Nucleic Acids Research, 2005, Vol. 33, Web Server issue W439 T-Reg Comparator provides an interface to a program developed in our group for the scanning of sequences with weight matrices (11). MATERIALS AND METHODS For weight matrix comparison we use a method that has already been introduced by De Moor and colleagues (9,10). It is based on a symmetrized, position-averaged Kullback– Leibler distance. To compare two weight matrices: the shorter one is moved along the other and all shifted positions that satisfy the following three conditions are considered. First, at least half of the shorter matrix has to overlap with the longer matrix. Second, this overlap has to be at least four positions long. And third, the overlapping part of at least one matrix has to have a position-averaged entropy below one with respect to the natural logarithm. For each such shift, a position- normalized dissimilarity score is calculated for the overlap- ping part, and the smallest dissimilarity score is used to measure the overall similarity between the two matrices. Our web service works on the T-Reg database. The T-Reg database is an in-house relational database on transcriptional regulation that currently contains data from Jaspar and Transfac version 8.4 (1,2). Data from Jaspar and Transfac Public is freely accessible on our web site. However, T-Reg cannot be made publicly available for download because it contains data from Transfac that we are not allowed to redis- tribute. The Jaspar data was obtained from the project web site, http://jaspar.cgb.ki.se. The input consists of a set of weight matrices or sets of sequences, which can be entered into a text ﬁeld or uploaded from a ﬁle. In the current version of the program we support several matrix formats: MEME, Transfac and Jaspar ﬁle for- mats and a raw data format. Sequence sets are given in FASTA format, or simply one sequence per line, and are then used to generate weight matrices. If the user inputs an alignment, weight matrices are constructed by counting the occurrences of bases in each position. If, instead, the user inputs unaligned binding site sequences, DiAlign (12) is utilized to compute a multiple alignment ﬁrst. After specifying the input, the user chooses the set of matrices with which to make the compar- ison. For example, it is possible to restrict the comparison to the set of available vertebrate matrices in the public version of Transfac. Smaller sets of matrices lead to shorter computation times and results that can be easily interpreted. The dissimil- arity score ranges from 0 to 5. We recommend a cutoff of 0.8 or 0.5, where the latter produces more speciﬁc results. After all pairwise comparisons have been made, the applica- tion returns a table that contains the following information: the name and the consensus of the input matrix and a list of matrices with divergence smaller than the given cutoff. For these matri- ces, the name, the overlap, the orientation, the shift, the actual dissimilarity score and the consensus of the best match are provided. Further, a grouping of the transcription factors into coarse classes based on the structure of the DNA-binding domain is given in the annotation. Hyperlinks guide the user to the web pages of the source databases and additional infor- mation, Jaspar, Transfac Public, Transfac or TESS (13). If the matrix stems from the non-public part of Transfac, the hyperlink points to the Biobase website (http://www.biobase.de), where access is restricted to licensed users. A standalone version of T-Reg Comparator will be made available at our web site. As a natural step in studies on transcriptional regulation, a user can start a sequence search with the input matrices. The annotation with the PWM hits on the sequence is based on an elaborate, statistically sound method that has previously been developed in our group (11). The software is freely available at http://genereg.molgen.mpg.de/ProﬁleStats/index.shtml. Two aspects of this method should be mentioned here. First, in the process of constructing a scoring matrix from the PWM, we use a regularization method that does not change the overall nucleotide composition of the proﬁle and regular- izes each position relative to its signal strength. Second, for the ﬁnal scoring matrices, we calculate exact score distributions under a background and a signal model for motif sequences. This allows the determination of the scanning cutoff using statistical considerations, which is better than making an ad hoc decision such as setting the cutoff to 80% of the PWM’s overall score range. In the current interface the user can choose an accepted false-negative rate. The output consists of a list of all the matches better than the cutoff. The orientation, the position and the false-positive estimate for each hit are printed out. RESULTS AND DISCUSSION We demonstrate the functionality of T-Reg Comparator on an example (Figures 1 and 2). We have constructed an example MEME ﬁle from a promoter analysis of ribosomal protein genes. The ﬁle is available in the Supplementary material or via the help page of T-Reg Comparator. The three weight matrices represent the typical cases that occur during motif discovery endeavours. Motif 1 is quite unspeciﬁc and matches other unspeciﬁc matrices best. It shows weak similarity to binding sites of the STAT family of transcription factors. When compared against Jaspar, the PWM Motif 8 matches MA0028 for the transcription factor Elk-1 best. The sequence logo for MA0028 is depicted in Figure 1 and the comparison is illustrated in Figure 2. Motif 8 resembles a typical binding site of a factor of the ETS family because it contains the charac- teristic core motif GGAA. The two positions preceding GGAA Figure 1. Sequence logo representation of the binding specificity of the tran- scription factor Elk-1, copied from the Jaspar web site, http://jaspar.cgb.ki.se (identifier MA0028). The height of each column indicates the information content of the corresponding position. The sizes of the characters represent the relative frequency of the corresponding bases.