BACKGROUND: Cis-regulatory modules (CRMs) are short stretches of DNA that help 
regulate gene expression in higher eukaryotes. They have been found up to 1 
megabase away from the genes they regulate and can be located upstream, 
downstream, and even within their target genes. Due to the difficulty of finding 
CRMs using biological and computational techniques, even well-studied regulatory 
systems may contain CRMs that have not yet been discovered.
RESULTS: We present a simple, efficient method (HexDiff) based only on hexamer 
frequencies of known CRMs and non-CRM sequence to predict novel CRMs in 
regulatory systems. On a data set of 16 gap and pair-rule genes containing 52 
known CRMs, predictions made by HexDiff had a higher correlation with the known 
CRMs than several existing CRM prediction algorithms: Ahab, Cluster Buster, 
MSCAN, MCAST, and LWF. After combining the results of the different algorithms, 
10 putative CRMs were identified and are strong candidates for future study. The 
hexamers used by HexDiff to distinguish between CRMs and non-CRM sequence were 
also analyzed and were shown to be enriched in regulatory elements.
CONCLUSION: HexDiff provides an efficient and effective means for finding new 
CRMs based on known CRMs, rather than known binding sites.