Computational discovery of TFBS can have two types of FP predictions. One type is the identification of an incorrect motif from a set of upstream regions known to bind to a TF of interest as described above (see Methods). The second type of FP error is the background discovery rate of the correct motif using upstream regions that do not bind to the TF. To simulate this rate for STE12-like binding site recovery we repeated the analysis as described above starting with 51 randomly chosen yeast promoters. We find that positional clustering identifies STE12-like sites in <5% of results, compared with 10-15% for Gibbs sampling alone. Thus, using positional clustering, the performance of computational motif discovery is enhanced not only by improving the positive predictive value in promoters of genes co-regulated by STE12, but also by decreasing the false discovery of STE12-like sites by 10%.