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130 Davuluriinteracting proteins and bind to sequence specific cis-regulatory elements orTF-binding sites (TFBSs) in gene promoters. These clusters of TFBSs, alwaysof different types, and some represented multiple times, which occur synergisticallyin gene regulatory regions, are known as cis-regulatory modules (2).Emerging evidence suggests that alternative states of promoter activity (activationor repression) are influenced by interconnected assembly of transcriptionalregulatory networks and epigenetic modifications at a chromatin template (3).A key component in this epigenetic machinery is the occurrence of histonemodifications around the promoter region of a gene (4–6), and different combinationsof histone modifications may act synergistically or antagonistically toaffect gene expression (7). It is known that acetylation of lysine 9 at histone H3(H3-K9) is linked to transcriptional activation (8), whereas dimethylation of thesame lysine seems to specify transcriptional repression (9). Regarding this,coregulatory proteins, which often possess chromatin modulating activities,appear to act cooperatively with partner TFs to establish patterns of geneexpression, and thus, provide considerable functional flexibility in specifyingtranscriptional activation or repression (3,10).Consequently, modeling the cis-regulatory modules in the activated orrepressed target promoters of specific TFs is required to elucidate the transcriptionalregulatory machinery. The first step in modeling the cis-regulatory modulesis specifically identifying the TFBSs in the target promoters of TFs. Which genepromoters are targets of a given TF is partly determined by the DNA-bindingdomain of the TF protein. This domain allows the TF to bind to its specific TFBSin the target gene promoter. Extensive molecular research has provided a wealthof such information about experimentally characterized gene promotersequences, TFs, and their binding sites (TFBS). Databases such as JASPAR (11),TRANSFAC (12), TRRD (13), and TFD (14) provide information about TFs andexperimentally known TFBSs. In theory, the availability of these resources alongwith gene promoter databases, such as MPromDb (15), DBTSS (16), TRED (17),and EPD (18), should have made the task of finding TF target promoters astraightforward approach. For example, one can scan the promoter sequences ina genome of interest for the location of TFBS by using programs, such asMATCH (19), which uses TRANSFAC position weight matrices (PWMs). ThesePWM-based scanning programs are extremely useful for the identification ofpotential TFBS in a small promoter region around the Transcription Start Site(TSS) of a gene of interest, but produce too many false-positive predictionswhen applied to multiple promoters at genome level.Consequently, determination of the TF targets is a daunting task (20).Recent programs have greatly improved in their TFBS prediction accuracyby incorporating sequence conservation information through phylogeneticfootprinting (21–25) and by modeling the cis-regulatory modules (3,26–28).