A number of these tools have been developed by the Edwards and Shields labs [7-11] and made available as part of the SLiMSuite package and online as webservers (http://bioware.ucd.ie) [9-10,12-14], with two new tools, SLiMPrints and QSLiMFinder, currently in preparation for submission, and SLiMMaker to be added soon. The main tools that form the SLiMSuite package/servers are as follows:
- SLiMFinder [8,13]: de novo SLiM prediction based on a statistical model of over-represented motifs in unrelated proteins.
- SLiMDisc [7,12]: de novo SLiM prediction based on heuristic ranking of over-represented motifs in unrelated proteins.
- SLiMPred : de novo SLiM/MoRF prediction in single proteins based machine learning of motif attributes.
- SLiMSearch : biological context (disorder & conservation) for searches of pre-defined motifs with under- and over-representation statistics, correcting for evolutionary relationships.
- SLiMSearch 2.0 : biological context (disorder & conservation) and ranking for proteome-wide searches of pre-defined motifs.
- SLiMPrints (in prep.): de novo SLiM/MoRF prediction in single proteins from statistical clustering of conserved disordered residues.
- QSLiMFinder (server coming soon): Query-based variant of SLiMFinder with increased sensitivity and specificity.
- CompariMotif : Motif-motif comparison tool.
- SLiMMaker (coming soon): Simple tool for converting aligned peptides or SLiM occurrences into a regular expression motif.
- GOPHER : Automated orthologue prediction and alignment algorithm. Used for conservation-based masking (SLiMFinder/SLiMSearch) and prediction (SLiMPrints).
- GABLAM  (server coming soon): BLAST-based protein similarity scoring and clustering. Used for SLiMFinder and SLiMSearch adjustments for evolutionary relationships.
 Tompa P (2011) Unstructural biology coming of age. Curr Opin Struct Biol 21: 419;  Babu MM et al. (2011) Intrinsically disordered proteins: regulation and disease. Curr Opin Struct Biol 21:432;  Diella F et al. (2008) Understanding eukaryotic linear motifs and their role in cell signaling and regulation. Front Biosci 13:6580;  Davey NE et al. (2012) Attributes of short linear motifs. Mol Biosyst 8:268;  Davey NE, Trave G & Gibson TJ (2011) How viruses hijack cell regulation. Trends Biochem Sci 36:159;  Davey NE, Edwards RJ & Shields DC (2010) Computational identification and analysis of protein short linear motifs. Front Biosci 15:801;  Davey NE, Shields DC & Edwards RJ (2006): SLiMDisc: short, linear motif discovery, correcting for common evolutionary descent. Nucleic Acids Res. 34:3546;  Edwards RJ, Davey NE & Shields DC (2007): SLiMFinder: A probabilistic method for identifying over-represented, convergently evolved, short linear motifs in proteins. PLoS ONE 2:e967;  Edwards RJ, Davey NE & Shields DC (2008): CompariMotif: Quick and easy comparisons of sequence motifs. Bioinformatics 24:1307;  Davey NE et al. (2010): SLiMSearch: a webserver for finding novel occurrences of short linear motifs in proteins, incorporating sequence context. Lecture Notes in Bioinformatics 6282:50;  Mooney C et al. (2012): Prediction of short linear protein binding regions. J Mol Biol 415:193;  Davey NE, Edwards RJ & Shields DC (2007): The SLiMDisc server: short, linear motif discovery in proteins. Nuc Acids Res 35:W455;  Davey NE et al. (2010): SLiMFinder: a web server to find novel, significantly over-represented, short protein motifs. Nuc Acids Res 38:W534;  Davey NE et al. (2011): SLiMSearch 2.0: biological context for short linear motifs in proteins. Nuc Acids Res 39:W56.