The protocol of the JUMPn software aims to facilitate biological interpretation of quantitative proteomics data. The software is able to organize the whole protein into tier hierarchy of co-expression clusters and the protein interaction modules. Notably, the JUMPn software streamlines the analysis of co-expression clustering, pathway enrichment, and PPI module detection.
It provides a user-friendly interface and interactive visualization of the data and the networks. While we successfully demonstrated the application of JUMPn in whole proteome data analysis, the method is readily extendable to other data types, including phosphoproteomics analysis and interactome data from affinity purification mass spectrometry. To set up the JUMPn software, download the source code from the website and install the software on the command line terminal.
To launch the JUMPn software, change the current directory to the execution folder by typing cd execution"on the terminal, and then enter the text on the terminal to launch the JUMPn on the web browser. On the JUMPn homepage, click on the Commence Analysis"button to start the JUMPn analysis, then in the bottom left corner of the Commence Analysis"page, click on the Upload Demo B-Cell Proteomic Data"button to view a notification of the success of the data upload. In the bottom right corner of the page, click on the Submit JUMPn Analysis"button to initiate the demo run using default parameters.
A progress bar will denote the course of the analysis. Wait until the progress bar is fulfilled. Once the demo run is finished, a dialogue box will appear with the success run message and the absolute path to the result folder, then click on Continue to Results"tab.
As the webpage guides to the co-expression cluster results by WGCNA algorithm, click on the View Results"on the dialogue window to continue. On the left of the Results Page 1 WGCNA Output page, find the protein co-expression patterns and use the Select the Expression Format"to navigate between two figure formats. Boxplot figure will be selected by default.
Select Trends"tab to display the trends plot, with each line representing individual protein abundance across samples. The color of each line represents how close the expression pattern is to the co-expression cluster consensus. On the right of the WGCNA output page, the Pathway Ontology Enrichment Heatmap"can be viewed.
In a heatmap, the most highly enriched pathways for each cluster will be displayed together. Next, scroll down the webpage to view the expression pattern for individual proteins. Use the drop-down box, select the co-expression cluster to examine proteins from each cluster, then select a specific protein in the table upon which the bar plot below the table will be automatically updated to reflect its protein abundance.
For specific protein names, use the Search"box on the right side of the table. Later, look at the protein-protein interaction or PPI results by clicking on the Results Page 2 PPI Output"on the top. To obtain the results for a specific co-expression cluster, click on Select the Co-Expression Cluster"tab.
The displays of all figure panels will be updated for the newly selected cluster. On the left figure panel, view the PPI networks for the selected co-expression cluster by using the select by group dropdown box and highlighting individual PPI modules within the network, then go for the selected network layout format box to change the network layout. Using mouse and trackpad, zoom in or zoom out of the PPI network to view the gene names of each node in the network.
When zoomed in, select and click a certain protein to highlight the protein and its network neighbors. In the network, drag a certain node to change its position in the layout and reorganize the network layout. On the right panel of the PPI result page, study the co-expression cluster level information that assists interpretation of PPI results.
The co-expression pattern on the selected cluster can be viewed as boxplot by default. Select Trends"to show trends plot for the a co-expression pattern and then opt for pathway bar plot to show significantly enriched pathways for the co-expression cluster with the pathway circle plot, view significantly enriched pathways for the co-expression cluster in the circle plot format. To view results on the individual PPI module level, scroll down the result page to PPI output webpage and expand the select the module dropdown box to select a specific PPI module for display.
View the PPI module on the left panel, manipulate the network display as explained before. On the right panel, view the pathway ontology enrichment results, then use Select the Pathway Annotation Style"dropdown box and tick barplot tab to show more information and displays about significantly enriched pathways for the selected PPI module. Tick circle plot to show significantly enriched pathways for the selected PPI module in the format of a circle plot and use heatmap to show significantly enriched pathways and the associated gene names.
Go for Table"tab to display the detailed pathway enrichment results, including the name of pathways, ontology terms, gene names and the P value by Fisher's exact test. Follow the absolute path printed on the top of the results pages and find the publication spreadsheet table. JUMPn is developed with the R shiny platform for a user friendly interface and integrates three major functional modules.
Co-expression clustering analysis, pathway enrichment analysis, and PPI network analysis. After each analysis results are automatically visualized in our adjustable via the R shiny widget functions and readily downloadable as publication tables and Microsoft Excel format. For PPI network analysis, a composite PPI network was compiled by combining STRING, BioPlex, and in web IM databases.
The final merged PPI network covers more than 20, 000 human genes with 1, 100, 000 edges. This comprehensive interactome is included and published in a bundle with the JUMPn software for sensitive PPI analysis. Network based approaches that leverage both co-expression and protein interaction networks can be used as an additional method for the inference of activity in transcription factors and kinases.
