A major challenge to identifying transcription factor binding sites (TFBSs) is that they tend to be short, degenerate, and occur frequently throughout the genome. Analysis of a single sequence usually leads to an abundance of false positive predictions of TFBSs. Several programs have been developed to respond to this challenge based on two principles. First, functional regulatory elements are often conserved evolutionarily; therefore, identifying TFBSs that are conserved or aligned in multiple sequences may effectively filter false positive predictions (Ovcharenko et al. 2005) . Second, gene expression often results from coordinate activation of multiple, proximal regulatory elements; therefore identifying TFBSs in clusters, rather than isolation, may enhance confidence in the functionality of predicted TFBSs. These principles have been leveraged, albeit differently, by rVISTA (http:// gsd.lbl.gov/vista/index.shtml; Loots et al. 2002; Loots and Ovcharenko 2004) , which is a member of the VISTA suite of tools and is also integrated with zPicture, and the newly launched Mulan (http://mulan.dcode.org/; Ovcharenko et al. 2005) . Both servers use profiles of transcription factor binding sites from the TRANSFAC database (http://www.biobase.de; Matys et al. 2003) . Because TRANSFAC and other similar resources only contain information for known transcription factor binding sites, they are inherently incomplete. Furthermore, existing tools do not address other important regulatory features such as properties related to protein-protein interactions and chromatin structure, and clusters of binding sites that may have been reshuffled between organisms over evolutionary time (Loots et al. 2002) .