Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data

Abstract

Protein networks have become a popular tool for analyzing and visualizing the often long lists of proteins or genes obtained from proteomics and other high-throughput technologies. One of the most popular sources of such networks is the STRING database, which provides protein networks for more than 2000 organisms, including both physical interactions from experimental data and functional associations from curated pathways, automatic text mining, and prediction methods. However, its web interface is mainly intended for inspection of small networks and their underlying evidence. The Cytoscape software, on the other hand, is much better suited for working with large networks and offers greater flexibility in terms of network analysis, import, and visualization of additional data. To include both resources in the same workflow, we created stringApp, a Cytoscape app that makes it easy to import STRING networks into Cytoscape, retains the appearance and many of the features of STRING, and integrates data from associated databases. Here, we introduce many of the stringApp features and show how they can be used to carry out complex network analysis and visualization tasks on a typical proteomics data set, all through the Cytoscape user interface. stringApp is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/stringapp .

Keywords: Cytoscape; STRING database; functional enrichment; network analysis; network visualization; protein networks; proteomics data.

Figure 1.

Highlighting various stringApp features in a screenshot of a small STITCH network in Cytoscape. Edge colors indicate type of interaction (green for protein-compound and gray for protein−protein interactions) and node colors are arbitrary. The results panel (right) shows the 3D structure of the currently selected node CDK7 (indicated by yellow node color) and provides links to other related resources. The STRING enrichment table panel (bottom) lists the enriched terms for this network (FDR-corrected p-value <0.05) with their category, term name description, FDR-corrected p-value and the enriched genes. The Filter STRING Enrichment table dialog (left) demonstrates the available options for filtering enriched terms by category and redundancy.

Figure 2.

STRING network of proteins with significantly regulated phosphorylation sites detected in a phosphoproteomics study of ovarian cancer. Log-ratios between disease and healthy tissues for the most significant site for each protein were mapped to the nodes using a blue− white−red gradient. Proteins without any interaction partners within the network (singletons) are omitted from the visualization.

Figure 3.

Clustered protein association network with proteins colored by the phosphorylation cluster to which they were assigned in the original analysis. Network clustering was performed using the Markov clustering (MCL) implementation in the clusterMaker2 Cytoscape app. The 13 proteins associated with Ovary epithelial cancer according to DISEASES are represented by bigger nodes. Clusters consisting of one node only are omitted from the visualization.

Figure 4.

Functional analysis of the largest cluster obtained by Markov clustering (Figure 3). The top-2 enriched terms after redundancy filtering were visualized as split donut charts around the nodes annotated with those terms. CDK12 is highlighted by bigger node size because it is associated with Ovary epithelial cancer according to DISEASES. The three kinases CDK7, CDK12, and CDK13 are highlighted in green based on annotations from the Pharos database.