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dc.contributor.author
Olorin, Emily  
dc.contributor.author
O'Brien, Kevin T.  
dc.contributor.author
Palopoli, Nicolás  
dc.contributor.author
Pérez Bercoff, Åsa  
dc.contributor.author
Shields, Denis C.  
dc.contributor.author
Edwards, Richard J.  
dc.date.available
2020-09-04T15:12:48Z  
dc.date.issued
2015-08  
dc.identifier.citation
Olorin, Emily; O'Brien, Kevin T.; Palopoli, Nicolás; Pérez Bercoff, Åsa; Shields, Denis C.; et al.; SLiMScape 3.x: a Cytoscape 3 app for discovery of Short Linear Motifs in protein interaction networks; F1000 Research Ltd; F1000Research; 4; 477; 8-2015; 1-11  
dc.identifier.issn
2046-1402  
dc.identifier.uri
http://hdl.handle.net/11336/113226  
dc.description.abstract
Short linear motifs (SLiMs) are small protein sequence patterns that mediate a large number of critical protein-protein interactions, involved in processes such as complex formation, signal transduction, localisation and stabilisation. SLiMs show rapid evolutionary dynamics and are frequently the targets of molecular mimicry by pathogens. Identifying enriched sequence patterns due to convergent evolution in non-homologous proteins has proven to be a successful strategy for computational SLiM prediction. Tools of the SLiMSuite package use this strategy, using a statistical model to identify SLiM enrichment based on the evolutionary relationships, amino acid composition and predicted disorder of the input proteins. The quality of input data is critical for successful SLiM prediction. Cytoscape provides a user-friendly, interactive environment to explore interaction networks and select proteins based on common features, such as shared interaction partners. SLiMScape embeds tools of the SLiMSuite package for de novo SLiM discovery (SLiMFinder and QSLiMFinder) and identifying occurrences/enrichment of known SLiMs (SLiMProb) within this interactive framework. SLiMScape makes it easier to (1) generate high quality hypothesis-driven datasets for these tools, and (2) visualise predicted SLiM occurrences within the context of the network. To generate new predictions, users can select nodes from a protein network or provide a set of Uniprot identifiers. SLiMProb also requires additional query motif input. Jobs are then run remotely on the SLiMSuite server (http://rest.slimsuite.unsw.edu.au) for subsequent retrieval and visualisation. SLiMScape can also be used to retrieve and visualise results from jobs run directly on the server. SLiMScape and SLiMSuite are open source and freely available via GitHub under GNU licenses.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
F1000 Research Ltd  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/  
dc.subject
SHORT LINEAR MOTIFS  
dc.subject
SLIMSCAPE  
dc.subject
CYTOSCAPE  
dc.subject
PROTEIN INTERACTION NETWORK  
dc.subject.classification
Ciencias de la Información y Bioinformática  
dc.subject.classification
Ciencias de la Computación e Información  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
SLiMScape 3.x: a Cytoscape 3 app for discovery of Short Linear Motifs in protein interaction networks  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2020-08-28T18:46:59Z  
dc.journal.volume
4  
dc.journal.number
477  
dc.journal.pagination
1-11  
dc.journal.pais
Reino Unido  
dc.description.fil
Fil: Olorin, Emily. University of New South Wales; Australia  
dc.description.fil
Fil: O'Brien, Kevin T.. Universidad de Dublin; Irlanda  
dc.description.fil
Fil: Palopoli, Nicolás. Fundación Instituto Leloir; Argentina. University of Southampton; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.description.fil
Fil: Pérez Bercoff, Åsa. University of New South Wales; Australia  
dc.description.fil
Fil: Shields, Denis C.. Universidad de Dublin; Irlanda  
dc.description.fil
Fil: Edwards, Richard J.. University of Southampton; Reino Unido. University of New South Wales; Australia  
dc.journal.title
F1000Research  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.12688/f1000research.6773.1  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://f1000research.com/articles/4-477