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DNA motif discovery is an important task since it helps to better understand the regulation of the transcription in the protein synthesis process. This paper introduces a novel method for the task of DNA motif finding where the proposed method adopts machine-learning approach by the use of a well-known clustering algorithm, Fuzzy C-Means. The method is explained in detail and tested against DNA sequences extracted from the genome of Saccharomyces cerevisiae and Escherichia coli organisms. Experimental results suggest that the algorithm is efficient in finding statistically interesting features existing in the DNA sequences. The comparison of the algorithm with the well-known motif finding tools, MEME and MDScan, which are built on statistical and word-enumerative models, shows the advantages of the proposed method over the existing tools and the promising direction of the machine-learning approach.

Chromatin immunoprecipitation on microarray (ChIP-chip) experiments are a powerful tool for the detection of in vivo protein-DNA binding activity essential to the regulation of gene expression. Coupled with newly introduced tiling microarrays that can interrogate select parts or even an entire genome with high resolution, Chip-chip technology allows for the unbiased mapping of DNA-binding proteins throughout the genome. However, the increased resolution from tiling microarrays results in large, noisy, and correlated data sets that require powerful and computationally efficient analysis methods. In this chapter we first introduce ChIP-chip technology and tiling microarrays. Then we discuss methods for analyzing ChIP-chip data to obtain a list of putative protein-DNA interaction regions. Furthermore, we consider relevant follow-up analyses such as correlating ChIP-chip data with gene expression and searching for known and novel DNA-binding motifs in the putative binding regions.