compared to state-of-art motif-finding tools from the literature such as MEME and MDScan, as well. Experimental results suggest that the proposed methods are highly promising for DNA motif-finding task.