W282 Nucleic Acids Research, 2010, Vol. 38, Web Server issue diﬀerent RNA regulatory motifs based on homology to known motifs(13). Recently, we developed a novel computational approach for predicting and mapping SF binding sites of known SFs that considers both the genomic environment and the evolutionary conservation of SF cis-regulatory elements. The method was used to test experimentally validated sequences showing a high accuracy of 93% and a relatively low false-positive rate of 1% on the tested data. In addition, the method was applied to diﬀer- ent sets of exons and introns, and detected an enrichment of SF binding sites in diﬀerent types of AS events, such as cassette exons, alternative 50-splice sites and alternative 30-splice sites (14). Here, we present a web service for mapping SF binding sites on human genomic sequences. As a default, SFmap searches for experimentally deﬁned binding motifs (stored in our database) within the given sequence. In addition to the motifs available on the web server, the user may provide any other RNA motif of interest (of length 4–10 nt). SFmap output includes a link to the results in two diﬀerent formats, both in text and in a visualized display of the motifs as custom anno- tation tracks in the UCSC human genome (HG) browser (15). The SFmap web service is free and open to all users with no login requirement. SFmap METHODOLOGY Accurate mapping of SF binding sites is challenging, mainly due to the very low information content present in these relatively short and degenerative sequences (4). To enable accurate prediction of SF binding sites, the SFmap server implements the conservation of score (COS) using the weighted rank approach algorithm (COS(WR)) developed recently by Akerman et al. (14). Speciﬁcally, our method searches for the existence of a deﬁned consensus motif, ideally motifs that have been conﬁrmed experimentally to bind known SFs. The list of motifs that were tested and veriﬁed is given in Table 1. The COS(WR) algorithm exploits two major attributes of functional binding sites on RNA: their preference to cluster in regulatory regions in order to enhance binding aﬃnity and the tendency of the regulatory region to be conserved in evolution. COS(WR) scores are calculated in two steps: ﬁrst, the weighted rank (WR) function is applied to calculate the clustering propensity of the motif; second, evolutionary conservation is estimated by further weighting the WR scores with the COS function. The COS function relaxes positional constraints imposed by sequence alignments, therefore it is ideal for estimating the evolutionary conservation of regulatory motifs. A detailed description of the algorithm is given in Akerman et al. (14). As described in Akerman et al. (14), the algorithm was tested on a set of experimentally validated sequences that bind speciﬁc factors and a control set of non-binding sites (56 and 502 sequences for positives and controls, respect- ively) and displayed a high 93% accuracy with a relatively low 1% false-positive rate. The results were obtained using a predeﬁned set of parameters to calculate the WR Table 1. List of SF-binding motifsa SF SF2ASF 9G8 SC35 Tra2alpha Tra2beta SRp20 SRp40 SRp55 hnRNPA1 hnRNPAB hnRNPH/F MBNL NOVA1 PTB CUG-BP YB1 FOX1/2 Motifs crsmsgw, ugrwgvh acgagagay, wggacra gryymcyr, ugcygyy gaagaggaag gaagaa, ghvvganr, aaguguu cuckucy, wcwwc yywcwsg yrcrkm uagaca, uagagu, uagggw auagca ggcgg, gggug, uguggg, uugggu ygcuky ycay cucucu, ucuu ugcug caaccacaa ugcaug aThe SFs and their related motifs were extracted from the literature and are based on experimental data as detailed in Akerman et al. (14). function: the signiﬁcant cut-oﬀ for the major hit, the sub- optimal cut-oﬀ for the multiple hits and the window size. The COS(WR) algorithm performance was compared with the SF binding motif predictor ESEﬁnder (6,12), which is currently the only method available for mapping binding motifs of known SFs. As shown in Akerman et al. (14), the COS(WR) algorithm demonstrated signiﬁcant higher speciﬁcity compared to ESEﬁnder motif predictions for the four SR proteins available in ESEﬁnder. SFmap INPUT AND OUTPUT The current SFmap version is designed for predicting and mapping SF binding sites in the HG. In addition, non-human RNA sequences are also accepted as input but will be processed without considering evolutionary conservation. As an input, SFmap requires a sequence or list of sequences in FASTA format that can be ex- tracted from diﬀerent assemblies of the HG. In addition, a user can choose to upload the sequence coordinates (chromosome: start-end:strand). Furthermore, the user is prompted to select the motifs of interest from a list of veriﬁed motifs (Table 1). As a default, all motifs stored in our database will be selected. Optionally, the user can add custom motifs, 4–10 nt-long, using IUPAC symbols. Custom motifs will be predicted by the same algorithm used to map other motifs in our database. To search for splicing motifs, the program runs the input sequence against the HG and retrieves the ﬂanking sequences from the genome, as well as the human–mouse alignment of the region required for calculating the COS(WR) score. If no match is found, WR will be calculated for the central sequence excluding the terminal positions (i.e. window size/2 from each side). In cases where human–mouse align- ments are not available, SFmap will employ the WR al- gorithm, which does not consider evolutionary conservation and thus does not require a sequence