Programs that use a motif to search (i.e., scan) a sequence database for matches (i.e., predicted transcription factor binding sites) fall into two general categories. One approach is to employ a training set of transcription factor binding sites and a scoring scheme to evaluate predictions [2-8]. The scoring scheme is often based on information theory [9], and the training set is used to empirically determine a score threshold for reporting of the predicted transcription factor binding sites. The second method relies on a rigorous statistical analysis of the predictions, based upon modeled assumptions. Briefly, the statistical significance of a sequence match to a motif can be assessed through the determination of type I error (p-value): the probability of observing a match with a score as good or better in a randomly generated search space of identical size and nucleotide composition. The smaller the p-value, the less likely that the match is due to chance alone. Staden [10] presented an efficient method that exactly calculates this probability, and Neuwald et al. [11] described an implementation of this method.