An interpolated Markov model polishes Gibbs sampling´s ability in detecting regulatory elements

Microarray techniques provide new methods to find coregulated genes based on their coexpression profiles. Under the assumption that coregulated genes share cis acting regulatory elements, it is important to investigate the upstream sequences controlling the transcription of these genes. A modified Gibbs sampling algorithm with background interpolated Markov model (IMM) has been developed to detect regulatory elements in the upstream regions of translation start site of coexpressed genes. Simulated data are used to test our algorithm successfully. Results show that the improved Gibbs sampling has better performance in extracting less-conserved elements than algorithms with single nucleotide independent model and fixed higher-order Markov models. Then, upstream sequences of two clusters of coexpressed genes from Saccharomyces cerevisiae under diauxic shift conditions are analyzed, several putative motifs that may be involved in the pathway are found.