Cytoscape.js 2023 update: a graph theory library for visualization and analysis.

SUMMARY: Cytoscape.js is an open-source JavaScript-based graph library. Its most common use case is as a visualization software component, so it can be used to render interactive graphs in a web browser. It also can be used in a headless manner, useful for graph operations on a server, such as Node.js. This update describes new features and enhancements introduced over many new versions from 2015 to 2022. AVAILABILITY AND IMPLEMENTATION: Cytoscape.js is implemented in JavaScript. Documentation, downloads and source code are available at http://js.cytoscape.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Surveilling and Tracking COVID-19 Patients Using a Portable Quantum Dot Smartphone Device.

The ability to rapidly diagnose, track, and disseminate information for SARS-CoV-2 is critical to minimize its spread. Here, we engineered a portable smartphone-based quantum barcode serological assay device for real-time surveillance of patients infected with SARS-CoV-2. Our device achieved a clinical sensitivity of 90% and specificity of 100% for SARS-CoV-2, as compared to 34% and 100%, respectively, for lateral flow assays in a head-to-head comparison. The lateral flow assay misdiagnosed ∼2 out of 3 SARS-CoV-2 positive patients. Our quantum dot barcode device has ∼3 times greater clinical sensitivity because it is ∼140 times more analytically sensitive than lateral flow assays. Our device can diagnose SARS-CoV-2 at different sampling dates and infectious severity. We developed a databasing app to provide instantaneous results to inform patients, physicians, and public health agencies. This assay and device enable real-time surveillance of SARS-CoV-2 seroprevalence and potential immunity.

Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap.

Pathway enrichment analysis helps researchers gain mechanistic insight into gene lists generated from genome-scale (omics) experiments. This method identifies biological pathways that are enriched in a gene list more than would be expected by chance. We explain the procedures of pathway enrichment analysis and present a practical step-by-step guide to help interpret gene lists resulting from RNA-seq and genome-sequencing experiments. The protocol comprises three major steps: definition of a gene list from omics data, determination of statistically enriched pathways, and visualization and interpretation of the results. We describe how to use this protocol with published examples of differentially expressed genes and mutated cancer genes; however, the principles can be applied to diverse types of omics data. The protocol describes innovative visualization techniques, provides comprehensive background and troubleshooting guidelines, and uses freely available and frequently updated software, including g:Profiler, Gene Set Enrichment Analysis (GSEA), Cytoscape and EnrichmentMap. The complete protocol can be performed in ~4.5 h and is designed for use by biologists with no prior bioinformatics training.

AutoAnnotate: A Cytoscape app for summarizing networks with semantic annotations.

Networks often contain regions of tightly connected nodes, or clusters, that highlight their shared relationships. An effective way to create a visual summary of a network is to identify clusters and annotate them with an enclosing shape and a summarizing label. Cytoscape provides the ability to annotate a network with shapes and labels, however these annotations must be created manually one at a time, which can be a laborious process. AutoAnnotate is a Cytoscape 3 App that automates the process of identifying clusters and visually annotating them. It greatly reduces the time and effort required to fully annotate clusters in a network, and provides freedom to experiment with different strategies for identifying and labelling clusters. Many customization options are available that enable the user to refine the generated annotations as required. Annotated clusters may be collapsed into single nodes using the Cytoscape groups feature, which helps simplify a network by making its overall structure more visible. AutoAnnotate is applicable to any type of network, including enrichment maps, protein-protein interactions, pathways, or social networks.