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Background: The identification of transcription factor binding sites (TFBSs)and cis-regulatory modules (CRMs) is a crucial step in studying geneexpression, but the computational method attempting to distinguish CRMs fromNCNRs still remains a challenging problem due to the limited knowledge ofspecific interactions involved. Methods: The statistical properties ofcis-regulatory modules (CRMs) are explored by estimating the similar-word setdistribution with overrepresentation (Z-score). It is observed that CRMs tendto have a thin-tail Z-score distribution. A new statistical thin-tail test withtwo thinness coefficients is proposed to distinguish CRMs from non-codingnon-regulatory regions (NCNRs). Results: As compared with the existingfluffy-tail test, the first thinness coefficient is designed to reducecomputational time, making the novel thin-tail test very suitable for longsequences and large database analysis in the post-genome time and the secondone to improve the separation accuracy between CRMs and NCNRs. These twothinness coefficients may serve as valuable filtering indexes to predict CRMsexperimentally. Conclusions: The novel thin-tail test provides an efficient andeffective means for distinguishing CRMs from NCNRs based on the specificstatistical properties of CRMs and can guide future experiments aimed atfinding new CRMs in the post-genome time.

A statistical thin-tail test of predicting regulatory regions in the Drosophila genome