Assessment of clustering algorithms for unsupervised transcription factor binding site discovery

Identification of transcription factor binding sites is a key task to understand gene regulation mechanism to discover gene networks and functions. Clustering approach is proved to be useful when finding such patterns residing in promoter regions of co-regulated genes. Four clustering algorithms, Self-Organizing Map, K-Means, Fuzzy C-Means and Expectation-Maximization are studied in this paper to discover motifs in datasets extracted from Saccharomyces cerevisiae, Escherichia coli, Droshophila melanogaster and Homo sapiens DNA sequences. Required modifications to clustering algorithms in order to adapt them to motif finding task are presented through the paper. Then, their motif-finding performances are discussed carefully and evaluated against a popular motif-finding method, MEME.

► This paper considers clustering approach for TFBS identification.
► Four clustering algorithms are studied; Self-Organizing Map, K-Means, Fuzzy C-Means and Expectation maximization.
► Experimental results prove that machine learning methods, specifically clustering techniques, are proper means to perform DNA motif discovery.