Since TFBS are predicted computationally by local optimization strategies, we evaluate the extent to which one of these strategies, Gibbs sampling, identifies the same set of segments in repeated runs using the same input data. Identifying stably recurring motifs requires clustering of related results which, in turn, requires definition of 'related'. Sequence similarity based clustering is impaired by the combination of sequence variation within motifs, the short length of TF-binding sites, and aligning motifs of different lengths. Instead of using sequence based clustering, we chose to cluster results by position, counting the number of times Gibbs sampling identifies each nucleotide in the promoter (Figure 1 ). We find that Gibbs sampling predictions, generated using BioProspector (8) are power-law distributed over nucleotide position (Supplementary Figure S2) . Gibbs sampling converges on the majority of nucleotides very infrequently, and a small number of nucleotides very frequently. Thus, the most frequently recurring nucleotides appear in as few as 10% of results. Moreover, we find the power-law distribution of results is robust to Gibbs sampling algorithm and scoring scheme (data not shown). We can hypothesize that the most frequently occurring positions are the most biologically significant. Thus, discarding the least frequent Gibbs sampling results may yield higher accuracy and robust identification of biologically insignificant positions.