Some statistical properties of regulatory DNA sequences, and their use in predicting regulatory regions in the Drosophila genome: the fluffy-tail test

We present a novel statistical method, the "fluffy-tail test", to recognise regulatory DNA. We exploit one of the basic informational properties of regulatory DNA: abundance of over-represented transcription factor binding site (TFBS) motifs, although we do not look for specific TFBS motifs, per se . Though overrepresentation of TFBS motifs in regulatory DNA has been intensively exploited by many algorithms, it is still a difficult problem to distinguish regulatory from other genomic DNA.We show that, in the data used, our method is able to distinguish cis-regulatory modules by exploiting statistical differences between the probability distributions of similar words in regulatory and other DNA. The potential application of our method includes annotation of new genomic sequences and motif discovery.The transcription rate of genes is dictated primarily by interactions between DNA-binding transcription factors. Comparatively short sequences (several hundred to several thousand base pairs, depending on thespecies) upstream or downstream of the transcription start site often play a major role in the regulation of gene expression. Specific sites within such regions are recognized by regulatory proteins (transcription factors), which act upon binding as transcriptional repressors or activators, controlling the rate of transcription. The identification of regulatory regions, which are generally composed of dense clusters of target transcription factor binding sites, forms an essential step in understanding the regulatory interactions that govern the spatial and temporal expression of individual genes (see for example [1,2]) and genetic regulatory networks, (see for example [3]).Ultimately, this task is accomplished experimentally using techniques such as empirical deletion analysis, direct binding measurements, and co-precipitation of protein-DNA complexes. However, experimental verification is expensive and time consuming. Therefore, to address the growing volumes of avail