Identification of GABR cis-regulatory sequences As described above in Introduction, identifying functional TFBS in mammals is difficult due in part to inclusion of decoy sequence from long upstream regions and lack of information on co-regulation of genes. Positional clustering, as shown above, is more robust to noisy input than Gibbs sampling alone, and thus may be better suited to identify de novo cis-regulatory elements in mammalian promoters that are coordinately regulated. To test this possibility, we chose seven mammalian GABR promoters (GABRps) whose activity is potentially altered in response to status epilepticus as identified through change in mRNA levels of the gene products (10) . For each set, the initial promoters were analyzed using Gibbs sampling with positional clustering (solid triangles) and without (open triangles). Two Gibbs sampling approaches were applied to each dataset: a Gibbs sampler procedure similar to BioProspector (8) (row A), and MotifSampler (39) (row B). Row C shows the combination of both sampling procedures, along with positional clustering of the combined results. x-axis counts over addition of DSs. We evaluated the positive predictive value of each technique on each dataset, and found positional clustering generally improved the PPV through addition of 100% random DSs. (31, 35) . We also included orthologous rodent promoters in the input sets (45) . Orthologous promoters were included to provide more instances of binding sites in the input set than would be expected by random, allowing for easier detection of the sites. Inclusion of orthologous promoters has the additional effect of selectively amplifying evolutionarily conserved binding sites. Such binding sites are more likely to have major functional roles in the regulation of the GABR receptor. Thus, sensitivity to such sequences is improved at the expense of sensitivity to species-specific binding sites. With this effect in mind, we require all GABR-binding site predictions to exist in orthologous promoters. Since the mechanisms of co-regulation for the seven GABRs are unknown, hypothetical co-regulation models were evaluated by querying all 2 7 possible subsets of the seven GABRps. Clustering results on nucleotide positions and selecting the most frequently occurring positions, we predicted 13 functional TF-binding sites.