e20 Nucleic Acids Research, 2007, Vol. 35, No. 3 After receiving a set of upstream regions co-regulated by a shared TF as input, computational methods aim to predict the binding positions of that TF (6–8,18). Given a set of input promoters, motif detection algorithms identify a set of short, oligonucleotide segments hypothesized to bind to the TF of interest. The predicted sequences can be used to construct a position weight matrix (PWM) representing the average nuc- leotide frequencies for each position in the site (19). Ideally, computational detection will return all sequences that bind to every TF with biologically relevant function in those upstream regions. However, since the source of binding speciﬁcity for TFs is not well understood (20), heuristic approaches and ad hoc multiple alignment based scoring schemes are used to identify locally optimal solutions (17). Each local optimum that exists in a given set of promoters may correspond to distinctly different motifs, and may score differently relative to each other according to different scoring schemes. Binding site prediction algorithms are generally con- founded by several factors: degeneracy in the binding site; the unknown length of the binding site; the relatively large length of promoters; and the inclusion of DSs in the input sets (17,21,22). As a result as few as 10% of predicted posi- tions correspond to biologically functional binding sites (23). Due, in part, to the low accuracy rate, computational binding site identiﬁcation has been of limited use (23). Problems identifying binding sites are further exacerbated in mamma- lian genomes by larger promoter regions (24) and scarcity of reliable information on co-regulation of genes. Thus, the most demanding test of efﬁcacy for TFBS identiﬁcation approaches lies in their application to mammalian systems and subsequent validation of predictions. Because of computational complexity of the problem, Gibbs sampling is often used to identify binding positions (18). In this paper, we present a new strategy that clusters Gibbs sampling results at each input nucleotide—a technique we term positional clustering—to improve accuracy of pre- dicted TF binding. We evaluate the efﬁcacy of our approach using known examples of binding and regulation in yeast and experimentally testing predicted TF-binding sites upstream of the subunit genes coding for the heteromeric mammalian neu- rotransmitter receptor system, the type A g-aminobutyric acid receptor (GABAAR). The GABAAR is the major inhibitory neurotransmitter receptor in the central nervous system (CNS) (25,26) with important roles in development (27,28) and disease (29–31). The receptor is believed to be a pentamer made up of multiple subunits that come from at least four different subunit classes (a, b, g and d) (32). At least 19 genes code for the various subunits that differentially combine to form numerous pharmacologically distinct GABAA receptor iso- forms (29,30). Isoform utilization depends in part on the relative abundance of the subunits, which may change under various conditions (33–35). Understanding subunit regulatory mechanisms may provide insight into GABAA receptor isoform usage and related phenotypes (36). In the current study, we test the ability of positional clus- tering to detect known TF-binding sites in a series of increas- ingly noisy sets of yeast promoters, and found marked improvement in the percentage of correct predictions over Gibbs sampling alone. We also present de novo predictions PAGE 2 OF 9 of TF-binding sites in promoter regions of GABAA receptor subunit genes (GABRs) whose expression is altered (either up-regulated or down-regulated) in an animal model of tempo- ral lobe epilepsy (35). Positional clustering identiﬁed a number of putative cis-regulatory sites, many of which correspond to known binding elements for TFs found in the CNS. Mobility shift assays showed several predicted GABR-binding sequences speciﬁcally bind nuclear proteins derived from primary neocortical neurons kept in culture. Furthermore, a particular non-consensus GABR putative regulatory sequence was shown to have a functional role in cultured cortical neurons demonstrating the efﬁcacy of positional clustering in detecting functional regulatory elements in mammals. METHODS Saccharomyces cerevisiae promoter selection We identiﬁed S.cerevisiae genes predicted at high conﬁdence (P < 0.001) to be regulated by the TF STE12 in YPD growth media, according to whole-genome TF location data (14). For the 51 identiﬁed genes, we collected upstream intergenic pro- moters. Intergenic regions were truncated at 1 kb upstream of the gene’s TSS. GABR promoter selection We selected for study a set of six GABRs: GABRA1, GABRA4, GABRB1, GABRB3, GABRD and GABRE. Pro- moters were extracted for each gene, including two alternative ﬁrst exons of the GABRB3 (37), giving a set of seven promot- ers. The length of each promoter was: GABRA1, 3733 bp; GABRA4, 1546 bp; GABRB1, 1353 bp; GABRB3 (exon 1), 1310 bp; GABRB3 (exon 1A), 2080 bp; GABRD, 6625 bp; and GABRE, 5278 bp. We augmented the input set with orthologous promoters from rat, with the exception of GABRB3 for which an orthologous gene from mouse was used. In total, 14 promoters upstream of six GABRs were selected for analysis. Evaluating long-term Gibbs sampling behavior For a given input set of promoters, we ran the Gibbs sampler BioProspector (8) 400–550 times, evenly distributed across all motifs widths from 6–15 bp. We used a third-order back- ground model derived from appropriate genomic promoters. We collected the best three results from each BioProspector run. We counted the number of times BioProspector identi- ﬁed each nucleotide in the input set. For each promoter, we identiﬁed the maximally occurring nucleotide, and extracted all positions identiﬁed by BioProspector >35% of the maxi- mum. We clustered together neighboring positions into puta- tive TFBS. As a dust ﬁlter, we removed all putative TFBSs <6 bp long (Figure 1). For sets of S.cerevisiae promoters, we used 1200 results from 400 BioProspector runs in our evaluation. For GABRps, we considered all 127 non-empty subsets of the seven pro- moters (orthologous sequences were always considered together). We used results from 70 000 BioProspector runs, evenly distributed across all promoter subsets, in our analysis. In addition to dust ﬁltering, we required putative TFBSs