PAGE 7 OF 9 Nucleic Acids Research, 2007, Vol. 35, No. 3 e20 Figure 4. Three putative transcription factor binding sites form DNA–protein complexes in neocortical nuclear extracts. Neocortical nuclear extracts from E18 rat embryos were incubated with three 32P-radiolabeled probes from human GABRB1, GABRD and GABRB3. Cold wild-type oligonucleotides were used to define specificity through competition. Cold oligonucleotides were added at 100-fold excess over probe. The conditions for each lane are as indicated. Specific binding complexes are shown using asterisks (*). The probe sequences are as follows: (A) GABRB1: AATACGGTCCCTACT, (B) GABRD: ACTTAATTTGATTCCAT and (C) GABRB3: CGTGCCGGGG- CGCGGCGGA. Figure 5. EMSA of three putative TF binding sites form DNA–protein complexes in neocortical and fibroblast nuclear extracts. Neocortical (NEO) and fibroblast (FIB) nuclear extracts from E18 rat embryos were incubated with three 32P-radiolabeled probes from human A4 and D receptor subunits. Cold wild-type oligonucleotides were used to define specificity through competition. Cold oligonucleotides were added at 100-fold excess over probe. The conditions for each lane are as indicated. Specific binding complexes are shown using asterisks (*). The probe sequences are as follows: (A) GABA- A4: AGCGCGGGCGAGTGTGAG CGCGAGTGTGCGCACGCCGCGGG, (B) GABA-A4: GTGCACACACACGCCCACC GCGGCTCGGG and (C) GABA-D: TGACCGTAGTAGA. signiﬁcance of our predicted regulatory motifs, we evaluated the effects of transfecting neurons with double-stranded oligonucleotides containing one of the GABRA4 novel bind- ing motifs (dsA4O), as described above. GABRA4 is espe- cially interesting given that it is regulated by brain derived neurotrophic factor (BDNF) after status epilepticus (31,53). Transfection with the dsA4O produced a signiﬁcant down- regulation of GABRA4 gene expression in neocortical neurons as monitored by quantitative real-time RT–PCR with no Figure 6. Double-stranded oligonucleotide functional assay for GABRA4 regulation. Primary cultures of rat neocortical neurons were treated with DOTAP (N-[1-(2,3-dioleoyloxy)propyl]-N,N,N-trimethylammonium methyl- sulfate) alone (Mock) or with DOTAP and phosphothioate oligonucleotides from either a cAMP response element (CRE Decoy) or a sequence from the GABA-A4 promoter predicted using positional clustering (GABA-A4 Decoy) (GTGCACACACACGCCCACCGCGGCTCGGG). mRNA was harvested after 24 h, and real-time RT–PCR was performed with GABA-A4 specific primers. Error bars refer to individual experiments; i.e. different platings of cells from different animals. Data was normalized to rRNA levels, and expressed as relative mRNA levels (GABA-A4/rRNA). Results are shown as mean ± SEM, N ¼ 3, asterisk indicates significantly different from control at the 95% confidence interval. change after MOCK transfection or transfection with a dsO containing three copies of a cAMP regulatory element (CRE) (Figure 6). DISCUSSION How reliable are the binding site predictions returned by Gibbs sampling based TFBS identiﬁcation algorithms? We began by evaluating the stability of binding site predictions via repeated runs of Gibbs sampling. To quantify the robust- ness of predictions, we counted the number of Gibbs sam- pling results at each nucleotide position in the input set (Figure 1) over a large number of repeated trials. We ﬁnd that the most frequently returned positions better predict TF binding sites than the maximally scoring motifs from Gibbs sampling (Figures 2 and 3). Since scoring functions are empirically derived and do not necessarily represent biologi- cal reality, the result is not altogether unexpected (17). How- ever, we ﬁnd that selecting frequently recurring positions allows ﬁltering of up to 90% of spurious sampling results caused by convergence on biologically uninformative local minima. Positional clustering allows unbiased aggregation of results from different motif widths, thus approximating the width of the binding site ‘for free’ (54). Next we show that positional clustering improves robust- ness to the addition of DSs (Figures 2 and 3). Such sequences arise from inclusion of promoter regions in input sets without direct binding to the TF either due to experimental error or computational mis-annotation (17,22). In the STE12 example studied, linear regression models indicate our approach will maintain an advantage over traditional Gibbs sampling through addition of up to 150% noise to the original signal (Supplementary Figure S4). Empirical data, however, show a sharp decrease in improvement close to the addition of 45 DSs, or roughly double the input set (Figure 2). Moreover, evaluations using promoters co-regulated by other TFs