alternative splicing ( as ) is a post - transcriptional process that generates multiple mrna isoforms from the same gene . in recent years , as has emerged as one of the major sources of diversity and functional complexity in humans and other vertebrates ( 1 ) . the as process requires a core splicing mechanism that binds to splicing signals around the exon / intron junctions . core splicing signals include : ( i ) the canonical 5-splice site (  gu in humans ) and the 3-splice site (  ag in humans ) , located at the 5- and 3-ends of the intron , respectively ; ( ii ) the polypyrimidine tract ( ppt ) located upstream of the 3-splice site ; and ( iii ) the branch site ( conserved  a in humans ) located upstream of the ppt . a web server for mapping splicing signals is available at http://sroogle.tau.ac.il ( 3 ) . the core splicing signals , however , are not sufficient for accurate recognition of exon / intron junctions . it is well established that additional cis - regulatory elements exist in the exons and the flanking introns that bind to different rna - binding proteins , acting as splicing factors ( sfs ) ( 4 ) . in general , the cis - regulatory elements are relatively short and degenerative sequences , and in many cases , are present in multiple copies on the rna ( 5 ) . these sfs can act as positive or negative effectors of the splicing reaction by interacting differentially with exonic or intronic splicing enhancers ( eses / ises ) and silencers ( esss / isss ) , respectively . in recent years , several methodologies for identifying sf binding sites have been developed ( 610 ) ; for review see ( 11 ) . most of the available methods for detecting cis - regulatory splicing elements , such as rescue - ese ( relative enhancer and silencer classification by unanimous enrichment ) ( 7 ) , pese ( 8) and the method by goren et al . ( 10 ) , use a de novo prediction approach for detecting motifs enriched in splicing regulatory regions . these methods are generally designed to detect cis - regulatory elements ; however , they do not attempt to assign a specific trans - factor to a detected cis - regulatory motif . for example , the rescue - ese web server http://genes.mit.edu/burgelab/rescue-ese/ searches for hexamers that are associated with ese based on their higher frequency of occurrence in exons versus introns , as well as their significantly higher frequency in exons with weak ( non - consensus ) splice sites compared to exons with strong ( consensus ) splice sites ( 7 ) . the esefinder web site presents a search interface for detecting significant matches of five eses related to four sr proteins : sf2/asf , sc35 , srp40 and srp55 . the eses position specific scoring matrices were derived using functional systematic evolution of ligands by exponential enrichment ( 12 ) . another strategy regrna http://regrna.mbc.nctu.edu.tw/ was recently developed for identifying 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 different sets of exons and introns , and detected an enrichment of sf binding sites in different types of as events , such as cassette exons , alternative 5-splice sites and alternative 3-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 410 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 annotation tracks in the ucsc human genome ( hg ) browser ( 15 ) . the sfmap web service is free and open to all users with no login requirement . 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 . specifically , our method searches for the existence of a defined consensus motif , ideally motifs that have been confirmed experimentally to bind known sfs . 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 . . 
 table 1.list of sf - binding motifssfmotifssf2asfcrsmsgw , ugrwgvh9g8acgagagay , wggacrasc35gryymcyr , ugcygyytra2alphagaagaggaagtra2betagaagaa , ghvvganr , aaguguusrp20cuckucy , wcwwcsrp40yywcwsgsrp55yrcrkmhnrnpa1uagaca , uagagu , uagggwhnrnpabauagcahnrnph / fggcgg , gggug , uguggg , uugggumbnlygcukynova1ycayptbcucucu , ucuucug - bpugcugyb1caaccacaafox1/2ugcaugthe sfs and their related motifs were extracted from the literature and are based on experimental data as detailed in akerman et al . ( 14 ) . list of sf - binding motifs the sfs and their related motifs were extracted from the literature and are based on experimental data as detailed in akerman et al . 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 , respectively ) 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 function : the significant cut - off for the major hit , the suboptimal 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 . ( 14 ) , the cos(wr ) algorithm demonstrated significant higher specificity compared to esefinder motif predictions for the four sr proteins available in esefinder . 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 extracted 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 ) . optionally , the user can add custom motifs , 410 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 alignments are not available , sfmap will employ the wr algorithm , which does not consider evolutionary conservation and thus does not require a sequence alignment . notably , the user can deliberately choose to implement the wr algorithm that does not consider conservation information by manually selecting the wr box in the sfmap front page . 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 significance 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 experimental model was calculated . finally , only motifs that were scored above the threshold are reported . for convenience , 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 coordinates . a separate list including the position of the predicted 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 sequences . 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 3-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 3-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 . ugugug motif , which has been described in the literature as being the minimal binding motif of tdp-43 ( 19 ) . dense ( a ) and a  full ( b ) format . in this example 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 ) . dense ( a ) and a  full ( b ) format . in this example 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 ) . 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 , 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 ) , 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 3-utr intron and the 3-utr exon . interestingly , when displaying the mammalian evolutionary conservation provided by the hg browser comparative genomic analysis track ( bottom of figure 1b ) , one can see that the entire 3-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 ) . 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 consideration , 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 3-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 degenerative binding motifs relying on compensating information 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 selecting 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 3-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 expect that sfmap will be sensitive but not highly selective , 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 can not 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 . 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 implements 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 preferences . once more detailed information on binding motifs is revealed from the experimental data , sfmap could be implemented to enhance high accurate genome - wide predictions of binding sites of sfs in particular and rna - binding proteins in general . elyahu pen research fund ; the israeli science foundation ( grant number 1297/09 to y.m.g . ) . funding for open access charge : the israeli science foundation ( grant number 1297/09 to y.m.g . ) .
alternative splicing ( as ) is a post - transcriptional process considered to be responsible for the huge diversity of proteins in higher eukaryotes . 
 as events are regulated by different splicing factors ( sfs ) that bind to sequence elements on the rna . 
 sfmap is a web server for predicting putative sf binding sites in genomic data ( http://sfmap.technion.ac.il ) . 
 sfmap implements the cos(wr ) algorithm , which computes similarity scores for a given regulatory motif based on information derived from its sequence environment and its evolutionary conservation . 
 input for sfmap is a human genomic sequence or a list of sequences in fasta format that can either be uploaded from a file or pasted into a window . 
 sfmap searches within a given sequence for significant hits of binding motifs that are either stored in our database or defined by the user . 
 sfmap results are provided both as a text file and as a graphical web interface .