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 robustness of predictions, we counted the number of Gibbs sampling 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 biological reality, the result is not altogether unexpected (17) . However, 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) .