The previously developed SLiMScape plugin for Cytoscape 2.x enabled the user to interactively run SLiMFinder for de novo SLiM discovery, or SLiMSearch for predicting novel occurrences of known SLiMs.
The SLiM discovery functions of SLiMScape have also been extended through the incorporation of QSLiMFinder and enrichment statistics for known SLiMs using SLiMProb (formerly called SLiMSearch 1.x).
I don't do it because I'm making a statement, I do it because it's … I don't know, slimming?
Only two additional motifs were returned, KE.K and Y.C.PG.L, neither of which are known SLiMs.
SLiMFinder default settings identify known SLiMs with 100% specificity, and have a low false discovery rate on random test data.
Results: QSLiMFinder was extensively benchmarked using known SLiM-containing proteins and simulated protein interaction datasets of real human proteins.
First, we investigated how well QSLiMFinder returned known motifs from the ELMBench datasets of known SLiM-containing proteins from the ELM database (Dinkel et al., 2012).
The number of SLiM-binding proteins remains to be elucidated, however, given the distribution of known SLiM-binding and -modifying domains in the human proteome, it is likely to be in a similar range [ 8, 45].
We excluded several sequence regions unlikely to contain SLiMs (globular domains, coiled-coils, collagen regions and signal peptides, as annotated in SwissProt), because no more than 15% of known SLiMs [ 12, 37, 38] occur in these regions.
However, these motif classes represent only a small fraction of known SLiMs and outside these examples little effort has been made, either bioinformatically or experimentally, to discover the full complement of motif instances.
