Dataset Information

CellWhere: graphical display of interaction networks organized on subcellular localizations.

ABSTRACT: Given a query list of genes or proteins, CellWhere produces an interactive graphical display that mimics the structure of a cell, showing the local interaction network organized into subcellular locations. This user-friendly tool helps in the formulation of mechanistic hypotheses by enabling the experimental biologist to explore simultaneously two elements of functional context: (i) protein subcellular localization and (ii) protein-protein interactions or gene functional associations. Subcellular localization terms are obtained from public sources (the Gene Ontology and UniProt-together containing several thousand such terms) then mapped onto a smaller number of CellWhere localizations. These localizations include all major cell compartments, but the user may modify the mapping as desired. Protein-protein interaction listings, and their associated evidence strength scores, are obtained from the Mentha interactome server, or power-users may upload a pre-made network produced using some other interactomics tool. The Cytoscape.js JavaScript library is used in producing the graphical display. Importantly, for a protein that has been observed at multiple subcellular locations, users may prioritize the visual display of locations that are of special relevance to their research domain. CellWhere is at http://cellwhere-myology.rhcloud.com.

SUBMITTER: Zhu L

PROVIDER: S-EPMC4489307 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

CellWhere: graphical display of interaction networks organized on subcellular localizations.

Zhu Lu L Malatras Apostolos A Thorley Matthew M Aghoghogbe Idonnya I Mer Arvind A Duguez Stéphanie S Butler-Browne Gillian G Voit Thomas T Duddy William W

Nucleic acids research 20150416 W1

Given a query list of genes or proteins, CellWhere produces an interactive graphical display that mimics the structure of a cell, showing the local interaction network organized into subcellular locations. This user-friendly tool helps in the formulation of mechanistic hypotheses by enabling the experimental biologist to explore simultaneously two elements of functional context: (i) protein subcellular localization and (ii) protein-protein interactions or gene functional associations. Subcellula ...[more]

PMID: 25883154

Similar Datasets

Project description:Computational methods have been widely used for the prediction of protein subcellular localization. However, these predictions are rarely validated experimentally and as a result remain questionable. Therefore, experimental validation of the predicted localizations is needed to assess the accuracy of predictions so that such methods can be confidently used to annotate the proteins of unknown localization. Previously, we published a method called ngLOC that predicts the localization of proteins targeted to ten different subcellular organelles. In this short report, we describe the accuracy of these predictions using experimental validations.We have experimentally validated the predicted subcellular localizations of 114 human proteins corresponding to nine different organelles in normal breast and breast cancer cell lines using live cell imaging/confocal microscopy. Target genes were cloned into expression vectors as GFP fusions and cotransfected with RFP-tagged organelle-specific gene marker into normal breast epithelial and breast cancer cell lines. Subcellular localization of each target protein is confirmed by colocalization with a co-expressed organelle-specific protein marker. Our results showed that about 82.5% of the predicted subcellular localizations coincided with the experimentally validated localizations. The highest agreement was found in the endoplasmic reticulum proteins, while the cytoplasmic location showed the least concordance. With the exclusion of cytoplasmic location, the average prediction accuracy increased to 90.4%. In addition, there was no difference observed in the protein subcellular localization between normal and cancer breast cell lines.The experimentally validated accuracy of ngLOC method with (82.5%) or without cytoplasmic location (90.4%) nears the prediction accuracy of 89%. These results demonstrate that the ngLOC method can be very useful for large-scale annotation of the unknown subcellular localization of proteins.

Dataset Information

CellWhere: graphical display of interaction networks organized on subcellular localizations.

Publications

CellWhere: graphical display of interaction networks organized on subcellular localizations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets