Nucleic Acids Research, 2007, Vol. 35, Web Server issue W457 Figure 2. Visualizations created by the SLiMDisc web server for proteins from the ELM database (2) with annotated 14-3-3 binding motifs. (a) A motif position map shows the position of the top 20 returned motifs relative to the UNIPROT annotated features of the protein. Returned patterns are displayed below each motif marker, enabling the user to easily identify similar, overlapping motifs. The graph includes several charge- related scores, Eisenberg hydropathy and protein region disorder as predicted by IUPRED. The graph also shows features which have been exclusively masked according to the user’s preferences (red: known domains; green: labelled ‘FOLD’, indicates other regions that IUPRED predicted to have a low disorder, i.e. are more likely to be folded). SA: Surface accessibility. (b) An ungapped alignment of the residues immediately surrounding the motif returned, showing the context of each occurrence. (c) An alignment (allowing gaps) of the orthologues of the RAF1 protein around the RSxSxP 14-3-3 binding motif. This visualization shows the conservation of the motif across several species. motif in a given protein. This allows easy incorporation of prior knowledge without lengthy post-processing of results and also reduces search times. SLiM scoring Having established a large number of motifs, SLiMDisc ranks them according to their information content and frequency of occurrence, adjusted for evolutionary rela- tionships (6). This scoring method has been shown to perform well in the rediscovery of known ELMs in protein– protein interaction data sets where a subset of the data set is known to contain a functional ELM. The support or number of occurrences of each motif is down-weighted according to the relatedness of the proteins containing the motif. Three methods are available for the calculation of the normalized support of a motif [Minimum Spanning Tree, Unique Homologous Segments and Unrelated Proteins normalization (described in previous work)], each of which has a varying level of strictness with regards to the treatment of relationships between proteins. Output SLiMDisc outputs a ranked list of putatively interesting motifs. In addition, the server produces several visualiza- tions (in graphical PDF format) that allow users to gain knowledge of the context of the motif. These include: protein similarity trees using the GABLAM algorithm (6); ungapped alignments of the regions of proteins containing each motif centred on the motif; alignments (allowing gaps) of orthologues of the proteins, generated by MUSCLE (9); motif maps showing the position of the motifs relative to other features (e.g. domains, transmembrane regions) in the proteins, including plots of charge, surface probability, hydrophobicity and disorder [based on an 11-amino acid window size (Figure 2)]. All outputs are also produced as plain text for easy use with other programs. Post-processing Two additional servers for post-processing of the SLiMDisc results are available upon completion of a SLiMDisc analysis: SLiMPickings and CompariMotif (see Supplementary Materials for details). Submission to each of these servers is achieved by pressing a single button on the SLiMDisc results page, and a range of post- processing options is available if the user wishes to depart from the default settings. The SLiMPickings server allows for easy re-ranking and post-ﬁltering of data based on user-deﬁned options. The server compiles data from SLiMDisc and analyses it to create various statistics such as SLiM conservation (conservation scores are calculated on orthologues returned from the GOPHER server), predicted region disorder using IUPRED (10) and several charge-related