After receiving a set of upstream regions co-regulated by a shared TF as input, computational methods aim to predict the binding positions of that TF (6) (7) (8) 18) . Given a set of input promoters, motif detection algorithms identify a set of short, oligonucleotide segments hypothesized to bind to the TF of interest. The predicted sequences can be used to construct a position weight matrix (PWM) representing the average nucleotide frequencies for each position in the site (19) . Ideally, computational detection will return all sequences that bind to every TF with biologically relevant function in those upstream regions. However, since the source of binding specificity for TFs is not well understood (20) , heuristic approaches and ad hoc multiple alignment based scoring schemes are used to identify locally optimal solutions (17) . Each local optimum that exists in a given set of promoters may correspond to distinctly different motifs, and may score differently relative to each other according to different scoring schemes.