PAGE 3 OF 9 Figure 1. Schematic diagrams of the positional clustering process. (1) Sets of putatively co-regulated genes are identified. (2) Gibbs sampling is iterated on the input set thousands of times across numerous motif widths. Results are clustered on promoter position, creating a per-nucleotide frequency of the long term recurrence of Gibbs sampling. (3) A linear threshold is used to isolate the most frequently recurring positions, discarding all positions which fall below the threshold. to occur both in the human and in the orthologous rodent promoter. Evaluating STE12-binding site recovery We used positive predictive value, PPV ¼ TP/ðTP þ FPÞ‚ to evaluate STE12-binding site predictions. We classiﬁed pre- dictions as true positive (TP) or false positive (FP) by compar- ison to the STE12-binding motif, TGAAACA, as determined by (14). For each sequence, we calculated distance from the known STE12 PWM using a modiﬁed local ungapped sequence alignment similar to that in (38). Alignments were scored as the sum of Pearson’s correlation coefﬁcient, rðXÞ ¼ covðX‚ PWMÞ sxsPWM ‚ between prediction X and the STE12 PWM across all aligned positions. Thus, scores ranged from zero, with no positions aligned, to seven, the length of the STE12 PWM. We observed a bimodal distribution of scores (Supplementary Figure S1), and chose the alignment score corresponding to the minima of the distribution (alignment score ¼ 4.5) as the threshold to classify predictions as TP or FP. Evaluating robustness to DSs We complemented the seed set of 51 STE12-bound promot- ers with 1–50 randomly chosen yeast promoters. We per- formed our motif detection procedure on each input set, and compared the PPV of putative TFBS with that of raw BioProspector results (Figure 2, solid lines). Nucleic Acids Research, 2007, Vol. 35, No. 3 e20 To evaluate the background rate of STE12-binding site recovery, we created a seed set of 51 randomly chosen S.cerevisiae promoters. We evaluated the percentage of STE12-like binding sites identiﬁed in the random seed set, as well as in versions of the seed set augmented with 1–50 randomly chosen yeast promoters (Figure 2, dashed lines). For additional yeast evaluations (HAP4, TEC1, YAP1 and YDR026C), we substituted for BioProspector an in-house implementation of the BioProspector algorithm. Comparisons of results from each implementation show the two implemen- tations to be approximately equivalent. Identification of known binding motifs in GABR predictions We ran MotifScanner (39) to search GABR promoters for all vertebrate TF-binding motifs found in TRANSFAC (40). For each promoter analyzed, we used a prior probability of 0.1 and the corresponding organism speciﬁc third-order promoter background model from Eukaryotic Promoter Database (EPD) (41).We considered positional overlap between Motif- Scanner predictions and putative TFBSs indicative of known binding motifs in our predictions. Electrophoretic mobility shift assay (EMSA) Double-stranded oligonucleotides for EMSA contained the following sequences: (i) GABRB1: AATACGGTCCCTACT, (ii) GABRD: ACTTAATTTGATTCCAT, (iii) GABRB3: CGTGCCGGGGCGCGGCGGA, (iv) GABRA4: AGCGCGGGCGAGTGTGAGCGCGAGT- GTGCGCACGCCGCGGG, (v) GABRA4: GTGCACACACACGCCCACCGCGGCT- CGGG and (vi) GABRD: TGACCGTAGTAGA. Nuclear extracts were prepared (42) and used for gel shift analysis after concentration (Microcon no. 10 columns, Amicon, MA). Quantiﬁcation was performed on EMSAs under condi- tions that yield a standard curve for band intensity. Double-stranded oligonucleotide functional analysis Single-stranded sense and antisense phosphorothioate oligonu- cleotides for the predicted GGCGGCGTGCACACACACGC- CCACCGCGG binding site are annealed by boiling sense and antisense oligonucleotides for 5 min at equal molar ratios in dH2O. Oligos are then cooled to room temperature and placed on ice. Transfections using DOTAP (Roche)/HEPES solutions are performed with oligonucleotides corresponding to wild- type, mutant or with DOTAP (Roche)/HEPES solution lacking oligonucleotides (MOCK) as described in (29). Effects of oligonucleotide application to neurons are assessed by real-time RT–PCR. RESULTS Gibbs sampling framework Since TFBS are predicted computationally by local optimiza- tion strategies, we evaluate the extent to which one of these strategies, Gibbs sampling, identiﬁes the same set of