As discussed above, log likelihood ratio alone is unlikely to distinguish the true binding sites from the background noise. Figure 6(a) shows a different view of Figure 1. The (inaccurate) predictions from MEME serve not only as false positives versus the planted motifs, but also perhaps the hardest to separate from the true binding sites. A simple horizontal line classifier obviously can not separate the true binding sites from the predictions. In Figure 6(b), we introduce a second number in each dataset: we performed a Kolmogorov-Smirnov test on the positions of the binding sites, and calculate its p-value assuming a uniform distribution as the background model. Now on the 2D plane, the axes correspond to the motifs' conservation in both sequence and position. It's easy to see that even a straight line classifier y - ax - b = 0 will separate the two sets decently. Let Prllr be the y value, the negative log p-value of the log likelihood ratio, Prpos be the x value, the negative log p-value of Kolmogorov-Smirnov test as explained above. Most true binding sites will fit aPrpos- Prllr+ b >0, and most false predictions of MEME will fit aPrpos- Prllr+ b < 0. The straight line in Figure 6(b) has parameters a = 13.5, b = 21.