PAGE 5 OF 9 Nucleic Acids Research, 2007, Vol. 35, No. 3 e20 Figure 3. Improvement and robustness of positional clustering on promoters bound to other yeast TFs. Sets of S.cerevisiae promoters bound by the TFs YAP1, TEC1, HAP4 and YDR026C were chosen according to ChIP-chip experiments (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. clustering improves accuracy through a broad range of ran- dom DSs (Figure 3). Improvement appears to be limited and unreliable only when sampling alone correctly identiﬁes the binding site in fewer than 20% of results. This result is consistent with our analysis of STE12-bound promoters (Figure 2), and may correspond to a lower limit for the efﬁcacy of positional clustering. compared with 25% and 27% improvement for BioProspector and MotifSampler, respectively. Additionally, clustering combined sampling improved 19 of the 22 datasets evaluated, whereas clustering of BioProspector and MotifSampler results improved 17 and 16 datasets, respectively. Thus, posi- tional clustering is an effective mechanism to integrate results from multiple sampling procedures. Integrating sampling strategies Recently, researchers have noted that complementary motif detection approaches can be used together to predict binding sites more effectively than either method alone (23). With this in mind, we evaluated positional clustering in terms of its ability to combine results from two different sampling implementations. For each dataset, an equal number of results from each approach were combined into a single dataset, and positional clustering was used to predict binding sites as described above (Figure 3C). We measured the average per- cent change in PPV for each TF on each dataset, and found positional clustering improved combined sampling by 94% 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 sam- pling 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 identi- ﬁed through change in mRNA levels of the gene products