W526 Nucleic Acids Research, 2006, Vol. 34, Web Server issue genome and ﬁltered sets have been produced using mouse–dog (canFam1) and mouse–opossum (monDom1) alignments. TFBScluster input The ﬁrst screen of TFBScluster requires the user to choose the reference genome (human or mouse) to determine the relevant genome annotation and external data used for the subsequent analysis of candidate regulatory regions. The next screen speciﬁes the number of different consensus sequences that can be present in a cluster of TFBSs. The choice is also made as to which dataset of TFBSs should be used, either in-house consensus sequences, conserved regulatory motifs from two other studies (29,30) or TRANSFAC v8.3 matrices. The next screen contains ﬁelds to specify the main parameters of candidate regulatory clusters, such as the number of motifs within a user deﬁned window of sequence. To ensure that a minimum set of TFBSs are free to bind their corresponding proteins simultaneously, an option is included to discard clusters that do not contain the required number of sites when overlapping TFBSs are discounted. By default TFBScluster will use dataﬁles of binding sites that are conserved between human–mouse or mouse–human genomes. The degree of conservation and therefore the stringency of the search can be increased by selecting sites that are also conserved in dog or opossum. In the human version of TFBScluster we have implemented additional approaches to increase the likelihood that TFBSs represent functional sites. Regulatory potential scores have been shown by others (31,32) to provide signiﬁcant enrich- ment of regulatory sequences. A ﬁlter has therefore been implemented that can restrict TFBScluster to sites located in areas of regulatory potential with scores greater than zero (based on the threshold suggested by UCSC http:// hgdownload.cse.ucsc.edu/goldenPath/hg17/regPotential/). It is also possible to only consider those motifs that are present within experimentally determined active promoters, using the ﬁbroblast cell line IMR90. This dataset is based on a recent study that identiﬁed active promoters using a microarray based chromatin immunopreciptation method to detect all RNA polymerase II preinitiation complexes assembled on DNA throughout the human genome (33). TFBScluster output The ‘short’ analysis returns the chromosomal start and end positions of all candidate clusters. This method is useful to gauge how many candidate regulatory clusters are found using a given set of search parameters. Moreover, as the data is returned in the general feature format (GFF; http://www. sanger.ac.uk/Software/formats/GFF/GFF_Spec.shtml), results ﬁles can be readily exported to other genome analysis programs. Users are advised to avoid ‘long’ searches using parameters that generate more than 5000 candidate clusters, due to the length of time it will take to complete. TFBScluster can provide a more comprehensive analysis of candidate clusters by selecting the ‘long’ analysis. Using the Ensembl API (34), this option will identify all genes that are localized to within 100 kb of a cluster. A single gene will be associated with a cluster if the cluster is located in an intron, otherwise up to two genes will be associated with a cluster by selecting the closest gene 50 and 30 to the cluster. In TFBScluster a gene is deﬁned as the footprint of all Ensembl gene transcripts on the genome sequence. All candidate clusters located in an exon are excluded from further analysis. Clusters can be restricted to genes that are annotated with EntrezGene (35) and/or UniProt-SwissProt (36,37) identiﬁers. The choice of SwissProt identiﬁers ensures that only those genes with experimentally characterized proteins are detailed in the results. TFBScluster has the ability to retain or exclude cluster candidates based on the presence of user supplied IUPAC consensus sequences. This facility can be useful, e.g. to reject clusters containing a consensus that is known not to be associated with the others. It can also be used to determine space and order constraints within the cluster by submitting consensus sequence patterns. For example, GATA*5–8*GATA will look for two Gata sites within 5 to 8 nt on the same strand. The ‘long’ analysis results page provides detailed informa- tion for each candidate cluster together with information about the genes that are potentially under their control. Access is also provided to other ﬁles containing subsets of information from the main results ﬁle, such as a list of Swissprot identiﬁers that can be used in external analyses (see http://hscl.cimr.cam.ac. uk/TFBScluster_examples/TFBScluster_ex_ﬁle.html). Links are provided to both UCSC (http://genome.ucsc.edu/cgi-bin/ hgGateway) and Ensembl (http://www.ensembl.org/index. html) genome browsers; they both indicate the location of a cluster (with 300 ﬂanking nucleotides) in relation to the sur- rounding chromosome annotation. Information is therefore provided to streamline the experimental veriﬁcation of inter- esting candidates; 300 nt of sequence ﬂanking the cluster is shown for the reference genome, as well as an alignment of the predicted cluster for the default genomes. The selection of clusters can be further constrained by only considering those that have been localized to genes that are expressed in a tissue speciﬁc manner. To achieve this, both the human and mouse versions of TFBScluster make use of information from the Gene Expression Atlas 2 (38). The Gene Expression Atlas 2 contains global gene expression proﬁles for 79 different human tissues and 61 different mouse tissues, together with extensive statistical analysis. For example, a TFBScluster analysis for muscle-speciﬁc binding site clusters can be focussed on those genes that show differential expression in muscle. To achieve this, TFBScluster would only retain those clusters that localize to genes with a speciﬁed fold over median expression in muscle when compared to the other 78 tissues. This important feature therefore allows the user to connect candidate cluster regions to a particular biological context in human or mouse. SOFTWARE AND ACCESS TFBScluster.pl and supporting programs are written in PERL and are accessible via a PERL CGI interface on a web server, hosted by the University of Cambridge. The PERL scripts are available on request. The run time of a submitted job is typically less than 15 min using the ‘short’ analysis. For ‘long’ analyses, run time is dependent on the number of candidate clusters. Although users are initially restricted to the TFBS consensus sequences already present in TFBScluster, we