The SLiMDisc server: short, linear motif discovery in proteins
Davey, Norman E., Edwards, Richard J. and Shields, Denis C. (2007) The SLiMDisc server: short, linear motif discovery in proteins. Nucleic Acids Research, 35, (suppl.2), 5pp. (doi:10.1093/nar/gkm400).
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Short, linear motifs (SLiMs) play a critical role in many biological processes, particularly in protein-protein interactions. Overrepresentation of convergent occurrences of motifs in proteins with a common attribute (such as similar subcellular location or a shared interaction partner) provides a feasible means to discover novel occurrences computationally. The SLiMDisc (Short, Linear Motif Discovery) web server corrects for common ancestry in describing shared motifs, concentrating on the convergently evolved motifs. The server returns a listing of the most interesting motifs found within unmasked regions, ranked according to an information content-based scoring scheme. It allows interactive input masking, according to various criteria. Scoring allows for evolutionary relationships in the data sets through treatment of BLAST local alignments. Alongside this ranked list, visualizations of the results improve understanding of the context of suggested motifs, helping to identify true motifs of interest. These visualizations include alignments of motif occurrences, alignments of motifs and their homologues and a visual schematic of the top-ranked motifs. Additional options for filtering and/or re-ranking motifs further permit the user to focus on motifs with desired attributes. Returned motifs can also be compared with known SLiMs from the literature. SLiMDisc is available at: http://bioware.ucd.ie/ approximately slimdisc/.
|Additional Information:||Nucleic Acids Research Advance Access published online on June 18, 2007|
|Subjects:||Q Science > QH Natural history > QH301 Biology|
|Divisions:||University Structure - Pre August 2011 > School of Biological Sciences
|Date Deposited:||04 Jul 2007|
|Last Modified:||27 Mar 2014 18:30|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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