BACKGROUND: The identification of transcription factor binding sites (TFBSs) and 
cis-regulatory modules (CRMs) is a crucial step in studying gene expression, but 
the computational method attempting to distinguish CRMs from NCNRs still remains 
a challenging problem due to the limited knowledge of specific interactions 
involved.
METHODS: The statistical properties of cis-regulatory modules (CRMs) are 
explored by estimating the similar-word set distribution with overrepresentation 
(Z-score). It is observed that CRMs tend to have a thin-tail Z-score 
distribution. A new statistical thin-tail test with two thinness coefficients is 
proposed to distinguish CRMs from non-coding non-regulatory regions (NCNRs).
RESULTS: As compared with the existing fluffy-tail test, the first thinness 
coefficient is designed to reduce computational time, making the novel thin-tail 
test very suitable for long sequences and large database analysis in the 
post-genome time and the second one to improve the separation accuracy between 
CRMs and NCNRs. These two thinness coefficients may serve as valuable filtering 
indexes to predict CRMs experimentally.
CONCLUSIONS: The novel thin-tail test provides an efficient and effective means 
for distinguishing CRMs from NCNRs based on the specific statistical properties 
of CRMs and can guide future experiments aimed at finding new CRMs in the 
post-genome time.