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SLiMScape: a protein short linear motif analysis plugin for Cytoscape.

O'Brien KT, Haslam NJ, Shields DC - BMC Bioinformatics (2013)

Bottom Line: Data is presented using Cytoscape's visualization features thus providing an intuitive interface for interpreting results.SLiMScape provides a platform for performing short linear motif analyses of protein interaction networks by integrating motif discovery and search tools in a network visualization environment.This significantly aids in the discovery of novel short linear motifs and in visualizing the distribution of known motifs.

View Article: PubMed Central - HTML - PubMed

Affiliation: UCD Conway, Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin, Ireland.

ABSTRACT

Background: Computational protein short linear motif discovery can use protein interaction information to search for motifs among proteins which share a common interactor. Cytoscape provides a visual interface for protein networks but there is no streamlined way to rapidly visualize motifs in a network of proteins, or to integrate computational discovery with such visualizations.

Results: We present SLiMScape, a Cytoscape plugin, which enables both de novo motif discovery and searches for instances of known motifs. Data is presented using Cytoscape's visualization features thus providing an intuitive interface for interpreting results. The distribution of discovered or user-defined motifs may be selectively displayed and the distribution of protein domains may be viewed simultaneously. To facilitate this SLiMScape automatically retrieves domains for each protein.

Conclusion: SLiMScape provides a platform for performing short linear motif analyses of protein interaction networks by integrating motif discovery and search tools in a network visualization environment. This significantly aids in the discovery of novel short linear motifs and in visualizing the distribution of known motifs.

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SLiMScape Interface. A. The SLiMScape interface displaying SLiMFinder results for the true positive motif LIG_PCNA, a SLiM which binds to Proliferating Cell Nuclear Antigen (PCNA). B. The results panel showing several highly significant results from this protein set. C. A network view showing SLiMs (red) and other nodes (beige). D. A structural view of the SLiM mediated binding between POLD3 (red) and PCNA (beige) via LIG_PCNA. This structural view is provided for example and is not part of the plugin.
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Figure 1: SLiMScape Interface. A. The SLiMScape interface displaying SLiMFinder results for the true positive motif LIG_PCNA, a SLiM which binds to Proliferating Cell Nuclear Antigen (PCNA). B. The results panel showing several highly significant results from this protein set. C. A network view showing SLiMs (red) and other nodes (beige). D. A structural view of the SLiM mediated binding between POLD3 (red) and PCNA (beige) via LIG_PCNA. This structural view is provided for example and is not part of the plugin.

Mentions: SLiMScape uses SLiMFinder to locate statistically over-represented sequences within sets of proteins, defined by selecting subsets of a protein interaction network. A variety of node selection methods are provided to automatically select protein sets and different types of motif can be targeted depending on this selection process. For example, SLiM-mediated interactions can be discovered using the “Batch Interactions” selection method. This will iteratively create protein sets containing each protein (hub) and its interactors (spokes). Figure 1 shows the results of this method on a network consisting of Proliferating Cell Nuclear Antigen (PCNA) and its interactors. Since, in a large network, this can be computationally demanding selection methods include carrying out a single search across a set of selected nodes, specified interactively through Cytoscape’s interface. Another selection method named “Attribute” creates protein sets based on a user-defined attribute. A useful example is subcellular location, which can be used to find subcellular targeting motifs.


SLiMScape: a protein short linear motif analysis plugin for Cytoscape.

O'Brien KT, Haslam NJ, Shields DC - BMC Bioinformatics (2013)

SLiMScape Interface. A. The SLiMScape interface displaying SLiMFinder results for the true positive motif LIG_PCNA, a SLiM which binds to Proliferating Cell Nuclear Antigen (PCNA). B. The results panel showing several highly significant results from this protein set. C. A network view showing SLiMs (red) and other nodes (beige). D. A structural view of the SLiM mediated binding between POLD3 (red) and PCNA (beige) via LIG_PCNA. This structural view is provided for example and is not part of the plugin.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750528&req=5

Figure 1: SLiMScape Interface. A. The SLiMScape interface displaying SLiMFinder results for the true positive motif LIG_PCNA, a SLiM which binds to Proliferating Cell Nuclear Antigen (PCNA). B. The results panel showing several highly significant results from this protein set. C. A network view showing SLiMs (red) and other nodes (beige). D. A structural view of the SLiM mediated binding between POLD3 (red) and PCNA (beige) via LIG_PCNA. This structural view is provided for example and is not part of the plugin.
Mentions: SLiMScape uses SLiMFinder to locate statistically over-represented sequences within sets of proteins, defined by selecting subsets of a protein interaction network. A variety of node selection methods are provided to automatically select protein sets and different types of motif can be targeted depending on this selection process. For example, SLiM-mediated interactions can be discovered using the “Batch Interactions” selection method. This will iteratively create protein sets containing each protein (hub) and its interactors (spokes). Figure 1 shows the results of this method on a network consisting of Proliferating Cell Nuclear Antigen (PCNA) and its interactors. Since, in a large network, this can be computationally demanding selection methods include carrying out a single search across a set of selected nodes, specified interactively through Cytoscape’s interface. Another selection method named “Attribute” creates protein sets based on a user-defined attribute. A useful example is subcellular location, which can be used to find subcellular targeting motifs.

Bottom Line: Data is presented using Cytoscape's visualization features thus providing an intuitive interface for interpreting results.SLiMScape provides a platform for performing short linear motif analyses of protein interaction networks by integrating motif discovery and search tools in a network visualization environment.This significantly aids in the discovery of novel short linear motifs and in visualizing the distribution of known motifs.

View Article: PubMed Central - HTML - PubMed

Affiliation: UCD Conway, Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin, Ireland.

ABSTRACT

Background: Computational protein short linear motif discovery can use protein interaction information to search for motifs among proteins which share a common interactor. Cytoscape provides a visual interface for protein networks but there is no streamlined way to rapidly visualize motifs in a network of proteins, or to integrate computational discovery with such visualizations.

Results: We present SLiMScape, a Cytoscape plugin, which enables both de novo motif discovery and searches for instances of known motifs. Data is presented using Cytoscape's visualization features thus providing an intuitive interface for interpreting results. The distribution of discovered or user-defined motifs may be selectively displayed and the distribution of protein domains may be viewed simultaneously. To facilitate this SLiMScape automatically retrieves domains for each protein.

Conclusion: SLiMScape provides a platform for performing short linear motif analyses of protein interaction networks by integrating motif discovery and search tools in a network visualization environment. This significantly aids in the discovery of novel short linear motifs and in visualizing the distribution of known motifs.

Show MeSH