alignment. Notably, the user can deliberately choose to implement the WR algorithm that does not consider con- servation information by manually selecting the WR box in the SFmap front page. This option will improve runtime, although at the expense of speciﬁcity. This option is obligatory when running non-human sequences. Since, for graphical display, SFmap uses the blat search algorithm (16) to determine input sequence coordinates in the HG, in cases where no match is found for the input sequence, results will be provided as a text ﬁle but will not be displayed visually. SFmap outputs the predicted motifs and their calculated COS(WR) scores mapped to the input sequence. The results are provided both as a text ﬁle and in a visualized display of the motifs on the UCSC HG browser as a custom track. In order to deﬁne the signiﬁ- cance of the score for each motif stored in our database, we calculated a threshold relative to a background model of random human sequences [for more details see (14)]. Furthermore, to accommodate motifs provided by the users, a theoretical threshold that best ﬁts the experimen- tal model was calculated. Finally, only motifs that were scored above the threshold are reported. For convenience, SFmap outputs the threshold calculated for each motif. The resulting text ﬁle includes a summary table of the motifs predicted by our algorithm and their location within the input sequence, including their genomic coord- inates. A separate list including the position of the pre- dicted motifs (hits) and the calculated score for each hit is given for each motif requested by the user. In cases where the input ﬁle includes multiple sequences, results are provided independently for each sequence (including a link to the text and the visualized display), as well as a summary ﬁle combining all motifs found in the input se- quences. It is important to note that since the COS(WR) algorithm does not require 100% similarity of the binding site to the motif, diﬀerent binding sites (related to the same SF or to diﬀerent factors) can be predicted at the same genomic location. An example of a visualized display of SFmap in dense and full representation is shown in Figure 1A and 1B, respectively. In this example, the sequence of the 30-untranslated region (UTR) of the SC35 gene was provided as the input to SFmap. SC35 is an SF from the SR protein family and was previously shown to be autoregulated by a cassette exon event at the 30-UTR of its own gene (17). In a recent paper by Dreumont et al. (18), it was shown that two other SFs, hnRNP H and the TAR DNA-binding protein (TDP-43), antagonize the binding of SC35 to the junction between the retained intron and the terminal exon. In this example, we selected the two binding motifs of SC35 and the three hnRNP H/F motifs from the list of motifs provided by SFmap. In addition, we used the ‘new motif’ option to map the ‘UGUGUG motif’, which has been described in the literature as being the minimal binding motif of TDP-43 (19). As shown in Figure 1, the SF hits are displayed as a box (Figure 1A) or a thin bar (Figure 1B), designating the ﬁrst position of the motif. In the ‘full’ display option (Figure 1B), bar height represents the score of the hit, Nucleic Acids Research, 2010, Vol. 38, Web Server issue W283 which is detailed in the accompanying text output. Each row represents the results for a single motif speciﬁed on the left-hand side of the row. As indicated by arrows on the gene display (at the bottom of the ﬁgures), the SC35 gene is located on the antisense strand of the chromosome. Since the HG browser always displays the sense strand, the results in this example should be read from right to left. As illustrated in the ﬁgure, most of the binding sites of all three SFs were mapped to the junction region between the retained 30-UTR intron and the 30-UTR exon. Interestingly, when displaying the mammalian evolution- ary conservation provided by the HG browser compara- tive genomic analysis track (bottom of Figure 1B), one can see that the entire 30-UTR is highly conserved and by itself could not explain the speciﬁc localization of the binding sites of the three diﬀerent SFs to the splicing junction. Overall, the results of SFmap are highly consistent with the experimental results presented in Dreumont et al. (18). DISCUSSION SFmap is designed to map SF binding sites in human genomic regions using the COS(WR) algorithm (14). The COS(WR) algorithm takes two important features of SF binding sites into account: their propensity to cluster and enhance binding aﬃnity and speciﬁcity; and the tendency of the genomic environment of the binding site to be conserved. Taking these features into consider- ation, the method was shown to perform very accurately with a relatively low false-positive rate (14). The method was tested on a ﬁxed set of experimentally deﬁned binding motifs, however, it can be applied to any other motif of interest. Thus, though SFmap was speciﬁcally designed to map SF binding sites, it could potentially be applied to map binding sites of other RNA binding protein motifs on the RNA that are assumed to bind in a similar fashion, such as the Pumilio RNA-binding protein, which was recently shown to bind the 30-UTR in a cooperative manner (20). Furthermore, the main advantage of the algorithm is that it derives standardized scores for any given motif so that predictions of diﬀerent binding sites are comparable. Nevertheless, it is very important to note that the COS(WR) score does not reﬂect the binding aﬃnity or functionality of the site. Moreover, since the COS(WR) algorithm has been designed to identify degen- erative binding motifs relying on compensating informa- tion from the sequence environment, it is likely that in some cases, SFmap will map diﬀerent binding motifs to the same genomic position. Even though diﬀerent motifs will most likely yield a diﬀerent score (both above the motif threshold), one should be very careful when select- ing the best scored motif. Notably, the existence of two SF hits in the same position could indicate competition between two factors, such as that reported in the splicing regulation of the 30-UTR of the SC35 gene (18) shown in the example in Figure 1. Moreover, we would like to emphasize that COS(WR) may not be suitable for uniquely indentifying binding sites of proteins that have very stringent binding preferences. In the latter cases, we