Cluster analysis of regulatory sequences with a log likelihood ratio statistics-based similarity measure

Upstream regions in the DNA sequence are characterized by the presence of short regulatory motifs, which function as target binding sites for transcription factors. Finding two genes with common motifs in their regulatory regions may aid users in identifying co-regulated genes or inferring regulatory modules. By modelling pattern occurrences in the regulatory regions with Poisson statistics, this paper presents a log likelihood ratio statistics-based distance measure to calculate pair-wise similarities between sequences. To perform cluster analysis of regulatory sequences, this paper introduces two clustering algorithms on the basis of the incorporation of the log likelihood ratio statistics-based distance into hierarchical clustering and Self-Organizing Map. The proposed approach has been tested on a synthetic dataset and a real biological example. The results indicate that, in comparison to traditional distance functions, the log likelihood ratio statistics-based similarity measure offers considerable improvements in the process of regulatory sequence-based gene classification.