  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.
