W456 Nucleic Acids Research, 2007, Vol. 35, Web Server issue Figure 1. Schema of the SLiMDisc server. The SLiMDisc server returns putatively functional motifs contained in the input data set. CompariMotif and SLiMPickings post-process the output of the SLiMDisc server adding functionality such as conservation scoring, re-ranking and comparison against known motifs. region (often motifs are enriched in disordered regions) and the evolutionary divergence of the motif in homologues. Examples of SLiMDisc applied to interaction data sets have been discussed previously when it was shown to successfully recover known shared linear motifs from the HPRD-curated protein interaction database (6). Here we describe the SLIMDisc web server, which implements this stand-alone program, and provides in addition important visualization tools to aid interpreta- tion of the results. This article illustrates the ﬂexibility of the SLiMDisc server through an example application to a real biological data set. METHOD OVERVIEW Concept SLiMDisc is a motif discovery method for ﬁnding putative functional motifs in a group of proteins with a common attribute (6). The strongest evidence for a functional motif is obtained when the same motif occurs in unrelated proteins, evolving by convergence. Searches for such motifs are often swamped by motifs that are shared in related proteins and identical by descent; SLiMDisc incorporates information about the evolutionary descent of each motif based on treatment of BLAST (7) local alignments to weight against this eﬀect. SLiMDisc overview SLiMDisc outputs a ranked list of putatively interesting SLiMs from an input data set of proteins. A schema of the method is given in Figure 1. Any protein regions deﬁned by users to be masked are removed from the data sets. Homology information for the data set is created using the GABLAM method (6). TEIRESIAS (8) is used to identify all motifs with or without ambiguity. Motifs are ﬁltered according to a number of optional criteria, including the evolutionary relatedness of the proteins containing the motif, information content and surface probability. Finally, the motifs are ranked according to information content and support, normalized for evolutionary relationships (6). Masking Masking of regions unlikely to contain motifs can greatly improve SLiM discovery. It has been observed that functional motifs occur much less frequently inside globular regions, coiled coils and, by deﬁnition, in regions inaccessible for interaction (2,5). It is clear that removal of these regions will decrease the size of the search space, thus decreasing noise introduced by randomly occurring shared patterns in the data set and increasing the likelihood of discovering any true, functional SLiMs. The SLiMDisc server allows a user to easily mask regions that, from prior knowledge of the data set, they believe to be unlikely to contain functional motifs. Regions such as transmembrane regions, protein domains and inaccessible residues can be masked since, for certain purposes, they are areas which may have a lower likelihood of containing motifs. Furthermore, the user may identify speciﬁc regions of the proteins in which to conﬁne the search (e.g. the cytoplasmic regions of a set of proteins) or to conﬁne the search based on prior knowledge of a region possibly containing a functional