Nucleic Acids Research, 2007, Vol. 35, Web Server issue W459 DISCUSSION We have created a web server for the SLiMDisc method for the discovery of putatively interesting motifs in protein data sets which haves some common attributes which may be explained by a shared motif. We introduced interactive masking (inclusive and exclusive) of annotated protein features on a protein by protein basis to decrease the search space and increase the likelihood of returning motifs of interest. We have also demonstrated how the use of visualization can be used to help distinguish biologically meaningful SLiMs from the noise of stochastically occurring back- ground motifs. SLiM discovery is a diﬃcult task that has often been compared with ﬁnding a needle in a haystack. Their short length, high ambiguity and low-binding aﬃnities makes them unfavourable for experimental or in silico discovery, and the lack of adequate training data sets makes it hard to develop robust discovery algorithms that are fully automated. Current methods are only likely to discover the most easily revealed motifs. At this time, SLiM discovery diﬃculties are best overcome by a combination of automated discovery followed by visual inspection of putatively functional motifs, and the supervised incorporation of as much expert knowledge as possible through data set masking and results ﬁltering. Motif attributes, such as motif conservation in close orthologues or common motif position relative to other protein features, can all point to true functionality that is most easily described by visualiza- tion. The overall context of a motif allows the user to gain conﬁdence in motifs and helps the user to separate true motifs from the background of randomly occurring motifs with high support. As more SLiMs are discovered and conﬁrmed experimentally, it is hoped that our overall knowledge of the area will increase and allow more sophisticated—and more accurate— algorithms to be developed. SUPPLEMENTARY DATA Supplementary Data are available at NAR Online. ACKNOWLEDGEMENTS This work was supported by Science Foundation Ireland. Funding to pay the Open Access publication charges for this article was provided by Science Foundation Ireland. Conﬂict of interest statement. None declared. REFERENCES 1. Neduva,V. and Russell,R.B. (2005) Linear motifs: evolutionary interaction switches. FEBS Lett., 579, 3342–3345. 2. Puntervoll,P., Linding,R., Gemund,C., Chabanis-Davidson,S., Mattingsdal,M., Cameron,S., Martin,D.M., Ausiello,G., Brannetti,B. et al. (2003) ELM server: A new resource for investigating short functional sites in modular eukaryotic proteins. Nucleic Acids Res., 31, 3625–3630. 3. Neduva,V. and Russell,R.B. (2006) Peptides mediating interaction networks: new leads at last. Curr. Opin. Biotechnol., 17, 465–471. 4. Neduva,V. and Russell,R.B. (2006) DILIMOT: discovery of linear motifs in proteins. Nucleic Acids Res., 34, W350–W355. 5. Neduva,V., Linding,R., Su-Angrand,I., Stark,A., de Masi,F., Gibson,T.J., Lewis,J., Serrano,L. and Russell,R.B. (2005) Systematic discovery of new recognition peptides mediating protein interaction networks. PLoS Biol., 3, e405. 6. Davey,N.E., Shields,D.C. and Edwards,R.J. (2006) SLiMDisc: short, linear motif discovery, correcting for common evolutionary descent. Nucleic Acids Res., 34, 3546–3554. 7. Altschul,S.F., Gish,W., Miller,W., Myers,E.W. and Lipman,D.J. (1990) Basic local alignment search tool. J. Mol. Biol., 215, 403–410. 8. Rigoutsos,I. and Floratos,A. (1998) Combinatorial pattern discov- ery in biological sequences: The TEIRESIAS algorithm. Bioinformatics, 14, 55–67. 9. Edgar,R.C. (2004) MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics, 5, 113. 10. Dosztanyi,Z., Csizmok,V., Tompa,P. and Simon,I. (2005) IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content. Bioinformatics, 21, 3433–3434. 11. Balla,S., Thapar,V., Verma,S., Luong,T., Faghri,T., Huang,C.H., Rajasekaran,S., del Campo,J.J., Shinn,J.H. et al. (2006) Minimotif Miner: a tool for investigating protein function. Nat. Methods, 3, 175–177. 12. Bairoch,A., Apweiler,R., Wu,C.H., Barker,W.C., Boeckmann,B., Ferro,S., Gasteiger,E., Huang,H., Lopez,R. et al. (2005) The Universal Protein Resource (UniProt). Nucleic Acids Res., 33, D154–D159. 13. Gene Ontology Consortium. (2006) The Gene Ontology (GO) project in 2006. Nucleic Acids Res., 34, D322–D326. 14. Gould,S.J., Keller,G.A., Hosken,N., Wilkinson,J. and Subramani,S. (1989) A conserved tripeptide sorts proteins to peroxisomes. J. Cell. Biol., 108, 1657–1664.