Recently, researchers have noted that complementary motif detection approaches can be used together to predict binding sites more effectively than either method alone (23) . With this in mind, we evaluated positional clustering in terms of its ability to combine results from two different sampling implementations. For each dataset, an equal number of results from each approach were combined into a single dataset, and positional clustering was used to predict binding sites as described above ( Figure 3C ). We measured the average percent change in PPV for each TF on each dataset, and found positional clustering improved combined sampling by 94% compared with 25% and 27% improvement for BioProspector and MotifSampler, respectively. Additionally, clustering combined sampling improved 19 of the 22 datasets evaluated, whereas clustering of BioProspector and MotifSampler results improved 17 and 16 datasets, respectively. Thus, positional clustering is an effective mechanism to integrate results from multiple sampling procedures.