VolcaNoseR is a web app for creating, exploring, labeling and sharing volcano plots

Comparative genomic and proteomic screens generate vast datasets, often visualized using volcano plots. A volcano plot displays effect size against significance, with points exceeding user-defined thresholds representing hits. While visually effective for identifying key results, traditional volcano plots often lack interactivity and easy access to the full dataset. Only a small subset of data points (hits) are usually annotated, obscuring the rest of the potentially valuable information. Existing software solutions for generating volcano plots are often commercial, script-based, or limited in customization and interactivity. Free web applications exist but usually lack interactive features and options for sharing plots along with their underlying data. This necessitates the development of a tool that overcomes these limitations. VolcaNoseR addresses this by offering a user-friendly web application for creating, exploring, and sharing interactive volcano plots, promoting data transparency and reuse in large-scale comparative genomics and proteomics studies.

The authors refer to several existing volcano plot generation methods, highlighting their limitations. Commercial software and script-based approaches are mentioned, along with existing web applications that lack interactivity or robust sharing capabilities. Specific examples of other web applications like VolcanoR, msVolcano, PlotsOfData, and PlotTwists are cited, serving as a context for VolcaNoseR’s novelty and advanced features.

VolcaNoseR is a web application developed using R and the Shiny package. Data input is facilitated through file uploads (CSV, TXT, XLS, XLSX) or direct URLs to online data repositories (CSV). The application supports multiple delimiters and spreadsheets with multiple sheets. After data upload, users select columns representing fold change and significance. The application visualizes data using a scatter plot, with the x-axis typically showing log2 fold change and the y-axis showing -log10 p-value. Users can customize point size and transparency, and interactively access data point details by hovering. A 90-degree rotated plot is also available. Thresholds for fold change and significance are set by the user, visually represented by dashed lines, and used to classify data as unchanged, decreased, or increased. Data points are color-coded accordingly. Top hits are automatically detected based on user-selectable criteria (Manhattan distance, Euclidean distance, absolute fold change, or significance), with the number of top hits adjustable. Users can annotate increased, decreased, or all significantly changed data points. Manually searching and selecting specific gene/protein names for annotation is also supported. The color scheme can be customized. The generated plot can be downloaded as PNG or PDF. Finally, VolcaNoseR incorporates a URL-based sharing feature, where all user settings (including data source if external) are encoded into a URL. This URL can be used to recreate and share the plot, allowing others to reproduce and modify the analysis.

VolcaNoseR effectively addresses the limitations of existing volcano plot generation tools by offering several key features: (1) Interactive exploration of data points – users can access detailed information about each data point by hovering; (2) Customizable visualization parameters – users have control over axes labels, point sizes, colors, and threshold lines; (3) Data filtering and ranking – users can filter and rank the data based on several criteria; (4) Flexible annotation and labeling – automatic annotation of top hits and manual annotation are both possible; (5) URL-based sharing of plots and settings – allows easy replication and sharing of analysis with others; (6) Support for various data formats – users can easily upload data from several sources; (7) Open-source and accessible – the tool is freely available and the source code is publically available. The authors demonstrate VolcaNoseR's functionality using proteomics data from a previous publication and genomics data from a CRISPR screen dataset. These examples illustrate VolcaNoseR’s capabilities to analyze and share large datasets, promoting data transparency and reusability. The application of VolcaNoseR to a published CRISPR screen dataset allowed for rapid identification of GNAS as a potential antiproliferative gene in RPE1 cells, a finding consistent with other literature.

VolcaNoseR significantly improves upon existing methods for generating and sharing volcano plots. The interactive nature of the app, coupled with the ability to easily share plots and data through URLs, dramatically increases the accessibility and reusability of large datasets. The examples provided clearly illustrate its effectiveness in analyzing and interpreting complex datasets from genomic and proteomic studies. The open-source nature of VolcaNoseR ensures community contribution and further development, promoting widespread adoption within the field.

VolcaNoseR provides a valuable tool for researchers working with large genomic and proteomic datasets. Its interactive features, flexible annotation options, and URL-based sharing mechanism address significant limitations of existing volcano plot generation tools. The application’s open-source nature facilitates community involvement and continued improvement. Future development could include advanced statistical analysis options and integration with other bioinformatics tools.

While VolcaNoseR addresses many limitations of existing tools, some limitations exist. The file size limitation on the Huygens server (1 MB) restricts the analysis of very large datasets. Larger files can be processed locally or via the shinyapps webserver (up to 10 MB), but this requires additional technical setup. Additionally, while it provides basic visualization and annotation, advanced statistical analyses might still need to be performed using other dedicated tools.