SLiMDisc (Short Linear Motif Discovery) is a python-based method to find shared motifs in proteins with little or no primary sequence similarity from a group of proteins with a common attribute - be it biological function, subcellular location or a common interaction partner. The method builds on the basic pattern discovery abilities of the TEIRESIAS algorithm, applying a number of filters to the returned motifs to upweight those present in evolutionarily distant sequences and downweight those primarily arising due to common evolutionary descent.

A key feature of this method is that it requires no pre-filtering of the dataset for evolutionarily conserved sequences and does not suffer from the potential loss of information (and SLiMs) incurred by arbitrarily retaining a single representative of any given group of homologous proteins. Furthermore, a number of filtering options are provided, giving the user a great deal of control over the type of motif returned.