W284 Nucleic Acids Research, 2010, Vol. 38, Web Server issue Figure 1. SFmap output shown in a ‘dense’ (A) and a ‘full’ (B) format. In this example, SFmap was run on the 30-UTR of the SC35 gene. The predicted sites for the motifs selected by the user are displayed on the UCSC HG Browser as a custom track. The top ﬁve motifs were selected from motifs that were stored in our database, and the last motif is a user-deﬁned motif. As can be observed, most of the predicted binding sites of the three SFs are mapped to the intron/exon junction previously shown to be regulated by these factors (18). expect that SFmap will be sensitive but not highly select- ive, and thus may obtain a higher number of false-positive results with relatively high scores. It is important to emphasize that though the COS(WR) algorithm was tested on a set of 30 diﬀerent experimentally veriﬁed motifs, we cannot fully anticipate prediction accuracy for motifs that have not been tested. Thus, though SFmap can direct researchers to regions in the sequences that are likely to bind SFs or other RNA-binding proteins, direct experimental evidence will still be required to conﬁrm the predictions. CONCLUSIONS In the post-genomic era, tools for mapping SF binding sites are in great need, both for basic research and for the detection of disease-associated mutations in RNA regulatory sequences. The SFmap server, which imple- ments the COS(WR) algorithm (14), has been designed for mapping binding sites of known proteins given their consensus motifs. Since the method is very general, it can be applied for predicting the motifs of other RNA-binding proteins that are expected to have similar binding prefer- ences. Once more detailed information on binding motifs